Previous Seminars at the MCB


September 24, 2008
2:30 to 4:00 pm

"A Human Proteome Project"

Dr. J.M Bergeron

Room 903, McIntyre Medical Building, 3655 Promenade Sir-William-Osler, Montreal, H3G 1Y6.

The objective of a human proteome project is to characterize quantitatively a representative protein for each protein coding gene in its major organ and intracellular site of expression in the context of its major protein partners. The concept of a single representative protein for each protein coding gene enables a gene centric approach to be implemented for the completion of the human proteome. A recently annotated human genome suggests ca 20,500 protein coding genes of which ca 8,000 have no direct evidence for a protein expressed and a further 5,000 where the evidence may be weak. Hence a human proteome project will provide the missing evidence for protein expression by predicted protein coding genes. Current efforts to define the proteome using antibodies monospecific to a representative protein for each protein coding gene are ongoing with almost a third of the human genome mapped in this way via the Human Protein Atlas. Current mass spectrometry efforts have enabled community standards to be implemented via test samples. The extension to label free methods to quantify proteins by tandem mass spectrometry as well as their quantitation using a serial dilution of heavy isotope labelled peptides diagnostic of each representative protein provides the framework to define protein abundance for each representative protein in its major site of expression. Using both tagged genes for expression in mice and human cell lines, a protein interaction map may be defined for each representative protein and confirmed by coimmunoprecipitations with a polyclonal antibody resource followed by tandem mass spectrometry to characterize protein partners. Computational biology to merge the data from the antibody directed mapping of the proteome along with the quantitative estimates of protein abundance via tandem mass spectrometry and protein partners also deduced by tandem mass spectrometry defines the resource of a human proteome.

[ past talks ]

April 2008

Thursday 04 April
2:30 - 3:30 pm

"High Throughput Microarray Platform: Validation and Use In Characterizing Kinase Inhibitors"

Paul S. Kayne
Applied Genomics, Bristol-Myers Squibb Co.

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

As gene expression profiling experiments become more complex, new tools are needed for successful prosecution. Improvements needed include larger batch sizes, higher throughput, increased reproducibility, and new methods of analysis. Here we present a validation of a new high throughput array system. Through automation and new designs, this system enables large scale experimental designs.

Understanding the mechanisms and effects of compounds is an area that has largely been underserved by gene expression profiling, due in part to modestly designed experiments. We will discuss our approach to understanding kinase inhibitors. We employ the high throughput arrays to undertake designs incorporating dose curves, and new methods for analysis. Taken together, we are able to better understand the actions of these inhibitors on multiple targets of action.


February 2008

Thursday, 14 February
2:30 to 3:30 pm

"Jumping to Coincidences: Defying Odds in the Realm of the Preposterous"

Dr. James Hanley
Dept. of Epidemiology & Biostatistics McGill University 1020 Pine Avenue West Montreal, Québec, H3A 1A2, Canada.

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

The calculation of probabilities is central to statistical inferences; however, researchers, especially those who are not trained in probability, often have difficulty (or err) when setting up correct probability calculations. Probabilities of seemingly rare events that are assessed after the fact are especially problematic.

In an early report, I described three examples where probability specialists themselves have been "near-sighted" in assessing or predicting usual and unusual events generated by state lotteries. In one example, a lottery official offered "data" which one should expect from a fair lottery; unfortunately, the logic used to "predict the usual" was faulty. In the two others, the unusual (what one should not often expect) was calculated to be much more unusual than it really was. Since then, the story of a fourth -- and seemingly very unusual -- lottery event, and the official statistical reaction to it, were carried in worldwide publications. Again, the reported reaction was based on faulty probability calculations.

These four faulty lottery calculations, and several new ones involving unusual births and birthdays, prompted me to bring together in one place my "case series" and share it with a larger audience. I argue that these variants of a common "probability blind spot" are not sufficiently appreciated and that we need to be very skeptical of after-the-fact probability calculations. These mis-calculations have serious implications for the interpretation of high-throughput or high-dimensional data, and indeed for the interpretation of the scientific literature in general.


January 2008

Thursday 24 January
2:30 to 3:30 pm

"The Three-Dimensional Architecture of Gene Regulation"

Dr. Josee Dostie
Department of Biochemistry McGill University McIntyre Medical Sciences Building 3655 Promenade Sir-William-Osler Montreal, Quebec, Canada

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Since publication of the human genome sequence, the new genomic challenge has been to understand how it works. To achieve this goal, intense efforts are underway to map all the genes and regulatory DNA sequences. Several groups such as the ENCODE and FANTOM consortiums have already identified a large number of functional units in mammalian genomes. These projects are expected to yield complete linear maps of genes and regulatory elements along chromosomes. However, these maps will not be sufficient to define how proper gene expression is orchestrated because the functional organization of genes and DNA elements is not linear. Indeed, a given element can regulate a distant gene or a gene located on a different chromosome without affecting the ones adjacent to it. Genes can be regulated by multiple elements and vice versa, contingent upon cellular conditions. Therefore, the functional connectivity between genes and elements must be mapped in order to understand how correct gene expression is obtained.

Recent studies demonstrate that DNA elements regulate genes by physically interacting with them. These studies demonstrate that genomes are organized into dynamic three-dimensional networks of physical contacts. Therefore, comprehensive mapping of physical interactions will reveal the functional connectivity of genomes. Physical interaction networks can be mapped with the “Chromosome Conformation Capture (3C) technologies. The goal of our laboratory is to define these networks with 3C technologies and to identify mechanisms that regulate them. We are using the Hox gene clusters as model systems to initiate our studies. Analysis of Hox domains will yield the first datasets towards reconstructing the molecular picture of a genome at high-resolution in vivo.


October, 2007

Wednesday, 24 October, 2007
3:30 to 4:30 pm

"Statistical Learning and Virtual Screening in Drug Discovery"

Dr. Hugh Chipman
Canada Research Chair in Mathematical Modeling Department of Mathematics and Statistics Acadia University Wolfville, Nova Scotia

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

High-throughput screening of compounds for biological activity is often an important first step in the drug discovery process. >From a statistical learning perspective, the results of screening process can be used to construct a model. Using various descriptors of molecular structure as inputs, we seek to predict activity. These descriptors can be easily calculated, but the activity is the outcome of more expensive screening procedures. Screening results for a part of the library constitute a training set, which can be used to build a model to predict activity. This model enables ``virtual screening'' in which activity is predicted rather than measured. This talk will describe a number of recent models developed for such virtual screening, including mixture discriminant analysis, decision trees, nearest neighbours, and ensemble models.


Wednesday, October 24, 2007
2:30 to 3:30 pm

"Design and Analysis of Pooled High Throughput Screening Data"

Dr S. Stanley Young
Asst. Dir. for Bioinformatics National Institute of Statistical Sciences Research Triangle Park, NC

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Discovery of biologically active compounds is often accomplished by screening large collections of compounds. Very few compounds are expected to have the activity of interest so testing compounds individually generates large amounts of mostly uninformative data. Dorfman (1943) proposed testing pools of individuals when looking for rare events. There is relatively little literature on using pools for drug discovery; informal conversations indicate that testing of pools has a checkered history. There is a level of paranoia that active compounds will be missed. There is also the somewhat complicated logistics of the decoding process.

So what are the statistical issues with pooling? There is some guiding theory for determining the number of compounds in a pool, k=SQRT(1/p) where p is the probability that a compound will be active. We know a lot about the structural features of our compounds. Can we use that knowledge to select compounds to pool together? What about the expensive, re-testing decoding process? Can we use the chemical structures of compounds in a pool to “put a statistical finger” on the active compound(s) in an active pool?

This presentation will address two questions: How to use chemical structural information to guide the constructing of pools. How to build a mathematical model for the prediction of biological activity when the tested objects are mixtures of compounds?

This is “a work in progress” so I will comment on future research directions. This is very much “team research.” I am working with Professor Jackie Hughs-Oliver of NCSU, and graduate students at NCSU. Some of this work is funded by the NIH Road Map project.


Wednesday, October 17, 2007
14:30 to 15:30 pm

"MC-Fold: A Novel RNA Folding Approach Based On Nucleotide Cyclic Motifs And Its Application To RNAi"

Dr Francois Major
Institut de Recherche en Immunologie et en Cancérologie and Département d'Informatique et de Recherche Opérationnelle Université de Montréal.

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

We changed the classical rationale underlying RNA structure prediction by incorporating the contributions of the non-Watson-Crick base pairs. To do so, we defined a new first-order object for representing nucleotide relationships in structured RNAs, which we call nucleotide cyclic motif (NCM) (1). In comparison to the classical stacks of Watson-Crick base pairs, the properties that make these particular building blocks appealing for structure determination are the facts that: i) they embrace indistinctly both canonical and non-canonical base pairs; ii) they precisely designate how any nucleotide in the sequence relates to others; and iii) two adjacent blocks are merged through a common base pair, allowing us to predict compatible chains of NCMs (2) and to assemble them in complete 3-D structures.

We show here how the new approach i) reproduces accurately consensus/experimental 3-D structures from sequence data (cf.hairpins, polymorphic RNAs, multi-branched RNAs), ii) incorporates multiple-sequence and low-resolution data, and iii) improves the structural bases of RNAi mechanisms, such as precursor processing and siRNA targeting.

1. Lemieux, S. and Major, F. (2006) Automated extraction and classification of RNA tertiary structure cyclic motifs. Nucleic Acids Res., 34, 2340-2346. 2. St-Onge, K., Thibault, P., Hamel, S. and Major, F. (2007) Modeling RNA tertiary structure motifs by graph-grammars. Nucleic Acids Res, 35, 1726-1736.


September, 2007

Wednesday, September 26, 2007
14:30 to 15:30 pm

“Modeling an evolutionary conserved circadian cis-element using functional and comparative genomics"

Dr Felix Naef
Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute for Experimental Cancer Research (ISREC)

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

I will discuss two problems in computational circadian biology:

1. Stochastic phase oscillators and circadian bioluminescence recordings cultured circadian oscillators from peripheral tissues were recently shown to be both cell autonomous and self-sustained. Therefore the dominant cause for amplitude reduction observed in bioluminescence recordings of cultured fibroblasts is desynchronization rather than the damping of individual oscillators. We propose a generic model for quantifying luminescence signals from biochemical oscillators, based on noisy phase oscillators. Our model incorporates three essential features of circadian clocks: the stability of the limit cycle, fluctuations, and inter-cellular coupling. The model is then used to analyze bioluminescence recording from immortalized and primary fibroblasts. Fits to population recordings allow to simultaneously estimate the stability of the limit cycle (or equivalently the stiffness of individual frequencies), the period dispersion, and the interaction strength between cells. Consistently with other work, coupling is found to be weak and insufficient to synchronize cells. Interestingly we find that frequency fluctuations remain correlated for longer than one clock cycle, which is confirmed from individual cell recordings. We discuss how to link the generic model with more microscopic models, which suggests mechanisms by which circadian oscillators resist fluctuations and maintain accurate timing in the periphery.

2. Modeling an evolutionary conserved circadian cis-element Circadian oscillator networks rely on a transcriptional activator called CLOCK. Identifying the targets of this heterodimeric bHLH transcription factor poses challenges and it has been difficult to decipher its specific sequence affinity beyond a canonical E-box motif, except perhaps for some flanking bases contributing weakly to the binding energy. Here we use a comparative genomics approach and first study of the conservation properties of the best-known circadian enhancer in the Drosophila melanogaster period gene. This shows a signal involving the presence of two closely spaced sequence motifs, a configuration that we can also detect in the other four prominent CLOCK targets genes in flies: timeless, vrille, Pdp1 and cwo. The examples allow training a probabilistic model that we can test using functional genomics datasets. We find that the sequences predicted from our model are overrepresented in promoters of genes induced in a recent study by a glucocorticoid receptor-CLOCK fusion protein. We then scanned the mouse genome with the fly model and found that many known CLOCK/BMAL1 targets harbor sequences matching our consensus. The phase of predicted cyclers in liver agreed with known CLOCK/BMAL1 regulation.

Dr. Felix Naef studied theoretical physics at the ETHZ and obtained his PhD from the EPFL in 2000. He then received postdoctoral training at the Center for Studies in Physics and Biology at the Rockefeller University (NYC) under the guidance of Prof. Magnasco. His research focuses on the modeling and interpretation of high-throughput functional data and the study of biomolecular oscillators. He joined ISREC as an associate scientist in the NCCR Molecular Oncology program in early 2004 and was nominated Tenure Track Assistant Professor in the School of Life Sciences at EPFL in 2005.


Wednesday, September 19, 2007
14:30 to 15:30 pm

"Adaptive Stimulation Design for the Treatment of Epilepsy"

Dr. Joelle Pineau
School of Computer Science, McGill University.

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Electrical stimulation has recently emerged as a promising therapy for patients with medically intractable epilepsy. However little is known about the best stimulation patterns to use, such that we get maximal seizure reduction, while also minimizing long-term damage to the brain. The overall goal of this project is to automatically optimize a closed-loop strategy for the control of deep brain stimulation using reinforcement learning methods.

In this talk I will present some of the key methodological challenges that must be addressed in order to automatically learn adaptive treatment strategies in this context. In particular I will discuss (1) the use of ensemble methods to automatically detect seizures, (2) the design of a computational model of epilepsy to provide synthetic training data for the reinforcement learning agent, (3) initial results of applying reinforcement learning using in vitro recordings.

This is joint work with Massimo Avoli, Aaron Courville, Arthur Guez, Philip de Guzman, and Robert Vincent.


Apr 2007

Apr 16
12:00 to 1:00 pm

"Probing sub-proteomes using affinity purification"

Dr. Daniel Figeys
The Ottawa Institute of Systems Biology, BMI, University of Ottawa

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

The concentration range of protein present in biological samples remains a serious challenge for proteomic technologies. The proteome is defined as the ensemble of the proteins in a sample; however, the reality is that a good portion of the proteome remains invisible because of detection and processing limitations in proteomic technologies. We have developed technologies to analyze specific portions of the proteome (sub-proteome) that combine affinity purification, mass spectrometry, and bioinformatics. In this presentation, we will provide some examples of sub-proteome studies. First, we will report our results on mapping protein-protein interaction for over 330 human genes using immunopurification coupled to mass spectrometry and bioinformatics. Using this approach, 2235 human proteins were observed to participate in 6463 interactions with the bait proteins. A suite of bioinformatic approaches were used to assess the validity of the results. Second, we will discuss the mapping of protein polyubiquitination sites using affinity purification coupled to the proteome reactor and mass spectrometry and will discuss the potential applications of this approach.


Apr 04
1:00 to 2:00 pm

"Conservation and Evolution of Mammalian Transcription Factor Binding Sites"

Guillaume BOURQUE, Ph.D.
Group Leader, Assoc Director, Computational & Mathematical Biology, Genome Institute of Singapore

Duff Medical Building, 3775 University Street, Room 507

Recent analyses of transcription factor occupancy using chromatin immunoprecipiration Paired-End-diTag (ChIP-PET) and microarray based (ChIP-Chip) approaches have help unravel the genome-wide binding sites of many proteins in both the human and mouse genome. We make use of a collection of such experiments (5 ChIP-PETs and 1 ChIP-Chip) to contrast the properties of the binding regions for different mammalian transcription factors (p53, ER, cMyc, NF-kB, Oct4 and Sox2). Using two distinct conservation metrics relying on either regions of high sequence similarity in whole-genome vertebrate alignments or presence of cross-species sequence binging motifs, we demonstrate that the overall proportion of binding sites that appear to be under positive selection varies from ~10% to ~30%. Although limited in numbers, we confirm the significance of these conserved binding motifs by showing: 1) that they exhibit a stronger association with regulated genes, and 2) that they are more likely to be detected in another specie by an independent ChIP assay. Interestingly, we also uncover that the distribution of conserved binding sites relative to genes varies considerably for different transcription factors (e.g. majority of conserved binding sites are proximal for cMyc byt distant for ER) suggestive of a diverse spectrum of regulatory modes of action. Finally, leveraging on the ability of the ChIP-PET approach to detect binding sites even in repeat-rich regions, we show that p53, Oct4-Sox2 and ER binding regions are significantly biased towards distinctive families of repeats (ERV1, ERVK and MIR respectively) and that those can lead to up to 20% of specie-specific binding sites. Overall, our results advocate for very dynamic regulatory machinery in eukaryotic genomes where the conserved binding sites are likely to play an important but fragmentary role


Apr 02
12:00 to 1:00 pm

Cellular signaling networks: from regulation to information superhighways

Dr. Edwin Wang
National Reseach Council, Biotechnology Research Institute

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

During the last 50 years, cellular signaling data and information have been generated worldwide and accumulated in literature. In recent years, high-throughput technologies allow to generating a large mount of DNA sequence, microarray and protein data. We manually curated signaling events from literature and combined with other high-throughput data to construct a human signaling network. By integrative analysis of the network with other types of high-throughput biological data, we explored the regulation and signal propagation on the human signaling network. I will summarize the principles of gene regulation, protein phosphorylation and signal information superhighways of the network.


Apr 02
9:30 am

"Computational methods for reconstructing biomolecular systems"

Chen-Hsiang Yeang, Post-doctoral Researcher
University of California, Santa Cruz

McConnell Engineering Building, Room 437

Recent progress in high throughput technologies has generated an enormous amount of data which allow us to study biology at systems level. Two research directions arise from computational systems biology: to reconstruct a biomolecular system by integrating multiple sources of data and to study the evolution of the components within the system. In this talk I will present research works in each of those directions. In the first part, I will describe a computational model capturing the co-evolution between the components in a molecular system. It extends the continuous-time Markov process of sequence substitution by rewarding co-variation and penalizing single-site transitions in the rate matrix. The model accurately predicts the secondary interactions of tRNA and 16S rRNA molecules, and identifies the tertiary interactions which do not follow typical Watson-Crick or GU base pairing rules. We then apply the model to screen co-evolving amino acid pairs among all the protein domain families in the Pfam database. The inferred pairs are highly enriched with the domains which are physically or functionally coupled. Among the top 100 inferred family pairs, 82 occur in the same proteins or share the same functional annotations. By inspecting the 3D protein structures, we find many co-evolving positions are either close and exhibit compensatory substitution across species, or located at functionally important sites of the proteins. In the second part, I will describe a constraint-based modeling framework of inferring gene regulatory networks by data integration. The model treats each piece of evidence as a constraint over attributes in the system and builds a joint probabilistic graphical model from all constraints, and applies statistical inference algorithms to calculate attribute values. To demonstrate its use we apply this framework to four problems. First we infer the causal/functional order of genes in the regulatory network of colon cancer invasiveness using RNAi knock-down expression data. Second we infer the causal order and combinatorial functions of the regulatory network for the surface roughness phenotype of Vibrio cholerae, using multiple knock-out expression data. Third we identify the active pathways and edge directions/signs in the physical interaction network of yeast that explain the knock-out expression data. Finally, we propose information theoretic criteria for suggesting new knock-out experiments that


Mar 2007

Mar 27
1:30 to 2:30 pm

"Large-scale prediction of human protein-protein interactions and analysis in the context of the nucleolar subnetwork"

Michelle Scott
The Barton Group, School of Life Sciences, University of Dundee, Scotland UK

McIntyre Building, 9th floor, Room 903

Although the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%. We investigated the prediction of human protein-protein interactions by combining orthogonal protein features within a probabilistic framework. The features include co-expression, orthology to known interacting proteins and the full-Bayesian combination of subcellular localization, co-occurrence of domains and post-translational modifications. A novel scoring function for local network topology was also investigated. This topology feature greatly enhanced the predictions and together with the full-Bayes combined features, made the largest contribution to the predictions. The most accurate predictor identifies 37606 human interactions, 32892 (80%) of which are not present in other publicly available large human interaction datasets, thus substantially increasing the coverage of the human interaction map. A subset of the 32892 novel predicted interactions have been independently validated. Comparison of the prediction dataset to other available human interaction datasets, estimates the false positive rate of the new method to be below 80% which is competitive with other methods. Since the new method scores and ranks all human protein pairs, smaller subsets of higher quality can be generated thus leading to even lower false positive prediction rates. The set of interactions predicted in this work increases the coverage of the human interaction map and will help determine the highest confidence human interactions. We also have recently begun analyzing the human protein interaction predictions in the context of the nucleolar subnetwork, including the identification of the main nucleolar complexes and the prediction of the main residency compartments of nucleolar proteins identified by mass spec.


Mar 26
12:00 to 1:00 pm

"Protein interaction networks in bacteria - from sequence to function"

Peter Uetz, Associate Investigator
The Institute of Genomic Research

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Most bacterial genomes encode hundreds of uncharacterized proteins. Even in well-annotated genomes the biological role of many characterized genes remains little understood. Protein interaction networks can place proteins into a functional context and provides foundation for developing a physical basis for the myriad of dynamic processes in a cell. Surprisingly, no bacterial proteomes have been systematically interrogated for binary interactions between their proteins and thus their overall topologies remain unknown although protein complexes have been systematically purified from E. coli and analyzed by mass spectrometry. Here we present the protein interaction network for the syphilis spirochete Treponema pallidum and compare it to other prokaryotic interaction datasets. T. pallidum encodes 1,039 proteins, 729 (or 70%) of which interact via 3,687 unfiltered interactions as revealed by systematic yeast two-hybrid screens. A filtered high-confidence dataset of 1,634 interactions links 601 proteins including 283 proteins of previously unknown function (124 conserved in other species, 159 spirochete-specific). We estimate that our screen covered at least 28% of all protein interactions in this species thus suggesting the complete interactome of a small bacterium to contain on the order of 5,000 protein interactions. Based on our high-confidence interactions, we predict 280,606 homologous interactions for 147 completely sequenced bacterial genomes. As a proof of concept we predicted new components of the flagellar apparatus in E. coli and B. subtilis and experimentally confirmed their role in bacterial motility, demonstrating that this approach is generally applicable in microbial functional genomics.


Mar 12
12:00 to 1:00 pm

"Probing the link between RNAi and epigenetics"

Dr. Thomas Duchaine, Assistant Professor
McGill Cancer Center & Department of Biochemistry, McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

The discovery that double-stranded RNA (dsRNA) and its functional small RNA derivatives (siRNAs) could mediate potent and specific gene silencing in a wide variety of species sparked a revolution in molecular biology. Originally hailed as a powerful tool to study gene function, it is now clear that similar phenomena lie at the heart of a complex set of gene regulatory networks. A recognized group of related silencing mechanisms broadly conserved in eukaryotes is now generally known as RNA-mediated interference, or RNAi. Emerging evidence suggests that one possible outcome for RNAi is the modification of histone marks on chromatin, which is the basis for epigenetic regulation. While the molecular basis for this function is still poorly characterized in animals, it is thought to be mediated by self-enforcing loops we termed endoRNAi. In endoRNAi, an open reading frame or a non-coding chromatin locus somehow triggers the generation of small RNAs, which are loaded in RNAi effector complexes and converge back on the loci. Such genetic regulation loops have most recently been observed in a wide variety of species (including mammals), involve conserved components, and are critical both for the regulation of specific genetic loci and in a broader sense for the packaging of heterochromatin and the maintenance of the genome’s integrity. I will present the evidence behind this emerging and major aspect of RNAi biology. I will also describe our work based on an integrated framework of C. elegans genetic and biochemistry which supports the implication of histone-binding proteins in endoRNAi.


Mar 05
12:00 to 1:00 pm

"The Challenge of Deriving Meaningful Descriptions of the Physical Interactome of Saccharomyces cerevisiae"

Dr. Shoshana Wodak, Professor
University of Toronto, Department of Biochemistry and Structural Biology, Department of Medical Genetics

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Reliable information on the physical and functional interactions between the gene products is an important prerequisite for deriving meaningful system-level descriptions of cellular processes. The available information about protein interactions in Saccharomyces cerevisiae has been vastly increased recently by two comprehensive tandem affinity purification/ mass-spectrometry (TAP/MS) studies. However, using somewhat different approaches these studies produced diverging descriptions of the yeast interactome, clearly illustrating the fact that converting the purification data into accurate sets of protein-protein interactions and complexes remains a major challenge. We review the major analytical steps involved in this process, with special focus on the task of deriving complexes from the network of binary interactions. Applying the MCL procedure to an alternative yeast interaction network, recently derived by combining the data from the two latest TAP/MS studies, we produce a new description of yeast protein complexes. Several objective criteria suggest that this new description is more accurate and meaningful than those previously published. The same criteria are also used to gauge the influence that different methods for deriving binary interactions and complexes may have on the results. It is also shown that employing identical procedures to process the latest purification datasets significantly improves the convergence between the resulting interactome descriptions.


Mar 01
9:30 a.m.

Evolutionary Escape on Fitness Landscapes

Niko Beerenwinkel
Program for Evolutionary Dynamics, Harvard University

McConnell Engineering Building, Room 437

The evolution of HIV within individual patients is associated with disease progression and failure of antiretroviral drug therapy. Using graphical models we describe the development of HIV drug resistance mutations and show how these models improve predictions of the clinical outcome of combination therapy. We present combinatorial algorithms for computing the risk of escape of an evolving population on a given fitness landscape. The geometry of fitness landscapes and the underlying gene interactions are analyzed in an attempt to generalize the notion of pairwise epistasis to higher-order genetic systems. Finally, we discuss the new and exciting prospects for analyzing viral genetic variation that arises from recent pyro-sequencing technology.


Feb 2007

Feb 21
12:00 to 1:00 pm

"Systems Approach to Understanding Death vs Survival Responses of Epithelial Cells to Growth Factor and Cytokine Stimuli"

Douglas Lauffenburger Professor of Bioengineering
Massachusetts Institute of Technology

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Cell behavioral functions are governed by biomolecular networks that translate stimulatory cues (e.g., ligand/receptor binding interactions, mechanical stresses, pathogen infection, and other environmental insults) into intracellular signals which regulate transcriptional and post-transcriptional, metabolic, and cytoskeletal processes that effect proximal and ultimate cell responses. While there is a growing body of work enhancing our understanding of how intracellular signals are generated by stimulatory cues, an exceptionally difficult challenge at the present time is to understand how these signals operate in integrated manner to govern cell behavioral responses. We are undertaking efforts to address this question by means of a combination of quantitative, dynamic protein-centric experimental manipulations and measurements with a spectrum of computational mining and modeling approaches. Particular application problems of our interest include cell migration, proliferation, differentiation, activation, and death, with an emphasis on ascertaining how effectiveness of prospective therapeutics might be predicted. This talk will present an overview of our perspective and approach, along with a specific example vignette focused on epithelial death-vs-survival responses to growth factor and cytokine stimuli.


Jan 2007

Jan 15
12:00 to 1:00 pm

"Peptide retention time prediction from high-throughput proteomic data"

Corey Yanofsky
Biomedical Engineering, McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

In high-throughput proteomics experiments, complex mixtures of peptides usually undergo chromatographic separation prior to identification by mass spectrometry. Chromatographic separation was originally incorporated into the experimental design principally to limit the complexity of the peptide mixture entering the mass spectrometer at any one time. Over the course of years, large databases have been built to store information, including chromatographic retention time information, about peptides identified experimentally. This creates the possibility of using retention time data as an extra dimension to improve the identification of peptides based on mass spectra. However, there is considerable variability in the elution time for a particular peptide which must be corrected before the information can be use for peptide identification. At McGill University, many thousands of proteomic experimental runs have been run using a variety of chromatographic protocols. These runs have different chromatographic dead times, elution gradients, and experimental precisions, all of which interfere with a straightforward prediction of future retention times from previous data. To overcome these complications, we have developed a Bayesian model to estimate the physical property of peptides underlying experimental retention times, explicitly modelling varying chromatographic dead time, elution gradient, and run-specific variance as separate effects. The model was fit using a data set of 113160 peptide identifications, comprising 6681 unique peptides in 3163 runs, thus providing estimates of the “true” retention time (relative to a chosen reference run) of the 6681 peptides. A cross-validation study was performed, showing that the predictive error of the model was typically wthin +/- 2 minutes. (This covers about 8% of the total elution time for a run with a 60 minute gradient and 10 minutes of dead time.) A second stage was necessary to predict the retention times of peptides which are not present in the data set, so the results of the first stage were used to fit a peptide-sequence-based retention time model. In a leave-one-out cross-validation study, the sequence-based model was able to predict retention times with an overall accuracy of +/- 5 minutes, which covers about 20% of the total elution time for a run with a 60 minute gradient and 10 minutes of dead time.


Jan 08
12:00 to 1:00 pm

"Gene expression profiling of the breast tumor microenvironment"

Greg Finak
Centre for Bioinformatics, McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

The role of the cellular microenvironment in breast tumorigenesis has become an important research area. However little is known about gene expression changes in stromal tissue and if these correlate with disease progression and outcome. In order to gain a better understanding of the breast tumor microenvironment, we have used microarray-based gene expression profiling of laser capture microdissected breast tumor and matched normal stroma to identify a novel gene expression signature that is predictive of poor prognosis in breast cancer patients. This predictor is independent of known clinical risk factors and published molecular predictors, and is predictive in other independently published breast cancer microarray data sets in multivariate analysis. The predictor identified from stromal tissue has features of a hypoxia transcriptional response, angiogenic response, and a differential immune response in subsets of patients with different clinical outcome, highlighting the importance of these responses to the tumor microenvironment. Our results present some of the first evidence in clinical breast tumor samples, of signatures from the stromal microenvironment that predict outcome. Our data support published animal models that implicate type II macrophages and the immune response in tumor progression and metastasis. These results highlight the complex relationship between the tumor and its microenvironment, and underline the role that the stroma plays in tumor progression.


Dec 2006

Dec 06
2:30 to 3:30 pm

RNA editing as a drug target in trypanosomatid pathogens

Reza Salavati
Assistant Professor, McGill University, Institute of Parasitology

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

RNA editing in trypanosomatid parasites converts many of their mitochondrial mRNAs into mature (translatable) mRNAs for components of an essential energy generating system. RNA editing is catalyzed by a multi-protein complex (editosome) of 20 protein components that is essential for the stages of the pathogen that cause disease in humans. This indicates the important possibility that the process and thus each of the numerous components of the complex are potential drug targets. Database analyses have identified homologs of the genes in all major trypanosomatid pathogen species so that drug design can explore common features of the proteins for the possible development of drugs that will be effective against this entire group of pathogens. We are currently in the process of structural studies of the RNA editing ligases with the long term goal of the overall functional structure of the editosome and its utility in structure based drug design. Our preliminary data on this will be reported for this talk.


