Friday, DEC 1, 2006
SYSTEMS BIOLOGY: REGULATORY NETWORKS

Invited speaker: Eric Davidson, California Institute of Technology
Title: Experimental System Analysis of the Genomic  Regulatory Code for Embryonic Development    Abstract: Sea urchins are now among the most advanced experimental systems for analysis of genomic regulatory networks. This talk will concern the gene regulatory network (GRN) for endomesoderm specification in sea urchin embryos. The network, which now contains ~50 genes, mainly regulatory genes, is a powerful tool for both explanation and prediction, extending from the phenomenology of development to cis-regulatory functions at the nodes of the GRN. The GRN is formulated on the basis of prior knowledge of the developmental process, detailed observations on spatial and temporal patterns of gene expression, and a large-scale perturbation analysis. The GRN is represented in computational models that permit predictions of cis-regulatory inputs at the nodes of the GRN, and that display the gene regulatory transactions that are active or inactive in given spatial domains of the embryo at given times. Among the computational methodologies developed to support the GRN analysis is a new application of interspecific sequence comparisons which has proven experimentally to be remarkably useful for rapid identification of cis-regulatory elements. Experimental verification of cis-regulatory predictions has been obtained in remarkable detail for many key nodes of the GRN, indicating that it provides a true representation of encoded genomic regulatory logic for early development. It is now possible, by comparing different GRNs, to perceive some principles of circuit design for embryogenesis.

Invited speaker: Martha Bulyk, Harvard Medical School
Title: Genomic Analyses of Transcription Factors and Cis Regulatory Elements
Abstract: The interactions between transcription factors (TFs) and their DNA binding sites are an integral part of the cellular regulatory networks that control gene expression. We have developed in vitro protein binding microarray (PBM) technologies that allow the rapid, high-throughput characterization of the DNA binding site sequence specificities of TFs in a single day. We are currently performing PBM experiments on a large number of yeast and mouse TFs, as well as TFs from other organisms. We plan to use PBM data on the DNA binding specificities of metazoan TFs for more accurate prediction of cis regulatory modules within the vast noncoding portions of those organisms' genomes. Specifically, we are inferring co-regulation by sets of TFs through an integrated analysis of gene expression data, TF binding site motif data, and prediction of cis- regulatory modules.

Accepted Paper: David Orlando, Duke University
Title: A Probabilistic Model for Cell Cycle Distributions in Synchrony Experiments
Abstract: Synchronized populations of cells are often used to study dynamic processes during the cell division cycle. However, the analysis of time series measurements made on synchronized populations are confounded by the fact that populations lose synchrony over time. Time series measurements are thus averages over a population distribution that is broadening over time. Moreover, direct comparison of measurements taken from multiple synchrony experiments is difficult, as the kinetics of progression during the time series are rarely comparable. Here, we present a flexible mathematical model that describes the dynamics of population distributions resulting from synchrony loss over time. The model was developed using S. cerevisiae, but we show that it can be easily adapted to predict distributions in other organisms. We demonstrate that the model reliably fits data collected from populations synchronized by multiple techniques, and can accurately predict cell cycle distributions as measured by other experimental assays. To indicate its broad applicability, we show that the model can be used to compare global periodic transcription data sets from different organisms: S. cerevisiae and S. pombe.

Accepted Paper: Tomer Shlomi, Tel Aviv University
Title: A Genome-Scale Computational Study of the Interplay between Transcriptional Regulation and Metabolism
Abstract: This paper presents a new method, Steady-state Regulatory FBA (SR-FBA), for predicting gene expression and metabolic fluxes in a large-scale integrated metabolic-regulatory model. Using SR-FBA to study the metabolism of Escherichia coli, we find that gene expression is more strongly coupled with varying growth media conditions than the reactions' flux activity. The extent to which the different levels of metabolic and transcriptional regulatory constraints determine metabolic behavior is comprehensively quantified: Metabolic constraints determine the flux activity state of 45-51% of metabolic genes, depending on the growth media, while transcription regulation determines the flux activity state of 13-20% of the genes. A considerable number of 36 genes are redundantly expressed, that is, they are expressed even though the fluxes of their associated reactions are zero, indicating that they are not optimally tuned for cellular flux demands. The non-determined state of the remaining ~30% of the genes suggests that they may represent metabolic variability within a given growth medium. Overall, SR-FBA enables one to address a host of new questions concerning the interplay between regulation and metabolism.

