Roadmap to Systems Biology

1. ESE 680-003Special topics in electrical and systems engineering: Systems BiologyPappas Kumar Rubin Julius Halász Roadmap to Systems Biology

2. What next? • Cellular processes come down to molecular interactions • Rate laws • Kinetic constants • Differential equations • … so all we need to do is get all the reactions ,rate laws, constants, put them into a computer  virtual cell

3. What next? • Easier said than done: • Processes not typically known in detail • Kinetic constants • Not measurable/Not measurable in vivo • Meaningless • High dimensional, nonlinear systems • Yet often simple behavior: emergence • Even if individual processes can be studied, the cost of going through all of them is prohibitive

4. What next? • Biologists have “told us so”: • Reductionism doesn’t work • There are exceptions to all “laws” • Qualitative descriptions are more meaningful • Source of limitations • Experimental input • Lack of fundamental understanding of processes • Lack of appropriate mathematical “language”

5. What next? • Systems/quantitative biology today: • No mathematically expressed principles • Several qualitative principles • Robustness • Redundancy • Driven by experimental data • Certain clusters of modeling activity • Physics, circa 1670 (before Newton) • Incremental progress on many fronts • Best approach is to try to be useful to biology

6. Some of the fronts • Genetic network identification • Metabolic networks • Signaling • Cycles (cell, circadian) • Mesoscopic / stochastic phenomena • Synthetic biology • Software tools

7. Genetic network identification • Microarrays • One of the most spectacular advances in experimental technique • Typical of “high-throughput” approach • Made possible by • Genome sequencing projects of the 1990’s • Semiconductor, microchip technology

8. Genetic network identification • Microarrays • Chips with a grid of RNA* microprobes • Each probe has a different sequence* • Probes represent genes • Probes hybridize to mRNA from a sample • Optical (fluorescence) readout • Parallel measurement of gene expression • Commercially available for several organisms • Affymetrix – “the Microsoft of biotechnology”

9. Gene network identification • What can we learn from high throughput, semi-quantitative, perhaps time resolved, gene expression data? • Identification of transcription networks • Ignore all details of interactions • Focus on the existence of an influence of Gene A onto Gene B • Various levels of abstraction, from on/off to Hill coefficients

10. Gene network identification • Next lecture • Papers by Collins, Liao • A whole industry has been spawned • Lots of room for new ideas coming from computer science/hybrid systems • Challenge: connect with biological knowledge

11. Metabolic networks • Another “breadth-first” approach • Made possible by arduous work of many postdocs, PubMed, and other databases • Metabolic reactions curated into comprehensive databases • Stoichiometric information on hundreds of concurrent chemical reactions • The workings of the chemical factory

12. Metabolic networks • The state of the system is the vector of all metabolite concentrations c. • Each reaction is represented by an integer vector: A + B  3X [-1, -1, 3, 0] 2A + B  Y [-2, -1, 0, 1] • Stoichiometric matrix S • Vector of reaction rates v • External fluxes of metabolites f

13. Metabolic networks • At steady state, c is constant • The state of the metabolic network is v • Many possible solutions • Feasiblity cone • Which state is picked by nature? • Determined by unknown kinetic details • Models postulate optimization principles

14. Metabolic networks • Many papers: • Palsson, Church • Lecture by Marcin Imielinski (?) • Lots of linear algebra

15. Signaling • Multi-cellular organisms are similar to highly organized societies • Every cell has the same genetic information • Yet they are highly specialized/differentiated • Widely different phenotypes, functions • The organism works because each cell does what it is supposed to Signaling ensures that cells act properly

16. Signaling • In cancer, the signaling machinery breaks down • Wrong signals and/or wrong interpretation • Cells differentiate into the wrong type • They grow when they are not supposed to • Stop listening to the system commands • Take a life of their own (tumors)

17. Signaling • Signaling tells cells to do everything • Lack of certain signals triggers cell suicide (apoptosis) • Signals are carried by special molecules in the organism • Hormones, growth factors • There are specialized receptors on the cell surface • Receptors transduce signals (binding of their ligand) into the cytosol (the inside of the cell) • Signaling cascades originate in the initial binding event • Complicated networks of multistep phosphorylation reactions • Eventually they control gene expression

18. Signaling • Signaling malfunctions result from small mutations • Lack of signaling • Uninduced signals • Over/under- amplification • A few well studied networks • EGF Erb/Her • A few well studied cell lines

19. Cell signaling • Huge literature • Lecture: Avi Ghosh (Drexel)

20. Mesoscopic phenomena • Face the reality of small molecule numbers • Stochastic nature of reactions • Well established simulation methods • Often ignored, wrongly

21. Mesoscopic phenomena • A few important results • Lambda phage (Arkin) • Lac system (van Oudenaarden) • Competence (Elowitz) • Relevant experimental results • Well delimited, controlled, yet live system • Lecture by Mustafa Khammash

22. Cycles • Complicated control systems • Make sure that actions are taken in the correct sequence • Cell cycle • Papers by Tyson • Circadian cycle • Papers by Doyle

23. Synthetic biology • From simple genetic switches • To tumor killing bacteria • In between: synthesis of artemisin (Keasling)

24. Software • Large industry • Lots of potential for new work • Largely ten years behind in modeling • Focus on languages standardization,.. • Still very important