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

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
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 Jul 22;20(11):
The Cell as a System
Maurya et al. Systems biology of macrophages. Adv Exp Med Biol. 2007; 598:
A Biological Process as a System
Subramaniam et al. The Macrophage Lipidome
Ogawa et al. Molecular Determinants of Crosstalk between Nuclear Receptors and Toll-like Receptors. Cell Sep 9;122(5):
A Biochemical Pathway as a System
Maurya MR and Subramaniam S. A kinetic model for calcium dynamics in RAW Cells: 1. Mechanisms, parameters and dose response. Biophysical Journal Aug; 93: A kinetic model for calcium dynamics in RAW Cells: 2. Knockdown response and long-term response. Biophysical Journal Aug; 93:
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 Nov 9;101(45):
Physiological Function as a System
Avidor-Reiss et al. Decoding cilia function: defining specialized genes required for compartmentalized cilia biogenesis. Cell May 14;117(4):
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