1
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Kochen MA, Hellerstein JL, Sauro HM. First-order ultrasensitivity in phosphorylation cycles. Interface Focus 2024; 14:20230045. [PMID: 38344405 PMCID: PMC10853695 DOI: 10.1098/rsfs.2023.0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/24/2024] [Indexed: 05/09/2024] Open
Abstract
Cellular signal transduction takes place through a network of phosphorylation cycles. These pathways take the form of a multi-layered cascade of cycles. This work focuses on the sensitivity of single, double and n length cycles. Cycles that operate in the zero-order regime can become sensitive to changes in signal, resulting in zero-order ultrasensitivity (ZOU). Using frequency analysis, we confirm previous efforts that cascades can act as noise filters by computing the bandwidth. We show that n length cycles display what we term first-order ultrasensitivity which occurs even when the cycles are not operating in the zero-order regime. The magnitude of the sensitivity, however, has an upper bound equal to the number of cycles. It is known that ZOU can be significantly reduced in the presence of retroactivity. We show that the first-order ultrasensitivity is immune to retroactivity and that the ZOU and first-order ultrasensitivity can be blended to create systems with constant sensitivity over a wider range of signal. We show that the ZOU in a double cycle is only modestly higher compared with a single cycle. We therefore speculate that the double cycle has evolved to enable amplification even in the face of retroactivity.
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Affiliation(s)
- Michael A. Kochen
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | | | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
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2
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Kochen MA, Hellerstein JL, Sauro HM. Sensitivity and Frequency Response of Biochemical Cascades. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557821. [PMID: 37781602 PMCID: PMC10541101 DOI: 10.1101/2023.09.14.557821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Signal transduction from a cell's surface to cytoplasmic and nuclear targets takes place through a complex network of interconnected pathways. Phosphorylation cycles are common components of many pathways and may take the form of a multi-layered cascade of cycles or incorporate species with multiple phosphorylation sites that effectively create a sequence of cycles with increasing states of phosphorylation. This work focuses on the frequency response and sensitivity of such systems, two properties that have not been thoroughly examined. Starting with a singularly phosphorylated single-cycle system, we compare the sensitivity to perturbation at steady-state across a range of input signal strengths. This is followed by a frequency response analysis focusing on the gain and associated bandwidth. Next, we consider a two-layer cascade of single phosphorylation cycles and focus on how the two cycles interact to produce various effects on the bandwidth and damping properties. Then we consider the (ultra)sensitivity of a doubly phosphorylated system, where we describe in detail first-order ultrasensitivity, a unique property of these systems, which can be blended with zero-order ultrasensitivity to create systems with relatively constant gain over a range of signal input. Finally, we give an in-depth analysis of the sensitivity of an n-phosphorylated system.
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Affiliation(s)
- Michael A Kochen
- Department of Bioengineering, University of Washington, Seattle, WA 98105
| | | | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98105
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3
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Derbalah A, Al‐Sallami H, Hasegawa C, Gulati A, Duffull SB. A framework for simplification of quantitative systems pharmacology models in clinical pharmacology. Br J Clin Pharmacol 2020; 88:1430-1440. [DOI: 10.1111/bcp.14451] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/13/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
| | | | | | - Abhishek Gulati
- Clinical Pharmacology and Exploratory Development, Astellas Pharma Global Development Northbrook Illinois USA
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4
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Kolpakov F, Akberdin I, Kashapov T, Kiselev L, Kolmykov S, Kondrakhin Y, Kutumova E, Mandrik N, Pintus S, Ryabova A, Sharipov R, Yevshin I, Kel A. BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data. Nucleic Acids Res 2020; 47:W225-W233. [PMID: 31131402 PMCID: PMC6602424 DOI: 10.1093/nar/gkz440] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/02/2019] [Accepted: 05/11/2019] [Indexed: 12/16/2022] Open
Abstract
BioUML (homepage: http://www.biouml.org, main public server: https://ict.biouml.org) is a web-based integrated environment (platform) for systems biology and the analysis of biomedical data generated by omics technologies. The BioUML vision is to provide a computational platform to build virtual cell, virtual physiological human and virtual patient. BioUML spans a comprehensive range of capabilities, including access to biological databases, powerful tools for systems biology (visual modelling, simulation, parameters fitting and analyses), a genome browser, scripting (R, JavaScript) and a workflow engine. Due to integration with the Galaxy platform and R/Bioconductor, BioUML provides powerful possibilities for the analyses of omics data. The plug-in-based architecture allows the user to add new functionalities using plug-ins. To facilitate a user focus on a particular task or database, we have developed several predefined perspectives that display only those web interface elements that are needed for a specific task. To support collaborative work on scientific projects, there is a central authentication and authorization system (https://bio-store.org). The diagram editor enables several remote users to simultaneously edit diagrams.
