Investigate Data Dependency for Dynamic Gene Regulatory Network Identification through High-dimensional Differential Equation Approach

Tao Lu, Min Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Gene regulation plays a fundamental role in biological activities. The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. We proposed a comprehensive statistical procedure for ODE model to identify the dynamic GRN. In this article, we applied this model to different segments of time course gene expression data from a simulation experiment and a yeast cell cycle study. We found that the two cell cycle and one cell cycle data provided consistent results, but half cell cycle data produced biased estimation. Therefore, we may conclude that the proposed model can quantify both two cell cycle and one cell cycle gene expression dynamics, but not for half cycle dynamics. The findings suggest that the model can identify the dynamic GRN correctly if the time course gene expression data are sufficient enough to capture the overall dynamics of underlying biological mechanism.

Original languageEnglish
Pages (from-to)2377-2391
Number of pages15
JournalCommunications in Statistics: Simulation and Computation
Volume45
Issue number7
DOIs
StatePublished - Aug 8 2016

Keywords

  • Data dependency
  • Dynamic model
  • Gene regulatory network
  • Time course gene expression data

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