Invited speaker: Daphne Koller, Stanford University
Title: Genetic Variation and Regulatory Networks: Mechanisms and Complexity
Abstract: Sequence polymorphisms affect gene expression by perturbing the complex network of regulatory interactions. Standard methods (e.g., Yvert et al., 2004) attempt to associate each gene expression phenotype with genetic polymorphisms.  This talk describes a novel probabilistic method, called Geronemo, which aims to understand the mechanism by which genetic changes perturb gene regulation. Geronemo automatically constructs a set of co-regulated genes (modules), whose regulation can involve both sequence variations and expression of regulators. By exploiting the modularity of biological systems, Geronemo reveals regulatory relationships that are indiscernible when genes are considered in isolation, allowing the recovery of intricate combinatorial regulation.  By incorporating both expression and genotype of regulators, Geronemo captures cases where the effect of sequence variation on its targets is indirect. We applied Geronemo to a dataset from the progeny generated by a cross between laboratory (BY) and wild (RM) isolates of S. cerevisiae. Geronemo produced novel hypotheses about genetic perturbations in the yeast regulatory network, including transcriptional regulation, signal transduction, and chromatin modification. Our global analysis highlights some of the key differences between the regulatory network of the BY and RM strains.  In particular, our results suggest that a significant part of individual expression variation in yeast arises from evolution in the regulatory regions and the coding sequences of a small number of chromatin modifiers.   Moreover, our analysis suggests an intriguing hypothesis, supported by subsequent wet-lab experiments, which elucidates a pathway associated with p-bodies, a recently discovered protein complex that helps regulate mRNA degradation.

Accepted paper : Celine Lefebvre, Columbia University
Title: A context-specific network of protein-DNA and protein-protein interactions reveals new regulatory motifs in human B cells
Abstract: Recent genome wide studies in yeast have started to unravel the complex, combinatorial nature of transcriptional regulation in eukaryotic cells, including the concerted regulation of proteins involved in complex formation. In this work, we use a Bayesian evidence integration framework to assemble an integrated network, including both protein-DNA and protein-protein interactions, in a specific cellular context (human B cells). We then use it to study common interaction motifs between protein complexes and regulatory programs, using an enrichment analysis approach. Specifically, we compared the frequency of mixed interaction motifs in the real network against random networks of equivalent connectivity. These motifs include sets of target genes regulated by multiple interacting transcription factors, and gene sets encoding same complex proteins regulated by different transcription factors.

Accepted paper : Matthew A. Zapala
Title: High-Density QTL Mapping to Identify Phenotypes and Loci Influencing Gene Expression Patterns in Entire Biochemical Pathways
Abstract: Associating SNPs to gene expression levels has been useful in identifying transcriptional networks in what is called a "genetical genomic" analysis. These types of studies suffer from multiple comparison bias as thousands of gene expression signals are tested against thousands of SNPs. Moreover, the biological meaning of a cis or trans association is difficult to discern. We have taken an integrative approach using univariate, aggregate and multivariate statistics to associate gene expression values to SNPs both individually and as gene sets belonging to biochemical pathways. SNPs that not only perturb the expression of single genes but also entire pathways are identified. We analyzed public gene expression data on hematopoetic stems cells from 22 BXD mice. 1093 unique SNPs from the Wellcome Trust SNP genotype set are available for these mice. Genes were grouped into GO, KEGG, Biocarta and Mousepath pathways based on their Entrez ID. The aggregate and multivariate analyses identified genes previously identified in univariate studies. More importantly, these methods identified novel associations that occurred in gene sets related to hematopoetic stem cells.

Invited speaker: Bing Ren, UC San Diego
Title: Decoding the human genome - a chip-chip approach
Abstract: The sequencing of the human genome has been essentially completed now, but the function for the vast majority of the genomic sequence is still unknown.  In particular, our knowledge of the transcriptional regulatory sequences remains poor to non-existent, preventing a systematic understanding of the gene regulatory mechanisms in human cells. I'll describe a high throughput experimental approach, combining ChIP-on-chip with high-resolution genome-tiling arrays, for mapping the transcriptional regulatory elements in the human genome.  Using this strategy, my laboratory has been able to systematically identify promoters, enhancers and insulator elements throughout the human genome, and characterize the common feature of chromatin architecture associated with these sequences.  We have further developed computational algorithms that exploit the chromatin feature to identify new promoters and enhancers in the human genome. Our results thus provide the first global view of gene regulation in human cells, offer new insights into the transcriptional regulatory networks, and facilitate the functional annotation of the human genome.

Accepted paper: Xuefeng Zhou, Washington University in Saint Louis
Title: UV-B Responsive MicroRNA Genes in Arabidopsis thaliana
Abstract: MicroRNAs are small, non-coding RNAs that play critical roles in post-transcriptional gene regulation. In plants, mature microRNAs pair with complementary sites on mRNAs and subsequently lead to cleavage and degradation of the mRNAs. Many microRNAs target mRNAs that encode transcription factors, therefore, they regulate the expression of many down-stream genes. In this study, we carry out a survey of Arabidopsis microRNA genes in response to UV-B radiation, an important adverse abiotic stress. We develop a novel computational approach to identify microRNA genes induced by UV-B radiation and characterize their functions in regulating gene expression. We report that in {em A. thaliana} 16 microRNA genes in 11 microRNA families are up-regulated under UVB stress condition. We also discuss putative transcriptional down-regulation pathways triggered by the induction of these microRNA genes. Moreover, our approach can be directly applied to miRNAs responding to other abiotic and biotic stresses and extended to miRNAs in other plants and metazoans. This study shows that machine learning approaches coupled to statistical analysis hold significant promise for predicting gene expression activity under certain conditions.

Accepted paper: Rachel P. McCord, Harvard University
Title: Inferring condition-specific transcription factor function from DNA binding specificities and gene expression
Abstract: Numerous genomic and proteomic datasets are permitting the elucidation of transcriptional regulatory networks in the yeast Saccharomyces cerevisiae. However, prediction of the condition-dependence of regulatory network interactions has been challenging, since most protein-DNA interactions identified in vivo are from assays performed in just one or a few cellular conditions. Here, we present a novel method that aims to predict the condition-specific functions of S. cerevisiae transcription factors (TFs) by integrating 1,327 microarray gene expression datasets and comprehensive TF binding site data from protein binding microarrays (PBMs). Importantly, our method does not impose arbitrary thresholds for calling target regions "bound", but rather allows all the information derived from a TF binding experiment to be considered. The results of this analysis for characterized TFs show that this method can identify physical, environmental, and genetic interactions, as well as distinct sets of genes that might be activated or repressed by a single TF in particular conditions. This approach could be used to predict functions of uncharacterized TFs and to suggest conditions for directed in vivo experiments.

Invited speaker: Ron Shamir, Tel Aviv University
Title: Modeling and expansion of signaling pathways
Abstract: One of the major challenges in systems biology is the analysis of large scale genome-wide data vis-.-vis prior knowledge about a particular cellular network. Here we introduce an extended computational framework that combines formalization of available qualitative knowledge in a probabilistic model, and integration of high throughput experimental data.  Using our methods, it is possible to learn an improved model with better fit to the experiments. In particular, we improve the model in terms of (a) refining the relations among model components, and (b) expanding the model to include new components that are transcriptionally regulated by the model. We demonstrate our methodology on the yeast response to hyper-osmotic shock. We show that our integrative approach synergistically combines qualitative and quantitative evidence into concrete novel biological hypotheses.