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Affiliation(s)
- Fedor Kolpakov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Ilya Akberdin
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | | | - Llya Kiselev
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Semyon Kolmykov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation.,Institute of Cytology and Genetics SB RAS, Novosibirsk 630090, Russian Federation
| | - Yury Kondrakhin
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Elena Kutumova
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Nikita Mandrik
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Sergey Pintus
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Anna Ryabova
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Ruslan Sharipov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Ivan Yevshin
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation
| | - Alexander Kel
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation.,geneXplain GmbH, 38302 Wolfenbüttel, Germany.,Institute of Chemical Biology and Fundamental Medicine, SB RAS, Novosibirsk 630090, Russian Federation
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5
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Greene J, Daniell J, Köpke M, Broadbelt L, Tyo KE. Kinetic ensemble model of gas fermenting Clostridium autoethanogenum for improved ethanol production. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.04.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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6
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Sabatino A, Frezzato D. Tagged-moiety viewpoint of chemical reaction networks. J Chem Phys 2019; 150:134104. [DOI: 10.1063/1.5081675] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Andrea Sabatino
- Department of Chemical Sciences, University of Padova, Via Marzolo 1, I-35131 Padova, Italy
| | - Diego Frezzato
- Department of Chemical Sciences, University of Padova, Via Marzolo 1, I-35131 Padova, Italy
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7
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Application of Parameter Optimization to Search for Oscillatory Mass-Action Networks Using Python. Processes (Basel) 2019. [DOI: 10.3390/pr7030163] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Biological systems can be described mathematically to model the dynamics of metabolic, protein, or gene-regulatory networks, but locating parameter regimes that induce a particular dynamic behavior can be challenging due to the vast parameter landscape, particularly in large models. In the current work, a Pythonic implementation of existing bifurcation objective functions, which reward systems that achieve a desired bifurcation behavior, is implemented to search for parameter regimes that permit oscillations or bistability. A differential evolution algorithm progressively approximates the specified bifurcation type while performing a global search of parameter space for a candidate with the best fitness. The user-friendly format facilitates integration with systems biology tools, as Python is a ubiquitous programming language. The bifurcation–evolution software is validated on published models from the BioModels Database and used to search populations of randomly-generated mass-action networks for oscillatory dynamics. Results of this search demonstrate the importance of reaction enrichment to provide flexibility and enable complex dynamic behaviors, and illustrate the role of negative feedback and time delays in generating oscillatory dynamics.
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8
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Choi K, Medley JK, König M, Stocking K, Smith L, Gu S, Sauro HM. Tellurium: An extensible python-based modeling environment for systems and synthetic biology. Biosystems 2018; 171:74-79. [PMID: 30053414 PMCID: PMC6108935 DOI: 10.1016/j.biosystems.2018.07.006] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Revised: 07/18/2018] [Accepted: 07/18/2018] [Indexed: 10/28/2022]
Abstract
Here we present Tellurium, a Python-based environment for model building, simulation, and analysis that facilitates reproducibility of models in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source simulation environment composed of multiple libraries, plugins, and specialized modules and methods. Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution. Two interfaces are provided, one based on the Spyder IDE which has an accessible user interface akin to MATLAB and a second based on the Jupyter Notebook, which is a format that contains live code, equations, visualizations, and narrative text. Tellurium uses libRoadRunner as the default SBML simulation engine which supports deterministic simulations, stochastic simulations, and steady-state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from complex algorithms for bifurcation analysis to multidimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and analysis for both novices and experts. AVAILABILITY tellurium.analogmachine.org.
