Modeling of gene regulatory network dynamics using threshold logic.
Gene regulation modeling is one of the most active research topics in systems biology. The aim of modeling gene regulation is to understand how individual genes function and interact with each other to create complex biological phenomena. In this paper we propose a novel gene regulatory model based on threshold logic. The approach is developed by a combination of threshold logic properties and perceptron learning techniques. This work does not focus on determination of the pair-wise interactions among genes. Instead, the objective of this work is to generate a model that will describe and predict phenomena associated with a biological system. The utility of the approach is demonstrated by modeling a cellular system of 50 genes. The model could effectively replicate both the steady state and the transient behavior of genes.
School of Computing and Informatics, Arizona State University, Tempe, Arizona, USA. tejaswi@asu.edu
Tejaswi
T
Gowda
Sarma
S
Vrudhula
Seungchan
S
Kim
D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008128:Logic; D008957:Models, Genetic; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D049490:Systems Biology / Q000379:methods