research in bioinformatics and systems biology · sized evolutionary algorithms. proceedings of the...

ResearchResearch in in BioinformaticsBioinformatics andandSystemsSystems BiologyBiology

Miguel Rocha & Isabel RochaMiguel Rocha & Isabel RochaUn. Un. MinhoMinho -- PortugalPortugal

Outline

��SomethingSomething aboutabout mymy institutioninstitution��GroupGroup historyhistory & & membersmembers��ResearchResearch lineslines��AvailableAvailable data & software data & software toolstools��CurrentCurrent projectsprojects��PublicationsPublications��CollaborationCollaboration��TeachingTeaching

Something about my institution

�� Universidade do MinhoUniversidade do Minho�� 34 34 yearsyears ((foundedfounded inin 1973)1973)

�� AboutAbout 16000 16000 studentsstudents; ; aboutabout 2000 2000 postpost--graduategraduatestudentsstudents

�� 1200 1200 teachingteaching staff (staff (aboutabout 850 850 withwith PhDPhD))

�� 600 600 nonnon--teachingteaching staffstaff

�� 2 2 campicampi: Braga : Braga andand GuimarãesGuimarães

�� BScBSc, , MScMSc andand PhDPhD degreesdegrees inin allall areasareas ofofknowledgeknowledge: : sciencessciences, , engineeringengineering, , healthhealth, , lawlaw, , social social sciencessciences, , educationeducation, , humanitieshumanities andand artsarts

Group history & members

�� TwoTwo mainmain subsub--groupsgroups thatthat cooperatecooperate ininBioinformaticsBioinformatics / / SystemsSystems BiologyBiology ResearchResearch::

�� BioPSEgBioPSEg groupgroup –– CenterCenter BiologicalBiological EngineeringEngineering ––IBBIBB

•• BioprocessBioprocess optimizationoptimization andand controlcontrol ((recombinantrecombinantproteinprotein productionproduction, , foodfood ingredientsingredients))

•• MetabolicMetabolic EngineeringEngineering ((E. E. colicoli, S. , S. cerevisiaecerevisiae, H. , H. pyloripylori

•• BiologicalBiological modelsmodels

•• MembersMembers: : –– PhDPhD: Eug: Eugéénio Ferreira, Isabel Rocha, Ana Velosonio Ferreira, Isabel Rocha, Ana Veloso

–– PhDPhD studentsstudents: Rafael Costa : Rafael Costa andand SSóónia Carneironia Carneiro

–– MScMSc studentsstudents: Orlando Rocha, Joana Castro : Orlando Rocha, Joana Castro andand Carina Carina DurãesDurães

Group history & members

�� TwoTwo mainmain subsub--groupsgroups thatthat cooperatecooperate ininBioinformaticsBioinformatics / / SystemsSystems BiologyBiology ResearchResearch::�� CCTC / CCTC / DepDep. . InformaticsInformatics

•• ResearchResearch inin MachineMachine LearningLearning, Data , Data MiningMining, , ModelingModeling, , OptimizationOptimization algorithmsalgorithms

•• ExpertiseExpertise inin EvolutionaryEvolutionary ComputationComputation andand Artificial Artificial Neural Neural NetworksNetworks

•• MembersMembers: : –– PhDPhD: Miguel Rocha (PI), Rui Mendes, : Miguel Rocha (PI), Rui Mendes, AnAnáálialia LourenLourençço (o (postpost--docdoc researcherresearcher))

–– MScMSc studentstudent: Eduardo Valente, Jos: Eduardo Valente, Joséé P. PintoP. Pinto

–– BIC BIC grantgrant: Paulo Maia: Paulo Maia

Main research lines

�� OptimizationOptimization ofof fedfed--batchbatch fermentationfermentation processesprocesses

�� Use Use ofof optimizationoptimization methodsmethods ((namelynamely EvolutionaryEvolutionaryComputationComputation) to ) to performperform thethe optimizationoptimization ofof feedingfeedingprofilesprofiles andand otherother parametersparameters ofof fedfed--batchbatchfermentationfermentation processesprocesses

�� ToolsTools for for inin silicosilico metabolicmetabolic engineeringengineering

�� ComputationalComputational toolstools for for thethe simulationsimulation ofof metabolicmetabolicnetworksnetworks andand optimizationoptimization ofof strainsstrains ((e.ge.g. . selectingselectinggene gene knockoutsknockouts) for ) for givengiven productionproduction goalsgoals

Other research lines

�� ModelingModeling ofof fedfed--batchbatch fermentationfermentation processesprocesses

�� Use Use ofof Neural Neural NetworksNetworks to to modelmodel kineticskinetics inin fedfed--batchbatchfermentationfermentation processesprocesses

�� InformationInformation retrievalretrieval andand TextText miningmining toolstools

for for biomedicalbiomedical literatureliterature

�� InformationInformation retrievalretrieval andand TextText miningmining toolstools to to extractextractusefuluseful informationinformation fromfrom biomedicalbiomedical literatureliterature for for thetheconstructionconstruction ofof metabolicmetabolic andand regulatoryregulatory modelsmodels

Other research lines

�� ToolsTools for for biologicalbiological modelmodel constructionconstruction, , simulationsimulation andand visualizationvisualization

�� SteadySteady--statestate metabolicmetabolic modelsmodels, , regulatoryregulatory networksnetworks

�� BioBio--visualizervisualizer –– software software tooltool for for visualizingvisualizing metabolicmetabolicandand regulatoryregulatory modelsmodels andand omicomic datadata

�� Experimental design for Experimental design for determinationdetermination ofof kinetickineticstructurestructure andand parametersparameters inin intracellularintracellular modelsmodels

Available data & software tools

�� Software for Software for optimizationoptimization ofof fedfed--batchbatch fermentationfermentationprocessesprocesses

�� Software for Software for modelingmodeling ofof fedfed--batchbatch fermentationfermentationprocesses (ODEprocesses (ODE’’s s andand ANN)ANN)

�� OptGeneOptGene –– software for software for inin silicosilico metabolicmetabolicengineeringengineering

�� BioVisualizerBioVisualizer

�� DatabaseDatabase for for regulatoryregulatory informationinformation

�� ToolsTools for for retrievalretrieval andand TextText MiningMining overover biomedicalbiomedicalliteratureliterature ((underunder developmentdevelopment))

Current projects : FCT

�� MOBioProMOBioPro –– ModelingModeling andand optimzationoptimzation ofof BiologicalBiologicalProcessesProcesses

�� PortuguesePortuguese FCT; 2005 FCT; 2005 –– 20082008

�� 73 000 euros73 000 euros

�� recSysBiorecSysBio –– MetabolicMetabolic EngineeringEngineering

�� PortuguesePortuguese FCT; 2005 FCT; 2005 –– 20082008

�� NewNew projectproject approvedapproved, , relatedrelated to to applicationapplication ofofGridGrid ComputingComputing to to BioinformaticsBioinformatics problemsproblems

�� UnderUnder evaluationevaluation: FCT : FCT projectsprojects, , PhDPhD / / postpost--docdocgrantsgrants

Current projects: cooperation

�� CoordinationCoordination actionaction withwith U.VigoU.Vigo

�� AnalysisAnalysis ofof microarraymicroarray datadata

�� 2007 2007 –– 2008 ; 1400 + 8000 euros2008 ; 1400 + 8000 euros

�� CooperationCooperation withwith UC UC IrvineIrvine

�� MachineMachine LearningLearning applicationapplication inin SystemsSystems BiologyBiology

�� 2007 ; 14 000 euros2007 ; 14 000 euros

Current projects: cooperation

�� CollaborativeCollaborative projectproject withwith DupontDupont, USA (, USA (inin silicosilico

MetabolicMetabolic EngineeringEngineering toolstools))

�� ProjectsProjects underunder evaluationevaluation: :

�� EU EU coordinationcoordination actionaction ((MetabolicMetabolic EnginneringEnginnering –– headedheadedbyby DTU DTU -- DenmarkDenmark))

�� IberianIberian NanotechnologyNanotechnology InstituteInstitute (Molecular (Molecular SystemsSystemsBiologyBiology ofof H. H. PyloriPylori –– withwith U. Vigo; FICHUVI, IPATIMUP)U. Vigo; FICHUVI, IPATIMUP)

�� MITMIT--PortugalPortugal (Experimental Design (Experimental Design inin SystemsSystems BiologyBiology ––withwith CSAIL CSAIL -- MIT)MIT)

Publications

�� Rocha, M., Pinto, J.P., Rocha, I., Ferreira, E.C. Rocha, M., Pinto, J.P., Rocha, I., Ferreira, E.C. EvaluatingEvaluating EvolutionaryEvolutionary AlgorithmsAlgorithms andandDifferentialDifferential EvolutionEvolution for for thethe OnlineOnline OptimizationOptimization ofof FermentationFermentation Processes. Processes. LectureLecture Notes Notes inin

ComputerComputer ScienceScience, 4447, 236, 4447, 236--246, 2007.246, 2007.