Nov 2006

Nov 22
2:30 to 3:30 pm

Metabolomics: A novel tool for stress diagnosis and gene function elucidation

Dr. Ajjamada Kushalappa
Department of Plant Sciences, McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Metabolomics, the dynamics of metabolome of an organism, has been applied to diagnose stress, and to elucidate gene functions, in plants, animals and microbes. Understanding of plant defense mechanisms at the molecular/metabolic level could lead to novel strategies to manage destructive crop diseases. With the accumulation of information on plant and pathogen genomics, there is an increasing demand for knowledge on functions of plant/pathogen genes involved in stress-resistance. The knowledgebase on metabolomics of plant-pathogen interactions is not only complementary to transcriptomics and proteomics but also has special advantages, as it is often the downstream result of gene expression. Plants produce thousands of metabolites, but not all are present at a given time. Though the metabolome (all the metabolites) of a species is an expression of its genome, the type and the amounts of different metabolites produced vary with the environment. When attacked by a pathogen, plants shift from primary to various secondary metabolic pathways, changing from growth to stress-defense strategies. Exposure of plants to stressful environment can increase the chance of detecting stress-related-metabolites. Most likely, resistance to stress results from synergistic effects of various types of defense mechanisms (structural and biochemical) operating together. However, specific plant defense is due to subsets of activation and suppression molecules produced by plants in response to pathogen attack. The dynamics of metabolites, in plants (homeostasis) following stress, can be related to genes and gene expressions to better understand plant defense, and further used to improve resistance of plant cultivars to stress. Crop plants grown in the field and the harvested produce in storage are exposed to several biotic (pathogens, parasites, etc.) and abiotic (physical and chemical environment) stress, leading to severe loss in yield. The dynamics of metabolite profiles following stress can be modeled (ultivariate analysis) and can be used in the detection, diagnosis and management of stress. We have used metabolite profiling to: a) detect biotic stress of fruits and vegetables, and b) discriminate resistance and explain the functions of resistance genes in plant cultivars against disease. Univariate and multivariate analyses have been used to identify stress related biomarker metabolites. The problems and progress of metabolite profiling, technology development to discriminate diseases of fruits and vegetables in storage, and identification of functions of disease resistance gene/QTL in wheat to defend attack by fungal pathogens will be discussed.


Nov 15
2:30 to 3:30 pm

"A panorama of the protein complexes formed by the general transcription machinery in mammals"

Benoit Coulombe
Director, Gene Transcription Laboratory, Director, Proteomics Discovery Platform, Institut de recherches cliniques de Montreal (IRCM)

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

We have performed a survey of protein complexes containing transcription and RNA processing factors in the soluble compartment of mammalian cells using protein affinity purification coupled to mass spectrometry. High confidence interactions were selected computationally. Thirty-four tagged polypeptides yielded a network of 842 interactions. Remarkably, the network is significantly enriched in proteins known to regulate the formation of protein complexes, uncovering a novel regulatory mechanism that targets the transcription machinery. The network also contains previously-uncharacterized proteins to which we have started inferring functions. The RNA polymerase II (RNAPII)-associated proteins (RPAPs) are physically and functionally associated with RNAPII, forming an interface between the enzyme and chaperone and scaffolding proteins. The newly-discovered methylphosphate cap synthase MePCS is part of a 7SK/U6 snRNP complex containing RNA processing and transcription factors, including the elongation factor P-TEFb. Our results define the first high-density interaction network involving the cellular machinery that interprets mammalian genomes.


Nov 08
2:30 to 3:30 pm

Microfluidic systems for miniaturizing and parallelizing biological experimentation

David Juncker
Assistant Professor, Department of Biomedical Engineering, McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Miniaturization, parallelization, and integration are the concepts that drove the advances of microelectronics and of computer science, and which have lead to the “digital revolution”. Miniaturization and parallelization have started to bear on the life sciences with the emergence of large-scale cDNA microarrays and high throughput sequencers. Here I will present three microfluidic technologies that we have developed or are currently developing, and discuss how they may enable the miniaturization and parallelization of proteomics and cell biology. The first technology is the microfluidic capillary system, which we have used for ultra-miniaturized protein analysis, and which can be cloned into large arrays for large scale proteomics. The second technology is an electrostatic valve necessary for building integrated microfluidic processors which can in turn be used for large scale cell studies. The third technology is a microfluidic probe that can be used to “write” and “erase” patterns and objects on surfaces with a microfluidic jet. The versatility of the probe is illustrated with a series of processing examples, including protein microarrays, arrays of surface gradients, and the selective staining and contact-free removal of single living cells. We will use the probe for changing the microenvironment of cells and study the cellular behavior in response to different stimuli. Large scale proteomics and cell biological experiments in well defined microenvironments will drive systematic and quantitative biological studies, and provide a rich source of data for bioinformatics.


Oct 2006

Oct 25
2:30 - 3:30 pm

Model-based clustering for gene expression profiles

Dr. David A. Stephens
Department of Mathematics and Statistics, McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

In this talk I will outline a computationally-efficient method for the hierarchical clustering of time course gene expression data. The specific applications on which I shall focus are the immune defense system of the malaria vector, mosquito Anopheles Gambiae, and the developmental cycle of the malaria parasite Plasmodium Falciparum. The motivation for the method is Bayesian, but rather than computing full posterior quantities, the method computes the optimal clustering under the restrictions of hierachical clustering. The method can be extended to co-clustering problems, where parallel experiments are carried out for the same set of genes under different environmental conditions. Finally, I will discuss dynamic models that aim to capture the co-regulation of sets of genes, and how these models can be used to construct templates for the underlying clusters.


Sep 2006

Sep 20
2:30 - 3:30 pm

Whole-genome comparative and regulatory genomics

Mathieu Blanchette
School of Computer Science - McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

This talk will describe how a whole-genome computational prediction and analysis of human regulatory regions can yield important insights into gene regulation, and how genome evolution, and in particular computationally reconstructed ancestral DNA sequences, can help in this process. I will first describe a approach to the detection of cis-regulatory modules that exploits both inter-species comparison and binding site clustering. The analysis of the ~120,000 modules identified by this algorithm reveals a number of interesting observations regarding the overall distribution properties of the modules, but also regarding the properties of the individual transcription factors predicted to bind them. These properties include association to particular expression patterns or function, co-occurrences of binding sites for pairs of transcription factors, and broad regulatory network properties. In the second part of the talk, I will briefly introduce a joint project with Dr. David Haussler and Dr. Webb Miller, aiming to reconstruct the complete genome of ancestral mammals. I will focus on how this ancestral sequence information can help our study of the evolution of regulatory mechanisms in mammals, and how these sequences can be used to predict human regulatory regions more accurately.


Sep 13
2:30 - 3:30 pm

Transcriptional Regulation: Character and Capabilities

Ted Perkins
School of Computer Science - McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Transcriptional regulation, discovered roughly a half-century ago by Jacob and Monod, is one of the major means by which the concentrations of gene products are controlled. However, the relationships between transcription factor concentrations and the expression level or transcription rate of a gene are very complex. As a result, there is a lack of consensus on how to express these relationships---that is, on what kinds of models capture the essential features of transcriptional regulation without eliminating important details. This is a problem, of course, for those interested in modeling transcriptional regulation. But it is also a problem for those who want to understand and convey the behavior of particular genes, as well as those who want to engineer genes. This talk will be a three-part story on the character of transcriptional regulation. In the first part, I will survey some of the important historical milestones in our understanding of transcriptional regulation, beginning with the seminal work of Jacob and Monod. In the second part, I will describe lessons learned from detailed modeling of "high-fidelity" expression data sets, including some of my own work on the segmentation network of Drosophila melanogaster. In the final part, I will describe a thought experiment: Given what we know about transcriptional regulation, what kinds of relationships between transcription factors and target gene are possible?


May 2006

May 01
12:00-1:00pm CANCELLED

Genome rearrangement with imperfect gene orders

Nadia El-Mabrouk
University of Montreal

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

A fundamental problem arising in comparative genomics is to determine the evolutionary distance between two or more genomes. The genome rearrangement approach infers divergence history in terms of global mutations involving the movement, inversion, duplication, insertion and loss of chromosomal segments. For this purpose, the genome is modeled as a set of chromosomes, each consisting of a linear order of signed genes. Genome rearrangements have been widely studied by bioinformaticians over the last decade. The major focus has been to infer the most economical scenario of elementary operations transforming one linear order of genes into another. Mxost of these studies assume a perfect pre-annotation of the genomes, involving a perfect orthology correspondence between genes and a perfect knowledge of gene orders. However, in may cases, the similarity scores given by the local alignment tools (such as BLAST or FASTA) are too ambiguous to conclude to a homology, and using different parameters and cut-off values may lead to different sets of orthologs. On the other hand, despite the increase in the number of sequencing projects, the choice of candidates for complete genome sequencing is usually limited to few model organisms and species with major economical impact. For the large majority of species, all we have are partial maps of genes (or other markers) produced by methods of law resolution that are inevitably missing some genes and fail to order some sets of neighboring genes. In addition, different protocols give rise to different maps with markers from one map not ordered with respect to markers of another map. In this presentation, I'm interested in generating gene orders from imperfect datasets involving: 1- genes with unclear orthology relationships; 2- partial gene orders obtained from the concatenation of different gene maps. In the first case, I will present an algorithm that assigns orthology relationships between genes of the same family based on their genomic context. In the second case, I will show how a partial gene order can be linearized with respect to a reference gene order.


Apr 2006

Apr 24
12:00 - 1:00

Dynamical Systems Biology: The next step in integrative biology and bioinformatics?

Mads Kaern, Ottawa Institute of Systems Biology, Department of Cellular and Molecular Medicine, Department of Physics
University of Ottawa

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Much research is currently focused on obtaining comprehensive maps of the systems that control cellular dynamics by identifying and cataloguing the complete repertoire of protein-DNA and protein-protein interactions that take place within cells. While these efforts have been immensely successful, it is becoming increasingly clear that the resulting molecular interaction networks are not sufficient to fully understand the intricacies of cellular regulation. This is in part due to complex systems-level features, such as multi-layered feedback control and epigenetic variability, whose effects on cellular dynamics typically cannot be deduced from static and qualitative information. In this talk, I highlight some of the strategies used in our lab to gain further insight into the dynamics and design principles of transcriptional regulatory networks in yeast. The goal of this research is to develop and implement new systems biology methodologies that can shed further light on complex cell regulatory control systems.


