TY - JOUR
T1 - Investigate Data Dependency for Dynamic Gene Regulatory Network Identification through High-dimensional Differential Equation Approach
AU - Lu, Tao
AU - Wang, Min
N1 - Publisher Copyright:
© 2016, Copyright © Taylor & Francis Group, LLC.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/8/8
Y1 - 2016/8/8
N2 - 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.
AB - 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.
KW - Data dependency
KW - Dynamic model
KW - Gene regulatory network
KW - Time course gene expression data
UR - http://www.scopus.com/inward/record.url?scp=84975230097&partnerID=8YFLogxK
U2 - 10.1080/03610918.2014.902224
DO - 10.1080/03610918.2014.902224
M3 - Article
AN - SCOPUS:84975230097
VL - 45
SP - 2377
EP - 2391
JO - Communications in Statistics - Simulation and Computation
JF - Communications in Statistics - Simulation and Computation
SN - 0361-0918
IS - 7
ER -