A method for estimating Hill function-based dynamic models of gene regulatory networks.

ABSTRACT: Gene regulatory networks (GRNs) are quite large and complex. To better understand and analyse GRNs, mathematical models are being employed. Different types of models, such as logical, continuous and stochastic models, can be used to describe GRNs. In this paper, we present a new approach to identify continuous models, because they are more suitable for large number of genes and quantitative analysis. One of the most promising techniques for identifying continuous models of GRNs is based on Hill functions and the generalized profiling method (GPM). The advantage of this approach is low computational cost and insensitivity to initial conditions. In the GPM, a constrained nonlinear optimization problem has to be solved that is usually underdetermined. In this paper, we propose a new optimizat

SUBMITTER: Ehsan Elahi F 

PROVIDER: S-EPMC5830732 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature