Special topics in electrical and systems engineering: Systems Biology

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

2. Organizational issues • Schedule: MW 9:30 – 11:00 • Room: Towne 303 • Instructors: • George Pappas: pappasg@seas.upenn.edu (TBA) • Vijay Kumar: kumar@me.upenn.edu • Harvey Rubin: rubinh@mail.med.upenn.edu • Agung Julius: agung@seas.upenn.edu (Tue 3-4) • Adam Halasz: halasz@grasp.upenn.edu (Mon 11-12) • Website: www.seas.upenn.edu/~agung/ese680.htm • Default mailing list for registered students

3. Prerequisites • Mathematics • calculus (functions, derivatives, integrals, ordinary differential equations) • linear algebra (vectors, matrices, linear transformations) • Programming • working experience with a programming language, such as C or MATLAB • Biology • useful but not required beyond introductory level • a review of necessary notions will be provided • several concise introductory papers are available (e.g. Sontag05)

4. Overview of molecular systems biology

5. What we mean by systems biology • Many ways to look at it: • Biological applications where the mathematical framework is an organic part of the scientific investigation much like in physics • Application of systems theory to biological networks • Quantitative models summarizing the usual narrative from molecular biology • Has led to the development of its own specific mathematical results: in control, linear algebra, Markov processes

6. What we mean by systems biology • Systematic and quantitative investigation of cellular functions, cells, and organisms • Based on knowledge of the underlying molecular, chemical, physical processes • Main approach is mathematical modeling which relies crucially on computers

7. In the context of biology • Systems biology straddles the gap between • Molecular biology (bottom-up, focused on parts) • Physiology (top-down, focused on the whole) • Made possible by revolution in experimental analysis methods • Sequencing of several entire genomes • High throughput methods (e.g. microarrays) • Single molecule tracking • Detailed experimental information available allows the top-down and bottom-up approaches to finally meet • Specific new challenges: complexity, computability, emerging properties • Mathematics, computation and computer science no longer confined to supportive ‘bioinformatics’ role • Need for a model-centered approach previously not common in biology

8. In the context of engineering • Complex systems: a cell is comparable in complexity to a jumbo jet • Many different degrees of freedom: biological systems are inhomogeneous, not well amenable to methods from statistical physics • Closest mathematical disciplines are related to engineering: linear systems, control theory, finite automata, hybrid systems • Important difference: more analysis, less synthesis* (*synthetic biology notwithstanding)

9. The object of systems biology • Cells are sophisticated chemical factories • External substances processed to provide energy, cellular material = metabolism • Sophisticated processes performed by specialized molecules whose blueprints are encoded in the DNA • Genes encoded in DNA are converted into proteins = gene expression • Gene expression controlled by current needs of metabolism and external conditions

10. The object of systems biology • The elements of cellular processes are now individually known (at least in principle) • Databases collect information on the various ‘networks’ at work in cells • metabolic network (900+ reactions in E.coli) • genetic network (1k in E.coli, 100k human) • protein-protein interaction network • Putting these elements together in a rational* model that reproduces the functionality of the system and has predictive power

11. The uses of computational models • Repositories of current knowledge • A model summarizes the available information • Source of questions posed to experiment • Often lack if relevant information becomes evident only when we try to use the existing information • Predictions of system behavior • Behavior under experimentally inaccessible circumstances • Values of quantities that are difficult to measure

12. Expectations from systems biology • Health care: • Understanding diseases as malfunctions of normal cells or the interaction of cells with pathogens • Personalized medicine: can take into account individual characteristics, conditions • Biotechnology • Design and production of cells with desired properties • Production of cheap drugs • Energy

13. Examples of methods • Cells as dynamical systems = ordinary differential equations for the time evolution of genes, proteins and their interactions • Nonlinear couplings, time delays, high dimensions • Feedback loops generate robust patterns • Well stirred reactors: no spatial detail • Elements of control theory • Metabolic networks = characterization of the collection of metabolic reactions using linear algebra • Reactions defined by their stoichiometric coefficients • State of the metabolism is a convex combination • No kinetic information (reactions can have any rate)

14. Examples of methods • Stochastic models = describe reactions in terms of discrete numbers of molecules inside one cell • Closer to true first-principle modeling than ODEs • Often reduce to ODEs* • Often introduce additional behaviors • Spatial models = take into account the spatial extension of cells • ODEs become PDEs (partial differential equations) • Very important in signalling • May be combined with stochastic considerations

15. Examples of methods • Discrete automata e.g. Petri nets • Represent metabolic networks as graphs • Boolean networks • Genes represented as logical variables • Hybrid dynamical systems • Continuous variables and discrete transitions

16. Advantages of studying systems biology • Interdisciplinary field • Much less social structure – better chances of breaking through • Varied sources of funding • Many problems where you can be the first one

17. Advantages of studying systems biology • Promising field • Interdisciplinary • Lots of opportunities now

18. Course outline • Format: • regular lectures (33%) • guest lectures (12%) • paper review (30%) • lab (25%) • Grading • participation (20%) • final project, report and presentation(80%)

19. Topics • Overview of systems biology • Introductory notions of cellular biology • Kinetic description of transcription, translation and gene regulation in genetic networks • Nonlinear dynamics in bio-molecular networks • Metabolic network analysis • Stochastic modeling of biochemical reactions • Signalling pathways • Spatial dynamics • Systems biology and control • Hybrid systems modeling and analysis of biomolecular systems

20. References • Several textbooks can be found on Amazon: • Klipp, Szallasi, Alon, Alberghina, • They are quite expensive and beyond the scope of this course • Recent special edition of Nature on systems biology • Review of Sontag at ECC 2006

21. Useful information • Search engines: Pubmed, Google scholar, ScienceDirect • go through the Penn network to take advantage of numerous institutional subscriptions • From home you can either use a Penn proxy for PubMed or use the Penn library site to retrieve papers • Journals: Science, Nature, PNAS, Biophysical Journal, IEE Systems Biology, BMC (online only), Journal of {Molecular, Computational, Theoretical} Biology • Many conferences, special journal issues