Computational Systems Biology

Transcription

1 Computational Systems Biology Vasant Honavar Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence, Learning, & Discovery Iowa State University

2 Can a biologist fix a radio? We would eventually find how to open the radios and will find objects of various shape, color, and size [...]. We would describe and classify them into families according to their appearance. We would describe a family of square metal objects, a family of round brightly colored objects with two legs, round-shaped objects with three legs and so on. Because the objects would vary in color, we will investigate whether changing the colors affects the radio s performance. Al-though changing the colors would have only attenuating effects (the music is still playing but a trained ear of some people can discern some distortion), this approach will produce many publications and result in a lively debate. Y. Lazebnik. Can a biologist fix a radio? Or, what I learned while studying apoptosis. Cancer Cell

3 Biology, circa 19 th and 20 th centuries All science is either stamp collecting or physics Biology has largely been about stamp collecting Collecting, cataloging, organizing, and describing: species cells, tissues, organs genomes, genes, regulatory elements and proteins protein structures and protein-protein, protein-dna, protein-rna complexes gene expression profiles Ernest Rutherford

4 Biology: from stamp collecting to physics Biology has been largely a descriptive science akin to physics before Newton We have been limited by Our instruments of observation Our ability to construct predictive models Modern systems biology is about Approaching biology as fundamentally an information science Transforming biology into a predictive science Integration, rather than reduction

5 Biology: From stamp collecting to physics Computation : Biology :: Calculus : Physics Universality of computation: Any structure or process that is describable can be described in the form of computer programs Differential equations (stoichiometric models, kinetic models) Undirected and directed graphs Boolean networks State transition systems e.g., Petri nets Probabilistic models Grammars

6 Computational Systems Biology Building and testing predictive models in biology requires system-wide observational and experimental data To construct a cellular network model, we minimally need a list of the molecular players a list of the influences of one set of players on another ways to structure and query the data computational approaches to construct descriptive and predictive models from the data ways to generate testable hypothesis from the model

7 Computational Systems Biology: Data Connectivity without activity: yeast 2 hybrid Activity without connectivity: microarray Activity and connectivity: pathway inference

8 Yeast Gene regulatory network 1276 regulatory interactions among 682 proteins Analysis Network motifs Topological properties Modules Comparison across multiple networks Maslov, S., Brookhaven

9 Computational Systems Biology: Finding Network motifs

10 Computational Systems Biology: Protein networks Modules and programming

11 Computational Systems Biology: Modeling metabolic pathways Jeong et al, Nature, 407, , 2000.

12 Computational Systems Biology: Network Reconstruction Direct analysis Indirect analysis e.g., promoter analysis

13 Computational Systems Biology: Network reconstruction Dana Pe er, 2003

14 Computational Systems Biology: Pathway inference

15 Computational Systems Biology: Pathway inference

16 Computational systems biology: Different data, Different models

17 Computational systems biology Building and testing predictive models in biology requires Working with high dimensional, noisy, sparse, heterogeneous data Integration across Disciplines Levels of abstraction DNA, mrna, proteins Macromolecular Networks Cells Tissues Organs Organisms.. Spatial and temporal scales Computational thinking among biologists

18 How to Computational Systems Biology: Challenges Fully decipher the (digital) information content of the genome Do all-vs-all comparisons of 1000s of genomes Extract protein and gene regulatory networks from the above Reliably integrate disparate multi-scale data types Construct predictive models from large-scale, sparse, multidimensional data Convert static network maps into dynamic mathematical models Identify cellular signatures for cellular states (e.g. healthy vs. diseased) Build models across multiple scales of time & space

19 Computational systems biology Building and testing predictive models in biology Requires advances in High level modeling languages Databases and knowledge bases Mathematical analysis techniques Data and knowledge integration tools Data mining algorithms Simulation tools Presents challenges in computer science, mathematics, statistics, control theory, engineering

20 Computational Systems Biology Physics BIOLOGY Computational Systems Biology Computational Models COMPUTATION TECHNOLOGY Stamp Collecting

21 Computational Systems Biology at ISU Can build on strengths Biological sciences GDCB, BBMB, EEOB Computational sciences Computer Science, Statistics, Mathematics Physical Sciences Engineering Chemical and Biological Engg., Electrical and Computer Engg. Interdisciplinary centers and initiatives LH Baker Center for Bioinformatics and Biological Statistics Center for Integrated Animal Genomics Complements university priorities in Biological sciences Information sciences E-science