Computational Systems Biology: An Introduction

1. Computational Systems Biology:An Introduction Eytan Ruppin, 2012

2. השראה: קוראים לי ריי קורצווייל ואני אחיה לנצח • בגיל 17 הוא לימד מחשב להלחין מוזיקה, בגיל 27 המציא את הסורק, ובעשורים הבאים הפך למיליונר בזכות מאות פטנטים וחזה את מהפכות האינטרנט והסלולר. עכשיו, בגיל 60, נביא ההייטק ריי קורצווייל גילה שאנחנו בדרך לחיי נצח

3. 1. Molecular biology – a (very) quick recap..

4. The Cell Basic unit of life. Carries complete characteristics of the species. All cells store hereditary information in DNA. All cells transform DNA to proteins, which determine cell’s structure and function. Two classes: eukaryotes (with nucleus) and prokaryotes (without). http://regentsprep.org/Regents/biology/units/organization/cell.gif

5. http://www.ornl.gov/hgmis/publicat/tko/index.htm DNA protein RNA The hard disk One program Its output transcription translation

6. Gene expression Pre-mRNA Mature mRNA DNA protein transcription splicing translation

7. Gene expression Gene Pre-mRNA Mature mRNA DNA protein transcription splicing translation Transcription factors (TFs) control transcription by binding to specific DNA sequence motifs.

8. The Human Genome: numbers • 23 pairs of chromosomes • ~3,200,000,000 bases • ~25,000 genes • Gene length: 1000-3000 bases, spanning 30-40,000 bases • ~1,000,000 protein variants

9. Model Organisms • Eukaryotes; increasing complexity • Easy to store, manipulate. • Budding yeast • 1 cell • 6K genes • Nematode worm • 959 cells • 19K genes • Fruit fly • vertebrate • 14K genes • mouse • mammal • 30K genes

10. High-throughput measurement Protein-protein interaction (PPI): yeast two-hybrid Protein-DNA (transcriptional) interactions: chip-on-chip Genetic interactions DNA protein RNA Genome: Sequencing technologies Transcriptome: Microarrays Proteome: Various assays

11. 2. Systems Biology

12. The Reductionist Approach to Biological Research • Explanations of things ought to be continually reduced to the very simplest entities • Identifying individual genes, proteins and cells, and studying their specific functions

13. The Reductionist Approach to Biological Research (20th century biology) Explanations of things ought to be continually reduced to the very simplest entities Can this approach explain the behavior of a complex system?

14. Building models from parts lists • High throughput technologies signal the end of reductionism in biology

15. Why Build Models?(Jay Bailey, 1998) • 1. To organize disparate information into a coherent whole • 2. To think (and calculate) logically about what components and interactions are important in a complex system. • 3. To discover new strategies • 4. To make important corrections to the conventional wisdom • 5. To understand the essential qualitative features

16. "One is neither too scrupulous and sincere, nor too subjected to nature; but one is more or less master of his model, and especially of his means of expression"

17. When one is a master of his own model..

18. So what is Systems Biology? • The study of the mechanisms underlying complex biological processes as integrated systems of many interacting components. • collection of large sets of experimental data • proposal of mathematical models that might account for at least some significant aspects of this data set • accurate computer solution of the mathematical equations to obtain numerical predictions, • assessment of the quality of the model by comparing numerical simulations with the experimental data. • First described in 1999 by Leroy Hood – Director of the Institute for Systems Biology

19. What’s it good for? • Basic Science/”Understanding Life” • Predicting Phenotype from Genotype • Understanding/Predicting • Metabolism • Cellular signal trasduction • Cell-Cell Communication • Pathogenicity/Toxicity • Biology in silico.. Virtual life..

20. PubMed abstracts indicate a growing interest in Systems Biology Human genome completed

21. 3. Biological Networks

22. From genomics to genetic circuits • The coordinated action of multiple gene products can be viewed as a network

23. Transcriptional Regulatory Network • Nodes – transcription factors (TFs) and genes; • Edges – directed from transcription factor to the genes it regulates • Reflect the cell’s genetic regulatory circuitry • Derived through: ▲ Chromatin IP ▲ Microarrays S. cerevisiae 1062 TFs, X genes 1149 interactions

24. Protein-Protein Interaction (PPI) Networks • Nodes – proteins; • Edges – interactions • Reflect the cell’s machinery and signlaing pathways. • High-throughput experiments: ▲Protein coIP ▲Yeast two-hybrid S. cerevisiae 4389 proteins 14319 interactions

25. Metabolic Networks • Nodes – metabolites; Edges – biochemical reactions • Reflect the cell’s metabolic circuitry • Derived through: ▲ Biochemistry knowledge ▲Metabolic flux measurements S. cerevisiae 1062 metabolites 1149 reactions

26. Systems Biology: Network States

27. There are many sources of informationabout biological networks

28. Biological networks operate in thecrowded intra‐cellular environment

29. 4. How do we model the complex biological processes encoded in these networks?

30. Modeling the Network Function • A dynamic system with differential equations • Requires unknown data on kinetic constants and concentrations Kinetic models Approx. kinetics • Constraint-based modeling Constraint-based analysis • Boolean and discrete models, bayesian models, linear models, etc Conventional functional models • Topological analysis • Degree distribution, motifs, functional modules Topological analysis Abstraction level Signaling Metabolic Regulatory PPI 30

31. Types of models • Data models – reconstruction • Topological – structure of networks • Steady state – linear algebra • Dynamic states – ODEs • Thermal fluctuations – noise, stochastic ODEs • Sensitivity – MCA, etc

32. Interim Summary • The genotype‐phenotype relationship is fundamental in biology • Systems biology promises to make this relationship mechanistic • The core paradigm is a four step process – Components‐>networks‐>in silico models‐>phenotype • Network reconstruction is foundational to the field and a common denominator • Models are built to describe steady states (capabilities) and dynamics states

33. And now to Monty Python something completely different.. • The Future as seen at Present

34. New upcoming Data • The revolution in genome sequencing technologies • Which leads also to new gene expression technologies • microRNA chips • Large scale protein abundance data • Large scale metabolomics data • Completing the identification of cellular networks • Large scale individual cell measurements in high temporal resolution

35. Research Questions & Challenges – I. Basic Science • The riddle of embryonic development • How are cells regulated? • The riddle of `junk’ DNA and the hidden world of mRNA • How and to what extent does the normal cellular genotype determine the phenotype? – Epigenetics.. • The emergent properties of tissues and organs • Evolutionary systems biology – the search for LUCA, the origins of multi-cellularity, the ascent of man..

36. Research Questions & Challenges – II. Applications • Charting the pathophysiology of human diseases • Identifying new drugs and combinations of drugs • Stems cell research and tissue and organ replacement • Whats can microrganisms do for you? • Metagenomics and the art of sailing.. • Personalized Medicine

37. Some potential computational avenues • Genome association & CN studies • New approaches for modeling integrated cellular functions – in silico cellular biology • Models of tissues and systems • “Interfacing” with the community and the literature..

38. My lab: Don’t ask what Sysbio can do for you, ask what you can do for Sysbio  • Studying cancer metabolism and predicting and testing new anti-cancer therapies • Computational methods for predicting biomarkers and disease diagnosis • Searching for new antibiotics that are resistant to resistance… • Building and studying the gut metabolome • The evolution of human brains.. • Metabolism of stem cells, Alzheimer’s disease, diabetes. • Fighting aging and extending human lifespan