Friday14thofApril20179H30–18H00CentredeRecherchesInterdisciplinaires(CRI)-FacultédeMédecine,siteCochinPort-Royal,ailesud,2ème

étage-UniversitéParisDescartes-24,rueduFaubourgSaintJacques-75014Paris

NozhaBoujemaa-ResearchDirectorInBigDataDefenseMaster’sTheses

PhDPosterSessionEscapeGame

FreeBreakfast-FreeBuffet-FreeDrinks-FreeEntrance

Program

AmphithéâtreLUTON-24rueduFaubourgSaintJacques75014Paris

14thofApril2017

9H00 Breakfast

KeynoteSpeaker10H00 NozhaBoujemaa,ResearchDirector,AdvisortotheChairman&CEOofInriaInBigData

10H55 Coffeebreak

11H20 ThibaultCorneloup-SpatialstatisticsfortheanalysisoftheorganizationofP-bodies 11H45 CharlesBernard-Assessingthephenotypicheterogeneityofacellpopulationviasinglecell

RNASeqdataanalysis12H10 AhmedSaadawi-IntegrativemodelingoftheTREXcomplex12H35EléonoreBellot-Emergenceofcomplexityandandindancechoreography

13H00 Lunch

14H00SebastiánSosa-Carrillo-Parameterssearchingforasynthetictoggleswitch14H25 MislavAcman-Evaluationofmonotonicregressionmethodforthepredictionofcomplex

phenotypesfromtranscriptomicdata 14H50 PresentationofCRIdatascienceclub:UrszulaCzerwinska 15H05 EscapeGame 16H05 PosterpresentationPHD&Servringcoffeeatthesametime

OlgaSeminck:

PredictingProcessingCostofAnaphoraResolution

AamirAbbasi:Towardsafastbrain-machineinterfaceintegratingartificialsomatosensoryfeedback

ChristopheCoste:TraitLevelAnalysisOfMultitraitPopulationProjectionMatrices

16H45UriBarenholz–InvitedSpeaker

17H30 Buffet

SpatialstatisticsfortheanalysisoftheorganizationofP-bodies

ThibaultCorneloup

CentredeRecherchesInterdisciplinaires

InternatModelingandimageanalysisteam,morphogenesis,signalization,modellingdepartment,INRA-Versailles

P-bodysuits are ribonucleoproteins complex without membrane, located in the cytoplasm of eukaryotic cells and involved in the degradation and the translational repression of mRNA. Within the framework of the project ARNPbodyStruc (D. Weil), the IJPB team modelling (p. Andrey) has for objective to develop a 3D model of the architecture of P-bodysuits resting on data of MET. A prerequisite is to develop tools to compare predictions of the model and MET observations PUTS it on the basis of the distributions of golden particles which, coupled with antibodies, are used to reveal the spatial location of proteins involved in the assembly of P-bodysuits.

Because of the limited character of the data obtained by immuno-MET, we adopted an approach of statistical analysis of limited processes to make this comparison. The approach consists in characterizing the distributions of golden balls by confronting them with models of spatial distribution and to quantify the gap between observations and spatial model by means of distance functions (Andrey and al 2010).

By using random models and classic functions of distance, we have already established the feasibility and the interest of this statistical approach and showed that the protein ddx6 follows a non-random distribution there clusters which correspond probably to the association of ddx6 in ARNm (Ernoult-Lange and al 2012). New functions of distance and new spatial models must be however developed to characterize with a finer resolution the distributions of golden balls. The objective will be the analysis of distributions radial roads by C++ language, the analysis of alignments, and the analysis of multivariate patterns for the comparison of distributions of different proteins such as DDX6, LSM14 and GE1.

