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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 : pappasg@seas.upenn.edu (TBA) Vijay Kumar: kumar@me.upenn.edu

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Special topics in electrical and systems engineering: Systems Biology


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special topics in electrical and systems engineering systems biology

Special topics in electrical and systems engineering:Systems Biology

ESE 680-003

Pappas Kumar Rubin

Julius Halász

organizational issues
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
prerequisites
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)
what we mean by systems biology
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
what we mean by systems biology6
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
in the context of biology
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
in the context of engineering
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)
the object of systems biology
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
the object of systems biology10
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
the uses of computational models
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
expectations from systems biology
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
examples of methods
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)
examples of methods14
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
examples of methods15
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
advantages of studying systems biology
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
advantages of studying systems biology17
Advantages of studying systems biology
  • Promising field
  • Interdisciplinary
  • Lots of opportunities now
course outline
Course outline
  • Format:
    • regular lectures (33%)
    • guest lectures (12%)
    • paper review (30%)
    • lab (25%)
  • Grading
    • participation (20%)
    • final project, report and presentation(80%)
topics
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
references
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
useful information
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