Abstract
Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.
Highlights
While a great deal of the advances in physics and chemistry have stemmed from reductionist approaches where the subject of interest, such as an atom or particle, is systematically isolated from the rest of the world in a strictly controlled environment, biology defies such a paradigm by encompassing the whole range of spatial and temporal scales present in nature: ranging from the molecules being observed to the observer built from molecules
How is the synthesis of proteins affected by the surrounding anatomy, and vice-versa? How does the environment interfere with the control of gene expression? How do species, products of genetic programs, interact with the environment? How do cells, initially with identical molecular composition, differentiate to produce the myriad of tissues and functions in an organism? To answer such questions will correspond to unveiling the final secrets of life
Conservation of network motifs is seen during evolution, it has been shown by Hormozdiari et al Figure 6 - The three main types of biological networks: (a) a transcriptional regulatory network has two components: transcription factor (TF) and target genes (TG), where Transcription factors (TFs) regulates the transcription of TGs; (b) protein-protein interaction networks: two proteins are connected if there is a docking between them; (c) a metabolic network is constructed considering the reactants, chemical reactions and enzymes
Summary
While a great deal of the advances in physics and chemistry have stemmed from reductionist approaches where the subject of interest, such as an atom or particle, is systematically isolated from the rest of the world in a strictly controlled environment, biology defies such a paradigm by encompassing the whole range of spatial and temporal scales present in nature: ranging from the molecules being observed to the observer built from molecules. Lems to be addressed are: (i) to organize the ever increasing experimental results from complex biological systems (e.g. protein-protein interaction, gene expression profiles, metabolic pathways) into suitable respective representations and models; (ii) to be able to simulate the dynamics of such models under varying conditions, so as to unveil important biological patterns and structures; and (iii) to find the means for effectively connecting such models at the several spatial and time scales involved.
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