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Affiliation(s)
- Kiri Choi
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, WA 98195, USA.
| | - J Kyle Medley
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, WA 98195, USA.
| | - Matthias König
- Institute for Biology, Institute for Theoretical Biology, Humboldt University, Berlin, Germany.
| | - Kaylene Stocking
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, WA 98195, USA.
| | - Lucian Smith
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, WA 98195, USA.
| | - Stanley Gu
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, WA 98195, USA.
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, WA 98195, USA.
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9
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Bedaso Y, Bergmann FT, Choi K, Medley K, Sauro HM. A portable structural analysis library for reaction networks. Biosystems 2018; 169-170:20-25. [PMID: 29857031 DOI: 10.1016/j.biosystems.2018.05.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 04/30/2018] [Accepted: 05/28/2018] [Indexed: 09/30/2022]
Abstract
The topology of a reaction network can have a significant influence on the network's dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper, we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with extensive on-line documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it simple to install on any standard Python distribution. The bulk of the source code is licensed under the open source BSD license with other parts using as either the MIT license or more simply public domain. All source is available on GitHub (https://github.com/sys-bio/Libstructural).
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Affiliation(s)
- Yosef Bedaso
- Department of Bioengineering, William H. Foege Building, Box 355061, Seattle, WA 98195-5061, USA.
| | | | - Kiri Choi
- Department of Bioengineering, William H. Foege Building, Box 355061, Seattle, WA 98195-5061, USA.
| | - Kyle Medley
- Department of Bioengineering, William H. Foege Building, Box 355061, Seattle, WA 98195-5061, USA
| | - Herbert M Sauro
- Department of Bioengineering, William H. Foege Building, Box 355061, Seattle, WA 98195-5061, USA.
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10
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Greene JL, Wäechter A, Tyo KEJ, Broadbelt LJ. Acceleration Strategies to Enhance Metabolic Ensemble Modeling Performance. Biophys J 2017; 113:1150-1162. [PMID: 28877496 DOI: 10.1016/j.bpj.2017.07.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 06/22/2017] [Accepted: 07/11/2017] [Indexed: 01/01/2023] Open
Abstract
Developing reliable, predictive kinetic models of metabolism is a difficult, yet necessary, priority toward understanding and deliberately altering cellular behavior. Constraint-based modeling has enabled the fields of metabolic engineering and systems biology to make great strides in interrogating cellular metabolism but does not provide sufficient insight into regulation or kinetic limitations of metabolic pathways. Moreover, the growth-optimized assumptions that constraint-based models often rely on do not hold when studying stationary or persistor cell populations. However, developing kinetic models provides many unique challenges, as many of the kinetic parameters and rate laws governing individual enzymes are unknown. Ensemble modeling (EM) was developed to circumnavigate this challenge and effectively sample the large kinetic parameter solution space using consistent experimental datasets. Unfortunately, EM, in its base form, requires long solve times to complete and often leads to unstable kinetic model predictions. Furthermore, these limitations scale prohibitively with increasing model size. As larger metabolic models are developed with increasing genetic information and experimental validation, the demand to incorporate kinetic information increases. Therefore, in this work, we have begun to tackle the challenges of EM by introducing additional steps to the existing method framework specifically through reducing computation time and optimizing parameter sampling. We first reduce the structural complexity of the network by removing dependent species, and second, we sample locally stable parameter sets to reflect realistic biological states of cells. Lastly, we presort the screening data to eliminate the most incorrect predictions in the earliest screening stages, saving further calculations in later stages. Our complementary improvements to this EM framework are easily incorporated into concurrent EM efforts and broaden the application opportunities and accessibility of kinetic modeling across the field.
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Affiliation(s)
- Jennifer L Greene
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois
| | - Andreas Wäechter
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois.
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11
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Snowden TJ, van der Graaf PH, Tindall MJ. Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends. Bull Math Biol 2017; 79:1449-1486. [PMID: 28656491 PMCID: PMC5498684 DOI: 10.1007/s11538-017-0277-2] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Accepted: 03/30/2017] [Indexed: 01/31/2023]
Abstract
Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.
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Affiliation(s)
- Thomas J Snowden
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK.,Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG, UK
| | - Piet H van der Graaf
- Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG, UK.,Leiden Academic Centre for Drug Research, Universiteit Leiden, Leiden, 2333 CC, Netherlands
| | - Marcus J Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK. .,The Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6AX, UK.
