J. Comput. Biol. - An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(3404) distribut(989) bayesian(671) }
{ model(2656) set(1616) predict(1553) }
{ can(774) often(719) complex(702) }
{ imag(2830) propos(1344) filter(1198) }
{ method(2212) result(1239) propos(1039) }
{ perform(999) metric(946) measur(919) }
{ estim(2440) model(1874) function(577) }
{ data(3963) clinic(1234) research(1004) }
{ inform(2794) health(2639) internet(1427) }
{ take(945) account(800) differ(722) }
{ design(1359) user(1324) use(1319) }
{ state(1844) use(1261) util(961) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ problem(2511) optim(1539) algorithm(950) }
{ import(1318) role(1303) understand(862) }
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{ concept(1167) ontolog(924) domain(897) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ general(901) number(790) one(736) }
{ visual(1396) interact(850) tool(830) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ medic(1828) order(1363) alert(1069) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Gene regulatory networks (GRNs) play a central role in sustaining complex biological systems in cells. Although we can construct GRNs by integrating biological interactions that have been recorded in literature, they can include suspicious data and a lack of information. Therefore, there has been an urgent need for an approach by which the validity of constructed networks can be evaluated; simulation-based methods have been applied in which biological observational data are assimilated. However, these methods apply nonlinear models that require high computational power to evaluate even one network consisting of only several genes. Therefore, to explore candidate networks whose simulation models can better predict the data by modifying and extending literature-based GRNs, an efficient and versatile method is urgently required. We applied a combinatorial transcription model, which can represent combinatorial regulatory effects of genes, as a biological simulation model, to reproduce the dynamic behavior of gene expressions within a state space model. Under the model, we applied the unscented Kalman filter to obtain the approximate posterior probability distribution of the hidden state to efficiently estimate parameter values maximizing prediction ability for observational data by the EM-algorithm. Utilizing the method, we propose a novel algorithm to modify GRNs reported in the literature so that their simulation models become consistent with observed data. The effectiveness of our approach was validated through comparison analysis to the previous methods using synthetic networks. Finally, as an application example, a Kyoto Encyclopedia of Genes and Genomes (KEGG)-based yeast cell cycle network was extended with additional candidate genes to better predict the real mRNA expressions data using the proposed method.

Resumo Limpo

gene regulatori network grns play central role sustain complex biolog system cell although can construct grns integr biolog interact record literatur can includ suspici data lack inform therefor urgent need approach valid construct network can evalu simulationbas method appli biolog observ data assimil howev method appli nonlinear model requir high comput power evalu even one network consist sever gene therefor explor candid network whose simul model can better predict data modifi extend literaturebas grns effici versatil method urgent requir appli combinatori transcript model can repres combinatori regulatori effect gene biolog simul model reproduc dynam behavior gene express within state space model model appli unscent kalman filter obtain approxim posterior probabl distribut hidden state effici estim paramet valu maxim predict abil observ data emalgorithm util method propos novel algorithm modifi grns report literatur simul model becom consist observ data effect approach valid comparison analysi previous method use synthet network final applic exampl kyoto encyclopedia gene genom keggbas yeast cell cycl network extend addit candid gene better predict real mrna express data use propos method

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