�� Rocha, I.Rocha, I., , ForsterForster, J., , J., NielsenNielsen, J. Design , J. Design andand applicationapplication ofof genomegenome--scalescale reconstructedreconstructedmetabolicmetabolic modelsmodels. . Gene Gene EssentialityEssentiality atat GenomeGenome scalescale: : ProtocolsProtocols andand BioinformaticsBioinformatics (A. (A. OstermanOsterman EdEd.). .). InIn seriesseries: : MethodsMethods inin Molecular Molecular BiologyBiology ((JohnJohn WalkerWalker, , EdEd.), Humana .), Humana PressPress ((ininpresspress).).

�� Rocha, M., Pinto, J.P., Rocha, I., Ferreira, E.C. Rocha, M., Pinto, J.P., Rocha, I., Ferreira, E.C. OptimizationOptimization ofof BacterialBacterial StrainsStrains withwith VariableVariable--SizedSized EvolutionaryEvolutionary AlgorithmsAlgorithms. . ProceedingsProceedings ofof thethe 2007 IEEE 2007 IEEE SymposiumSymposium onon ComputationalComputational

IntelligenceIntelligence inin BioinformaticsBioinformatics andand ComputationalComputational BiologyBiology (CIBCB 2007)(CIBCB 2007), , HonoluluHonolulu, , HawaiiHawaii, , AprilApril 11--5, 2007, IEEE 5, 2007, IEEE ComputationalComputational IntelligenceIntelligence SocietySociety, (ISBN: 1, (ISBN: 1--42444244--06980698--6), CD6), CD--ROM, ROM, pppp. . 331331--337, 2007.337, 2007.

�� Carneiro, S., Rocha, I., Ferreira, E.C. Carneiro, S., Rocha, I., Ferreira, E.C. ApplicationApplication ofof a a genomegenome--scalescale metabolicmetabolic modelmodel to to thetheinferenceinference ofof nutritionalnutritional requirementsrequirements andand metabolicmetabolic bottlenecksbottlenecks duringduring recombinantrecombinant proteinproteinproductionproduction inin EscherichiaEscherichia colicoli. . Microbial Microbial CellCell FactoriesFactories 5(Suppl 1):P52, 3 5(Suppl 1):P52, 3 pppp., 2006.., 2006.

�� PatilPatil, K. R., Rocha, I., , K. R., Rocha, I., ForsterForster, J., , J., NielsenNielsen, J. , J. EvolutionaryEvolutionary programmingprogramming as a as a platformplatform for for ininsilicosilico metabolicmetabolic engineeringengineering. . BMC BMC BioinformaticsBioinformatics 6:308, 2005.6:308, 2005.

�� ... http://... http://biopseg.deb.uminho.ptbiopseg.deb.uminho.pt//

Collaboration

Network of Excellence – EURECCA

Coordination ActionDTU - DenmarkUVigo - Spain

ISU - USA

MIT - USAUCI - USA

UPorto - Portugal

Regulatory Model Inference using Text

Mining Tools

Experimental Design for determination of

metabolic model structure and parameters

Data Mining Tools for Model Inference

Algorithms for Optimization of

Microbial strains

Development of an unified Software

Platform

Molecular Simulation for Enzyme

Optimization

European Union

EURECCA NoESYSINBIO CA

IGC - Portugal

Computational Biology Collaboratorium

DupontUSA

Computational Tools

for Metabolic

Engineering

Teaching

�� MIT PortugalMIT Portugal

�� CoordinationCoordination ofof thethe ComputationalComputational BiosystemasBiosystemas SciencesSciences& & EngineeringEngineering module module ofof thethe BioBio--engineeringengineering systemssystemsadvancedadvanced coursecourse / / PhDPhD programprogram

�� MScMSc inin BioinformaticsBioinformatics

�� StartingStarting SeptemberSeptember 20072007

�� MScMSc inin InformaticsInformatics

�� OptionalOptional curricular curricular unitunit (30 (30 ectsects))

�� MScMSc inin BiologicalBiological andand BiomedicalBiomedical EngineeringEngineering

�� BioinformaticsBioinformatics / / SystemsSystems BiologyBiology

Evolutionary Computation for the Optimization ofFermentation Processes

Miguel Rocha Jose Pinto Isabel Rocha Eugenio Ferreira

Universidade do Minho (Portugal)

June 21, 2007

M.Rocha, J.Pinto, I.Rocha, E.Ferreira EC for the Optimization of Fermentation Processes

Motivation

Valuable products such as recombinant proteins, antibioticsand amino-acids are produced using fermentationtechniques.

There is a great economic incentive to the optimization ofthese processes. But, these are complex, involve nonlinearbehavior and time-varying properties.

Thus, there is the need to consider appropriate quantitativemathematical models and robust optimization techniques,to handle such features.

Motivation

Fed-batch fermentation processes

In fed-batch fermentation processes there is an addition ofnutrients along the process; the system states change from alow initial to a very high final product concentration.

White box mathematical models are developed, based ondifferential equations that represent the mass balances of thestate variables.

This dynamic behavior motivates the development ofoptimization methods to find the optimal input feedingtrajectories, given a performance index (PI) that measuresthe process productivity.

This is typically solved before the beginning of thefermentation process (open-loop optimal control or offlineoptimization).

Previous work on Offline optimization

For simple bioreactors, the problem can be solvedanalytically, but it becomes too complex when the number ofvariables increases.

Numerical methods:Gradient based methods: based on local sensitivities of theobjective function to guide adjustments in control trajectories.Dynamic programming: a systematic backward search,combined with the system’s simulation, is used to find theoptimal path through the defined grid;

Previous work on Offline optimization

For simple bioreactors, the problem can be solvedanalytically, but it becomes too complex when the number ofvariables increases.

Numerical methods:Gradient based methods: based on local sensitivities of theobjective function to guide adjustments in control trajectories.Dynamic programming: a systematic backward search,combined with the system’s simulation, is used to find theoptimal path through the defined grid;

Evolutionary Computation for Offline optimization

The aim is to find the feeding trajectory, represented as avector of real-valued variables, that yields the best PI.

Each variable will encode the amount of substrate to be fedinto the bioreactor, in a given time interval. The solution willbe given by their temporal sequence.

Size of the solution is reduced by using linear interpolation.

Evaluation process: running a numerical simulation of theODEs in the model (using a linearly implicit-explicitRunge-Kutta scheme, giving as input the feeding values in thesolution.

Fitness is calculated according to the PI for each case.

The work in our group was devoted to the development andcomparison of several meta-heuristics, from the field ofEvolutionary Computation to the previous problem, namely:

Evolutionary Algorithms with a real-valued representationDifferential Evolution (DE)Particle Swarm Optimization

Experiments conducted with several case studies shown thesuperiority of DE algorithms in this task.

The work in our group was devoted to the development andcomparison of several meta-heuristics, from the field ofEvolutionary Computation to the previous problem, namely:

Evolutionary Algorithms with a real-valued representationDifferential Evolution (DE)Particle Swarm Optimization

Experiments conducted with several case studies shown thesuperiority of DE algorithms in this task.

Differential Evolution

A variant of the DE algorithm called DE/rand/1 wasconsidered that uses a binomial crossover.

The following scheme is followed for each individual i at eachgenerarion:

1 Randomly select 3 individuals r1, r2, r3 distinct from i ;2 Generate a trial vector based on: ~t = ~r1 + F · (~r2 −~r3)3 Incorporate coordinates of this vector with probability CR;4 Evaluate the candidate and use it in the new generation if it is

at least as good as the current individual.

Differential Evolution

A variant of the DE algorithm called DE/rand/1 wasconsidered that uses a binomial crossover.

The following scheme is followed for each individual i at eachgenerarion:

1 Randomly select 3 individuals r1, r2, r3 distinct from i ;2 Generate a trial vector based on: ~t = ~r1 + F · (~r2 −~r3)3 Incorporate coordinates of this vector with probability CR;4 Evaluate the candidate and use it in the new generation if it is

at least as good as the current individual.

Real valued Evolutionary Algorithm

The EA uses real-valued representations and the followingmutation/crossover operators:

Random Mutation;Gaussian Mutation;Two-Point crossover;Arithmetical crossover.

In each generation half of the population is kept from theprevious generation. Selection is performed by using a rankingof the individuals and applying a roulette wheel scheme.

Real valued Evolutionary Algorithm

The EA uses real-valued representations and the followingmutation/crossover operators:

Random Mutation;Gaussian Mutation;Two-Point crossover;Arithmetical crossover.

In each generation half of the population is kept from theprevious generation. Selection is performed by using a rankingof the individuals and applying a roulette wheel scheme.

Limitations of offline optimization

Even when the models used for offline optimization arereliable, in a real environment several sources of noisecontribute to changes in the observed values of the variables.

Also, it is likely that there exists a time-variance of yield andkinetic parameters not contemplated in the models.