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12
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Snowden TJ, van der Graaf PH, Tindall MJ. A combined model reduction algorithm for controlled biochemical systems. BMC SYSTEMS BIOLOGY 2017; 11:17. [PMID: 28193218 PMCID: PMC5307760 DOI: 10.1186/s12918-017-0397-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Accepted: 01/18/2017] [Indexed: 02/05/2023]
Abstract
BACKGROUND Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often, however, not all portions of a model are of equal interest in a given setting. In such situations methods of model reduction offer one possible approach for addressing the issue of complexity by seeking to eliminate those portions of a pathway that can be shown to have the least effect upon the properties of interest. METHODS In this paper a model reduction algorithm bringing together the complementary aspects of proper lumping and empirical balanced truncation is presented. Additional contributions include the development of a criterion for the selection of state-variable elimination via conservation analysis and use of an 'averaged' lumping inverse. This combined algorithm is highly automatable and of particular applicability in the context of 'controlled' biochemical networks. RESULTS The algorithm is demonstrated here via application to two examples; an 11 dimensional model of bacterial chemotaxis in Escherichia coli and a 99 dimensional model of extracellular regulatory kinase activation (ERK) mediated via the epidermal growth factor (EGF) and nerve growth factor (NGF) receptor pathways. In the case of the chemotaxis model the algorithm was able to reduce the model to 2 state-variables producing a maximal relative error between the dynamics of the original and reduced models of only 2.8% whilst yielding a 26 fold speed up in simulation time. For the ERK activation model the algorithm was able to reduce the system to 7 state-variables, incurring a maximal relative error of 4.8%, and producing an approximately 10 fold speed up in the rate of simulation. Indices of controllability and observability are additionally developed and demonstrated throughout the paper. These provide insight into the relative importance of individual reactants in mediating a biochemical system's input-output response even for highly complex networks. CONCLUSIONS Through application, this paper demonstrates that combined model reduction methods can produce a significant simplification of complex Systems Biology models whilst retaining a high degree of predictive accuracy. In particular, it is shown that by combining the methods of proper lumping and empirical balanced truncation it is often possible to produce more accurate reductions than can be obtained by the use of either method in isolation.
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Affiliation(s)
- Thomas J Snowden
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK.,Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG, UK
| | - Piet H van der Graaf
- Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG, UK.,Leiden Academic Centre for Drug Research, Universiteit Leiden, Leiden, NL-2333 CC, Netherlands
| | - Marcus J Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX, UK. .,The Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6AX, UK.
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13
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Haraldsdóttir HS, Fleming RMT. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks. PLoS Comput Biol 2016; 12:e1004999. [PMID: 27870845 PMCID: PMC5117560 DOI: 10.1371/journal.pcbi.1004999] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 05/25/2016] [Indexed: 12/12/2022] Open
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 computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. Conserved moieties are transferred between metabolites in internal reactions of a metabolic network but are not synthesised, degraded or exchanged with the environment. The total amount of a conserved moiety in the metabolic network is therefore constant over time. Metabolites that share a conserved moiety have interdependent concentrations because their total amount is constant. Identification of conserved moieties results in a concise description of all concentration dependencies in a metabolic network. The problem of identifying conserved moieties has previously been formulated in terms of the stoichiometry of metabolic reactions. Methods based on this formulation are computationally intractable for large networks. We show that reaction stoichiometry alone gives insufficient information to identify conserved moieties. By first incorporating additional data on the fate of atoms in metabolic reactions, we developed and implemented a computationally tractable algorithm to identify conserved moieties and their atomic structure.
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Affiliation(s)
- Hulda S. Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ronan M. T. Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- * E-mail:
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14
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Jamshidi N, Raghunathan A. Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods. Front Microbiol 2015; 6:1032. [PMID: 26500611 PMCID: PMC4594423 DOI: 10.3389/fmicb.2015.01032] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 09/11/2015] [Indexed: 12/12/2022] Open
Abstract
Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.
