Analysis of complex metabolic behavior through pathway decomposition

<p>Abstract</p> <p>Background</p> <p>Understanding complex systems through decomposition into simple interacting components is a pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially with its size.</p> <p>Results</p> <p>Here, we present a new method for decomposition of metabolic flux distributions into elementary flux modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic engineering of <it>Escherichia coli </it>and for understanding the survival of <it>Mycobacterium tuberculosis </it>(MTB) during infection.</p> <p>Conclusions</p> <p>Our method can achieve computational time improvements exceeding 2000-fold and requires only several seconds to generate elementary mode decompositions on genome-scale networks. These improvements arise from not having to generate all relevant elementary modes prior to initiating the decomposition. The decompositions from our method are useful for understanding complex flux distributions and debugging genome-scale models.</p

Complex networks theory for analyzing metabolic networks

One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure

On functional module detection in metabolic networks

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models

Using in silico models to simulate dual perturbation experiments: procedure development and interpretation of outcomes.

BackgroundA growing number of realistic in silico models of metabolic functions are being formulated and can serve as 'dry lab' platforms to prototype and simulate experiments before they are performed. For example, dual perturbation experiments that vary both genetic and environmental parameters can readily be simulated in silico. Genetic and environmental perturbations were applied to a cell-scale model of the human erythrocyte and subsequently investigated.ResultsThe resulting steady state fluxes and concentrations, as well as dynamic responses to the perturbations were analyzed, yielding two important conclusions: 1) that transporters are informative about the internal states (fluxes and concentrations) of a cell and, 2) that genetic variations can disrupt the natural sequence of dynamic interactions between network components. The former arises from adjustments in energy and redox states, while the latter is a result of shifting time scales in aggregate pool formation of metabolites. These two concepts are illustrated for glucose-6 phosphate dehydrogenase (G6PD) and pyruvate kinase (PK) in the human red blood cell.ConclusionDual perturbation experiments in silico are much more informative for the characterization of functional states than single perturbations. Predictions from an experimentally validated cellular model of metabolism indicate that the measurement of cofactor precursor transport rates can inform the internal state of the cell when the external demands are altered or a causal genetic variation is introduced. Finally, genetic mutations that alter the clinical phenotype may also disrupt the 'natural' time scale hierarchy of interactions in the network

Rigidity and flexibility of biological networks

The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

The Origin, Succession, and Predicted Metabolism of Bacterial Communities Associated with Leaf Decomposition.

Intraspecific variation in plant nutrient and defensive traits can regulate ecosystem-level processes, such as decomposition and transformation of plant carbon and nutrients. Understanding the regulatory mechanisms of ecosystem functions at local scales may facilitate predictions of the resistance and resilience of these functions to change. We evaluated how riverine bacterial community assembly and predicted gene content corresponded to decomposition rates of green leaf inputs from red alder trees into rivers of Washington State, USA. Previously, we documented accelerated decomposition rates for leaves originating from trees growing adjacent to the site of decomposition versus more distant locales, suggesting that microbes have a "home-field advantage" in decomposing local leaves. Here, we identified repeatable stages of bacterial succession, each defined by dominant taxa with predicted gene content associated with metabolic pathways relevant to the leaf characteristics and course of decomposition. "Home" leaves contained bacterial communities with distinct functional capacities to degrade aromatic compounds. Given known spatial variation of alder aromatics, this finding helps explain locally accelerated decomposition. Bacterial decomposer communities adjust to intraspecific variation in leaves at spatial scales of less than a kilometer, providing a mechanism for rapid response to changes in resources such as range shifts among plant genotypes. Such rapid responses among bacterial communities in turn may maintain high rates of carbon and nutrient cycling through aquatic ecosystems.IMPORTANCE Community ecologists have traditionally treated individuals within a species as uniform, with individual-level biodiversity rarely considered as a regulator of community and ecosystem function. In our study system, we have documented clear evidence of within-species variation causing local ecosystem adaptation to fluxes across ecosystem boundaries. In this striking pattern of a "home-field advantage," leaves from individual trees tend to decompose most rapidly when immediately adjacent to their parent tree. Here, we merge community ecology experiments with microbiome approaches to describe how bacterial communities adjust to within-species variation in leaves over spatial scales of less than a kilometer. The results show that bacterial community compositional changes facilitate rapid ecosystem responses to environmental change, effectively maintaining high rates of carbon and nutrient cycling through ecosystems

A critical review of the formation of mono- and dicarboxylated metabolic intermediates of alkylphenol polyethoxylates during wastewater treatment and their environmental significance

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 Taylor & Francis.Alkylphenoxyacetic acids, the metabolic biodegradation products of alkylphenol ethoxylates, are commonly found in wastewaters and sewage effluents. These persistent hydrophilic derivatives possess intrinsic estrogenic activity, which can mimic natural hormones. Their concentrations increase through the sewage treatment works as a result of biodegradation and biotransformation, and when discharged can disrupt endocrine function in fish. These acidic metabolites represent the dominant alkylphenolic compounds found in wastewater effluent and their presence is cause for concern as, potentially, through further biotransformation and biodegradation, they can act as sources of nonylphenol, which is toxic and estrogenic. The authors aim to assess the mechanisms of formation as well as elimination of alkylphenoxyacetic acids within conventional sewage treatment works with the emphasis on the activated sludge process. In addition, they evaluate the various factors influencing their degradation and formation in laboratory scale and full-scale systems. The environmental implications of these compounds are considered, as is the need for tertiary treatment processes for their removal