Saturday, DEC 2, 2006
PROTEIN INTERACTIONS

Invited speaker: Mark Gerstein,  Yale University
Title: Understanding Protein Function on a Genome-scale using Networks
Abstract: My talk will be concerned with topics in proteomics, in particular predicting protein function on a genomic scale. We approach this through the prediction and analysis of biological networks -- both of protein-protein interactions and transcription-factor-target relationships. I will describe how these networks can be determined through integration of many genomic features and how they can be analyzed in terms of various simple topological statistics. I will discuss the accuracy of various reconstructed quantities.

Accepted paper: Jianhua Ruan, Washington University in St. Louis,
Title: Identification and evaluation of functional modules in gene co-expression networks
Abstract: Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. In this paper, we first propose a simple method to construct gene co-expression networks from Microarray data, and introduce an efficient spectral clustering algorithm to identify natural communities, which are relatively densely connected sub-graphs, in the network. To evaluate the effectiveness of our approach and its advantage over existing methods, we propose a novel method to measure the agreement between the gene communities and the modular structures in other reference networks, including protein-protein interaction networks, transcriptional regulatory networks, and gene networks derived from gene annotations. We test the proposed method on two large-scale gene expression data in Yeast and Arabidopsis. The results show that the clusters identified by our method are functionally more coherent than the clusters from several standard clustering algorithms, such as k-means, self-organizing maps, and spectral clustering, and have high agreement to the modular structures in the reference networks.

Accepted paper: Xin Lu, University of California, San Diego
Title: Inverse correlation of connectivity and modulation
Abstract: Asthma is a complex polygenic disease involving the interaction of many genes through a complex gene interaction network. We mapped the gene expression data of a murine model onto known mouse gene network, studied the topological structure of the network, the correlation between the topology and differential expression, and the Gene Ontology (GO) classifications. We showed that the highly modulated genes tend to be less connected peripheral down stream effective genes while highly connected core regulator genes tend to have lower level of modulation. Therefore we argue that studies solely depending on differential expression may not be able to identify the most important regulatory gene for diseases; instead we need to rely on the combination of gene network and gene expression to find the key regulators.

Accepted paper: Igor Ulitsky, Tel Aviv University
Title: Pathway redundancy and protein essentiality revealed in the S. cerevisiae interaction networks
Abstract: The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Our analysis on S. cerevisiae reveals 140 between-pathway models that explain 3,765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify “pivot” proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins.

Accepted paper: Derek A Ruths, Rice University
Title: De novo Signaling Pathway Predictions based on Protein-Protein Interaction, Targeted Therapy and Protein Microarray Analysis
Abstract: Mapping intra-cellular signaling networks is a critical step in developing an understanding of and treatments for many devastating diseases. The predominant ways of discovering pathways in these networks are knockout and pharmacological inhibition experiments. However, experimental evidence for new pathways can be difficult to explain within existing maps of signaling networks. In this paper, we present a novel computational method that integrates pharmacological intervention experiments with protein interaction data in order to predict new signaling pathways that explain unexpected experimental results. Biologists can use these hypotheses to design experiments to further elucidate underlying signaling mechanisms or to directly augment an existing signaling network model. When applied to experimental results from human breast cancer cells targeting the epidermal growth factor receptor (EGFR) network, our method proposes several new, biologically-viable pathways that explain the evidence for a new signaling pathway. These results demonstrate that the method has potential for aiding biologists in generating hypothetical pathways to explain experimental findings. Our method is implemented as part of the PathwayOracle toolkit and is available from the authors upon request.

Accepted paper: Cenk Sahinalp, Simon Fraser University
Title: Not All Scale Free Networks are Born Equal: the Role of the Seed Graph in PPI Netwok Emulation
Abstract: The (asymptotic) degree distributions of the best known "scale free" network models are all similar and are independent of the seed graph used. Hence it has been tempting to assume that networks generated by these models are similar in general. In this paper we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the "right" seed graph, the duplication model captures many topological features of publicly available PPI networks very well.

