Computational Systems Biology

Rückblick übermathematische System-ModellierungSystembiologie

Daten-Integration/Datenbanken

SBML &Systembiologie-Werkzeuge

Literatur: E. Kipp et al.Systems Biology in Practice

Vorlesung System-Biophysik 6. Feb. 2008

Inhalte : Biophysik der Systeme

1. Einleitung2. Evolution + Spieltheorie3. Nichtlineare Systemdynamik4. Raumzeitliche Strukturbildung5. Biologische Netzwerke6. Genetische Netzwerke7. Immunsystem

http://www.schwerpunkt-biophysik.physik.lmu.de

Evolutionbeschreibt die Entstehung und zeitliche Entwicklung von „Spezien“

Entstehung molekularer VielfaltPhysikalische Voraussetzungen, die Mutation und Selbstvermehrung erlaubenSynthese von Makromolekülen (Miller-Urey Experiment)

Nichtgleichgewichtssituationen

Hydrothermale Windeals molekulare Brutstätte

Die Rolle von Vesikelnals Reaktions-Kompartments

Theorien zur Evolutionsdynamik

Darwin: Selbstvermehrung (Replikation), Mutation, Selektion

A. Eigensches EvolutionsmodellGeburtenrate, Sterberate, + Selektionsdruck durch Begrenzung der ResourcenMolekulare Evolution : Replikation durch autokatalytische Prozesse–RNA WeltHyperzyklen (beschreibt Enzym-RNA WW)(die produktivste, bzw. kooperativste Spezie gewinnt)

B: SpieltheorienPopulationsabhängige Selektion „Es gewinnt die beste (stabilste)Strategie (nicht notwendigerWeise die produktivste Spezie)

Raum-zeitliche Strukturbildung

Raum-zeitliche Strukturbildung mit diffusivem Transport1. Belousov Zhabotinsky Reaktion2. Diffusionslimitierte Aggregation (DLA) führt zu fraktalen Strukturen3. Aktivator-Inhibitor Modell „Wie Schnecken sich in Schale werfen“ * lokale Selbstverstärkung und langreichweitige Hemmung * spontane Musterbildung durch lokale Fluktuationen * Reaktions-Diffusionsgleichung

Biochemische Netzwerke

Metabolische Netzwerke sind durch eine Netzwerktopologie(pathway) und biochemische Ratengleichungen beschrieben.

S-Systeme : einfache nichtlineare Näherung mit numerischen Vorteilen

!

E + Sk1" # " ES

k2" # " E + P

!

k"1# $ $

Enzymatische ReaktionenMichaelis-Menton-KinetikInhibierung, Regelung

Bakterielle Chemotaxis

Bakterielle Chemotaxis: - Biased random walk - Adaption durch Methylierung „Integral feedback control“

- Ultrasensitiveness- Adaptivness- Robustness

Eukaryotische Chemotaxis

Signalübermittlung durch Botenstoffe (c-AMP)Erregbares System (autokatalytische Oszillationen)Biochemisches Netzwerk(synchronisationsfähig)Theorie: Zelluläre Automaten(Zellen folgen dem Gradientenchemischer Wellen)

Schleimpilz : Dictyostelium Discoideum

- second messenger - Gradient sensing - Zell-Polarisation - Zellbewegung

Genregulation

* Wichtiges Beispiel: die Laktose-Regulation (lac-operon)* Vereinfachte Beschreibung vonGenregulation durch boolesche Algebra* Kontinuierliche Beschreibungdurch Differentialgleichungen

Stochastische Genexpression

• extrinsisches und intrinsisches Rauschen• Quorum Sensing

Modellierung mit Modulen und MotivenBeispiel circadian systems

RNA Regulation: mRNA, RNAi, ribozymes

RISC: RNAi-induzierter silencing complex

Netzwerktheorie

Netzwerke haben eine hierachische Struktur - Komponenten, Blöcke, funktionelle Module, System

Universelle Eigenschaften komplexer Netzwerke * „small world property“ (kurze Verbindungswege) * skaleninvarianz (Verteilung der „connectivity“) * Starke Tendenz zu Clustern

