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Intervention and Control of Gene Regulatory Networks: Theoretical Framework and Application to Human Melanoma Gene Regulation

The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks, and their dynamics are used to understand the mechanisms and characteristics of biological cells as well as to search for possible remedy to various diseases such as cancer. In classical biological experiments, cell function is ascertained based on rough phenotypical and genetic behavior. On the other hand, the use of dynamical system models allows one to analytically explore biological hypotheses. Current research in cancer biology indicates that global, systemic molecular interactions are pivotal in understanding cellular dynamics, and in designing intervention strategies to combat genetic diseases. In particular, most genetic ailments, such as cancer, are not caused by a single gene, but rather by the interaction of multiple genes. Global, holistic approaches to the study of biological systems reveal the dynamic nature of cellular networks, which provide an important framework for drug discovery and design. The massive amounts of information that omics (e.g., genomics, proteomics, metabolomics) high-throughput sequencing technology generate marked a great leap forward in computational methods for analyzing and interpreting biological data. However, it remains a major challenge to design optimal intervention strategies in order to affect the time evolution of gene activity in a desirable manner. One of the main aims of modern biological research is focused on intervening in biological cell dynamics in order to alter the gene regulatory network and avoid undesirable cellular states; e.g., metastasis. The development of effective control approaches for therapeutic intervention within genetic regulatory networks requires new models and powerful tools for understanding and managing complex networks. This Chapter is organized as follows. In Section 1.2, we review the main research streams in inference of genetic regulatory networks. In particular, we discuss the ad-