ABSTRACT
The general problem of reconstructing a biological interaction network from temporal evolution data is tackled via an approach based on dynamical linear systems identification theory. A novel algorithm, based on linear matrix inequalities, is devised to infer the interaction network. This approach allows to directly taking into account, within the optimisation procedure, the a priori available knowledge of the biological system. The effectiveness of the proposed algorithm is statistically validated, by means of numerical tests, demonstrating how the a priori knowledge positively affects the reconstruction performance. A further validation is performed through an in silico biological experiment, exploiting the well-assessed cell-cycle model of fission yeast developed by Novak and Tyson.
Subject(s)
Algorithms , Artificial Intelligence , Gene Expression Profiling/methods , Gene Expression Regulation/physiology , Proteome/metabolism , Signal Transduction/physiology , Computer Simulation , Linear Models , Models, BiologicalABSTRACT
The goal of this paper is to provide a novel procedure for the identification of nonlinear models which exhibit a quadratic dependence on the state variables. These models turn out to be very useful for the description of a large class of biochemical processes with particular reference to the genetic networks regulating the cell cycle. The proposed approach is validated through extensive computer simulations on randomly generated systems.
