Cellularstatetransitions(i.e.frompluripotenttocommitted,replicativetoquiescent,etc.)requirethe coordinatedregulationofthousandsofgenes.Therapeuticallyharnessingthesetransitionsholdsgreat promiseforhumanhealth;?forinstance,autologousstemcelltherapyhasbeensuccessfullyusedin regenerativemedicineandcancertreatments,amongothers.Whilesomeofthekeyregulatoryswitchesare known,thefieldlacksasystems-levelunderstandingofthegenomiccircuitsthatcontrolthesetransitions, informationthatiscriticalforinformedclinicalintervention.Here,wewilldevelopanintegratedcomputational frameworktoidentifycoregeneregulatorycircuitsfromlargegenenetworksandpredicttheirdynamicsand regulatoryfunctionswithouttheneedofdetailednetworkkineticparameters.Advancesingenomicsprofiling technologyhaveenabledthemappingofgeneregulatorynetworks,thuswenowhavethecapacitytoidentify combinatorialinteractionsamonggenesandthemasterregulatorsofstatetransitions.Somesystemsbiology approacheshavesimulatedthedynamicsofageneregulatorycircuit,buttraditionalmethodssufferfromtwo keyissues.First,thereisnorationalruletochooseanappropriatesetofregulatorgenesinalargenetworkto model.Second,sinceitishardtodirectlymeasuremostnetworkkineticparametersfromexperiment, modelingresultsarebasedonasetofguessedparametersthatcanbelessthanoptimal,limitingthe applicationofmathematicalmodelingtolargesystemsandthepredictionpowerofsystemsbiology.To addresstheseissues,werecentlydevelopedalgorithmnamedrandomcircuitperturbation(RACIPE).RACIPE generatesanensembleofcircuitmodels,eachofwhichcorrespondstoadistinctsetofrandomkinetic parameters,anduniquelyidentifiesrobustfeatures,suchasclustersofstablegeneexpressionstates,by statisticalanalysis.WewillfurtherenhanceRACIPEalgorithmsforlargesystemsandnewdataanalysistools usingmachinelearning.Thisapproachwillconvertatraditionalnonlineardynamicsproblemintoadata analysisproblem,anessentialstepforextendingtheapplicationofgenecircuitmodelingtolargesystems.It alsoprovidesanovelstrategytointegratetop-downgenomicsapproacheswithbottom-upmathematical modeling.Thealgorithmswillbetestedandrefinedusingliterature-basedgenenetworks,publicgenomics data,anddatafromcollaboration,withafocusoncelldifferentiationindevelopmentalprocessesandstate transitionsinoncogenesis.Successoftheprojectwillresultinacomprehensivetoolkitthatwillunveilthegene regulatorymechanismofcellulardecision-makinginanycellofinterest.Thealgorithmicdevelopmentis expectedtohaveabroadimpactonnotonlybasicresearchinsystemsbiologybutalsoshedlighton therapeuticinterventioningenomicmedicine.

Public Health Relevance

TOPUBLICHEALTH Mosthumancomplexdiseasesaremultigenic,butourunderstandingoftheinteractionsbetweengenesin healthystatesandhowtheyaredisruptedindiseasestatesislacking.Computationalalgorithmswillbe designedtoconstructandmodelgeneregulatorycircuitsofhighercomplexitytofillanimportantgapinour understandingofthemaintenanceandtransitionsamongcellularstatesandtheirabnormalbehaviorin disease.Inthisproposal,wewilldevelopasuiteofsystems-biologytoolstoextendgenecircuitmodelingto largesystemsandintegrategenomicsdatarelevanttofundamentalcellbiology,cancer,andotherhuman diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Unknown (R35)
Project #
5R35GM128717-02
Application #
9752643
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Resat, Haluk
Project Start
2018-08-01
Project End
2023-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Jackson Laboratory
Department
Type
DUNS #
042140483
City
Bar Harbor
State
ME
Country
United States
Zip Code
04609
Kohar, Vivek; Lu, Mingyang (2018) Role of noise and parametric variation in the dynamics of gene regulatory circuits. NPJ Syst Biol Appl 4:40