Understanding the roadmap of metabolism by pathway analysis.
ABSTRACT The theoretical investigation of the structure of metabolic systems has recently attracted increasing interest. In this chapter, the basic concepts of metabolic pathway analysis are described and various applications are outlined. In particular, the concepts of nullspace and elementary flux modes are explained. The presentation is illustrated by a simple example from tyrosine metabolism and a system describing lysine production in Corynebacterium glutamicum. The latter system gives rise to 37 elementary modes, 36 of which produce lysine with different molar yields. The examples illustrate that metabolic pathway analysis is a useful tool for better understanding the complex architecture of intracellular metabolism, for determining the pathways on which the molar conversion yield of a substrate-product pair under study is maximal, and for assigning functions to orphan genes (functional genomics). Moreover, problems emerging in the modeling of large networks are discussed. An outlook on current trends in the field concludes the chapter.
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ABSTRACT: The complexity of even comparatively simple biochemical systems necessitates a computational description to explore and eventually understand the dynamics emerging from the underlying networks of cellular interactions. Within this contribution, several aspects relating to a computational description of large-scale biochemical networks are discussed. Topics range from a brief description of the rationales for computational modeling to the utilization of Monte Carlo methods to explore dynamic properties of biochemical networks. The main focus is to outline a path towards the construction of large-scale kinetic models of metabolic networks in the face of incomplete and uncertain knowledge of kinetic parameters. It is argued that a combination of phenotypic data, large-scale measurements, heuristic assumptions about generic rate equations, together with appropriate numerical schemes, allows for a fast and efficient way to explore the dynamic properties of biochemical networks. In this respect, several recently proposed strategies that are based on Monte Carlo methods are an important step towards large-scale kinetic models of cellular metabolism.The Open Bioinformatics Journal 02/2011; 511:4-15.
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ABSTRACT: A homoserine auxotroph strain of Corynebacterium glutamicum accumulates storage compound trehalose with lysine when limited by growth. Industrially lysine is produced from C. glutamicum through aspartate biosynthetic pathway, where enzymatic activity of aspartate kinase is allosterically controlled by the concerted feedback inhibition of threonine plus lysine. Ample threonine in the medium supports growth and inhibits lysine production (phenotype-I) and its complete absence leads to inhibition of growth in addition to accumulating lysine and trehalose (phenotype-II). In this work, we demonstrate that as threonine concentration becomes limiting, metabolic state of the cell shifts from maximizing growth (phenotype-I) to maximizing trehalose phenotype (phenotype-II) in a highly sensitive manner (with a Hill coefficient of 4). Trehalose formation was linked to lysine production through stoichiometry of the network. The study demonstrated that the net flux of the population was a linear combination of the two optimal phenotypic states, requiring only two experimental measurements to evaluate the flux distribution. The property of linear combination of two extreme phenotypes was robust for various medium conditions including varying batch time, initial glucose concentrations and medium osmolality.Systems and Synthetic Biology 06/2013; 7(1-2).
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ABSTRACT: The liver is a fascinating organ and performs a wide range of functions necessary for life. Because the hepatocyte is the major functional cell type found in the liver, it is important that we better understand its role in health and disease. Functional hepatocytes have been derived from many sources, including human stem cell populations. These models offer new opportunities to further our understanding of human liver biology from diverse genotypes and, in the future, to facilitate the development of novel medicines or cell-based therapies. This review discusses limitations in current cell-based models and the advantages offered by pluripotent stem cell-derived hepatocytes.Annual Review of Pharmacology 01/2013; 53:147-59. · 21.54 Impact Factor