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Affiliation(s)
- Neema Jamshidi
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA ; Department of Radiological Sciences, University of California, Los Angeles Los Angeles, CA, USA
| | - Anu Raghunathan
- Chemical Engineering Division, National Chemical Laboratory Pune, India
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15
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Hucka M, Bergmann FT, Dräger A, Hoops S, Keating SM, Le Novére N, Myers CJ, Olivier BG, Sahle S, Schaff JC, Smith LP, Waltemath D, Wilkinson DJ. Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions. J Integr Bioinform 2015; 12:271. [PMID: 26528569 PMCID: PMC5457286 DOI: 10.2390/biecoll-jib-2015-271] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 08/04/2015] [Accepted: 09/04/2015] [Indexed: 11/18/2022] Open
Abstract
Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.
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Hucka M, Bergmann FT, Hoops S, Keating SM, Sahle S, Schaff JC, Smith LP, Wilkinson DJ. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core. J Integr Bioinform 2015; 12:266. [PMID: 26528564 PMCID: PMC5451324 DOI: 10.2390/biecoll-jib-2015-266] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 08/03/2015] [Accepted: 09/04/2015] [Indexed: 11/18/2022] Open
Abstract
Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
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Ingalls BP, Bembenek E. Exploiting stoichiometric redundancies for computational efficiency and network reduction. In Silico Biol 2014; 12:55-67. [PMID: 25547516 PMCID: PMC4923743 DOI: 10.3233/isb-140464] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Analysis of metabolic networks typically begins with construction of the stoichiometry matrix, which characterizes the network topology. This matrix provides, via the balance equation, a description of the potential steady-state flow distribution. This paper begins with the observation that the balance equation depends only on the structure of linear redundancies in the network, and so can be stated in a succinct manner, leading to computational efficiencies in steady-state analysis. This alternative description of steady-state behaviour is then used to provide a novel method for network reduction, which complements existing algorithms for describing intracellular networks in terms of input-output macro-reactions (to facilitate bioprocess optimization and control). Finally, it is demonstrated that this novel reduction method can be used to address elementary mode analysis of large networks: the modes supported by a reduced network can capture the input-output modes of a metabolic module with significantly reduced computational effort.
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Affiliation(s)
- Brian P. Ingalls
- Correspondence to: Brian P. Ingalls, Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada. Tel.: +1 519 888 4567/Ext. 35457; Fax: +1 519 746 4319;
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Identifying all moiety conservation laws in genome-scale metabolic networks. PLoS One 2014; 9:e100750. [PMID: 24988199 PMCID: PMC4079565 DOI: 10.1371/journal.pone.0100750] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Accepted: 05/26/2014] [Indexed: 12/04/2022] Open
Abstract
The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.
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Kim KH, Qian H, Sauro HM. Nonlinear biochemical signal processing via noise propagation. J Chem Phys 2014; 139:144108. [PMID: 24116604 DOI: 10.1063/1.4822103] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced ultra-sensitivity. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes.
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Affiliation(s)
- Kyung Hyuk Kim
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, Washington 98195, USA
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Thomas A, Rahmanian S, Bordbar A, Palsson BØ, Jamshidi N. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance. Sci Rep 2014; 4:3925. [PMID: 24473230 PMCID: PMC3905279 DOI: 10.1038/srep03925] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Accepted: 01/13/2014] [Indexed: 12/11/2022] Open
Abstract
Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.
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Affiliation(s)
- Alex Thomas
- 1] Department of Bioinformatics and Systems Biology, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Sorena Rahmanian
- 1] Department of Bioengineering, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Aarash Bordbar
- 1] Department of Bioengineering, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Bernhard Ø Palsson
- 1] Department of Bioengineering, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] Institute of Engineering and Medicine, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [3] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Neema Jamshidi
- 1] Institute of Engineering and Medicine, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [3] Department of Radiological Sciences, University of California, Los Angeles, BOX 951721, Los Angeles, CA 90095-1721
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Villafaña-Rojas J, González-Reynoso O, Alcaraz-González V, González-García Y, González-Álvarez V, Solís-Pacheco JR, Aguilar-Uscanga B, Gómez-Hermosillo C. Asymptotic Observers a tool to estimate metabolite concentrations under transient state conditions in biological systems: Determination of intermediate metabolites in the pentose phosphate pathway of Saccharomyces cerevisiae. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2013.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Maarleveld TR, Khandelwal RA, Olivier BG, Teusink B, Bruggeman FJ. Basic concepts and principles of stoichiometric modeling of metabolic networks. Biotechnol J 2013; 8:997-1008. [PMID: 23893965 PMCID: PMC4671265 DOI: 10.1002/biot.201200291] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Revised: 05/03/2013] [Accepted: 07/01/2013] [Indexed: 12/16/2022]
Abstract
Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organisms to cope with different environments. Constraint-based stoichiometric modeling of metabolic networks has become an indispensable tool for such studies. Herein, we review the basic theoretical underpinnings of constraint-based stoichiometric modeling of metabolic networks. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces, are explained with simple, illustrative examples. We emphasize the mathematical definitions and their network topological interpretations.
