Abstract:Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computatio… Show more
“…This utility is especially important for reconstructions whose content is at the edge of experimental biochemistry. Nevertheless, for other applications [24, 26, 39] it is necessary to know the fate of all the atoms in the metabolic network.Fig. 9Unbalanced thyroid peroxidase reaction.…”
Section: Discussionmentioning
confidence: 99%
“…For example, given atom mapping data for a stoichiometrically consistent metabolic network, the set of conserved moieties can be efficiently computed and identified [26]. Each conserved moiety corresponds to a particular identifiable molecular substructure that is invariant with respect to the chemical transformations of that network.…”
The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0223-1) contains supplementary material, which is available to authorized users.
“…This utility is especially important for reconstructions whose content is at the edge of experimental biochemistry. Nevertheless, for other applications [24, 26, 39] it is necessary to know the fate of all the atoms in the metabolic network.Fig. 9Unbalanced thyroid peroxidase reaction.…”
Section: Discussionmentioning
confidence: 99%
“…For example, given atom mapping data for a stoichiometrically consistent metabolic network, the set of conserved moieties can be efficiently computed and identified [26]. Each conserved moiety corresponds to a particular identifiable molecular substructure that is invariant with respect to the chemical transformations of that network.…”
The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0223-1) contains supplementary material, which is available to authorized users.
“…They involve conserved moieties, which are groups of atoms that remain intact in all reactions of a system. AMP, NAD + , and NADP + are prominent examples of conserved moieties in energy metabolism (Haraldsdóttir and Fleming, 2016). Consider the simple system in Fig.…”
Section: Cycles Are Resolved By Considering Global Dominance Equationsmentioning
Mechanistic models of biochemical systems provide a rigorous kinetics-based description of various biological phenomena. They are indispensable to elucidate biological design principles and to devise and engineer systems with novel functionalities. To date, mathematical analysis and characterization of these models remain a challenging endeavor, the main difficulty being the lack of information for most system parameters. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism that enables mechanistic modeling of complex biological processes without requiring previous knowledge of the parameter values involved. This is achieved by making use of a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are predicted in a second step. DST3 represents the most generally applicable implementation of the Design Space formalism to date and offers unique advantages over earlier versions. By expanding the capabilities of the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and guiding the design and optimization of novel synthetic circuits.
“…Similarly, the number of passive genes at a specific time of the process can be obtained via the number of active genes at this time point or vice versa. There are different ways of obtaining conserved cycles in a process such as Haousholder based methods [13], Gauss-Jordan elimination methods [11], methods based on atomic transition networks of species [14]. We refer to [11] and the references therein for a review on the methods of obtaining conservation relations in a biochemical process of interest.…”
Biochemical reaction systems involve many different species interacting via many different reaction channels. When the number of species and the abundance of species are so high, pure modeling approaches based on differential equations suffer from curse of dimensionality. If a system involves conserved cycles, abundances of some species can be obtained via algebraic relations which in turn will reduce the dimension of differential equations representing the dynamics of the system. In the present paper, we propose a numerical algorithm that uses Gauss-Jordan method to obtain conserved cycles in biochemical systems. We give this algorithm in Direct Method (DM), First Reaction Method (FRM) and Next Reaction Method (NRM) which obtain exact realizations of the state vector in stochastic modeling approach. We apply these three algorithms with/without using conservation relations to biochemical systems in different sizes and compare the computational costs of two different versions of each exact algorithm.
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