a model of iron metabolism in the human body
TRANSCRIPT
AModelofIronMetabolismintheHumanBodyTimothyBarry*1,MaryGockenbach*2,JigneshParmar3,PedroMendes3
1UniversityofMaryland,CollegePark,2UniversityofTexasatArlington,3CenterforQuantitativeMedicine,UConnHealth*Theseauthorscontributedequallytothiswork
Introduction Methods AdditionofHepcidin Data
Model
ParameterIdentifiability• Iron-relateddiseasesareprevalentthroughouttheworld.• Anemiaaffectsonequarteroftheworld’spopulation.• Hemochromatosis,adiseaseofironoverload,isthemost
commoninheriteddiseaseofgenemutationinCaucasians.• Understandingthemechanismsofironmetabolismwill
advanceprogresstowardsindividualizedtreatmentstrategies.
• Amathematicalmodelusingordinarydifferentialequations(ODE)isdevelopedtosimulateirondistributioninthemajororgansofthebody.
• Thismodelisbasedonpreviousworkmodelingironmetabolisminmice[6].
• Someparametervalues,includingthevolumeandironconcentrationoftheorgans,wereobtainedfromliterature.
• Theremainderwereestimatedusingexperimentaldatafromsubjectsinjectedwithradioactiveiron[1-3,5,7].
• Parametersetsweregeneratedbyminimizinganobjectivefunction(sumofsquaredresiduals)usingavarietyofoptimizationalgorithms.
• Thechosenparametersetwasrequired(1)toproduceasmallobjectivevalue,and(2)torenderthefollowingquantitiesclosetobiologicallyrealisticranges:
• ParameterestimateswerecarriedoutusingthesoftwareCOPASI,abiochemicalsystemsimulator[4].
ThisworkwascarriedoutatModelingandSimulationinSystemsBiology, anREUattheCenterforQuantitativeMedicine(CQM)atUConnHealth,andfundedbytheNationalScienceFoundation(DMS1460967).WethankDr.Reinhard Laubenbacher,directorofCQM.
[1]Berzuini,C.etal. (1978).ComputersandBiomedicalResearch 11,209-227.[2]Bonnet,John,etal. (1960).Blood 15.1,36-44[3]Ganz,T.(2011).Blood 117.17,4425-4433[4]Hoops,S.etal. (2006).Bioinformatics 22,3067-74[5]Huff,R.,etal.(1951).JournalofClinicalInvestigation 30.12Pt2,1512.[6]Parmar, J.etal (2017).BMCSystemsBiology 11.1,57.[7]Pollycove,M.etal.(1961).JournalofClinicalInvestigation 40.5,753.
• Themodeliscapableoffittingdatafrommultipleindividuals,andaccuratelysimulatesanemiaandtheeffectofmeals.
• Asrelevantdataisadded,parameteridentifiability willimprove.
• Withtheavailabilityofindividualdata,thismodelcouldbepersonalizedforuseinprecisionmedicine.
• Forexample,anindividual’sweight,height,andagecouldbeusedtoestimateorganvolumesandironconcentrations.
• Identifiabilityanalysisisatoolusedtoascertaintheextenttowhichestimatedparametersaredeterminedbydata.
• Toreplicatemealpatterns,ironabsorptionwaschangedfromacontinuousratetoasequenceofdiscreteevents.
• Theresultsofmealsimulationsaligncloselywithtimecoursedataofserumiron[3],andallowestimationofparametersrelatedtohepcidin.
• Oncethemodelwasaligned,thehepcidindatawasincludedinthemodelcalibration.
TotalIron PercentIroninRBC TransferrinSaturation3.5 g 84.1% 35.3%
• Irondeficiencyanemiaistheresultofinsufficientironinthediet.
• Wheninfectedwithapathogen,thebodyincreaseshepcidinproductionsoastodecreaseironconcentrationintheblood.Anemiaofchronicdiseaseresultswhenthisresponseissustainedoveranextendedperiodoftime.
• Simulationsofirondeficiencyanemiaandanemiaofchronicdiseasebroadlyreflectthesepathologies.
• Thevaluesofparametersforwhichdatawasavailable(e.g.,KEPO)aremorecertainthanthoseforwhichdatawasnotavailable(e.g.,kInBM).
Discussion
References
Acknowledgements
SimulationofIronDisorders
ModelCalibration
TotalIron PercentIroninRBC TransferrinSaturation2 – 5g 60 – 75% 24 – 40%
• Themodelconsistsof7compartmentsand23equations,whichdescribetheflowofironbetweencompartments,theirreversiblebindingofirontotransferrinintheplasma,andthesynthesisanddegradationofkeyregulatoryproteinshepcidinanderythropoetin(EPO).
• Ironentersthebodythroughdietaryintakeandleavesthroughintestinalcellshedding(representedaslossfromtheduodenum)andthroughhair,sweat,anddeadskin(representedaslossfromtherestofbody).
• AsanexampleoftheformofanODEinthemodel,theequationthatgovernsironfluxinthespleenis