References :

Andrey P, Kieˆu K, Kress C, Lehmann G, Tirichine L, et al. (2010) Statistical Analysis of 3D Images Detects Regular Spatial Distributions of Centromeres and Chromocenters in Animal and Plant Nuclei. PLoS Comput Biol 6(7): e1000853. doi:10.1371/journal.pcbi.1000853

Michèle Ernoult-Lange, Sonia Baconnais, Maryannick Harper, et al. (2012) Multiple binding of repressed mRNAs by the P-body protein Rck/p54. RNA:18: 1702-1715. doi: 10.1261/rna.034314.112

Arpon J, Gaudin P, Andrey P. (2017) A method for testing random spatial model on nuclear objects distributions. In review.

AssessingthephenotypicheterogeneityofacellpopulationviasinglecellRNASeqdata

analysis

CharlesBernard

EquipeStress&Cancer(FatimaMechta-Grigoriou)atInstitutCurie,Paris

Previousresearchledbymygrouprevealedthatinbreastandovariancancers,atleastfourdifferenttypesofCancerAssociatedFibroblasts(CAF)canbecharacterizedbyFACS,IHCandRNA-Seq.TheteamisnowtacklingtheheterogeneityexistingwithineachofthesefourCAFsubpopulations,usingsingle-cellRNA-seq.

Thefirstpartofthepresentationwillbededicatedtotheanalysisofthiscellularheterogeneity,applyingtheSPADEalgorithm(Spanning-treeProgressionAnalysisofDensity-normalizedEvents)onthesescRNAseqdata.SPADEallowsahierarchicalvisualizationofcellularsubtypeswithinapopulation,accordingtotheirsimilaritiesofexpressionpattern.TheoutputofSPADEissimilartoaphylogenetictreeandcanhelponetoinferhierarchybetweencellsofsimilarprofiles,wherebranchingpointsonthetreereflectmajorcellfatedecisions.

AnotherissueraisedbyscRNAseq,dueespeciallytothelowdetectionofawiderangeofgenes,istoidentifytherelevantassociationswhichcouldexistbetweengenes,intheframeofpairwisecomparisonsofgeneexpressions.Previousstudieshaveindeedshownthat«classical»correlationtestsfailatcapturingacertainnumberofmeaningfulassociationsfromabiologicalviewpoint.Inthesecondpartofthepresentation,Iwillthereforeintroducestatisticalmethodswhichsucceedatcapturingassociationsbetweentheexpressionoftwogenes,whichcanbenon-linear,non-monotonicorwhichcan'tevenbemodelledbyamathematicalfunction.

IwilltryfinallytoshowhowthesedetectionsofnovelassociationscanbecombinedwiththeoutputofSPADEhierarchicalclusteringtoconfirmthebiologicalrelevanceofpotentialmarker(s)(genes)usedtocharacterizethedifferentsubtypesidentifiedineachoftheCAFsubpopulations.

IntegrativemodelingoftheTREXcomplex

AhmedSaadawi

Structuralbioinformaticsunit(MichaelNilgesgroup),InstitutPasteur,Paris

TheprocessofnuclearexportofmRNAsissophisticatedandentailsdifferentsteps.Thefunctionalorganizationofsuchaprocessispivotalforthefidelityandmaintenanceofgeneexpression.OneofthecorecomponentsnecessitatedforsuccessfulmRNAsnuclearexportistheTREXproteincomplex.TheTREX(TRanscription-EXport)complexisanevolutionarilyconservedmultiproteincomplexthatplaysamajorroleinthefunctionalcouplingofdifferentstepsduringmRNAbiogenesis,includingmRNAtranscription,processing,decay,andnuclearexport.AlthoughthefunctionofTREXisrelativelywellcharacterized,itsstructureand/orarchitecturehasnotyetbeenfullyresolved.Herein,weaimatdeterminingthearchitectureoftheTREXmacromolecularcomplexusingintegrativemodeling-inparticularbyutilizingtheintegrativemodelingplatformorIMP.Themodelingprocessconsistsofafour-stagecomputationalcycleof1)gatheringsparsedatasuchaselectronmicroscopydensitymaps,SAXS,cross-linking,sequenceinformation(FASTA),X-raycrystallography(PDBs),etc.;2)representationofthesubunitsintoaninitialmodelandtranslationofthedataintospatialrestraints;3)samplingofdifferentconformationsusingMonteCarlosimulation;4)analyzingthedatabyclusteringthesampledmodelstodeterminehigh-probabilityconfigurationsorbest-scoringmodelsthatsatisfytheexperimentaldata.TheendpointwouldbetoobtainareasonablestructuralresolutionofTREX.

References:

1)Katahira,Jun."mRNAexportandtheTREXcomplex."BiochimicaetBiophysicaActa(BBA)-GeneRegulatoryMechanisms1819.6(2012):507-513.

2)Russel,Daniel,etal."Puttingthepiecestogether:integrativemodelingplatformsoftwareforstructuredeterminationofmacromolecularassemblies."PLoSBiol10.1(2012):e1001244.

3)Fernandez-Martinez,Javier,etal."StructureandFunctionoftheNuclearPoreComplexCytoplasmicmRNAExportPlatform."Cell167.5(2016):1215-1228.

Emergenceofcomplexityandandindancechoreography

ÉléonoreBellot

MobileLab,CRI(CentedeRecherchesInterdisciplinaires)

Complexsystemsaresystemscomposedofmanyinteractingcomponents,exhibitingglobalpropertiesemergingfromthelocalnon-linearinteractionsoftheseentities,sothatthesystemisnotreducibletoonelevelofdescription.Or,astoldshorter:'thewholeismorethanthesumofitsparts'.Itadressesdifferentconceptsasself-organization,collectivemotion,emergenceofpatterns.

Collectivemotionofflocksofbirdsisagoodexample,asbirdsactsaccordingtothenear-neighbours,andscaleeffectsleadstocomplexpatternsuchasspiralingwithoutanycentralcoordination,thancanbefullymodeledwithtoolsofstatisticalphysics.

Thefieldofcomplexsystemscutsacrossmanydisciplinessuchasphysics,biology,socialsciences,economy,management...anditalsorelatestosomechoreographicworksinwhichthebehaviorresultedisproducedbyexplicit-or-notinteractionrulesbetweenthedancers.Thiscanbeseenforexamplequiteeasilyinfolkdances:quitesimplelocalrulescreatenicedynamicalpatterns.

Thesechoreographic(inabroadmeaningincludingimprovisation)worksarethematterofstudyofthisinternship.

Theideaisfirsttotrytoconfronttheformalismofcomplexsystemstodanceworks,toseetowhatextenditcanberelevant.Then,helpedbyinterviewswithchoreographswhoseworksrelate,tohaveanideaoftheirapproachandunderstandingofthisfield,andalsohowdotheysharethisinformationwiththedancers.Lastly,theideawouldbetodevelopincollaborationwiththeartistsachoreographictoolboxofcomplexsystemstricks,freelyusableandeditableasanopeninspirationfordancers,tobroadentheusetheycanhaveofthisfieldintheirprojects.

Parameterssearchingforasynthetictoggleswitch

SebastiánSosa-Carrillo

CentredeRecherchesInterdisciplinaires

InternatLifewareteam,INRIASaclay

Nowadaystheuseofmodelstodescribebiologicalphenomenaisbeingmoreandmorecommon.Oneofthemostcomplicatedtaskinthisapproachistoinferthemodel’sparametersfromtherealexperimentaldata.Severalmethodshavebeendevelopedtoachievethisgoal.Forinstance,oneofthemistosocalledCMA-ES,whichaimstofindgoodmodelparametersbyrandomlysearchingintheparametersspaceinordertominimizethemismatchbetweenthesimulateddataandtherealexperimentaldata,thismismatchiscalled“thecost”.Someofthelimitationsofthismethodsarecausedduetothehigh-dimensionalparametersspaceandthefactthatthecostfunctioncanpresentmultiplelocalminima,soitcanbenotguaranteedthatthebestparametersarereturnedbythismethod.Inadditiontothis,moreconstraintscanbeimposedbytheobservedbehaviorofthesystemindifferentexperimentalconditions,aswellasthefactthatthemodelparametersneedabiologicalmeaningthatmustbecoherentwithwhatisknownaboutthesystem.Mygoalinthisinternshipistoconstraintheparameterssearchofourtoggleswitchmodelinordertofindparameterswhichaccomplishagoodfittingoftheexperimentaldata,whilealsobeingabletoreproducethesystembehaviorindifferentexperimentalconditions,andofcoursemaintainingtheabiologicalmeaningwhichisinagreementwiththecurrentknowledge.

