p systems model optimisation by means of evolutionary based search algorithms
DESCRIPTION
This talk was presented at the Bioinformatics track at GECCO 2010. The associated paper was nominated for best paper awardTRANSCRIPT
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PSystemModelOp/misa/onbyMeansofEvolu/onaryBasedSearch
Algorithms
C.García‐Mar+nez,C.Lima,
J.Twycross,M.Lozano,N.Krasnogor
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Outline
• Mo+va+on:Systems&Synthe+cBiology,PSystemsbasedmodeling
• Methods&ExperimentalSetup
• ResultsandDiscussion
• Conclusions
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•The Cell senses the environment and its own internal states•Makes Plans, Takes Decisions and Act•Evolution is the master programmer
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The Cell as an Intelligent (Evolved) Machine
Cell
Internal States
Environmental Inputs
Actions
Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009.
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•The Cell senses the environment and its own internal states•Makes Plans, Takes Decisions and Act•Evolution is the master programmer
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The Cell as an Intelligent (Evolved) Machine
Cell
Internal States
Environmental Inputs
Actions
Amir Mitchell, et al., Adaptive prediction of environmental changes by microorganisms. Nature June 2009.
Wikimedia Commons
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Network Motifs: Evolution’s Preferred Circuits•Biological networks are complex and vast•To understand their functionality in a scalable way one must choose the correct abstraction
•Moreover, these patterns are organised in non-trivial/non-random hierarchies
•Each network motif carries out a specific information-processing function
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“Patterns that occur in the real network significantly more often than in randomized networks are called network motifs” Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation
network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002)
Radu Dobrin et al., Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10.
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Y positively regulates X
Negative autoregulation
Positive autoregulation
The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector.The I1-FFL is a pulse generator and response accelerator
U. Alon. Network motifs: theory and experimental approaches. Nature Reviews Genetics (2007) vol. 8 (6) pp. 450-461
Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002)
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Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002)
•The correct abstract ions facilitates understanding in complex systems.
•Provide a route to engineering , programming and evolving cells and their models
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• Cells(andmostbiologists)don’tdodifferen/alcalculus!
• Psystemsareaexecutablespecifica/onsthatcloselymimicbiologicalreality.
• Theseareprogramsthatexplicitlymimictheinternalbehaviorofcellsystems.
• Theseprogramsareexecutedinavirtualmachinethatcapturestheintrinsicstochas5cityinherentinbiology
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FundamentalECChallenge
• Learningaprogramwithstochas/cbehaviorvs.learningaPsystem.
•A cell is a living example of distributed stochastic computing.
function f1(p1,p2,p3,p4){if (p1<p2) and (rand<0.5) print p3else print p4}
function f1(p1,p2,p3,p4){if (p1<p2) RND print p3 RNDelse RND print p4 RND}
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ModularAssemblyofPSystems
• Modules:setofrulesrepresen/ngmolecularinterac/onsthatoccuroNen.
• Elementalmodules:Degrada/on,complexa/on,unregulatedgeneexpression,nega/vegeneexpression,etc.
• Combinatorics:Combina/onofbasicmodules(building‐blocks)originatesmorecomplexmodules,allowingmodularandhierarchicalmodellingwithPsystems.
• Challenge:Explorethelargecombinatorialspaceofmodulesandcorrespondingparameters.
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Multi-Objective Optimisation in Morphogenesis
Rui Dilão, Daniele Muraro, Miguel Nicolau, Marc Schoenauer. Validation of a morphogenesis model of Drosophila early development by a multi-objective evolutionary optimization algorithm. Proc. 7th European Conference on Evolutionary Computation, ML and Data Mining in BioInformatics
(EvoBIO'09), April 2009.
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Parameter Optimisation in Metabolic Models
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A. Drager et al. (2009). Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Systems Biol ogy 2009, 3:5
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Evolving P Systems Structures
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F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331-338. ACM Publisher, 2008.
H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009
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Outline
• Mo/va/on:Systems&Synthe/cBiology,PSystemsbasedmodeling
• Methods&ExperimentalSetup
• ResultsandDiscussion
• Conclusions
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Methods&ExperimentalSetup
• Comparedifferentevolu/onaryalgorithmstoop/miseparameters(kine/cconstants)inPsystems.
• Fourtestcasesofincreasingdifficultyanddimension:1.TC1:Pulsegeneratorfordifferentini/alcondi/ons(13parameters).
2.TC2:SameproblemasTC1butwithalargerparameters’domain.
3.TC3:Moregeneralpulsegenerator:feed‐forwardloopmo/f(18parameters).
4.TC4:Bandwidthdetector(34parameters).
• Experimentalbudgetwasrestrictedto1000func/onevalua/ons.
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TargetModels
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TargetModels
•HighlyDimensional•Noisy&Uncertainoutcomes•Non‐lineari+es•ExpensiveFunc+onevalua+ons
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TargetModels
•HighlyDimensional•Noisy&Uncertainoutcomes•Non‐lineari+es•ExpensiveFunc+onevalua+ons
Op+misa+onHell!