Mar 2006

Mar 27
12:00-1:00 pm

"Development and analysis of StemBase, a database of stem cell gene expression data"

Miguel Andrade, Assistant Professor
University of Ottawa

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

We have developed StemBase [; 1], a database of gene expression data produced within the framework of the Stem Cell Genomics Project. StemBase contains data from more than 150 different samples of stem cells and their derivatives in human and mouse, obtained from more than 20 members of the Canadian Stem Cell Network. Most of the gene expression data are Affymetrix DNA microarray data with a small number of Serial Analysis of Gene Expression (SAGE) libraries. We are developing methods for the analysis of the data deposited in StemBase, for example to find markers of particular stem cell types. We are also doing analysis of the data. For example, we studied time series of differentiation of mouse embryonic stem cells into embryoid bodies to find genes whose expression levels were changing quickly before the cells started major differentiation [2]. References: [1] Perez-Iratxeta, et al. 2005. Study of stem cell function using microarray experiments. FEBS Letters. 579, 1795-1801 [2] Hailesellasse, K., et al. Submitted. Search for genes important in mouse embryonic stem cell differentiation.


Mar 22
9:30 to 10:30 am

Phylogenetic Estimation for Complex Evolutionary Processes

Li-San Wang, Department of Biology
University of Pennsylvania

McConnell Engineering Building 3480 University Street, Room MC437

Stochastic models of sequence evolution, since their introduction in the 1960s, have inspired the development of numerous computational and statistical methods for phylogeny reconstruction which have been widely successful in reconstructing the evolutionary history of genes and species. However, standard evolutionary models have two essential features, both of which are known to fail for a wide range of real biological data: (1) the domain of mutation is a concatenation of multiple independently distributed sites, each following a simple, identical stochastic process, and(2)the evolutionary history is a branching process (tree). This talk is an overview of my research on complex evolutionary processes -- processes that lack either of the two features of standard models. In both cases, new stochastical models need to be developed. Moreover, the inference of evolutionary histories under these models is much harder - in some cases simply computationally more intense, but in other cases posing significant and new algorithmic challenges. I will cover two such processes: the process of gene order evolution and the process of horizontal gene transfer. For each process, I will formulate the estimation problems, identify computational and statistical issues, and present our current results and future research directions.


Mar 20
12:00 to 1:00 pm

Integrating bioinformatics and molecular biology in the search for specific gene expression

Martina Stromvik
McGill University, Department of Plant Sciences

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

The full genome sequence of soybean (Glycine max (L.) Merr.) is not yet available, but it is believed to house between 40-60,000 genes. These numbers are based on contig assemblies of the 356,000 soybean ESTs available in the public domain, a tremendous source for gene expression studies. In themselves, the ESTs can be mined for genes of interest, but they have also been the basis for the development of both cDNA arrays and Affymetrix gene chips, making it possible to study the soybean transcriptome under different conditions. The talk will discuss a comprehensive approach to understanding specific gene expression, using relational database development for hypothesis generation, data storage and mining together with a “wet-lab” promoter isolation and characterization pipeline.


Mar 20
9:30 - 10:30 am

Estimating the significance of sequence motifs

Uri Keich, Assistant Professor, Computer Science Department
Cornell University

McConnell Engineering Building 3480 University Street, Room ENGMC 11

Efficient and accurate statistical significance evaluation is an essential requirement for motif-finding tools. One such widely used significance criterion is the p-value of the motif's information content or entropy score. Current computation schemes used in popular motif-finding programs can unwittingly provide poor approximations. We present an approach to a fast and reliable estimation of this p-value that can be applied more generally. We then show that in the context of twilight zone searches, or searches for relatively weak motifs, the paradigm of relying on entropy scores and their p-values can surprisingly lead to undesirable results. These lead us to consider alternative approaches to analyze the significance of motifs.


Mar 17
2:30 - 4:00 pm

New Methods for Detecting Lineage-Specific Selection

Adam Siepel, Assistant Professor, Biological Statistics & Computational Biology
Cornell University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

So far, most methods for identifying sequences under selection based on comparative sequence data have either assumed selectional pressures are the same across all branches of a phylogeny, or have focused on changes in specific lineages of interest. Here, we introduce a more general method that detects sequences that have either come under selection, or begun to drift, on any lineage. The method is based on a phylogenetic hidden Markov model (phylo-HMM), and does not require element boundaries to be determined a priori, making it particularly useful for identifying noncoding sequences. Insertions and deletions (indels) are incorporated into the phylo-HMM by a simple strategy that uses a separately reconstructed "indel history.'' To evaluate the statistical significance of predictions, we introduce a novel method for computing P-values based on prior and posterior distributions of the number of substitutions that have occurred in the evolution of predicted elements. We derive efficient dynamic-programming algorithms for obtaining these distributions, given a model of neutral evolution. Our methods have been implemented as computer programs called DLESS (Detection of LinEage-Specific Selection) and phyloP (phylogenetic P-values). We discuss results obtained with these programs on both real and simulated data sets.


Mar 10
12:00 PM

" Predicting biological functions at different scales "

DR. PEER BORK Head of Unit Structural & Computational Biology
EMBL (Heidelberg)

McIntyre Medical Sciences Building 3655 Promenade Sir William Osler Palmer Amphitheatre, Room 522


Mar 08
9:30 to 10:30 am

Whole-genome alignments and polytopes for comparative genomics

Colin Dewey, PhD
University of California, Berkeley

McConnell Engineering Building 3480 University Street, Room MC437

Whole-genome sequencing of many species has presented us with the opportunity to deduce the evolutionary relationships between each and every nucleotide. In this talk, I will present algorithms for this problem, which is that of multiple whole-genome alignment. The sensitivity of whole-genome alignments to parameter values can be ascertained through the use of alignment polytopes, which will be explained. I will also show how whole-genome alignments are used in comparative genomics, including the identification of novel genes, the location of micro-RNA targets, and the elucidation of cis-regulatory element and splicing signal evolution.


Feb 2006

Feb 27
12:00 to 1:00 pm

"Evolution of genome organization: estimating tempo and mode"

Miklós Csürös
Department of Computer Science and Operations Research, University of Montreal

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

I will describe two probabilistic models with the accompanying likelihood algorithms for the analysis of intron evolution and the evolution of gene family size. Recently completed genome sequences have been used for comprehensive analyses of exon-intron organization in orthologous genes of diverse organisms. I propose a method for estimating the number of introns lost or unobserved in all extant organisms, and show how to compute counts of intron gains and losses along the branches by using posterior probabilities. The methods are used to analyze the most comprehensive intron data set available presently, consisting of 7236 intron sites from eight eukaryotic organisms. The analysis shows a dynamic history with frequent intron losses and gains, and fairly --- albeit not as greatly as previously postulated --- intron-rich ancestral organisms. The second half of the talk is on the evolution of a gene family along a phylogeny. This work represents the first tractable probabilistic model that simulatenously handles the three main mechanisms that shape gene content: horizontal gene transfer, gene duplication, and gene loss. I illustrate the model by an application to the evolution of gene content in Preoteobacteria using the COG (Clusters of Orthologous Groups) database.


Feb 20
12:00 to 1:00 pm

"Ecoinformatics approaches to abundance across space and between species"

Brian McGill, Dept of Biology,
McGill University

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Although, lagging behind molecular biologists, ecology is beginning to recognize that informatics approaches can answer important questions that were not previously tractable. I will describe my research on how the abundance (number of individuals of a species in a given area) varies between species at one location (the species abundance distribution) and how the abundance of one species varies across space. For example, did you know that a species is rare in most of the locations where it is found? These questions have important implications for conservation and understanding the impacts of global warming on the biosphere. I will show how informatics approaches are providing exciting answers to these two important and century old questions.


Feb 13
12:00 - 1:00pm

"Alternative Splicing - Fact or Fiction"

Jacek Majewski, Assistant Professor,
McGill University, Department of Human Genetics

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Pre-mRNA splicing is emerging as a new important process in modifying mammalian proteomic diversity, modulating gene expression, and of course as a candidate factor influencing phenotypic diversity and genetic disease. I am interested in studying two interrelated issued: 1) how is splicing (and in particular alternative splicing) regulated - what sequence motifs and splicing factors are involved? and 2) how prevalent and meaningful are alternatively spliced isoforms; are there differences in splicing among individuals? I will talk about using combined bioinformatics and microarray approaches to tackle these issues.


Feb 10
2:30 to 3:30 pm

"A tree-based Gibbs motif sampler for unaligned orthologous upstream sequences"

Jens Lagergren
Stockholm Bioinformatics Center and Royal Institute of Technology, Sweden

Duff Medical Building, 3775 University Street, Seminar Room 321

Most studies of gene evolution focus on the coding part of genes rather than their regulatory regions. However, comparative genomics provides one of the most powerful approaches to identification of transcription factor binding sites; knowledge of how binding sites evolve facilitates construction of better identification algorithms. In phylogenetic foot-printing, putative regulatory elements are found in upstream regions of orthologous genes by searching for common motifs. Gibbs sampling is one successful method for finding common motifs. Since the orthologous sequences are related by a tree and differences between motif instances, in different upstream regions, are caused by mutational events along its edges, taking advantage of the tree in the motif search is an obvious as well as appealing idea. We describe the Tree-Based Gibbs motif sampler, which is a Gibbs sampler based on a general tree which takes unaligned sequences as input. An implementation of the tree based sampler will be described as well as in silico experimental results that show clear advantages of the method.


Feb 06
12:00 to 1:00 pm

Modeling Disease Outbreaks to Evaluate Automated Surveillance Systems

David Buckeridge, Assistant Professor
McGill University, Epidemiology, Biostatistics and Occupational Health

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

The current public health situation is one of great demand and opportunity for radical improvements in surveillance methods. Profound failures in information management in have resulted in considerable avoidable morbidity and mortality. At the same time, the increasing availability of routinely collected health data presents new opportunities for public health surveillance. One opportunity is the possibility of detecting outbreaks rapidly through automated, prospective analysis of routinely collected clinical and administrative data, but the effectiveness of this approach to surveillance is not well understood. Previous evaluations have been hampered by the lack of data containing know outbreak signals. In this presentation, I will describe the development and application of a simulation model designed to enable evaluation of outbreak detection in automated surveillance systems.


