Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented.By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach.In any biological system, various genetic interactions occur concurrently amongst different genes. While some genes interact almost instantaneously, other genes could have time delayed interactions (see Figure 1). From a biological perspective, instantaneous regulations represent the scenarios where the effect of a change in the expression level of a regulator gene is carried on to the regulated gene (almost) instantaneously. In such cases, the effect is reflected almost immediately in the regulated gene’s expression levela. On the other hand, in cases where regulatory interactions are time-delayed, its effect will be seen on the regulated gene after a finite time delay.Bayesian network and its extension, dynamic Baye