LMI-based Algorithm for the Reconstruction of Biological Networks

The general problem of reconstructing a biological network from temporal evolution data is tackled via an approach based on dynamical systems theory. In order to identify the dynamical model of the network an optimization algorithm, based on Linear Matrix Inequalities, is proposed. This approach allows to take into account, in the identification phase, both the experimental data and the a priori biological knowledge about the arcs of the network. Furthermore, the effectiveness of the proposed algorithm is improved by exploiting the assumption of scale-free structure, as usual in biological processes. The technique is validated against a well assessed case-study, that is the model of fission yeast cell cycle developed by Novak and Tyson.