Jan 2006

Jan 23
12:00 to 1:00 pm

Phylogenomics: the beginning of incongruence?

Herve Philippe
Department of Biochemistry, University of Montreal,

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

Until recently, molecular phylogenies based on a single or few orthologous genes often yielded contradictory results. Using multiple genes in a large concatenation was proposed to end these incongruences. Here we show that single gene phylogenies are often incongruent but these observed conflicts mostly lack statistically significant support. In contrast, the use of different tree reconstruction methods on different partitions of the concatenated super-gene leads to well-resolved, but, incongruent phylogenies. Therefore, phylogenomics opens the era of real (i.e. statistically significant) incongruence, instead of ending it. We argue that gathering a large amount of data is not sufficient to obtain a reliable tree because, given the current limitation of tree reconstruction methods, the quality of the input data is also primordial. We propose that selecting only data that contain a minimal amount of non-phylogenetic signal takes full advantage of phylogenomics and seriously reduces incongruence.


Jan 09
12:00 to 1:30

High-throughput Interactome Mapping using Protein Mass Spectrometry: Moving-Forward

Andrew Emili, PhD, Assistant Professor, Program in Proteomics and Bioinformatics
Banting and Best Department of Medical Research, Department of Medical Genetics and Microbiology, University of Toronto

Duff Medical Building, 3775 University Street, Main Amphitheatre 1

A key feature of the molecular organization of all organisms is the tendency of proteins to form larger complexes via/ /protein-protein interactions. Understanding how all the myriad of protein components of an organism operate together dynamically to mediate the fundamental cellular processes that collectively form the basis of a functional living cell will require a complete characterization of all the interacting proteins and protein complexes present in a cell, and how this change in response to physiological signals, developmental cues and disease processes. Using rigorous affinity tagging and purification procedures, we have elucidated a large-scale network of high-confidence interactions for ~700 gene products in /E. coli/ [Butland /et al/., Nature (2005) 433:531-7]., and for about ~3,000 gene products in budding yeast [Krogan /et al/; manuscript in preparation]. These networks include many novel interactions, and have provided insight into the putative functions of uncharacterized proteins. Since interacting proteins generally belong to the same pathway, and since genes are often conserved across evolution, companion comparative genomic studies have revealed evidence for the modular design and functional diversification of these networks across different phyla. Nevertheless, these are static maps, and we have only limited information regarding how dynamic these networks are during either normal or perturbed growth To address this issue, we are now developing a new analytical platform, consisting of basic liquid-chromatography-tandem mass spectrometry based shotgun profiling combined with advanced computer algorithms, statistical methods and software applications to support systematic large-scale identification, quantification and evaluation of the global patterns of soluble protein complexes across entire biological systems. The platform is designed to detect, quantify and track large numbers of protein complexes from feature-rich ion mass chromatograms, compensate for spurious fluctuations in recorded signal intensities, and reliably match related complexes across different datasets, allowing for comparative interactome studies under different experimental conditions. We hope to further refine this toolkit to enable routine genome-scale proteomic studies aimed at detecting pathological biological responses, such as biochemical signatures of disease, at the interactome level.


Nov 2005

Nov 14
12:30 to 1:30 pm

Data, hypotheses, and experiment planning: the informatics of systems biology

Dr. Hamid Bolouri, Professor Computational Biology
Institute for Systems Biology Seattle, WA, USA

McIntyre Medical Building, Room 1027


Oct 2005

Oct 19
2:30 - 3:30 pm

OMA, A Comprehensive, Automated Project for the Identification of Orthologs from Complete Genome Data

Gaston H. Gonnet
ETH Zurich, Institute of Computational Science

Duff Medical Building, Room 321

The OMA project is a large-scale effort to identify groups of orthologs from complete genome data, currently 240 species. The algorithm relies solely on protein sequence information and does not require any human supervision. It has several original features, in particular a verification step that detects paralogs and prevents them from being clustered together. The paralogy detection algorithm is provable correct and includes an interesting application of max edge-weight cliques. The resulting groups, whenever a comparison could be made, are highly consistent both with EC assignments, and with assignments from the manually curated database HAMAP. A highly accurate set of orthologous sequences constitutes the basis for several other investigations, including phylogenetic analysis and protein classification. Many of these trees shed light on previously unknown relations between species, most notably the realation between dog human and mouse.


Oct 14
11:00 - 12:00 noon

Probabilistic Models of the Endoplasmic Reticulum

Michael Hallett
McGill University School of Computer Science / McGill Centre for Bioinformatics

McIntyre Medical Building 10th Floor, Room 1034 (MacIntosh Lecture Theatre)


Sep 2005

Sep 15
4:00 pm

BIND and Blueprint's new resources for small-molecule interactions

Christopher W.V. Hogue
Principal Investigator, The Blueprint Initiative Samuel Lunenfeld Research Institute, Mount Sinai Hospital Associate Professor, Department of Biochemistry, University of Toronto

McIntyre Medical Building, Room 1034


May 2005

May 26
11h00 am

PatternHunter - Optimized Spaced Seeds for Homology Search Time

Dr. Bin Ma
University of Western Ontario

McConnell room #103

Given two DNA/protein sequences, homology search requires the finding of all pairs of substrings, each from one sequence, that are similar to each other. Traditionally, this is done by the Smith-Waterman algorithm, which is too slow for large genomes. The BLAST program significantly improved the speed by using consecutive seeds (a short strand of exact matches) to pre-select the homology regions. However, this also significantly reduces the sensitivity. I.e. many real homologies may be lost during the pre-selection. By replacing the consecutive seeds by optimized spaced seeds (several exact matches at some fixed positions), the sensitivity of the homology search is significantly improved. The spaced seeds, the reason of the sensitivity improvement, and the algorithm to select seeds are introduced.


May 20

Maximum Likelihood of Evolutionary Trees is Hard

Benny Chor
Tel-Aviv University

Lyman-Duff Medical Building, 3775 University St., Room 321

Understanding the origin and evolution of extant and extinct species is a fundamental scientific quest. Today, phylogenetic trees are widely used as the accepted evolutionary model, and are mostly based upon molecular sequences (amino acid or DNA) data. The space of candidate trees grows exponentially with the number of species, implying that even on modern computers, an exhaustive search over all trees is infeasible (except for few species, no more than approximately 20). Two reconstruction criteria most frequently used are maximum parsimony (MP), and maximum likelihood (ML). But are they solvable in a computationally efficient manner? Maximum parsimony (MP) was proved intractable almost 20 years ago (1986). The analogue question for maximum likelihood (ML) remained unsolved. This created a strange situation, because most practitioners believe that ML is computationally harder than MP. Namely computer programs running on the same sets of sequences tend to take much longer to solve ML than MP. Yet ML remained ``unclassified'', leaving the existence of an efficient ML algorithm a possibility. We resolve this question, and show that ML on phylogenetic trees is indeed computationally intractable (NP hard). Therefore, the reason why no ``magic bullet'', or efficient ML algorithm using clever algorithmic techniques was found, is because no such ``magic bullet'' exists. This is joint work with Tamir Tuller.


May 19

Protein subcellular localization: Analysis and prediction using the endoplasmic reticulum as a model organelle

Michelle Scott
McGill Centre for Bioinformatics

McIntyre Medical Building
3655 Promenade Sir William Osler,Rm 1034

Senior Seminar


Feb 2005

Feb 11
3:30 p.m.

Finding differentially expressed genes from Affymetrix microarray data

Angelo J. Canty
Dept of Developmental Biology, Hospital for Sick Children, Toronto

200 West, Sherbrooke str.
room SH-3420

In this talk I will describe the Affymetrix microarray platform for finding gene expression of large numbers of genes. These arrays are commonly used to look for genes which are differentially expressed between two populations. One of the most popular ways to find such genes is the Significance Analysis of Microarrays (SAM) methodology introduced by Tusher et al (2001) which I shall describe. I will talk about some of the issues which arise in such an analysis. I will also describe a mouse model in which we wish to study locus interactions in susceptibility to Type 1 Diabetes and how the basic SAM methods can be extended to such situations.

Note : Coffee and cookies will be served at 3:00 p.m.
A reception, with wine and cheese, will follow the talk.


Jan 2005

Jan 28
3:30 p.m.

A Statistician's View on Protein Folding

Ingo Ruczinski
Johns Hopkins University

McGill university
Burnside building, 805 W. Sherbrooke
room 1B39

The prediction of protein structure from its amino acid sequence and understanding the actual folding process are among the great unsolved challenges in molecular biology. Finding a solution to these problems and thereby understanding diseases such as Alzheimer would have a tremendous impact on public health. Many experiments carried out to understand the folding pathway from the unfolded state to the folded functional state generate data that have non-trivial statistical aspects, and in this seminar we will tell the tale mostly from a statistician's vantage point. We will also discuss some of the statistical and computational issues encountered in protein structure prediction, and present some applications of these methods such as the functional annotation of genes.

Note : Coffee and cookies will be served at 3:00 p.m.
A reception, with wine and cheese, will follow the talk.


Jan 28
4pm to 5pm

How can a Mathematician Cope with Phylogenetic Uncertainty?

David Bryant
McGill Centre for Bioinformatics

200 West, Sherbrooke Str., room SH-3420

Phylogenetics is the art of reconstructing evolutionary history, usually represented as an evolutionary tree (a phylogeny). The reconstruction of history is an inherently uncertain endeavour. There is the uncertainty we know (sampling error) and the uncertainty we know we don't know (modelling error). My approach has been to generalise phylogenetic trees and their associated stochastic models, giving phylogenetic networks. These networks can be used to represent error or uncertainty in our trees in a manner reminiscent of mixture models. I will describe applications of networks to the study of two billion year old divergences in the tree of life, and to more recent divergences in the United Nations. I also hope to touch on links with numerical mathematics, probability, statistics, combinatorics and algebraic geometry.


