Inferring gene regulatory networks with time delays using a genetic algorithm

NIMCE: A Gene Regulatory Network Inference Approach Based on Multi Time Delays Causal Entropy

Gene regulatory networks (GRNs)are involved in various biological processes, such as cell cycle, differentiation and apoptosis. The existing large amount of expression data, especially the time-series expression data, provide a chance to infer GRNs by computational methods. These data can reveal the dynamics of gene expression and imply the regulatory relationships among genes. However, identify the indirect regulatory links is still a big challenge as most studies treat time points as independent observations, while ignoring the influences of time delays. In this study, we propose a GRN inference method based on information-theory measure, called NIMCE. NIMCE incorporates the transfer entropy to measure the regulatory links between each pair of genes, then applies the causation entropy to filter indirect relationships. In addition, NIMCE applies multi time delays to identify indirect regulatory relationships from candidate genes. Experiments on simulated and colorectal cancer data show NIMCE outperforms than other competing methods. All data and codes used in this study are publicly available at https://github.com/CSUBioGroup/NIMCE.

A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation

Background

Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.

Results

In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified.

Conclusions

We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

Background

Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.

Results

In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.

Conclusion

Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.