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Affiliation(s)
- Timo R Maarleveld
- Life Sciences, Center for Mathematics and Computer Science, Amsterdam, The Netherlands; Systems Bioinformatics, Amsterdam Institute for Molecules Medicines and Systems, VU University Amsterdam, Amsterdam, The Netherlands; BioSolar Cells, Wageningen, The Netherlands
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Schryer DW, Vendelin M, Peterson P. Symbolic flux analysis for genome-scale metabolic networks. BMC SYSTEMS BIOLOGY 2011; 5:81. [PMID: 21605414 PMCID: PMC3130677 DOI: 10.1186/1752-0509-5-81] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Accepted: 05/23/2011] [Indexed: 02/06/2023]
Abstract
Background With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks. Results A symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast. Conclusions We were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.
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Affiliation(s)
- David W Schryer
- Laboratory of Systems Biology, Institute of Cybernetics at Tallinn University of Technology, Akadeemia tee 21, 12618 Tallinn, Estonia
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24
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Terzer M, Maynard ND, Covert MW, Stelling J. Genome-scale metabolic networks. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2011; 1:285-297. [PMID: 20835998 DOI: 10.1002/wsbm.37] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
During the last decade, models have been developed to characterize cellular metabolism at the level of an entire metabolic network. The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints. Here, we review large-scale metabolic network modeling, in particular, stoichiometric- and constraint-based approaches. Although many such models have been reconstructed, few networks have been extensively validated and tested experimentally, and we focus on these. We describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints on metabolic fluxes. We then discuss relatively successful approaches, including flux balance analysis (FBA), pathway analysis, and common extensions or modifications to these approaches. Finally, we describe techniques for integrating these approaches with models of other biological processes.
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Affiliation(s)
- Marco Terzer
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland
| | | | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jörg Stelling
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland
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Smallbone K, Simeonidis E, Swainston N, Mendes P. Towards a genome-scale kinetic model of cellular metabolism. BMC SYSTEMS BIOLOGY 2010; 4:6. [PMID: 20109182 PMCID: PMC2829494 DOI: 10.1186/1752-0509-4-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2009] [Accepted: 01/28/2010] [Indexed: 11/10/2022]
Abstract
Background Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour. Results We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system. Conclusions Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/.
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Affiliation(s)
- Kieran Smallbone
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7DN, UK
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26
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Abstract
Interactions among cellular constituents play a crucial role in overall cellular function and organization. These interactions can be viewed as being complementary to the usual "parts list" of genes and proteins and, in conjunction with the expression states of these parts, are key to a systems level understanding of the cell. Here, we review computational approaches to the understanding of the functional roles of cellular networks, ranging from "static" models of network topology to dynamical and stochastic simulations.
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Shaffer CA, Zwolak JW, Randhawa R, Tyson JJ. Modeling molecular regulatory networks with JigCell and PET. Methods Mol Biol 2009; 500:81-111. [PMID: 19399431 DOI: 10.1007/978-1-59745-525-1_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
We demonstrate how to model macromolecular regulatory networks with JigCell and the Parameter Estimation Toolkit (PET). These software tools are designed specifically to support the process typically used by systems biologists to model complex regulatory circuits. A detailed example illustrates how a model of the cell cycle in frog eggs is created and then refined through comparison of simulation output with experimental data. We show how parameter estimation tools automatically generate rate constants that fit a model to experimental data.
