A technique for preserving network structure in randomized Hi-C data.

Chromatin interaction data are frequently analyzed as a network to study several aspects of chromatin structure. Hi-C experiments are costly and there is a need to create simulated networks for quality assessment or result validation purposes. Existing tools do not maintain network properties during randomization.

Characterizing behavioural differentiation in gene regulatory networks with representation graphs.

We introduce the formal notion of representation graphs, encapsulating the state space structure of gene regulatory network models in a compact and concise form that highlights the most significant features of stable states and differentiation processes leading to distinct stability regions. The concept has been developed in the context of a hybrid system-based gene network modelling framework; however, we anticipate that it can also be adapted to other approaches of modelling gene networks in discrete terms. We describe a practical algorithm for representation graph computation as well as two case studies demonstrating their real-world application and utility.

Behavioral dynamics of bacteriophage gene regulatory networks.

We present hybrid system-based gene regulatory network models for lambda, HK022, and Mu bacteriophages together with dynamics analysis of the modeled networks. The proposed lambda phage model LPH2 is based on an earlier work and incorporates more recent biological assumptions about the underlying gene regulatory mechanism, HK022, and Mu phage models are new. All three models provide accurate representations of experimentally observed lytic and lysogenic behavioral cycles.

Network motif-based analysis of regulatory patterns in paralogous gene pairs.

Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as , and others. The availability of such networks provides an opportunity to compare gene regulatory processes at the whole genome level, and in particular, to assess similarity of regulatory interactions for homologous gene pairs either from the same or from different species.

Topological structure analysis of chromatin interaction networks.

Background: Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks.

Results: It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited.