Advances in the field of complex networks theory and network biology pave a new way to define human health through the study of networks of proteins, genes, metabolites, modules across cell signaling pathways, and clinical data.Combinations of large scale biological datasets and concepts from network theory, and systems biology produce new insights into the complex dynamic processes involved in human diseases such as cancer. To develop novel datadriven computational tools for discovering the insights of human diseases and for a new approach to multi-drug therapies for personalized therapeutics, it needs combinations of the high-quality set of human interactome networks, disease-specific expression data, and powerful network controllability algorithmics. Therefore, we address the issue of this thesis with the focus to integrate network biology and network controllability approach, to gain useful insight in the finding of the complex mechanism of cancer networks and open the door for a novel drug target approach called multi-drug therapeutics.
The first part of the thesis presents the network biology approaches to study the interactome of the biological systems and decode the wiring diagram of the cellular information processing systems. It reveals a variety of high-level intramolecular relationships including protein-protein interaction networks (PPI), protein compound interactions, gene regulatory interactions, and metabolic pathways. These interactions play a key role in the development of diseases and various types of cancers. One characteristic of such networks is that a small number of nodes in the networks are highly connected. Another characteristic is that a group of physically and functionally interconnected molecules driving to achieve a common biological process, have a modular structure. Further, through a minimum number of target nodes a full (partial) controllability of these intracellular network can be achieved.
The second part of the thesis presents the network controllability approach and some of the algorithms used in our case studies on different types of cancer PPI signaling networks. Recently, network control theory has been increasingly used in engineering and mathematics which also opens the way to investigate control principals for complex biological interaction networks through a minimum set of input (driver) nodes. According to control theory, a dynamical system may be steard such that its output is driven towards some desired final states (e.g target cancer essential proteins in PPI networks) via suitably-picked inputs (e.g. manipulating a set of driver proteins). Therefore, it is necessary to understand the dynamics of these complex networks, and their evolution rules (i.e., expressed as a system of linear equations) which govern the systems dynamics over time.
This doctoral thesis provides the target control theory approach fine tuned for the analysis of specific cancer signaling transduction PPI networks. The control approach presented here can be an impressive framework for effective development of multi drug-target therapeutics. We, therefore, expect that our approach can open a new way towards effective and efficient therapeutics target and a key resource towards personalized medicine in cancer