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Abstract
Probably one of the most characteristic features of a living system is its continual propensity to change as it juggles the demands of survival with the need to replicate. Internally these adjustments are manifest as changes in metabolite, protein, and gene activities. Such changes have become increasingly obvious to experimentalists, with the advent of high-throughput technologies. In this chapter we highlight some of the quantitative approaches used to rationalize the study of cellular dynamics. The chapter focuses attention on the analysis of quantitative models based on differential equations using biochemical control theory. Basic pathway motifs are discussed, including straight chain, branched, and cyclic systems. In addition, some of the properties conferred by positive and negative feedback loops are discussed, particularly in relation to bistability and oscillatory dynamics.
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Affiliation(s)
- Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
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Ramakrishnan N, Bhalla US. Memory switches in chemical reaction space. PLoS Comput Biol 2008; 4:e1000122. [PMID: 18636099 PMCID: PMC2440819 DOI: 10.1371/journal.pcbi.1000122] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2007] [Accepted: 06/10/2008] [Indexed: 11/27/2022] Open
Abstract
Just as complex electronic circuits are built from simple Boolean gates, diverse biological functions, including signal transduction, differentiation, and stress response, frequently use biochemical switches as a functional module. A relatively small number of such switches have been described in the literature, and these exhibit considerable diversity in chemical topology. We asked if biochemical switches are indeed rare and if there are common chemical motifs and family relationships among such switches. We performed a systematic exploration of chemical reaction space by generating all possible stoichiometrically valid chemical configurations up to 3 molecules and 6 reactions and up to 4 molecules and 3 reactions. We used Monte Carlo sampling of parameter space for each such configuration to generate specific models and checked each model for switching properties. We found nearly 4,500 reaction topologies, or about 10% of our tested configurations, that demonstrate switching behavior. Commonly accepted topological features such as feedback were poor predictors of bistability, and we identified new reaction motifs that were likely to be found in switches. Furthermore, the discovered switches were related in that most of the larger configurations were derived from smaller ones by addition of one or more reactions. To explore even larger configurations, we developed two tools: the “bistabilizer,” which converts almost-bistable systems into bistable ones, and frequent motif mining, which helps rank untested configurations. Both of these tools increased the coverage of our library of bistable systems. Thus, our systematic exploration of chemical reaction space has produced a valuable resource for investigating the key signaling motif of bistability. How does a cell know what type of cell it is supposed to become? How do external chemical signals change the underlying “state” of the cell? How are response pathways triggered on the application of a stress? Such questions of differentiation, signal transduction, and stress response, while seemingly diverse, all pertain to the storage of state information, or “memory,” by biochemical switches. Just as a computer memory unit can store a bit of 0 or 1 through electrical signals, a biochemical switch can be in one of two states, where chemical signals are on or off. This lets the cell record the presence/absence of an environmental stimulus, the level of a signaling molecule, or the result of a cell fate decision. There are a small number of published ways by which a group of chemical reactions come together to realize a switch. We undertook an exhaustive computational exploration to see if chemical switches are indeed rare and found, surprisingly, that they are actually abundant, highly diverse, but related to one another. Our catalog of switches opens up new bioinformatics approaches to understanding cellular decision making and cellular memory.
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Affiliation(s)
- Naren Ramakrishnan
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Upinder S. Bhalla
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
- * E-mail:
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Bruggeman FJ, Rossell S, Van Eunen K, Bouwman J, Westerhoff HV, Bakker B. Systems biology and the reconstruction of the cell: from molecular components to integral function. Subcell Biochem 2007; 43:239-62. [PMID: 17953397 DOI: 10.1007/978-1-4020-5943-8_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Frank J Bruggeman
- BioCenter Amsterdam, Free University Amsterdam, Faculty of Earth and Life Sciences, Department of Molecular Cell Physiology, The Netherlands
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Siso-Nadal F, Ollivier JF, Swain PS. Facile: a command-line network compiler for systems biology. BMC SYSTEMS BIOLOGY 2007; 1:36. [PMID: 17683566 PMCID: PMC1976619 DOI: 10.1186/1752-0509-1-36] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/10/2007] [Accepted: 08/03/2007] [Indexed: 11/10/2022]
Abstract
BACKGROUND A goal of systems biology is the quantitative modelling of biochemical networks. Yet for many biochemical systems, parameter values and even the existence of interactions between some chemical species are unknown. It is therefore important to be able to easily investigate the effects of adding or removing reactions and to easily perform a bifurcation analysis, which shows the qualitative dynamics of a model for a range of parameter values. RESULTS We present Facile, a Perl command-line tool for analysing the dynamics of a systems biology model. Facile implements the law of mass action to automatically compile a biochemical network (written as, for example, E + S <-> C) into scripts for analytical analysis (Mathematica and Maple), for simulation (XPP and Matlab), and for bifurcation analysis (AUTO). Facile automatically identifies mass conservations and generates the reduced form of a model with the minimum number of independent variables. This form is essential for bifurcation analysis, and Facile produces a C version of the reduced model for AUTO. CONCLUSION Facile is a simple, yet powerful, tool that greatly accelerates analysis of the dynamics of a biochemical network. By acting at the command-line and because of its intuitive, text-based input, Facile is quick to learn and can be incorporated into larger programs or into automated tasks.
