Modeling gene regulatory networks: Classical models, optimal perturbation for identification of network

Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption.  On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications.  This is not an unrealistic goal since genes which are regulated by gene regulatory networks (GRNs) are the core governors of life processes at the molecular level. In fact, manipulation of GRNs would be the ultimate strategy for optimal purposeful control of cell’s life.  GRNs are in charge of regulating the amounts of all the inter-cellular as well as intra-cellular molecular species produced all the time in all living organisms.  Manipulation of a GRN requires comprehensive knowledge about nodes and interconnections.  This paper deals with both aspects in networks having more than fifty nodes.  In the first part of the paper, restrictions of probabilistic models in modeling node behavior are discussed, i.e.: 1) unfeasibility of reliably predicting the next state of GRN based on its current state, 2) impossibility of modelling logical relations among genes, and 3) scarcity of biological data needed for model identification.  These findings which are supported by arguments from probability theory suggest that probabilistic models should not be used for analysis and prediction of node behavior in GRNs.  Next part of the paper focuses on models of GRN structure.  It is shown that the use of multi-tree models for structure for GRN poses severe limitations on network behavior, i.e. 1) increase in signal entropy while passing through the network, 2) decrease in signal bandwidth while passing through the network, and 3) lack of feedback as a key element for oscillatory and/or autonomous behavior (a requirement for any biological network).  To demonstrate that, these restrictions are consequences of model selection, we use information theoretic arguments.  At the last and the most important part of the paper we look into the gene perturbation experiments from a network-theoretic perspective to show that multi-perturbation experiments are not as informative as assumed so far.  A generally accepted belief among researches states that multi-perturbation experiments are more informative than single-perturbation ones, i.e., multiple simultaneously applied perturbations provide more information than a single perturbation.  It is shown that single-perturbation experiments are optimal for identification of network structure, provided the ultimate goal is to discover correct subnet structures.