Special topics in electrical and systems engineering systems biology
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Special topics in electrical and systems engineering: Systems Biology. ESE 680-003 Pappas Kumar Rubin Julius Halász. Organizational issues. Schedule: MW 9:30 – 11:00 Room: Towne 303 Instructors: George Pappas : [email protected] (TBA) Vijay Kumar: [email protected]

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Special topics in electrical and systems engineering systems biology l.jpg

Special topics in electrical and systems engineering:Systems Biology

ESE 680-003

Pappas Kumar Rubin

Julius Halász

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Organizational issues

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  • 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)

What we mean by systems biology l.jpg
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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Advantages of studying systems biology

  • Promising field

  • Interdisciplinary

  • Lots of opportunities now

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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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  • 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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  • 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