Bulletin of the American Physical Society
2005 APS March Meeting
Monday–Friday, March 21–25, 2005; Los Angeles, CA
Session L7: Modeling Large Scale Molecular Biological Data |
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Sponsoring Units: DBP DCOMP Chair: Orly Alter, University of Texas at Austin Room: LACC 408B |
Tuesday, March 22, 2005 2:30PM - 3:06PM |
L7.00001: Evolution and development: what can physics contribute Invited Speaker: The genome contains both the 'parts list' for an organism, the genes, as well as the instructions about how to assemble it. Many of the current genome sequencing projects have been motivated by the desire to compare sufficiently similar organism, to discover what parts of the genome are functional, and how they define the differences between the organisms. A particularly fruitful place to study gene regulation (the `assembly manual') is development from egg to adult. Current progress in deducing developmental regulatory networks from the genome will be illustrated with the early, head-tail patterning in the fly embryo. The recent sequencing of a second species of fly, has allowed us to computationally screen for interesting differences in the early embryonic patterning and test these experimentally. This provides a first glimpse of how regulation evolves. [Preview Abstract] |
Tuesday, March 22, 2005 3:06PM - 3:42PM |
L7.00002: Applying Logic Analysis to Genomic Data and Phylogenetic Profiles Invited Speaker: One of the main goals of comparative genomics is to understand how all the various proteins in a cell relate to each other in terms of pathways and interaction networks. Various computational ideas have been explored with this goal in mind. In the original phylogenetic profile method, `functional linkages' were inferred between pairs of proteins when the two proteins, A and B, showed identical (or statistically similar) patterns of presence vs. absence across a set of completely sequenced genomes. Here we describe a new generalization, logic analysis of phylogenetic profiles (LAPP), from which higher order relationships can be identified between three (or more) different proteins. For instance, in one type of triplet logic relation -- of which there are eight distinct types -- a protein C may be present in a genome iff proteins A and B are both present (C=A$\cap $B). An application of the LAPP method identifies thousands of previously unidentified relationships between protein triplets. These higher order logic relationships offer insights -- not available from pairwise approaches -- into branching, competition, and alternate routes through cellular pathways and networks. The results also make it possible to assign tentative cellular functions to many novel proteins of unknown function. Co-authors: Peter Bowers, Shawn Cokus, Morgan Beeby, and David Eisenberg [Preview Abstract] |
Tuesday, March 22, 2005 3:42PM - 4:18PM |
L7.00003: Genomic Signal Processing: Predicting Basic Molecular Biological Principles Invited Speaker: Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). \\ \\ The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of these two sets of states. \\ \\ Mapping genome-scale protein binding data using pseudoinverse projection onto patterns of RNA expression data that had been extracted by SVD and GSVD, a novel correlation between DNA replication initiation and RNA transcription during the cell cycle in yeast, that might be due to a previously unknown mechanism of regulation, is predicted. \\ \\ (1) Alter \& Golub, {\it Proc.~Natl. Acad.~Sci.~USA} {\bf 101}, 16577 (2004). \\ (2) Alter, Golub, Brown \& Botstein, {\it Miami Nat.~Biotechnol.~Winter Symp.~2004} (www.med.miami.edu/mnbws/alter-.pdf) [Preview Abstract] |
Tuesday, March 22, 2005 4:18PM - 4:54PM |
L7.00004: Protein interaction networks from literature mining Invited Speaker: The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the \textit{in house} literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. \textit{inhibit}, \textit{induce}) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with \textit{MLL} rearrangements, using our system, showed newly discovered signaling cross-talk. [Preview Abstract] |
Tuesday, March 22, 2005 4:54PM - 5:30PM |
L7.00005: Population Dynamics of Genetic Regulatory Networks Invited Speaker: Unlike common objects in physics, a biological cell processes information. The cell interprets its genome and transforms the genomic information content, through the action of genetic regulatory networks, into proteins which in turn dictate its metabolism, functionality and morphology. Understanding the dynamics of a population of biological cells presents a unique challenge. It requires to link the intracellular dynamics of gene regulation, through the mechanism of cell division, to the level of the population. We present experiments studying adaptive dynamics of populations of genetically homogeneous microorganisms (yeast), grown for long durations under steady conditions. We focus on population dynamics that do not involve random genetic mutations. Our experiments follow the long-term dynamics of the population distributions and allow to quantify the correlations among generations. We focus on three interconnected issues: adaptation of genetically homogeneous populations following environmental changes, selection processes on the population and population variability and expression distributions. We show that while the population exhibits specific short-term responses to environmental inputs, it eventually adapts to a robust steady-state, largely independent of external conditions. Cycles of medium-switch show that the adapted state is imprinted in the population and that this memory is maintained for many generations. To further study population adaptation, we utilize the process of gene recruitment whereby a gene naturally regulated by a specific promoter is placed under a different regulatory system. This naturally occurring process has been recognized as a major driving force in evolution. We have recruited an essential gene to a foreign regulatory network and followed the population long-term dynamics. Rewiring of the regulatory network allows us to expose their complex dynamics and phase space structure. [Preview Abstract] |
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