Degree distribution, Scale free networks, hierarchicalnetworks,metabolic pathways, robustness, two hybridscreen

Zelluläre Netze: Immunsystem

Humorale und zelluläre Immunantwort, Antikörperstruktur Antikörpervielfalt durch genetische RekombinationKlonale SelektionstheorieJernsche NetzwerktheorieT-Zellen : Unterscheidung von „Selbst“ und „Fremd“

Kognitive Systeme: - Immunsystem erkennt FremdstoffenLernfähige Systeme-erinnert Muster (z.B. Antikörper (Immunsystem) oder visuelle Muster (Neuronale Netze))

Zum Begriff „Bio-System“

InputOut-put

* Komponenten (Spezien)* Netzwerkartige Verknüpfungen (kinetische Raten)* Substrukturen (Knoten,Module, Motive)* Funktionelle Input => Output Relation

* Erforschung der „Bauprinzipen“ (reverse engineering)Vorsicht : Bauprinzip nicht „rational“ sondern Ergebnis eines Evolutionprozesses * Erstellung quantitativer Modelle zur Beschreibung des Systems

Eigenschaften

Ziel

Modellierungs- HierarchienBeispiel: Signalübertragung

Biochemische Ratengleichung

+ Definition von Reaktionsräumen

+ Diffusionsprozesse Reakt.-Diff- Gl.

+ stochastische Beschreibung

Kompexität von Signal-Netzwerken

Connection Maps: Signal Transduction Knowledge Environment www.stke.org

Computergestützte Modellierung- Systembiologie -

gene(tic) regulatory networks

protein interactions

networks

Citrate Cycle

Bio-Map[A.L.Barabasi]

GENOME

PROTEOME

METABOLOME

metabolic networks

The Systems Biology View

June 29

July 6

July 13

Informationstechnische Aspekte derSystem Biologie

– Model organisms as data sources– Data required beyond the ´omics– Standardization of in vitro/in vivo experiments and

their data– Standardization of databases, interoperability of

modeling software– => SBML (systems biology mark-up language)– Training

Systems Biology Definition

• Systems Biology integrates experimental and modelingapproaches to study the structure and dynamical propertiesof biological systems

• It aims at quantitative experimental results and buildingpredictive models and simulations of these systems.

• Current primary focus is the cell and its subsystems , butthe „systems perspective“ will be extended to tissues,organs, organisms, populations, ecosystems,..

The challenges of systems biology

“The data are accumulating and the computers are humming, what we are lacking arethe words, the grammar and the syntax of a new language…”Dennis Bray (TIBS 22(9):325-326, 1997)

“The most advanced tools for computer process description seem to be also the besttools for the description of biomolecular systems.”Ehud Shapiro (Biomolecular Processes as Concurrent Computation, Lecture Notes,2001)

“Although the road ahead is long and winding, it leads to a future where biology andmedicine are transformed into precision engineering.”Hiroaki Kitano (Nature 420:206-210, 2002)

“The problem of biology is not to stand aghast at the complexity but to conquer it.”Sydney Brenner (Interview, Discover Vol. 25 No. 04, April 2004)

Systems Biology (2)• Need insight in 4 key areas:

– Systems structures: cf. above

– Systems dynamics: eg sensitivity analysis, bifurcation analysis

– Control methods: mechanisms for minimizing malfunction

– Design methods: modify, construct biosystems with desired properties

• (Easy-to-use) Formula:

Second approximation (MPI Magdeburg graphic, 2002)Systems Biology = Biology + Informatics + Systems Engineering

First approximation (J. Schwaber, TJU, Nov 01): Systems Biology = Genomics + Systems Engineering

Life‘s Complexity Pyramid(Zoltvai-Barabasi, Science 10/25/02)

Systems Biology – just buzz for big bucks?

• „Systembiologie –alter Wein in neuenSchläuchen?“

(Laborjournal, 07-08/02)

• „...Not the first attempt atsystem-level understanding ..arecurrent theme in thescientific community“

(H. Kitano, ICSB 2000)

• BMBF „Systeme des Lebens“Project

– announced Dec 01, €50 m– liver cell focus– Initial awards for €15 m in Jan 03 to Uni

Freiburg, Tübingen & Rostock

• DARPA BioSPICE– larger part of Bio-computing project

started Fall 01 $60 m– Vision: provide bioscientists a standard,

scalable, easy-to-use modeling andsimulation environment

High-throughput technologydemands data integration

• Human Genome Project(Lauder et al. 2001, Venter et al. 2001)

• Whole-genome DNA arrays(monitoring the transcriptome level)

• Proteomic data (2D gels, mass spec.)

Microarray-Experiment

Example databases:

• www.pdb.org

SBMLEmerging Standards and Platforms for

Systems Biology

Systems Biology Markup-Language

The Need for a Model Exchange Language

SBML: Systems Biology Markup Language

• Language for representation and exchange ofbiochemical network models

• Problems addressed (after [HUFI02]):– users often need to work with complementary resources

from multiple tools => manual re-encoding in each tool– when simulators are no longer supported, encoded models

become unusable– Models published in peer-reviewed journals are not

straightforward to examine and test as they use specificrepresentation and environments. This also prevents a re-use strategy in building more complex models

The ERATO Systems Biology Workbench Project:A Simplified Framework for Application Intercommunication

Michael Hucka, Andrew Finney, Herbert Sauro, Hamid Bolouri

ERATO Kitano Systems Biology ProjectCalifornia Institute of Technology, Pasadena, CA, USA

Principal Investigators: John Doyle, Hiroaki Kitano

Collaborators:Adam Arkin (BioSpice), Dennis Bray (StochSim),Igor Goryanin (DBsolve), Andreas Kremling (ProMoT/DIVA),Les Loew (Virtual Cell), Eric Mjolsness (Cellerator),Pedro Mendes (Gepasi/Copasi), Masaru Tomita (E-CELL)

SBML History

• Software Platfoms for Systems Biology forum initiated April 2000 byERATO Symbiotic Systems Project Principal Investigators: H. Kitano (Keio U/Sony), J. Doyle (Caltech)

• Modeling/Simulation teams involved: Berkeley Biospice (Arkin, UC Berkeley) Cellerator (Shapiro/Mjolsness, Caltech) DBsolve (Goryanin, Glaxo-Wellcome Research, UK) E-Cell (Tomita, Keio U) Gepasi (Mendes, Virginia Tech) Jarnac (Sauro, Caltech/KGI) StochSim (Morton-Firth/Bray, Cambridge U) Virtual Cell (Schaff, U Connecticut) ProMoT/DIVA (Ginkel, MPI Magdeburg) CellML (Hedley, U Auckland & Physiome Sciences)

Motivations

• Observation: proliferation of software tools

• Researchers are likely to continue using multiple packages for the foreseeablefuture

• Problems with using multiple tools:– Simulations & results often cannot be shared or re-used– Duplication of software development effort

• No single tool is likely to do so in the near future– Range of capabilities needed is large– New techniques (⇒ new tools) evolve all the time

• No single package answers all needs– Different packages have different niche strengths– Strengths are often complementary

Project Goals & Approach

• Develop software & standards that– Enable sharing of modeling & analysis software– Enable sharing of models

• Goal: make it easier to share tools than toreimplement

• Two-pronged approach– Develop a common model exchange language

• SBML: Systems Biology Markup Language– Develop an environment that enables tools to interact

• SBW: Systems Biology Workbench

Systems Biology Markup Language (SBML)

• Domain: biochemical network models• XML with components that reflect the natural

conceptual constructs used by modelers in the domain• Reaction networks described by list of components:

– Beginning of model definition» List of unit definitions (optional)» List of compartments» List of species» List of parameters (optional)» List of rules (optional)» List of reactions

– End of model definition

SBML Key Characteristics• Based on XML (& further XML-based standards like MathML)• Releases (called „levels“) community driven ([sbml-discuss] list)

– Key authors: M. Hucka, A. Finney, H. Sauro– Level 1 published Mar 01– Level 2 to be published shortly

• Most tools mentioned already support SBML Level 1• Convergence with CellML actively pursued• Close affiliation with ERATO Systems Biology Workbench project

( www.sbw-sbml.org)

What is XML (Extensible Mark-Up Language)?• Developed by W3C 96/97 to overcome HTML

limitations• rapidly emerging IT industry standard for

structured documents

GML(IBM 70‘s)

SGML(Standard

GeneralizedMark-Up Language)

ISO 8879 (1986)HTML

(T. Berners-LeeCERN (1991)

XMLW3C (96/97)

SBML 2 Model Definition

SBML 2 Examples: Compartments

SBML 2Example for aRule

=> Need Tools!

