Special Issue on “ System Identification for Biological Systems ”

A remarkable feature of molecular biology over the last two decades has been the massive scaling up of its experimental techniques. The sequencing of the entire genome of organisms, the determination of the expression level of genes by means of DNA microarrays or reporter genes and the identification of proteins and their interactions by high-throughput proteomic methods have produced enormous amounts of data on different aspects of the functioning of cells. Nowadays, there is a general consensus among biologists on the need to complement time-course data characterizing genomic, proteomic and metabolic systems with formal methods for identifying dynamical models of networks of interactions. Indeed, reverse-engineering of regulatory networks enables computer-based simulation and in silico experiments that can be used for massive and rapid verification or falsification of biological hypotheses, replacing in certain cases costly and time-consuming in vitro or in vivo experiments. Moreover, in silico analysis of biological systems, beside promoting the understanding of cell functioning, underlies the design of interventions of biotechnological or biomedical relevance. For these reasons, the use of system identification techniques for the reconstruction of parameter and structure of biological systems is expected to play an increasingly important role in the field of systems biology. However, standard system identification methods are unlikely to work out of the box since they must cope with several challenges specific to biological systems. First, the structure of many interaction networks is unknown or highly uncertain and it has to be inferred on the basis of available data. Parsing all possible model structures is often computationally prohibitive and this calls for data-based model selection methods that are based on structural properties of the underlying systems. Second, full-blown models of biological systems often contain several parameters that cannot be reconstructed from available biological data. This raises the issue of simplifying existing models and resort to suitable parameterizations that are amenable to identification tasks. Third, the stochastic nature of chemical interactions might hamper the use of standard identification techniques and requires the development of novel and ad hoc methods. The aim of this special issue has been to collect very recent developments in system identification tailored to the reconstruction of biological processes. In the first paper, entitled " A Distribution Matching Method for Parameter Estimation and Model Selection in Computational Biology " , G. Lillacci and M. Khammash exploit the probability distribution of noise affecting the data for validating parameters inferred through an …