Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization

Proteins are composed by amino acids, which are created by genes. To understand how different genes interact to create different proteins, we need to model the gene regulatory networks (GRNs) of different organisms. There are different models that attempt to model GRNs. In this paper, we use the popular S-System to model small networks. This model has been solved with different evolutionary computation techniques, which have obtained good results; yet, there are no models that achieve a perfect reconstruction of the network. We implement a variation of particle swarm optimization (PSO), called dissipative PSO (DPSO), to optimize the model; we also research the use of an L1 regularizer and compare it with other evolutionary computing approaches. To the best of our knowledge, neither the DPSO nor L1 optimizer has been jointly used to solve the S-System. We find that the combination of S-System and DPSO offers advantages over previously used methods, and presents promising results for inferencing larger and more complex networks.