Example

S1

X2

X1K1· X0

k2 · S1

k3 · S1

X0

Example (cont.)<?xml version="1.0" encoding="UTF-8"?><sbml level="1" version="1"> <model name="simple">

<listOfCompartments> <compartment name="c1" /> </listOfCompartments>

<listOfSpecies> <specie name="X0" compartment="c1" boundaryCondition="true" initialAmount="1"/> <specie name="S1" compartment="c1"

boundaryCondition="false" initialAmount="0"/> <specie name="X1" compartment="c1"

boundaryCondition="true" initialAmount="0"/> <specie name="X2" compartment="c1"

boundaryCondition="true" initialAmount="0.23"/> </listOfSpecies>

Example (cont.)<?xml version="1.0" encoding="UTF-8"?><sbml level="1" version="1"> <model name="simple">

<listOfCompartments> <compartment name="c1" /> </listOfCompartments>

<listOfSpecies> <specie name="X0" compartment="c1" boundaryCondition="true" initialAmount="1"/> <specie name="S1" compartment="c1"

boundaryCondition="false" initialAmount="0"/> <specie name="X1" compartment="c1"

boundaryCondition="true" initialAmount="0"/> <specie name="X2" compartment="c1"

boundaryCondition="true" initialAmount="0.23"/> </listOfSpecies>

Example (cont.)<?xml version="1.0" encoding="UTF-8"?><sbml level="1" version="1"> <model name="simple">

<listOfCompartments> <compartment name="c1" /> </listOfCompartments>

<listOfSpecies> <specie name="X0" compartment="c1" boundaryCondition="true" initialAmount="1"/> <specie name="S1" compartment="c1"

boundaryCondition="false" initialAmount="0"/> <specie name="X1" compartment="c1"

boundaryCondition="true" initialAmount="0"/> <specie name="X2" compartment="c1"

boundaryCondition="true" initialAmount="0.23"/> </listOfSpecies>

Example (cont.)<listOfReactions>

<reaction name="reaction_1" reversible="false"> <listOfReactants> <specieReference specie="X0" stoichiometry="1"/> </listOfReactants> <listOfProducts> <specieReference specie="X0" stoichiometry="1"/> </listOfProducs> <kineticLaw formula="k1 * X0"> <listOfParameters> <parameter name="k1" value="0"/> </listOfParameters> </kineticLaw> </reaction>

<reaction name="reaction_2" reversible="false"> <listOfReactants> <specieReference specie="S1" stoichiometry="1"/> </listOfReactants> . . .

Systems Biology Workbench (SBW)• Simple framework for enabling application interaction

– Free, open-source (LGPL)– Portable to popular platforms and languages– Small, simple, understandable

SBW

VisualEditor

StochasticSimulator ODE-based

Simulator

ScriptInterpreter

DatabaseInterface

SBML 2 Hierarchy of Major Data Types

Features of SBW• Modules are separately-compiled executables

– A module defines services which have methods– SBW native-language libraries provide APIs

• C, C++, Java, Delphi, Python available now• … but can be implemented for any language

– APIs hide protocol, wire transfer format, etc.• Programmer usually doesn’t care about this level

• SBW Broker acts as coordinator– Remembers services & modules that implement them– Provides directory– Starts modules on demand

• Broker itself is started automatically– Notifies modules of events (startup, shutdown, etc.)

Whats Next ?

SB + Publishing / Experiments / Text Mining

SB + Medicine

SB + Nanotechnology

SB + Synthetic Biology

Ende