References:

Gardner,T.S.,Cantor,C.R.,&Collins,J.J.(2000).ConstructionofagenetictoggleswitchinEscherichiacoli.Nature,403(6767),339-342.

Hansen,N.,&Ostermeier,A.(2001).Completelyderandomizedself-adaptationinevolutionstrategies.Evolutionarycomputation,9(2),159-195.

Lugagne,J.B.(2016).Contrôletemps-réeld'unebasculegénétique(Doctoraldissertation,UniversitéParis7).

Evaluationofmonotonicregressionmethodforthepredictionofcomplexphenotypes

fromtranscriptomicdata

MislavAcman

SystemsBiologygroup(Schwikowskigroup),InstitutePasteur,Paris

Two-dimensionalmonotonicregressionisamachinelearningclassificationmethod,whichcanassociatequantitativestatesofbiomolecules,suchasmRNAabundance,withquantitativephenotypesofinterest.Theapproachisbasedonmonotonicregression[1],anditsmainadvantageisfastbrowsingthroughawiderangeoflinearandnon-linearrelationshipsbetweenpairsofpredictors.First,itfitsthebestmonotonicfunctiontoallpossiblepairsofpredictors(bothtrueandinversevalues)inthetrainingdataset.Then,byutilizingleave-one-outcross-validation(LOOCV),thepairsareassessedfortheirpredictiveperformance.Top-scoringpairsareselected.Theyconstituteasignaturethatisusedforfurtherpredictionsofthemethod.

Thusfar,monotonicregressionwassuccessfullyusedtoanalyseawholebloodtranscriptomedatasetofdenguepatientsinordertoidentifypatientswhoareprobabletodevelopasevereformofthedisease,whichrequireshospitalization.However,beforepresentingthistooltothebioinformaticscommunity,furtherevaluationofthemethodisneeded.Forthis,severalindependentdatasetswereselectedfromGeneExpressionOmnibusdatabase(GEO,https://www.ncbi.nlm.nih.gov/geo/)[2,3,4].Thedatasetswerethenusedtocomparethepredictivepowerandflexibilityofmonotonicregressiontopublishedresultsandothercommonlyusedclassificationmethods.

References:

1)Stout,QuentinF."Isotonicregressionformultipleindependentvariables."Algorithmica71.2(2015):450-470.

2)Berry,MatthewPR,etal."Aninterferon-inducibleneutrophil-drivenbloodtranscriptionalsignatureinhumantuberculosis."Nature466.7309(2010):973-977.

3)Zak,DanielE.,etal."AbloodRNAsignaturefortuberculosisdiseaserisk:aprospectivecohortstudy."TheLancet387.10035(2016):2312-2322.

4)Kong,SekWon,etal."Characteristicsandpredictivevalueofbloodtranscriptomesignatureinmaleswithautismspectrumdisorders."PLoSOne7.12(2012):e49475.