Accepted paper: Hailiang Huang, Johns Hopkins University
Title: Probabilistic Paths in Protein Interaction Networks
Abstract: Understanding how individual proteins are organized into complexes and pathways is a significant current challenge. We introduce new algorithms to infer protein complexes by combining seed proteins with a confidence-weighted network. Two new stochastic methods use averaging over a probabilistic ensemble of networks, and the new deterministic method provides a deterministic ranking of prospective complex members. We compare the performance of these algorithms with three existing algorithms. We test algorithm performance using three weighted graphs: a Naïve Bayes estimate of the probability of a direct and stable protein-protein interaction; a logistic regression estimate of the probability of a direct or indirect interaction; and a decision tree estimate of whether two proteins exist within a common protein complex. The best-performing algorithms in these trials are the new stochastic methods. The deterministic algorithm is significantly faster, whereas the stochastic algorithms are less sensitive to the weighting scheme.

Accepted paper: Haiyuan Yu, Yale University
Title: Developing a similarity measure in biological function space
Abstract: Many classifications of protein function such as Gene Ontology (GO) are organized in discrete categories within directed acyclic graph (DAG) structures. The computation of a numerical measure of functional similarity between two arbitrary proteins within such DAG structures is an important problem. Here we develop a simple probabilistic measure for this quantity. Our measure is based on counting the number of protein pairs that share exactly the same set of functional annotations in relation to the total number of classified pairs. We show such a measure is associated with a power-law distribution, allowing the quick assessment of the statistical significance of shared functional annotations. We formally compare it with other quantitative functional similarity measures (such as shortest path, lowest common ancestor, and information-theoretic similarity) and provide concrete metrics to assess differences. Finally, we provide a practical implementation for our probabilistic measure for GO and for the MIPS functional catalog and give two applications of it in specific functional genomics contexts.

Accepted paper: Alexandra Shulman-Peleg, Tel Aviv University
Title: Structural Similarity is a Prominent Feature of Genetic Interactions
Abstract: The study of gene mutants and their interactions is fundamental to understanding gene function and backup mechanisms within the cell. The recent availability of large scale genetic interaction networks in yeast and worm allows the investigation of the biological mechanisms underlying these interactions at a global scale. To date, less than 2% of the known genetic interactions in yeast or worm can be accounted for by sequence similarity. Here, we perform the first genome-scale structural comparison among protein pairs in the two species. We show that significant fractions of genetic interactions involve structurally similar proteins, spanning 7% and 14% of all known interactions in yeast and worm, respectively. We also identify several structural features that are predictive of genetic interactions and show their superiority over sequence-based features. Finally, we suggest putative mechanisms for genetic interactions among structurally similar proteins.

Invited speaker: Nevan Krogan, UCSF
Title: Genetic interactions reveal the functional relationships within and between protein complexes involved in chromosome biology 
Abstract: We present an epistatic miniarray profile (E-MAP) consisting of ~200,000 pair-wise genetic interaction measurements for 743 Saccharomyces cerevisiae genes involved in various aspects of chromosome function. The quantitative and high-density nature of this map enables systematic organization of these genes into epistasis groups, and further delineation of relationships among such functional modules yields many specific insights into transcriptional regulation, DNA replication/repair and chromatid segregation. Comparison of our genetic data with physical interaction data reveals that roughly half of physical interactions are between proteins that act in a coordinated manner to carry out common sets of functions and also allows the functional dissection of the remaining complexes. For example, we identify several functionally distinct modules within the Mediator complex and reveal how they communicate differentially with the RNA polymerase II transcriptional machinery. Our genetic interaction data also makes it possible to place distinct protein complexes into pathways, including one defined here in which RTT109-mediated acetylation of histone H3 on lysine 56 acts upstream of a cullin-containing ubiquitin ligase to ensure genomic integrity during DNA replication.