Dec 2004

Dec 16
4:00 pm

Mapping genetic networks and exploring cell cycle regulation using yeast functional genomics

Dr. Brenda Andrews
University of Toronto

McIntyre Medical Sciences Building
3655 Promenade Sir William Osler
room 1027


Oct 2004

Oct 01

Haplotype-based computational mapping of QTL using multiple inbred mouse strains

Jianmei Wang
Post doctoral fellow at Dept. of Genetics and Genomics, Roche Palo Alto

Duff Building, room 321

Traditionally, quantitative trait loci are identified by using experimental crosses of two inbred strains with different phenotypic trait values. We have developed a new computational method that identifies the genetic basis with much greater precision for phenotypic traits which vary among inbred mouse strains, using only trait values and genotype information of multiple parental inbred strains. A haplotype block structure of the mouse genome was constructed by organizing a dense set of SNPs into blocks based on allele data from 16 commonly used inbred strains1. The computational method identifies haplotypic blocks whose pattern correlates well with the strain trait values. This method correctly predicted the genetic basis for strain-specific differences in several biologically important traits. It was also used to identify a novel allele-specific functional element regulating H2-Ea gene expression2. 1. J. Wang et al., in Computational Genetics and Genomics: New Tools for Disease Biology, G. Peltz, Ed. Humana Press Inc., ToTowa, New Jersey, 2004. (To appear) 2. G. Liao, J. Wang et al. In Silico Genetics: Identification of A Novel Functional Element Regulating H2-Ea Gene Expression. Accepted by Science.


Sep 2004

Sep 21
7h00 pm to 8h30 pm

Nanotechnology: Big ideas about very little things

Peter Grutter
McGill University

Raymond Building, 21111 Lakeshore Road, Rm R2-045


Sep 21
4h30 pm

Microarray statistical analysis - Many choices, little consensus

Dr Robert Nadon
Dept of Human Genetics, McGill University & Genome Quebec Innovation Centre

3626 Saint Urbain Street, Rm 110


Sep 20
10h00 am to 11h00 am

Towards novel strategies for detecting RNA genes

Irmtraud Meyer
postdoctoral researcher at the Oxford Centre for Gene Function

Duff Medical Building, 3775 University Street, room 509, 5th floor

RNA genes are genes which are transcribed, but never translated into proteins. Although the vast majority of the transcriptional output of the human genome is thought to consist of non-protein-coding RNA, RNA genes are generally not annotated within the standard annotation procedure as they are notoriously hard to predict. However, as RNA genes have the potential to define an entire layer of yet unknown regulatory networks within the cell, we have to develop novel strategies for locating RNA genes within genomes. In order to successfully predict RNA genes within a genome, it is necessary to understand how they differ from other regions of the genome. One obvious expectation is that their RNA should exhibit a ``distinct'' pattern of secondary structure. However, any sub-sequence of the genome can fold into some secondary structure on RNA level by forming hydrogen bonds between complementary bases. To date, no adequate definition of structural ``distinctiveness'', that is able distinguish the secondary structure of non-functional RNA sequences from that of functional RNA sequences, has been found. One fundamental difference between RNA genes and intergenic regions which has not yet been exploited in RNA gene prediction, is that RNA genes are transcribed, whereas intergenic regions are not. Transcription is a directed process during which the 5' end of the RNA molecule is synthesized before its 3' end. We have demonstrated in a systematic computation study [1] that co-transcriptional folding significantly influences RNA genes in two ways which may provide the sequence signals required to successfully locate RNA genes within a genome. We have also developed [2] a novel algorithm which allows us to investigate important global structural properties of RNA genes. Using this algorithm, we have shown that RNA genes differ significantly from random RNA sequences. I will discuss our results and their implications for RNA gene finding. [1] I. M. Meyer and I. Miklos, Co-transcriptional folding is encoded within RNA genes, BMC Molecular Biology 2004, 10(5) [2] I. Miklos, I. M. Meyer and B. Nagy, Moments of the Boltzmann distribution for RNA secondary structures, submitted


Sep 20
12h00 pm

Mathematical models of gene regulation (without any mathematics!)

Dr Michael Mackay
McGill University

Montreal Children's Hospital, Rm C417


Sep 20
4h00 pm

Chromatin dynamics in gene regulatory coding

Dr Arndt Benecke
Institut des Hautes Études Scientifiques & Interdisciplinary Research Institute, Paris

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1101


Jul 2004

Jul 22
11:30 to 12:30

Measurement of Network information From N-point Correlations

Dr. Tane Ray
Dept. of Computer Science, Math and Physics, University of the West Indies, Cave Hill Campus, Barbados

Duff Medical Building
3775 University Street
Main Amphitheatre


Jun 2004

Jun 15

12h00 pm

Testing and building on a dispersal-assembly theory of ecological communities

Annette Ostling

Energy & Resources Group University of California Berkeley, CA

Stewart Biology Building
Room W4/12

Note: A. Ostling is a candidate for the position in Bioinformatics. There will also be a round table discussion following the seminar at 2:30 p.m. in the Coffee Room.


Jun 10

12h00 pm

Temporal variability and extinction risk of animal populations: from comparative analysis to the prediction of human impacts on endangered species

Pablo Inchausti
Université de Rennes 1 France

Stewart Biology Building
Room W4/12

Note: Pablo Inchausti is a candidate for the position in Bioinformatics. There will also be a round table discussion following the seminar at 2:30 p.m. in the Coffee Room.


Jun 08

4h00 pm


Donald Knuth

Université de Montréal
Pavillon André-Aisenstadt
Room 1140


Jun 08
12h00 pm

Spatial ecology, community structure, and global change

Brian McGill

Department of Fisheries & Wildlife Michigan State University

Stewart Biology Building
Room W4/12

Note: Brian McGill is a candidate for the position in Bioinformatics. There will also be a round table discussion following the seminar at 2:30 p.m. in the Coffee Room.


Jun 04
11:30 am

A structural perspective on complexes and cell-networks

Dr. Rob Russell

Structural Bioinformatics
EMBL, Meyerhofstrasse
Heidelberg, Germany

McIntyre Medical Building
ROOM 1034

Many current efforts are directed towards understanding large assemblies or networks of macromolecules. However, to date, limited attention has been paid to one of the best sources of interaction data: complexes of known three-dimensional structure. In this talk I will try to address this information gap, first by an overview of how structures can help interpret results from interaction discovery experiments like the two-hybrid system. I will then discuss a project aimed at providing as complete a structural picture as possible for whole cells using a combination of bioinformatics and electron microscopy. This has thus far provided new insights into many Yeast complexes, and provided a structure-based interaction network to use as a framework for future studies. Lastly, I will discuss briefly an attempt to uncover new protein-peptide mediated interactions, which are so central to the more transient parts of interaction networks.

Note: Pizza and refreshments available after the seminar in room 705.


Jun 03

Supertrees and phylogenies that make scents

Dr. Olaf Bininda-Emmonds

Technical University of Munich, Germany

Stewart Biology Building
Room W4/12

Note: Dr. Bininda-Emmonds is a candidate for the position in Bioinformatics. There will also be a round table discussion following the seminar at 2:30 p.m. in the coffee room.


May 2004

May 19

A User's Guide to Bayesian Phylogenetic

Bruce Rannala,
University of Alberta

Université de Montréal
Pavillon André-Aisenstadt
Local 6214 / Room 6214


May 18


Dr. Leonard Foster




May 05

Experimental bioinformatics approaches to proteomics

Ronald Beavis, Ph.D.

President, Beavis Informatics Ltd.
Candidate, Bioinformatics McGill University

Amphitheatre, Duff Medical Sciences Building
3775 University Street.


May 04

Functional genomics with yeast

Steve Oliver

School of Biological Sciences, Manchester, England.

McIntyre Medical Sciences Building, Room MCMED521


Apr 2004

Apr 23

Whole Genome Haplotype mapping using 100,000 single nucleotide polymorphisms and their implications on association studies

Carsten Rosenow, Ph. D. --
Senior Scientist Genomics Collaboration Genotyping Affymetrix Inc --

McGill, Strathcona Anatomy and Dentistry building Room 2/36 3640 University Street --


Apr 23

Pharmacogenetics ... Advanced health care for the future

Michael Phillips, Director --
Pharmacogenetics, Genome Quebec McGill University Innovation Centre --

Montreal Children's Hospital, 2300 Tupper, Rm C-417 --


Apr 22


Dr. Charles R. Scriver, Dr. Thomas J. Hudson, Dr. Jamie Engert, Dr. Damian Labuda, Dr. Jonathan Kimmelman, Dr. Marcus W. Feldman --
McGill University, Université de Montreal, Stanford University

Charles F. Martin Amphitheater, McIntyre Medical Sciences Bldg., 6th Floor --


Apr 20

Physical properties of gene expression microarray

Dr Yoshihiko Nagai --
McGill University & Genome Quebec Innovation Centre --

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1101 --


Apr 19

SNPs, Haplotypes and Complex Diseases

DR. Thomas J. Hudson, Director ,
mcgill university & genome quebec innovation centre

McIntyre Medical Sciences Building, 3655 Promenade Sir William Osler, room 1034 --


Dec 2003

Dec 15

Yeast functional genomics to study multidrug resistance

Dr. Bernard Turcotte
-- Department of Biology, McGill University

Stewart Biology Building, 1205 Dr Penfield Avenue, W4/12


Dec 11

Alain Denise
Université de Paris-Sud XI (Orsay)

Université de Montréal, Pavillon André-Aisenstadt, Local: 6214


Dec 10

Une approche in silico et in vivo pour la détection de sites de décalages en traduction dans les génomes eucaryotes

Alain Denise
Université de Paris-Sud XI (Orsay)

Université de Montréal, Pavillon André-Aisenstadt, Local: 3195


Dec 09

Cycles of nitrogen base interactions reveal RNA thermodynamics (and bring new hopes towards structure prediction)

Dr François Major
-- Université de Montréal

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1101. --


Dec 08

The Minimal Cycle Basis as a new paradigm to analyze RNA 3-D structures

Sébastien Lemieux
:: Bioinformatics Postdoctoral Fellow, Elitra Canada

McGill University, McConnell Engineering Building, Room 437 ::


Dec 04

Dr Mariano A. Garcia-Blanco
-- Dept of Genetics, Duke University Medical Center

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 903 --


Dec 03

Depicting reticulate evolution by means of reticulograms

Vladimir Makarenkov, Ph. D.
Associate professor at Université du Québec à Montréal

-- Université de Montréal, Pavillon André-Aisenstadt, Local: Local 3195


Dec 02

Sequence based prediction of endoplasmic reticulum resident proteins

Michelle Scott
McGill Centre for Bioinformatics

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1101. --


Dec 01

Role of chromatin acetylation by NuA4 in nuclear functions

Dr. Jacques Coté
-- Université Laval

Stewart Biology Building, 1205 Dr Penfield Avenue, W4/12


Dec 01

Proteomics: Too Much of a Good Thing?

