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Systems approaches to modelling pathways and networks.
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Low Degree Metabolites Explain Essential Reactions and Enhance Modularity in Biological Networks
Recently there has been a lot of interest in identifying modules at the level
of genetic and metabolic networks of organisms, as well as in identifying
single genes and reactions that are essential for the organism. A goal of
computational and systems biology is to go beyond identification towards an
explanation of specific modules and essential genes and reactions in terms of
specific structural or evolutionary constraints. In the metabolic networks of
E. coli, S. cerevisiae and S. aureus, we identified metabolites with a low
degree of connectivity, particularly those that are produced and/or consumed in
just a single reaction. Using FBA we also determined reactions essential for
growth in these metabolic networks. We find that most reactions identified as
essential in these networks turn out to be those involving the production or
consumption of low degree metabolites. Applying graph theoretic methods to
these metabolic networks, we identified connected clusters of these low degree
metabolites. The genes involved in several operons in E. coli are correctly
predicted as those of enzymes catalyzing the reactions of these clusters. We
independently identified clusters of reactions whose fluxes are perfectly
correlated. We find that the composition of the latter `functional clusters' is
also largely explained in terms of clusters of low degree metabolites in each
of these organisms. Our findings mean that most metabolic reactions that are
essential can be tagged by one or more low degree metabolites. Those reactions
are essential because they are the only ways of producing or consuming their
respective tagged metabolites. Furthermore, reactions whose fluxes are strongly
correlated can be thought of as `glued together' by these low degree
metabolites.Comment: 12 pages main text with 2 figures and 2 tables. 16 pages of
Supplementary material. Revised version has title changed and contains study
of 3 organisms instead of 1 earlie
Signatures of arithmetic simplicity in metabolic network architecture
Metabolic networks perform some of the most fundamental functions in living
cells, including energy transduction and building block biosynthesis. While
these are the best characterized networks in living systems, understanding
their evolutionary history and complex wiring constitutes one of the most
fascinating open questions in biology, intimately related to the enigma of
life's origin itself. Is the evolution of metabolism subject to general
principles, beyond the unpredictable accumulation of multiple historical
accidents? Here we search for such principles by applying to an artificial
chemical universe some of the methodologies developed for the study of genome
scale models of cellular metabolism. In particular, we use metabolic flux
constraint-based models to exhaustively search for artificial chemistry
pathways that can optimally perform an array of elementary metabolic functions.
Despite the simplicity of the model employed, we find that the ensuing pathways
display a surprisingly rich set of properties, including the existence of
autocatalytic cycles and hierarchical modules, the appearance of universally
preferable metabolites and reactions, and a logarithmic trend of pathway length
as a function of input/output molecule size. Some of these properties can be
derived analytically, borrowing methods previously used in cryptography. In
addition, by mapping biochemical networks onto a simplified carbon atom
reaction backbone, we find that several of the properties predicted by the
artificial chemistry model hold for real metabolic networks. These findings
suggest that optimality principles and arithmetic simplicity might lie beneath
some aspects of biochemical complexity
On the effects of alternative optima in context-specific metabolic model predictions
Recent methodological developments have facilitated the integration of
high-throughput data into genome-scale models to obtain context-specific
metabolic reconstructions. A unique solution to this data integration problem
often may not be guaranteed, leading to a multitude of context-specific
predictions equally concordant with the integrated data. Yet, little attention
has been paid to the alternative optima resulting from the integration of
context-specific data. Here we present computational approaches to analyze
alternative optima for different context-specific data integration instances.
By using these approaches on metabolic reconstructions for the leaf of
Arabidopsis thaliana and the human liver, we show that the analysis of
alternative optima is key to adequately evaluating the specificity of the
predictions in particular cellular contexts. While we provide several ways to
reduce the ambiguity in the context-specific predictions, our findings indicate
that the existence of alternative optimal solutions warrant caution in detailed
context-specific analyses of metabolism
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
Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways
This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Pey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.Background: The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results: We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions:
A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.Basque Governmen
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