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Evolu/onaryAlgorithms
• CovarianceMatrixAdapta/onAlgorithm(CMA‐ES)
• Differen/alEvolu/on(DE)
• Opposi/on‐BasedDifferen/alEvolu/on(ODE)
• Real‐CodedGene/cAlgorithm(GA)
• VariableNeighbourhoodSearchwithEvolu/onaryComponents(VNS‐ECsv1andv2)
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ExperimentalDetails
• Fitnessofagivencandidatesolu/onisgivenby:1. RunthecorrespondingPsystemwiththe
mul/compartmentGillespiestochas/csimula/onalgorithm(20runs).
2. Averagetheoutput/meseriesofallrunsandcalculatethedifferencetothetargetseries,usingtherandomlyweightedsummethod.
• Be\er(forguidingthesearch)thansimpleconsideringtheRMSE,par/cularlywhen/meseriesrangeroverdifferentscales.
• Op/misa/onresultsareaveragedover50runs.
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Outline
• Mo/va/on:Systems&Synthe/cBiology,PSystemsbasedmodeling
• ExperimentalSetup
• ResultsandDiscussion
• Conclusions
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Results
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Discussion
• AlgorithmsrankedaccordingtoRMSE.• Mann‐WhitneyUtestwithp‐value=0.05todeterminewhichalgorithmsperformsignificantlybe\erthanothers.
• ForTC1,mostalgorithmsperformequallywell,withexcep/ontoCMA‐ESandVNS‐EC1.
• ForTC2,wecanfindsignificantdifferencesbetweenalgorithms,whereGAisthebe\er.
• Reducingbiologicalknowledge(fromTC1toTC2)clearlyaffectstheperformanceofthealgorithms.
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Discussion
• ForTC3,manyalgorithmsperformsimilar,butDE,ODE,andGAseemtoperformslightlybe\er.SimilartoTC1butnowVNS‐EC2performsconsiderablyworse.
• ForTC4,wherethereisalargernumberofparameters,resultsaresignificantlydifferentfromotherproblems.
• VNS‐ECsnowperformsignificantlybe\erthanremainingapproaches.
• CMA‐ES,ODE,andDEperformsimilarly,whileGAistheleastcompe//ve.
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Discussion
• Ingeneral,GA,ODE,andDEperformbe\erforproblemswithfewparameters(13and18).GAperformsbe\erwhenbiologicalknowledgeisreduced.
• Ontheotherhand,VNS‐ECsperformbe\erforthelargerproblem(38parameters).
• Whyisthis?Thenumberofevalua/onsallowedissmall(1000).Let’shavealook…
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BestFitness
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BestFitness
• Twoimportantobserva/ons:1. ForTC4(largernumberofparameters),GA,DE,and
ODEreducetheirconvergencespeedsbecauseevolvingpopula/onsofindividualsconsumesmanyresources.However,VNS‐ECswhichfocusthesearchononesolu/onmakeabe\erusageofthereducedbudget.
2. Whentheprobleminvolvesfewerparameters,theallowedbudgetisenoughtoproperlyconvergeapopula/onofsolu/ons.Inthiscase,VNS‐ECsarenotcompe//veanymore.
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AverageModelFit
• TestCase1
• TestCase2
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AverageModelFit
• TestCase3
Forprotein1,allalgorithmshavesimilaroutputtothetarget.
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AverageModelFit
• TestCase4
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Outline
• Mo/va/on:Systems&Synthe/cBiology,PSystemsbasedmodeling
• Methods&ExperimentalSetup
• ResultsandDiscussion
• Conclusions
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Conclusions• Considered4testcasesofincreasingdifficulty.• Limitedcomputa/onalresources(1000evalua/ons)havebeenimposedgiventheincreased/metoevaluatecandidatesolu/ons.
• Forthisexperimentalsetup,ithasbeenfoundthat:1. Whennumberofkine/cconstantsissmall,GA,DE,andODEare
robustop/misers.2. Whennumberofparametersincreases,theVNS‐Ecsobtain
be\erresults.
• DE(and,notreported,PSO)giveagoodcompromiseofquality/speedandconfigura/oneffortforsmalltomediumsizeproblems.
• Forlargerproblems,VNS‐Ecs(withouttoomanyparameters)seemthewaytogo.
• Fitnesscriterionmustberevisited!!!
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Acknowledgements
•Jonathan Blake
•Claudio Lima
•Francisco Romero-Campero
•Karima Righetti
•Jamie Twycross
Integrated Environment
Machine Learning & Optimisation
Modeling & Model Checking
Molecular Micro-Biology
Stochastic Simulations
Members of my team working on SB2
EP/E017215/1
EP/H024905/1
BB/F01855X/1
BB/D019613/1
University of NottinghamProf. M. Camara, Dr. S. Heeb, Dr. G. Rampioni, Prof. P. WilliamsWeizmann Institute of ScienceProf. D. Lancet, Prof. I. Pilpel
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