Topologic properties of metabolic networks

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Over the past several decades, the biomedical research community has acquired an enormous amount of valuable data across a wide spectrum of the biological world. Nearly all these efforts reflect reductive, analytical approaches to investigating important biological questions, in which biologists typically have deliberately restricted their analyses to well-defined systems with relatively few components, implicitly attempting to reduce biological phenomena to the behavior of individual molecules. Yet, despite the value of these approaches, a discrete biological function cannot be attributed only to individual molecules. Instead, the robust behavior of biological systems arises to a large extent from complex interactions among its various building constituents (i.e., cellular networks). In this application, we propose to conduct a highly integrated program in which we will examine in quantitative terms the structure of complex metabolic networks that are required to maintain the proper function of a cell. This goal will be aided by two recent scientific developments: the emergence of integrated pathway-genome databases providing detailed connectivity maps of metabolic networks, and by theoretical advances in comprehending and quantifying the topology of complex (non-biological) networks. This research represents a unique collaboration between a theoretical physicist (A. -L. Barabasi), a physician-molecular biologist (Z. N. Oltvai), and a bacterial molecular geneticist (B. L. Wanner). Our aims are threefold: (1) We will analyze in quantitative terms the structure and functional activity of complex metabolic and genetic networks of model organisms, such as Escherichia coli; (2) We will examine the effects of perturbing the levels of proteins in central metabolic networks to aid in model building and to test rules that evolve from computer simulations of these models, including examining their tolerance to targeted mutations; and (3) We will attempt to develop an understanding of the dynamic changes that take place in metabolic networks in response to a changing environment, in studies of the Escherichia coli physiome. Understanding the principles of interactions among various metabolic network components of a living cell and the generic large-scale feature of metabolic networks will not only provide an important contribution to basic biology, but will also have applicability to translational research, such as pharmaceutical target identification.