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Massing JC, Gross T. Generalized Structural Kinetic Modeling: A Survey and Guide. Front Mol Biosci 2022; 9:825052. [PMID: 35573734 PMCID: PMC9098827 DOI: 10.3389/fmolb.2022.825052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach for formulating dynamical models to gain insights into dynamics and bifurcations of uncertain systems. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.
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Affiliation(s)
- Jana C. Massing
- Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany
- Helmholtz Centre for Marine and Polar Research, Alfred-Wegener-Institute, Bremerhaven, Germany
- Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl-von-Ossietzky University, Oldenburg, Germany
- *Correspondence: Jana C. Massing,
| | - Thilo Gross
- Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany
- Helmholtz Centre for Marine and Polar Research, Alfred-Wegener-Institute, Bremerhaven, Germany
- Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl-von-Ossietzky University, Oldenburg, Germany
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Cuperlovic-Culf M. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites 2018; 8:E4. [PMID: 29324649 PMCID: PMC5875994 DOI: 10.3390/metabo8010004] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/08/2018] [Accepted: 01/09/2018] [Indexed: 01/15/2023] Open
Abstract
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.
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Affiliation(s)
- Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council of Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada.
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Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Mathematical Modeling Approaches in Plant Metabolomics. Methods Mol Biol 2018; 1778:329-347. [PMID: 29761450 DOI: 10.1007/978-1-4939-7819-9_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.
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Affiliation(s)
- Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria.
- Department Biology I, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Austria.
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Childs D, Grimbs S, Selbig J. Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns. Bioinformatics 2015; 31:i214-20. [PMID: 26072485 PMCID: PMC4765856 DOI: 10.1093/bioinformatics/btv243] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase. Contact:dorothee.childs@embl.de Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dorothee Childs
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany
| | - Sergio Grimbs
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany
| | - Joachim Selbig
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany
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Gelius-Dietrich G, Desouki AA, Fritzemeier CJ, Lercher MJ. Sybil--efficient constraint-based modelling in R. BMC SYSTEMS BIOLOGY 2013; 7:125. [PMID: 24224957 PMCID: PMC3843580 DOI: 10.1186/1752-0509-7-125] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Accepted: 11/01/2013] [Indexed: 12/17/2022]
Abstract
Background Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users. Results Here, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model. Conclusions Due to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).
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Affiliation(s)
| | | | | | - Martin J Lercher
- Institute for Computer Science, Heinrich-Heine-University, Universitätsstr 1, 40225 Düsseldorf, Germany.
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Reznik E, Kaper TJ, Segrè D. The dynamics of hybrid metabolic-genetic oscillators. CHAOS (WOODBURY, N.Y.) 2013; 23:013132. [PMID: 23556969 DOI: 10.1063/1.4793573] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The synthetic construction of intracellular circuits is frequently hindered by a poor knowledge of appropriate kinetics and precise rate parameters. Here, we use generalized modeling (GM) to study the dynamical behavior of topological models of a family of hybrid metabolic-genetic circuits known as "metabolators." Under mild assumptions on the kinetics, we use GM to analytically prove that all explicit kinetic models which are topologically analogous to one such circuit, the "core metabolator," cannot undergo Hopf bifurcations. Then, we examine more detailed models of the metabolator. Inspired by the experimental observation of a Hopf bifurcation in a synthetically constructed circuit related to the core metabolator, we apply GM to identify the critical components of the synthetically constructed metabolator which must be reintroduced in order to recover the Hopf bifurcation. Next, we study the dynamics of a re-wired version of the core metabolator, dubbed the "reverse" metabolator, and show that it exhibits a substantially richer set of dynamical behaviors, including both local and global oscillations. Prompted by the observation of relaxation oscillations in the reverse metabolator, we study the role that a separation of genetic and metabolic time scales may play in its dynamics, and find that widely separated time scales promote stability in the circuit. Our results illustrate a generic pipeline for vetting the potential success of a circuit design, simply by studying the dynamics of the corresponding generalized model.
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Affiliation(s)
- Ed Reznik
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA
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