Paola Vera-Licona, Abdul Jarrah, Luis David Garcia-Puente, John McGee and Reinhard Laubenbacher 

Summary: This project concerns a problem at the heart of systems biology: the reconstruction of molecular regulatory networks from system-level experimental observations. While  there are many  algorithms  available that aim to infer network structure from experimental data, less emphasis has been placed on methods that utilize time series data effectively to infer both, structure and dynamical models of networks.

We propose an inference  algorithm within the  Boolean polynomial  dynamical system (BPDS) framework. The  algorithm is able to use time-course data, including network perturbations such as knock-out mutants and RNAi experiments. To infer wiring diagrams and dynamical models, it allows for  the incorporation of  prior  biological  knowledge,  and  it  is  robust  to  significant  levels  of  noise  in the data  used  for inference. It uses an evolutionary  algorithm for local  optimization withal an encoding  of  models as  BPDS. This  BPDS framework  allows an effective representation of the models  needed to be searched by our algorithm to improve computational performance. We validated the algorithm both in silico network and on microarray expression profiles.

Link  to  paper  preprint P. Vera-Licona,  A. Jarrah,  LD. Garcia,  J. McGee, R. Laubenbacher. An Algebra-Based Method to Infer the Structure and Dynamics of Gene Regulatory Networks. Submitted.

Below is included a Quick Start Guide, a folder with Sample Input Files to run the software for the method as well as the supplementary materials accompanying the paper.