Enabling the exploration of biochemical pathways

Reitz, Michael; Sacher, Oliver; Tarkhov, Aleksey; Trümbach, Dietrich; Gasteiger, Johann

doi:10.1039/b410949j

Cited by 48 publications

(31 citation statements)

References 7 publications

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“…These databases, focusing either solely on enzymes or on proteins in general, have been developed from the information found in the primary literature (UniProt, 1 wwPDB, 2 IntEnz, 3 ExplorEnz, 4 pFam, 5 CSA, 6,7 BRENDA, 8 PLD, 9 KEGG, 10 BioPath, 11 Promise 12 and MDB 13 ). With the advent of structure determination methods for proteins, and by using ingenious chemical and biochemical experimental designs, scientists have been able to propose catalytic mechanisms for many different enzymes.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Holliday

Mitchell

Thornton

2009

Journal of Molecular Biology

124

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Section: Introductionmentioning

confidence: 99%

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Holliday

Mitchell

Thornton

2009

Journal of Molecular Biology

124

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“…The availability of pathway information in databases like KEGG [81], BioPath [82], BioCyc [83] and others is steadily increasing. We illustrated that integrating this information with snapshot data yields additional insights.…”

Section: Discussionmentioning

confidence: 99%

ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics

Hasenauer

Hucho

et al. 2014

PLoS Comput Biol

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Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

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“…The BioPath.Database [23] (Molecular Networks GmbH) is a manually curated biochemical reaction database, in which the reactions are stoichiometrically balanced, and where the reaction centers, as well as the bonds broken and formed during a reaction, have been marked (http://www.molecular-networks.com/databases/biopath.html). It contains reactions originally derived from the Roche Applied Science “Biochemical Pathways” wall chart and from a more extensive monograph [24].…”

Section: Methodsmentioning

confidence: 99%

Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”

et al. 2014

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The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the “missing links” between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

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Enabling the exploration of biochemical pathways

Cited by 48 publications

References 7 publications

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics

Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”

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