Modeling and Analysis Techniques in Systems Biology.

1. Modeling and Analysis Techniques in Systems Biology. CS 6221 Lecture 1 P.S. Thiagarajan

2. Basic Info • P.S. Thiagarajan • COM2 #03 – 55 ; Tel Ext. 67998 • thiagu@comp.nus.edu.sg • www.comp.nus.edu.sg/~thiagu • Course web page: • www.comp.nus.edu.sg/~cs6221 • We will be using the IVLE system extensively.

3. Office Hours • Send mail first and fix an appointment.

4. Course Material • Selected Parts of the text book : • Systems Biology in Practice: E. Klipp, R. Herwig, A. Kowald, C. Wierling, H. Lehrach (Wiley) • Selected Survey papers, book chapters. • Lecture slides. • Research Articles.

5. Assignments • Lab Assignments • 3 • tool based (Cell Illustrator, COPASI, SimBio) • Individual

6. Term Papers • Read a paper or –more likely- a bunch of papers on a topic. • Summarize in the form of a term paper. • First assignment: Common • Second assignment: • More substantial • Can be aligned to your interests

7. Talk • Give talk based on the second term paper. • 25 + 5 minutes.

8. Grading (Tentative) • Lab assignments    45% (15 + 15 + 15) • Term papers        40% (15 + 25) • Talk: 15%

9. What is the Course About? • Computational systems biology • Computational aspects of systems biology. • Systems biology: • Not just focus on individual components. • genes, mRNAs, proteins, membranes, ligands …. • But study a system of such components and their interactions. • Many different views of systems biology.

10. Why Systems Biology? • Biology has traditionally –and extremely successfully!- focused on what individual parts of a cell do . • Bio-chemistry of large and small molecules • The structure of DNA and RNA • Proteins, ligands,…

11. Why Systems Biology? • But functionality of a system is determined crucially by the interactions of the parts. • Many fundamental biological processes are dynamic. • cell growith/division/differentiation • Metabolism,…. • Many diseases are marked by malfunctioning of these processes.

12. Why Systems Biology? • Advances in experimental technology are producing vast amounts of data concerning biological processes. • Which genes get expressed “when” in controlled conditions. • One would like to understand this data in a systemic way. • Enter: computational systems biology!

13. The CSB Approach • View selected biological processes as dynamical systems. • Model • Simulate • Analyze • Predict • Many research communities study dynamical systems …

14. What do we need ? • Biology for computer scientists. • basic biological sub-systems/processes • experimental techniques. • Modeling, analysis and simulation techniques. • Biologists as collaborators!

15. Current Status • Modeling techniques. • Mathematical • differential equations, Linear algebra, probability theory, statistics, Boolean networks, Markov chains, Bayesian networks,…. • CS-specific: • Automata, Petri nets, Hybrid functional Petri nets, hybrid automata, Bayesian networks/inferencing/learning, Markov chains, Model checking….

16. Current Status • Metabolism • Kinetics “laws” (models). • Enzyme kinetics, law of mass action, Michelis-Menten kinetics • Metabolic network models and flux analysis.

17. Current Status • Signal Transduction • Receptor-ligand interactions • Protein actors • signaling dynamics

18. Current Status • Other biological processes • biological oscillations • protein folding kinetics • cell cycle • Gene expression, regulation

19. Current Status • Modeling tools • Cell Illustrator, COPASI, SimBio, …..

20. What shall we do? • Selected basic topics. • To illustrate the current state of the field. • To critically examine what is missing. • To discuss promising lines of research.

21. What can CS offer? • We “know” how to deal with complex systems. • Hierarchy • silicon realization of circuits, digital design, micro-architectures, assemble language, programming languages, GUIs, … • separation of concerns. • concepts (models), techniques, tools at each layer and for connecting the layers.

22. What can CS offer? • Deal with other disciplines. • Multi-media • Control • Manufacturing • Communications • Business! • Using computing power via algorithms and data structures! • Computational thinking?!

23. What can CS offer? • Find the right level abstractions. • approximations • Handle distributed dynamics • Deal with hybrid behaviors • Build tools.

24. What the Course is NOT about. • We will not deal with: • Traditional “Bio-Informatics” topics • data mining, sequence analysis, … • Computational aspects of structural biology • Proteins structure, folding…

25. Contents • Bio-chemical networks • The basics of chemical kinetics • Three types of bio-chemical networks • Gene networks • Metabolic networks • Signaling pathways

26. Bio-pathways • Many studies of biological sub-systems boil down to studying: • bio-pathways • A network of bio-chemical reactions.

27. The hierarchy of bio-chemical networks Bio-Chemical reactions Metabolic pathways Signaling pathways Gene regulatory networks A network of Bio-Chemical reactions Interacting networks of Bio-Chemical reactions Cell functions

28. Biopathways

29. Gene Regulatory networks • Boolean models • Differential equations • Bayesian networks.

30. Metabolic pathways • Petri nets • Linear algebra • Flux analysis

31. Signaling Pathways • Differential equations. • Hybrid functional Petri nets • Hybrid automata • Stochastic models.

32. Our Research • ODEs based modeling. • Parameter estimation techniques • Stochastic approximations of ODEs dynamics. • Parameter estimation, sensitivity analysis • GPU implementations • Probabilistic (statistical ) model checking

33. Our Research • Collaboration with biologists: • Signaling pathways: • AKT/MAPK pathway • Complement pathway • TLR3-TLR7 signaling pathways • DNA damage/repair pathways • www.comp.nus.edu.sg/~rpsysbio

34. Expected Outcomes • Have a sound grasp of: • current modeling and simulation techniques (Signaling pathways) • Reaction kinetics • stochastic models and simulations • Analysis techniques: • Parameter estimation, sensitivity analysis

35. Expected Outcomes • Be aware of the limitations of current techniques and state of knowledge • Be ready to undertake modeling and simulation work.

36. Let us get started.

37. Basic Biology: Sources • Chapter 2 (Biology in a Nutshell) of the book “Systems Biology in Practice” by E. Klipp et.al. • Chapter 1 (Molecular Biology for Computer Scientists) of the book “Artificial Intelligence and Molecular Biology” by Lawrence Hunter. • The internet!

38. A major goal of biology • Understand the molecular biology of eukaryotic cells. • Cell: the basic building block. • Two major families: Prokaryotes and Eukaryotes. • Eukaryotes • More complex; genetic material is contained in the nucleus; • Most multi-cellular organisms are made up of eukroyotes.; WE are made up of these types of cells.

39. Cells • In multi-cellular organisms; • Cells are differentiated. • Different types of cells have different functions (and composition). • Groups of cells for specific functionalities • tissues. • we have 14 different types of tissues.

40. Source ?

41. Major Classes of Bio-Molecules • Carbohydrates • Lipids • Proteins • Nucleic acids

42. Proteins • Many functions! • Build up the cytoskeletal structure of the cell (the scaffolding) • Responsible for cell movements (motility) • Serve as catalytic enzymes for bio-chemical reactions. • Induce signal transductions. • Control transcriptions and translation of genes • Control degradation of proteins.

43. Proteins • Proteins consist of polypeptides. • Polypeptide - a LONG chain of amino acids bonded together by peptide bonds between adjacent amino acid residues. • The order of amino acids constituting a peptide is fundamental. • Primary structure • coded by genetic information

44. Proteins • 20 (23?) different amino acids • A protein can have 50 – 4000 amino acids sequence. (50 – 1000 is the typical range) • 201000 possible proteins! • Actually, only a tiny fraction is found in nature.

45. Nucleic Acids • DNA (Deoxyribonucleic acid) molecules store genetic information. • Present in all living organisms • RNA (Ribonucleic acid) takes part in a large number of processes. • Transferring hereditary information in the DNA to synthesize proteins.

46. The Central Dogma • First enunciated by Francis Crick in 1958[1] • re-stated in a Nature paper published in 1970:[2] • Three major classes of information-carrying biopolymers: • DNA, RNA, proteins • Information encoded as sequences of molecules.

47. The Central Dogma In principle there can be 9 types of transfers: Proteins DNA RNA Proteins DNA RNA

48. The Central Dogma The “simple” form of central dogma states: DNA RNA Proteins

49. The Central Dogma Information cannot be transferred back from protein to either protein or nucleic acid. 'once information gets into protein, it can't flow back to nucleic acid.'

50. Current Known Information Flows Special flows occur in retro viruses !