So, the experimental results are worse than the ones predictedafter offline optimization.

An alternative are online optimization algorithms that runsimultaneously with the real fermentation process,periodically generating new solutions, using measurements ofrelevant variables by sensors.

Online optimization

During the fermentation process, some variables can bemeasured, but their values are scarcely used for closed-loopoptimization.

It is possible to develop online optimization algorithmscapable of using this knowledge, by updating their internalmodels and generating new solutions.

EAs and DE are promising approaches to this task, since theykeep a population of solutions, easily adapted to performre-optimization.

Since a set of solutions is kept, a faster adaptation to newconditions is possible, while taking advantage of previousoptimization efforts.

Online optimization

A framework for online optimization

An online optimization framework based on EAs and DE isproposed, working as follows:

1 before the fermentation process starts, an offlineoptimization is conducted with EAs or DE.

2 After this step, whenever new information is available (thevalue of some state variables measured by sensors):

1 The online optimization algorithms (DE/EA) react byupdating their internal model and reaching a new bestsolution.

2 The new best solution is sent back to the fermentationmonitoring software.

Experiments on Online optimization

In our work, the performance of EAs and DE in the task ofonline optimization was evaluated.

Three case studies were used to perform offline andsimulated online optimization.

The relevant state variables were disturbed by adding noise atregular periods of time.

The behavior of both algorithms was compared and theperformance of the initial optimization after the perturbationsis evaluated.

Results on online optimization

In every case study, even a low level of noise is enough toclearly disrupt the results obtained by offline optimization.

The results of online optimization are quite close the onespredicted by offline optimization. Thus, the optimizationscheme is robust to the levels of noise studied.

The results exhibit graceful degradation with the increase ofthe level of noise.

A comparison of the results showa that DE seems to be moreeffective than the EAs in most cases (in some cases thedifference is not statistically significant).

Current work devoted to improvement of the algorithms andimplementation of online optimization interacting with thereal optimization.

Example I: Recombinant E. coli

Performance index:

PI =X (Tf )W (Tf )− X (0)W (0)

Model:

dt= (µ1 + µ2 + µ3)X − DX (2)

dt= (−k1µ1 − k2µ2)X +

Fin,SSin

W− DS (3)

dt= (k3µ2 − k4µ3)X − DA (4)

dt= (−k5µ1 − k6µ2 − k7µ3)X + OTR − DO (5)

dt= (k8µ1 + k9µ2 + k10µ3)X − CTR − DC (6)

dt' Fin,S (7)

Example II: Hybridoma reactor

Performance index:

∫ Tf

0−qMabXv (t)V (t) (8)

Model:dXv

dt= (µ− kd)Xv −

F1 + F2

VXv (9)

VGlcin −

F1 + F2

VGlc − qGlcXv (10)

VGlnin −

F1 + F2

VGln − qGlnXv (11)

dt= qLacXv −

F1 + F2

VLac (12)

dt= qAmmXv −

F1 + F2

VAmm (13)

dt= qMabXv −

F1 + F2

VMab (14)

dt= (F1 + F2) (15)

(16)M.Rocha, J.Pinto, I.Rocha, E.Ferreira EC for the Optimization of Fermentation Processes

Results obtained in case study I

U Initial optim. Initial+noiseDE EA DE EA

0.01 9.47 ± 0.00 8.85 ± 0.04 4.67 ± 0.70 4.79 ± 0.730.02 9.47 ± 0.00 8.83 ± 0.05 4.41 ± 0.75 4.69 ± 0.780.03 9.47 ± 0.00 8.81 ± 0.05 4.20 ± 0.76 4.35 ± 0.81

U Online opt.DE EA

0.01 9.11 ± 0.14 8.72 ± 0.140.02 8.80 ± 0.24 8.53 ± 0.250.03 8.47 ± 0.34 8.17 ± 0.35

Results obtained in case study II

U Initial optim. Initial+noiseDE EA DE EA

0.01 394.7 ± 0.2 386.3 ± 0.8 371.7 ± 8.5 367.9 ± 7.10.02 394.7 ± 0.2 385.2 ± 0.7 353.9 ± 14.9 351.2 ± 12.30.03 394.7 ± 0.2 386.1 ± 0.9 330.0 ± 23.5 343.0 ± 15.4

U Online opt.DE EA

0.01 386.2 ± 4.8 379.8 ± 3.80.02 374.1 ± 9.2 371.8 ± 8.30.03 364.5 ± 13.0 367.6 ± 11.0

research in bioinformatics and systems biology · sized evolutionary algorithms. proceedings of the...

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