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

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

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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)

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Overview of molecular systems biology

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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

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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

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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

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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)

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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

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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

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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

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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

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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)

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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

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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

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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

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Promising field Interdisciplinary Lots of opportunities now Advantages of studying systems biology

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

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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

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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

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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

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