Sunday, DEC 3, 2006
MASS SPECTROMETRY

Invited Speaker: Marshall Bern, Palo Alto Research Center
Title: Identification of Glycans and Glycopeptides using a mix of MS, MS/MS, and MS^n
Abstract: Glycans are carbohydrate modifications of proteins.  They play important roles in cell signaling, including sperm-egg binding and immune system function, and they have good potential as disease biomarkers.  Glycans cannot be analyzed like other protein modifications, such as phosphorylation, due to their high molecular weight and huge number of possible forms.  I will give an overview of the mass spectrometry and computational strategies used for glycan identification.

Accepted paper: Nathan J Edwards, University of Maryland
Title: Novel peptide identification from tandem mass spectra using expressed sequence tags and sequence database compression
Abstract: Peptide identification by tandem mass spectrometry is the dominant proteomics workflow for protein characterization in complex samples. Traditional search engines, which match peptide sequences with tandem mass spectra to identify the samples' proteins, use protein sequence databases to suggest peptide candidates for consideration. While the acquisition of tandem mass spectra is not biased towards well understood protein isoforms, this computational strategy is, failing to identify peptides from alternative splicing and coding SNP protein isoforms despite the acquisition of good quality tandem mass spectra. We propose, instead, that expressed sequence tags (ESTs) be searched. Ordinarily, such a strategy would be computationally infeasible due to the size of EST sequence databases, however we show that a sophisticated sequence database compression strategy, applied to human ESTs, reduces the sequence database size approximately thirty-five fold. Once compressed, our EST sequence database is comparable in size to other commonly used protein sequence databases, making routine EST searching feasible. We demonstrate that our EST sequence database enables the discovery of novel peptides in a variety of public datasets.

Accepted paper: Lu-yong Wang, Siemens Corporate Research
Title: Alignment of Mass Spectrometry Data by Clique Finding and Optimization
Abstract: Mass spectrometry (MS) is becoming a popular approach for quantifying the protein composition of complex samples. A great challenge for comparative proteomic profiling is to match corresponding peptide features from different experiments to ensure that the same protein intensities are correctly identified. Multi-dimensional data acquisition from liquid-chromatography mass spectrometry (LC-MS) makes the alignment problem harder. We propose a general paradigm for aligning peptide features using a bounded error model. Our method is tolerant of imperfect measurements, missing peaks, and extraneous peaks. It can handle an arbitrary number of dimensions of separation, and is very fast in practice even for large data sets. Finally, its parameters are intuitive and we describe a heuristic for estimating them automatically. We demonstrate results on single- and multi-dimensional data.

Invited Speaker: Forest White, MIT
Title: Quantitative Analysis of Cellular Signaling Network - Activity Relationships
Abstract: Signal transduction mediated by protein phosphorylation regulates many cellular biological processes.  Aberrations in protein phosphorylation due to kinase (or phosphatase) mutation or overexpression leads to dysregulation of cellular signaling and has been linked to a variety of pathologies, including cancer, autoimmune, and metabolic disorders.  Quantification of specific phosphorylation sites regulating signaling pathways involved in these pathological disorders should enable a better understanding of the genesis and progression of the disease state, potentially providing targets for more effective therapeutic intervention. 
To effectively monitor protein phosphorylation events governing signaling cascades, we have developed a methodology enabling the simultaneous quantification of tyrosine phosphorylation of specific residues on dozens of key proteins in a time-resolved manner.  We have recently extended our analysis of the EGFR signaling network to interrogate the effects of increased expression of HER2, an EGFR family member whose over-expression has been correlated with poor prognosis in several cancer sub-types, on the EGFR signaling network.  Application of bioinformatic tools has resulted in identification of several cohorts of tyrosine residues exhibiting self-similar temporal phosphorylation profiles, operationally defining dynamic modules in the EGFR signaling network.  We have also measured biological response to stimulation in this same system, generating a quantitative phenotypic data set describing the proliferation and migration rates.  To identify the phosphorylation sites most strongly correlated to biological response, partial least squares regression analysis of the phosphoproteomics and phenotypic data sets was performed, resulting in a weighted scoring for each phosphorylation site.