Dr. Scott D. Patterson
(Chief Scientific Officer,Farmal Biomedicines, LLC,Pasadena, CA)

McGill University, 3640 University street (1st floor, Room 1/15) --


Nov 2003

Nov 28

Analyse et modélisation de l'expression des gènes

Guillaume Bourque
Université de Montréal, Centre de Recherches Mathématiques.

Université de Montréal, Pavillon Principal, Local: G-815


Nov 25

New Perspectives on the Rational Design of Antiarrhythmic Agents

Dr. Jacques Beaumont,
SUNY Upstate Medical University, New York

McIntyre Medical Sciences Building, 3655 Prom. Sir William Osler, Room 1101 --


Nov 24

Role of oligomeric assembly in GPCR signaling

Dr Michel Bouvier
-- Dept of Biochemistry, Université de Montréal

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1034 --


Nov 21

La diversité d'ADN humain, les haplotypes et l'horloge génétique.

Damian Labuda
Université de Montréal, Département de biochimie.

Local: G-815, Pavillon Principal (Université de Montréal)


Nov 17

Hox gene function and regulation during mouse development

Dr Lucie Jeannotte
-- Université Laval

Stewart Biology Building, 1205 Dr Penfield Avenue, Rm W4/12


Nov 13

The priming mechanism of the primase in bacteriophage T7 DNA replication

Dr Masako Kato
-- Dept of Biological Chemistry and Molecular Pharmacology, Harvard. Dr Kato is a candidate for a position at McGill

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1027 --


Nov 12

Haplotypes histories as pathways of recombination and gene conversion.

Nadia El-Mabrouk
Professeure, Université de Montréal, Département d'informatique et de recherche opérationnelle.

Local 6214, Pavillon André-Aisenstadt (Université de Montréal)


Nov 12


-- Center for Computational Genomics and Center for Human Genetics, Case Western Research University School of Medicine, Cleveland, Ohio

Stewart Biology Building, 1205 Dr Penfield Avenue, Room W4/12


Nov 11

Exploratory data analysis using phylogenetic networks

Dr David Bryant
-- McGill Centre for Bioinformatics

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1101 --


Nov 10

A non-coding RNA is required for RNA polymerase II-dependent transcription repression in primordial germ cells

Dr Rui Goncalo Martinho
-- New York University Medical Center

Stewart Biology Building, 1205 Dr Penfield Avenue, Rm W4/12


Nov 10

Functional redundancy and understanding how Hox genes operate

Deneen M. Wellik,
University of Michigan Medical Center

Duff Medical Building, 3775 University Street, Amphitheatre.


Nov 07

L'observation de cycles d'interactions de bases azotées dans les ARN structurés révèlent de nouvelles règles thermodynamiques et apportent de nouveaux espoirs pour la prédiction.

François Major
Professeur Agrégé/Associate Professor, Université de Montréal, Département d'informatique et de recherche opérationnelle.

Local: G-815, Pavillon Principal (UdM, )


Nov 04

CellMap Proteomics

Dr. Alexander Bell
-- Professional Associate (Manager MS Data Interpretation) Montreal Proteomics Network --

Royal Victoria Hospital -- Room S-684


Nov 03

Bioinformatics for the discovery and analysis of regulatory region in the human genome

Dr Wyeth Wasserman (
Associate Professor, Dept. Medical Genetics UBC

Room 2/36 Strathcona building


Oct 2003

Oct 31

Learning Neural Computing From Electric Fish

Andree Longtin Ph.D.
Physics Department, University of Ottawa

Department of Physiology, McIntyre Medical Building, Room 1034 (F.C. MacIntosh Lecture Theatre)


Oct 30

The Atlas Assembler And What We Learned From The Rat

Paul Havlak
Department of Molecular and Human Genetics, the Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.

Université de Montréal, Local: 5340, Pavillon André-Aisenstadt


Oct 30

Translation and mRNA decay visibly connected: Tristertraprolin: 14-3-3 complexes regulate stress granule association and ARE-mRNA turnover

Dr Georg Stoecklin
-- Division of Rheumatology and Immunology, Brigham & Women's Hospital

McIntyre Medical Building, 3655 Promenade Sir William Osler, Rm 1027


Oct 29

Algorithmes et applications de l'analyses comparative de séquences

Mathieu Blanchette
Assistant Professor, McGill Center for Bioinformatics, McGill University

Local: 3195, Pavillon André-Aisenstadt (Université de Montréal, )


Oct 28

Comparative genomics of human/chimpanzee and mouse/rat

Saitou Naruya, Ph.D.
Professor. Division of Population Genetics, National Institute of Genetics, Mishima, Japan



Sep 2003

Sep 24

Seminars in Biomedical Engineering (BMDE-500D) SPECIAL SEMINAR Nanoscale biophysics in sensor development

Dr. Jay Nadeau
Jet Propulsion Laboratory, Astrobiology Research Element, California Institute of Technology, USA

Amphitheatre, Duff Medical Sciences Building 3775 University Street.


Jul 2003

Jul 03

Biased Gene Regulation Rules and Probable Order in Genetic Regulatory Networks.

Dr. Stuart Kauffman
Santa Fe Institute

Jonathan C. Meakins Amphitheatre McIntyre Medical Sciences Building 3655 Promenade Sir-William-Osler 5th Floor, Room 521.


May 2003

May 08

Tandem Repeat Identification: Algorithms for Locating Simple and Complex Patterns

Amy Hauth PhD, Computational Molecular Biology
University of Wisconsin

University of Montreal, Pavillon André-Aisenstadt, Room 3195


May 06

Identification of Tandem Repeats in Genome Sequences

Amy Hauth PhD, Computational Molecular Biology
University of Wisconsin

Univeristy of Montreal, Biochemistry Department, Pavillon Principal D-560


Apr 2003

Apr 07

RNA sequence alignments: structure prediction with phylogenetic trees and more

Viatcheslav (Slava) Akmaev
Genzyme Corporation Framingham, MA

McGill University Macdonald Engineering Building Room 388


Mar 2003

Mar 31

A Motion Planning Approach to Protein Foilding

Guang Song
Texas A&M University - Department of Computer Science

McGill University MacDonald Engineering Building Room 388


Feb 2003

Feb 26

Data-Driven Computer Simulation of Human Colon Cancer Cell

Mr. Colin C. Hill, Chief Executive Officer
President, Gene Network Sciences, Inc.



Feb 20

The Complexity of Reconstructing Evolutionary Trees

Valerie King
University of Victoria

Room 320, McConnell


Oct 2002

Oct 25

Description Logics - A Logical Foundation of the Semantic Web and its Applications

Volker Haarslev
Concordia University, Computer Science Department

Concordia University

MCB lab conference room


Oct 21

Proteins, petaflops and algorithms

William R. Pulleyblank (IBM Research, USA)
Director, Deep Computing Institute
Director, Exploratory Server Systems

IBM Research, USA

Macdonald Harrington G-10


Oct 17


Dalhousie University, Halifax NS

THE SPEAKER: W. Ford Doolittle received his BA and PhD degrees at Harvard and Stanford, respectively, and after postdoctoral work in Illinois and Colorado, joined the Department of Biochemistry (now Biochemistry and Molecular Biology) at Dalhousie. Since 1986, he has been Fellow and Director of the Evolutionary Biology Program of the Canadian Institute for advanced Research. He was awarded a Canada Research Chair in 2001. He is a Fellow of the Royal Society of Canada and a member of the US National Academy of Sciences.

Concordia University Science College
Main Auditorium, Room H-110
1455, de Maisonneuve Blvd. West


Oct 04

Surface Complementarity In Molecular Recognition

Dr. Vladimir Sobolev
Weizmann Institute of Science
Plant Science Department
Rehovot, 76100

Duff Medical Building
3775 University Street


Sep 2002

Sep 30

Computational Approaches for High-Throughput Biology

* Igor Jurisica, Ontario Cancer Institute
* Michael Hallett, McGill University

IBM Centre for Advanced Studies
8200 Warden Avenue
Markham, Ontario, Canada
L6G 1C7


Sep 26

"Genomics of two pathogens: DNA arrays of Campylobacter jejuni and genome sequencing of Actinobacillus pleuropneumoniae"

Dr. John Nash
Division of Biological Sciences, National Research Council
Ottawa, ON

Duff Medical Sciences Building
Room 507/509
3775 University Street, Montreal, Quebec
H3A 2B4


Sep 17

"Rickettsia, Tropheryma, and 50 others: Big lessons from small bacterial genomes"

Dr. Jean-Michel Claverie
Research Director

French National Research Center (CNRS, France)

Jonathan C. Meakins Amphitheatre
McGill University
McIntyre Medical Sciences Building
3655 Promenade Sir William Osler
5th Floor


Jul 2002

Jul 11

BioOpera: Process Support for Bioinformatics

Win Bausch
Information and Communication Systems Research Group Institute for Information Systems ETH Zuerich, Switzerland

Lecture Hall 1 (Ampitheatre) Duff Medical Building McGill University 3775 University Street Montreal, Quebec H3A 2B4 Canada


Nov 2001

Nov 08

Algorithms for Phylogenetic Footprinting

Mathieu Blanchette
University of Washington

McGill Centre for Bioinformatics


Jun 2001

Jun 04

Biological Information Processing: from DNA to Computation and Back

Lila Kari
Department of Computer Science University of Western Ontario London, Ontario Canada

McGill Centre for Bioinformatics


Jan 2001

Jan 19

A Polynomial Time Exact Disc- Covering Method

Jens Lagergren
Royal Institute of Technology (KTH) and Stockholm Bioinformatics Centre

McGill Centre for Bioinformatics


Nov 2000

Nov 2000

Une approche in silico et in vivo pour la détection de sites de décalages en traduction

Alain Denise
Professeur, équipe Bioinformatique. LRI, Université Paris-Sud.

Université de Montréal, Pavillon André-Aisenstadt, Local: Local 3195