Lecture: Computational Systems Biology Universität des Saarlandes, SS Introduction. Dr. Jürgen Pahle

Transcription

1 Lecture: Computational Systems Biology Universität des Saarlandes, SS Introduction Dr. Jürgen Pahle

2 Who am I? Dr. Jürgen Pahle Manchester Interdisciplinary Biocentre, The University of Manchester, United Kingdom since 2002 in the COPASI developer team contact:

3 Dates Lectures Tuesdays 16:00-17:30, MPI, room 021 between April 24, July 24, 2012 with the exception of May 1, 2012 (Tag der Arbeit) Tutorials/Exercises Thursdays 16:00-17:30, E 1 3, room 015 in selected weeks Please check the course website at frequently for updates, news and course material!!!

4 Exam July 31, :00-12:00 in E1 3, HS001 written or oral (depending on number of participants)

5 What is Systems Biology? "The combined study of biological systems through (i) investigating the components of cellular networks and their interactions, (ii) applying experimental high-throughput and whole-genome techniques, and (iii) integrating computational methods with experimental efforts." source: "Systems Biology - A Textbook" by Klipp et al., p.xvii

6 What is Systems Biology? "Biological processes are the result of complex and dynamic interactions within and between cells, organs and entire organisms. Systems biology is a field of research which aims to enhance our understanding of and even predict such processes of life. It follows an interdisciplinary approach and combines the latest experimental methods in biology with knowledge and technologies in the fields of mathematics, computer science, physics and engineering. This iterative cycle of laboratory experiments and modelling explains the special potential of systems biology." source:

7 Further resources sbiology and many more

8 Related disciplines Bioinformatics gene/protein sequence alignment biological databases phylogenetic trees... Biophysics molecular dynamics protein folding predictions... These disciplines are often referred to as computational biology

9 Some important terms in vitro - experiments done in, e.g. a test tube in vivo - experiments done in the living organism in silico - experiments/simulations done in the computer

10 The model Central element of systems biology research: simplified (mathematical) representation of the biological processes in an organism the process of creating and refining a model is called modelling specific algorithms can be used to calculate/predict the behaviour of a modelled biological system

11 Motivation Why should somebody want a mathematical model of biological phenomena? "Impossible experiments become possible" Hypotheses can easily be tested Other mathematical methods can be applied to an existing model (stability analysis, parameter estimation, etc.) I have come to believe that one's knowledge of any dynamical system is deficient unless one knows a valid way to numerically simulate that system on a computer D.T. Gillespie

12 Motivation (cont.) Models as repositories, and means of communicating of biological knowledge or hypotheses. Modelling immediately exposes gaps in the current knowledge!

13 Advantages of computational modelling Modeling drives conceptual clarification. Modeling highlights gaps in knowledge or understanding. Modeling provides independence of the modeled object. Time and space may be stretched ad libitum Solution algorithms can be used independently of the concrete system Modeling is cheap compared to experiments Models exert by themselves no harm on animals or plants or the environment Modeling can assist experimentation. Modeler has full knowledge and control over all aspects of the model Model results in mathematical terms allow for generalization. Visualisation Modeling allows for making well-founded and testable predictions source: "Systems Biology - A Textbook" by Klipp et al., p. 7

14 Systemic approach Reactions in an organism do not occur isolated! "Biological processes are the result of complex and dynamic interactions within and between cells, organs and entire organisms [..]" Interactions of multiple elements can lead to behaviour that is not immediately evident from the behaviour of the single elements Emergent properties "The whole is more than the sum of its parts"

15 Emergent properties Interaction of relatively simple components can lead to very complex behaviour

16 Models? Wind tunnnel Crash test dummies Model trains Weather forecast Mouse models...

17 Weather forecast source: German weather service

18 Weather forecast model creation mathematical model of major physical interactions related to weather phenomena source: German weather service

19 Weather forecast initial values for all points in geometry apply model to geometry apply initial values to geometry model geometry (~ 39 M points) source: German weather service

20 Weather forecast simulate spatial model (preferably on a fast computer) source: German weather service

21 TV weather forecast source: German weather service

22 Processes in living systems chemical reactions physical interactions e.g. electrical signal transduction in nerve cells

23 Systems biology model major reaction pathways of metabolism

24 Systems biology model model creation model of metabolism (chemical reactions of metabolic pathways)

25 Systems biology model initial values for each point in geometry (from experiments and biological databases) apply model to geometry apply initial values to geometry cell geometry (thousands of grid points)

26 Systems biology model simulate spatial model (preferably on a fast computer)

27 Systems biology model simulate temporal model

28 How-To (Biochemical modeling) Compartments (Nucleus, Cytosol,...) Metabolites (Proteins, Enzymes, Ions,...) Reactions (Decay,...) Kinetics (Velocity of reactions) Simulation: How does the system change over time? Analysis of the model: Which parts influence the behavior most? Which states are stable (steady state, oscillations)?

29 From cells to models How do we go about modelling cellular processes? real system mathematical description? Most aspects have to be neglected (e.g. cell geometry) A lot of abstration is involved (e.g. activities)

30 "All models are wrong but some are useful" Box, G.E.P. (1979) Robustness in the strategy of scientific model building in Robustness in Statistics (R.L. Launer and G.N. Wilkinson, Eds.), Academic Press "[..] the practical question is how wrong do they have to be to not be useful" Box, G.E.P. & Draper, N.R. (1987). Empirical ModelBuilding and Response Surfaces. Wiley. pp. 74

31 Different types of models Different scopes of models genome-wide, e.g. YEASTNET consensus model of Yeast metabolism, single reactions or small pathways Different levels of abstraction phenomenologic, "black-box" approach detailed mechanisms of single reactions

32 Different types of models (cont.) Different length and time scales

33 Different types of models (cont.) Structural/Qualitative models Components and their relations are described, e.g. using a graph representation Kinetic/Quantitative models Components and their interactions are assigned precise values, e.g. species concentrations or reaction fluxes. Study of how these values change over time.

34 Different types of models (cont.) Spatially homogeneous models Space is neglected, e.g. no concentration gradients. Temporal behaviour only is studied. Spatial(ly explicit) models Space is represented explicitly, e.g. partial differential equations system.

35 Different types of kinetic models Different levels of detail: microscopic models: only a few particles and the corresponding forces are simulated (molecular dynamics, ligand binding), computationally expensive!!! mesoscopic models: single particles are distinguishable, but acting forces and positions of the particles are neglected macroscopic models: particles of one type are grouped together, only the particle numbers (or the concentrations) are considered, systems are assumed homogeneous Macroscopic models: deterministic models: ordinary differential equation systems stochastic models: the system is modeled as random process hybrid models: mix of deterministic and stochastic elements

36 Mathematical formalisms Different mathematical formalisms can be used to describe the different model types: graphs ordinary/partial differential equations stochastic models, master equation, stochastic differential equations Petri nets π-calculus cellular automata...

37 Different models for the same system

38 Models Statements System state Variables, constants, parameters

39 Parameters and variables Parameters are items that are independent of the system, i.e. are set by outside agents (causes) Variables are items of the system whose values are determined exclusively by the parameters (effects) State of the system is a set of values for all variables One set of parameters determines unambiguously the variables One set of variables can potentially be caused by many parameter sets

40 The central modelling question Given a model of a system: how do the parameters affect the state of the system? Answers explain: which parameters have the highest effect on desired outcomes (e.g. drug design) what properties of the model are more fragile or robust which parameters need accurate estimates (experimental design) etc.

41 Preview Topics: Model building, editing, kinetic functions, simulation Software, standards, databases Structural analysis Sensitivities, Metabolic Control Theory Optimization Parameter estimation Stochastic simulation

42 Iterative modelling cycle knowledge formation forward modelling behaviour: simulation results Model Knowledge inverse modelling behaviour: experimental measurements knowledge retrieval text mining Publications Literature

43 Model creation / refinement knowledge formation forward modelling behaviour: simulation results Model Knowledge inverse modelling behaviour: experimental measurements knowledge retrieval text mining Publications Literature

44 Simulation, analysis, parameter scanning/sampling knowledge formation forward modelling behaviour: simulation results Model Knowledge inverse modelling behaviour: experimental measurements knowledge retrieval text mining Publications Literature

45 Optimisation & Parameter fitting knowledge formation forward modelling behaviour: simulation results Model Knowledge inverse modelling behaviour: experimental measurements knowledge retrieval text mining Publications Literature

46 Explanatory models ( predictive models) Conceptual models Aim: understand generic principles Parameter values are not important per se... but should be realistic. Optimal in some sense Accurate models Aim: understand a real phenomenon Parameter values are very important... and need to be estimated from data User: theoretician User: experimentalist Explore the model: Find best model given: Possible behaviours Existing knowledge Parameter ranges Data

47 Aims of the lecture Getting familiar with the common workflow and techniques of computational systems biology Understanding the purpose, strengths and weaknesses of the different methods and their computational/mathematical basis Becoming able to use the tools to investigate the behaviour of biological/biochemical systems Maybe, also getting some idea how to extend or improve the tools that are available at the moment...

48 Mendes group COPASI (COmplex PAthway SImulator) Software for the simulation and analysis of Kummer group biochemical networks Tool kit with a variety of different methods: Deterministic, stochastic and hybrid simulation methods Metabolic Control Analysis, Elementary Flux Mode Analysis, Sensitivity Analysis Parameter Scanning, Optimization, Parameter Fitting User-friendly GUI, runs under Mac, Linux, Windows and Solaris and command line version Artistic license/open-source reads and writes SBML, etc.

49 Next week no lecture (1.5. Tag der Arbeit) or exercise!