Shankar Subramaniam University of California at San Diego

1. Data to Biology Shankar Subramaniam University of California at San Diego

2. Many Dimensions of Biology • Scales: Molecules, Networks, Cells, Tissues… • Granularity: Structure, Function, Phenotype, Physiology… • Development: Stem cells, Differentiation, Tissue Engineering… • Species: Microorganisms, Unicellular Eukaryotes, Insects, Plants, Animals… • Length/Time: fempto, nano, micro, …. • Cell Processes: Metabolism, Regulation, Signaling… • Models: Micro, Meso, Macro…. • Model Systems: Microbes, Yeast, Worm/Fly, Plant, Mouse, Rat, Human UCSD-Bioinformatics & Systems Biology Group

3. CELLULAR RESPONSE TO STIMULUS Cell State1 State2 State i Input Response • proteins • peptides • amino acids • nucleotides • retinoids Gene Expression State: genes, proteins, metabolites, ions…… The Parts List Problem! UCSD-Bioinformatics & Systems Biology Group

4. Automated sequencing machines at the Center for Genome Research in the Whitehead Institute UCSD-Bioinformatics & Systems Biology Group

5. Deconstructing Biology • Analysis of components, interactions and phenotypes – in context • Multiscale and high throughput measurements • Integration of data and knowledge • Coarse grained views of the system • Understanding larger scale function • Quantitative prediction of response to input at the systems level • Study of dynamical behavior of systems • Perturbation of components to produce changes in systemic response • Building dynamical models of systems

6. Challenges in building biochemical models • Complexity of proteomic states and interactions • Integration of diverse data to infer biochemical interactions and modules • Accounting for the temporal state of biochemical models

7. DATA, MEASUREMENTS AND INTEGRATION Papin, Gianchandani and Subramaniam, Current Opinions in Biotechnology 2004

8. Characterizing Biochemical Models - Reconstruction Pradervand, Maurya and Subramaniam Genome Biology 2006

9. Basic Challenges for Systems Biology • How will we define and characterize a biological system? • How can we obtain the information on components of the system (qualitative and quantitative; static and dynamical)? Technologies and computational methods? • What mechanisms can we infer from the system behavior? • What are realistic models of a system? • How can we measure/compute input-phenotype characteristics of the system? • How will the model of the system be validated experimentally?

10. Biological Systems and Models - some Examples… • The Genome as a System • Guda et al. MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteins. Bioinformatics. 2004 Jul 22;20(11):1785-94. • The Cell as a System • Maurya et al. Systems biology of macrophages. Adv Exp Med Biol. 2007; 598: 62-79. • A Biological Process as a System • Subramaniam et al. The Macrophage Lipidome. 2008. • Ogawa et al. Molecular Determinants of Crosstalk between Nuclear Receptors and Toll-like Receptors. Cell. 2005 Sep 9;122(5):707-21. • A Biochemical Pathway as a System • Maurya MR and Subramaniam S. A kinetic model for calcium dynamics in RAW 264.7 Cells: 1. Mechanisms, parameters and dose response. Biophysical Journal. 2007 Aug; 93: 709-728. A kinetic model for calcium dynamics in RAW 264.7 Cells: 2. Knockdown response and long-term response. Biophysical Journal. 2007 Aug; 93: 729-740. • A Functional Module as a System • Bornheimer et al. Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction. Proc Natl Acad Sci U S A. 2004 Nov 9;101(45):15899-904. • Physiological Function as a System • Avidor-Reiss et al. Decoding cilia function: defining specialized genes required for compartmentalized cilia biogenesis. Cell. 2004 May 14;117(4):527-39. • An Organ as a System • Bhargav et al. Pathways associated with cardiomyogenesis from embryonic stem cells. • A Disease as a System • Sears et al., Insulin Resistance – A systems physiology study Proc. Natl. Acad. Sci. 2009.