PredictingProcessingCostofAnaphoraResolution

OlgaSeminck

UniversitéParisDiderotLaboratoiredeLinguistiqueFormelle,EcoleDoctoraleFrontièresduVivant

Anaphoraresolution,forhumanspeakers,canbemoreorlesscostlydependingonvariousfactorslikeambiguity,syntacticcomplexityandsemanticplausibility.Thevariationofcosthasbeenmeasuredbymanystudiesinpsycholinguistics,throughexperimentalparadigmslikeself-pacedreading,oreye-tracking.Ourprojectaimsatdevisingasystem,inspiredbycurrentNLPcoreferenceresolutionsystems,thatcanpredictaprocessingcostforanaphoraresolution,whichcanbeevaluatedbyrunningoursystemonhumandatacomingfrompsycholinguisticexperiments,oreye-trackingcorporae.g.theDundeeCorpus(Kennedyetal.2003).Inspiredbysurprisaltheory(Hale2001)andtheentropyreductionhypothesis(Hale2006),weproposeacontinuous,incrementalmeasurethatassignsprocessingcosttoanaphora.Ourmeasurereflectshowcertainaprobabilisticanaphoraresolutionsystemisaboutitsdecisions.Todoso,withasimpleanaphoraresolutiontool,wecomputeaprobabilitydistributionoverallantecedentcandidatesofananaphorandcalculateentropyoverit.Wehypothesizethattheentropyoverthisdistributioncanbeseenastheprocessingcostoftheresolutionoftheanaphor.Sothesmallertheentropy,thelessprocessingcostthatispredicted.Afirststudyweconductedontwobiasesthatwerediscoveredbypsycholinguists(SubjectAssignmentStrategyandParallelFunctionHypothesis(e.g.Crawleyetal.1990))showedthatourmodelwasabletosimulatehumanperformanceinthesematters:itassignedthepronounsinawaycomparabletohumanparticipantsandthecostitpredictedcorrespondedtoreadingtimesrecordedinself-pacedreadingexperiments.

References:

RosalindACrawleyetal.(1990).Theuseofheuristicstrategiesintheinterpretationofpronouns.In:JournalofPsycholinguisticResearch19.4,245–264

JohnHale(2001).AprobabilisticEarleyparserasapsycholinguisticmodel.In:ProceedingsofthesecondmeetingoftheNorthAmericanChapteroftheAssociationforComputationalLinguisticsonLanguagetechnologies.AssociationforComputationalLinguistics,1–8

JohnHale(2006).Uncertaintyabouttherestofthesentence.In:CognitiveScience30.4,643–672

AlanKennedyetal.(2003).Thedundeecorpus.In:Proceedingsofthe12thEuropeanconferenceoneyemovement

Towardsafastbrain-machineinterfaceintegratingartificialsomatosensory

feedback

AamirAbbasi

UnitédeNeuroscience,InformationetComplexité(UNIC),CNRS,Gif-sur-Yvette.

Inthisproject,wewillinvestigatetheimpactofsomatosensoryfeedbackonlearningamotortask,usingaBrainMachineInterface(BMI)set-up.Wewillcombineelectrophysiology,optogeneticsandanexternalprosthesistoimplementaclosedloopsensorimotorBMIinthemouse.Onthemotorside,theactivityofsingleunitsontheprimarymotorcortexwilldriveaprosthesiscarryingareward.Onthesensorysideinordertoinvestigaterulesofneuralcodingwewillperformpatternedstimulationdirectlyinthebarrelfieldoftheprimarysomatosensorycortexrepresentingthewhiskersofthemouse.Severalpatternsofsensorystimulationwillbetested.Biomimeticpatternswillconsistofactivatingtheregionsrepresentingthewhiskersinapatterncorrespondingtothemovementofanobjectalongthesnout,thusmimickingthemovementofprosthesisinspace.Anotherapproachwillbetoapplyarbitraryfixedrulesofstimulation,andusetheadaptivepropertiesofthenetworktolinkthespatiotemporalpatternsofstimulationtotheinformationaboutthepositionoftheprosthesisinspace.Overall,withourinvestigationweaimtostudythepotentialofnaturalsomatosensoryinputsbio-mimickeryindevelopingefficientBMIs,bothintermsofprosthesislearningandreliability.

References:

ArduinP.-J.,FregnacY.,ShulzD.E.&Ego-StengelV.(2014)Bidirectionalcontrolofaone-dimensionalroboticactuatorbyoperantconditioningofasingleunitinratmotorcortex.FrontNeurosci8,206.