Overall, by combining mass spectrometry-based analysis of protein phosphorylation with phenotypic measurements and computational modeling, we are now able to identify sections of the signaling network that correlate strongly with biological response to cell perturbation.  This approach should yield novel insights into the regulation of biological decisions on the network scale. 

Accepted paper: Mingzhou Song, New Mexico State University
Title: A Linear Discrete Dynamic System Model for Temporal Gene Interaction and Regulatory Network Influence in Response to Bioethanol Conversion Inhibitor HMF for Ethanologenic Yeast
Abstract: A linear discrete dynamic system model is constructed to represent the temporal interactions among significantly expressed genes in response to bioethanol conversion inhibitor 5-hydroxymethylfurfural for ethanologenic yeast Saccharomyces cerevisiae. This study identifies the most significant linear difference equations for each gene in a network. A log-time domain interpolation addresses the non-uniform sampling issue typically observed in a time course experimental design. This system model also insures its power stability under the normal condition in the absence of the inhibitor. The statistically significant system model, estimated from time course gene expression measurements during the earlier exposure to 5-hydroxymethylfurfural, reveals known transcriptional regulations as well as potential significant genes involved in detoxification for bioethanol conversion by yeast.

Accepted paper: Jacob D Feala, University of California, San Diego
Title: Flexibility in energy metabolism supports hypoxia tolerance in Drosophila flight muscle: metabolomic and computational systems analysis
Abstract: The fruit fly Drosophila melanogaster offers promise as a genetically tractable model for studying adaptation to hypoxia at the cellular level, but the metabolic basis for extreme hypoxia tolerance in flies is not well known. Using H1 NMR spectroscopy, metabolomic profiles were collected at 4 time points under 0.5% oxygen. Accumulation of lactate, alanine, and acetate suggested that these are end products of anaerobic metabolism in the fly. A constraint-based model of ATP-producing pathways was built using the annotated genome, an existing human mitochondria model, and the literature. Hypothesized pathways for producing acetate and alanine were added, then system-wide adaptation to hypoxia was investigated in-silico using the refined model. Simulations supported the hypothesis that the ability to flexibly convert pyruvate to these three byproducts might convey hypoxia tolerance by improving both the ATP/H+ ratio and the efficiency of glucose utilization.

Accepted paper: Yin Wu, Indiana Unversity at Bloomington
Title: A Computational Approach for the Identification of Site-specific Protein Glycosylations through Ion-Trap Mass spectrometry
Abstract: Glycosylation is one of the most common post-translational modifications (PTMs) of proteins, the characterization of which is commonly achieved utilizing mass spectrometry (MS). However, its applicability is currently limited by the lack of computational tools capable of autmoated interpretation of high throughput MS experiments which would allow the characterization of glycosylation sites and their microheterogeneities. We present here a computational approach which overcomes this problem and allows the identification and assignment of the microheterogeneities of glycosylation sites of glycoproteins from liquid chromatography ion-trap-based mass spectrometry (LC/MS) data. This method was implemented in a software tool and tested on several model glycoproteins. The results demonstrate the potential of our computational approach in automating the high throughput identification of glycoproteins.

Accepted paper: Hans-Michael Kaltenbach, Bielefeld University
Title: Markov Additive Chains and Applications to Fragment Statistics for Peptide Mass Fingerprinting
Abstract: Peptide mass fingerprinting is an important technique to identify a protein from its fragment masses obtained by mass spectrometry after enzymatic fragmentation. Recently, much attention has been given to the question of how to evaluate significance of identifications; results have been developed mostly from a combinatorial perspective. In particular, existing methods generally do not capture the fact that the same amino acid can have different masses because of, e.g., isotopic distributions or variable chemical modifications. We offer several new contributions to the field: we introduce probabilistically weighted alphabets, where each character can have different masses according to a probability distribution, and random weighted strings as a fundamental model for random proteins. We develop a general computational framework, Markov Additive Chains, for various statistics of cleavage fragments of random proteins, and obtain general formulas for these statistics. Special results are given for so-called standard cleavage schemes (e.g., Trypsin). Computational results are provided, as well as a comparison to proteins from the SwissProt database.