ArduinP.-J.,FregnacY.,ShulzD.E.&Ego-StengelV.(2013)"Master"neuronsinducedbyoperantconditioninginratmotorcortexduringabrain-machineinterfacetask.JNeurosci33(19),8308-8320.

BensmaiaS.J.&MillerL.E.(2014)Restoringsensorimotorfunctionthroughintracorticalinterfaces:progressandloomingchallenges.NatRevNeurosci15(5),313-325.

TRAITLEVELANALYSISOFMULTITRAITPOPULATIONPROJECTIONMATRICES

ChristopheCoste

Laboratoired'Eco-anthropologieetEthnobiologie/UMR7206Equipe"AnthropologieEvolutive",MNHN,Muséedel'Homme

Inmostmatrixpopulationprojectionmodels,individualsarecharacterizedaccordingtousuallyoneortwotraits;suchasage,stage,sizeorlocation.Abroadtheoryofmultitraitpopulationprojectionmatrices(MPPMs)incorporatinglargernumberoftraitswaslongheldbackbytimeandspacecomputationalcomplexityissues.Asaconsequence,nostudyhasyetfocusedontheinfluenceofthestructureoftraitsdescribingalife-cycleonpopulationdynamicsandlife-historyevolution.

Wepresenthereanovelvector-basedMPPMbuildingmethodologythatallowstocomputationally-efficientlymodelpopulationscharacterizedbynumerousandlargetraits,andextendsensitivityanalysesforthesemodels.Wethenpresentanewmethod,thetraitlevelanalysisconsistinginfoldinganMPPMonanyofitstraitstocreateamatrixwithalternativetraitstructurebutsimilarasymptoticproperties.ByaddingorremovingoneorseveraltraitstotheMPPM,andanalyzingtheresultingchangesinspectralproperties,thisallowsinvestigatingtheinfluenceofthetraitstructureontheevolutionoftraits.

Weillustratethisbymodelinga3-trait(age,parityandfecundity)populationdesignedtoinvestigatetheimplicationsofparity-fertilitytrade-offsinacontextoffecundityheterogeneityinhumans.TheTraitlevelanalysis,comparingmodelsofthesamepopulationmodeledwithdifferenttraits,demonstratesthatthesensitivityoffitnesstoage-specificfertilitydiffersbetweencaseswithorwithoutfertility-paritytrade-offs.Moreoveritshowsthatage-specificfertilityhasverydifferentevolutionarysignificancedependingonwhetherheterogeneityisaccountedfor.Thisisbecausetrade-offscanvarystronglyinstrengthandevendirectiondependingonthetraitstructureusedtomodelthepopulation.

UriBarenholzfromWeizmannInstitute

Designprinciplesofautocatalyticcyclesconstrainenzymekineticsandforcelowsubstratesaturationatfluxbranchpoints

Asetofchemicalreactionsthatrequireametabolitetosynthesizemoreofthatmetaboliteisanautocatalyticcycle.Wefindthatmostofthereactionsinthecoreofcentralcarbonmetabolismarepartofcompactautocatalyticcycles.Ouranalysisshowsthatsuchmetabolicdesignsmustmeetspecificconditionstosupportstablefluxes,henceavoidingdepletionofintermediatemetabolites.Autocatalyticcyclesarethereforesubjectedtoconstraintsthatmayseemcounter-intuitive:theenzymesofbranchreactionsoutofthecyclemustbeoverexpressedandtheaffinityoftheseenzymestotheirsubstratesmustberelativelyweak.

WeuserecentquantitativeproteomicsandfluxomicsmeasurementstoshowthattheaboveconditionsholdforfunctioningcyclesincentralcarbonmetabolismofE.coli.Ourworkdemonstratesthatthetopologyofametabolicnetworkcanshapethekineticparametersofenzymesandleadtoseeminglywastefulenzymeusage.