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Affiliation(s)
- Fernando Siso-Nadal
- Gene Network Sciences, 53 Brown Road, Ithaca, New York 14850, USA
- Centre for Non-linear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
| | - Julien F Ollivier
- Centre for Non-linear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
| | - Peter S Swain
- Centre for Non-linear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
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Kremling A, Saez-Rodriguez J. Systems biology--an engineering perspective. J Biotechnol 2007; 129:329-51. [PMID: 17400319 DOI: 10.1016/j.jbiotec.2007.02.009] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2006] [Revised: 01/23/2007] [Accepted: 02/19/2007] [Indexed: 01/01/2023]
Abstract
The interdisciplinary field of systems biology has evolved rapidly over the last years. Different disciplines have aided the development of both its experimental and theoretical branches. One field, which has played a significant role is engineering science and, in particular chemical engineering. Here, we review and illustrate some of these contributions, ranging from modeling approaches to model analysis with a special focus on technique which have not yet been substantially exploited but can be potentially useful in the analysis of biochemical systems.
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Affiliation(s)
- A Kremling
- Systems Biology Group, Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
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Vass MT, Shaffer CA, Ramakrishnan N, Watson LT, Tyson JJ. The JigCell model builder: a spreadsheet interface for creating biochemical reaction network models. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2006; 3:155-64. [PMID: 17048401 DOI: 10.1109/tcbb.2006.27] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Converting a biochemical reaction network to a set of kinetic rate equations is tedious and error prone. We describe known interface paradigms for inputing models of intracellular regulatory networks: graphical layout (diagrams), wizards, scripting languages, and direct entry of chemical equations. We present the JigCell Model Builder, which allows users to define models as a set of reaction equations using a spreadsheet (an example of direct entry of equations) and outputs model definitions in the Systems Biology Markup Language, Level 2. We present the results of two usability studies. The spreadsheet paradigm demonstrated its effectiveness in reducing the number of errors made by modelers when compared to hand conversion of a wiring diagram to differential equations. A comparison of representatives of the four interface paradigms for a simple model of the cell cycle was conducted which measured time, mouse clicks, and keystrokes to enter the model, and the number of screens needed to view the contents of the model. All four paradigms had similar data entry times. The spreadsheet and scripting language approaches require significantly fewer screens to view the models than do the wizard or graphical layout approaches.
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Affiliation(s)
- Marc T Vass
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0106, USA.
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Lee DY, Yun C, Cho A, Hou BK, Park S, Lee SY. WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks. Bioinformatics 2006; 22:1150-1. [PMID: 16543278 DOI: 10.1093/bioinformatics/btl091] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
SUMMARY WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.
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Affiliation(s)
- Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea
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Vallabhajosyula RR, Chickarmane V, Sauro HM. Conservation analysis of large biochemical networks. Bioinformatics 2005; 22:346-53. [PMID: 16317075 DOI: 10.1093/bioinformatics/bti800] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Large biochemical networks pose a unique challenge from the point of view of evaluating conservation laws. The computational problem in most cases exceeds the capability of available software tools, often resulting in inaccurate computation of the number and form of conserved cycles. Such errors have profound effects on subsequent calculations, particularly in the evaluation of the Jacobian which is a critical quantity in many other calculations. The goal of this paper is to outline a new algorithm that is computationally efficient and robust at extracting the correct conservation laws for very large biochemical networks. RESULTS We show that our algorithm can perform the conservation analysis of large biochemical networks, and can evaluate the correct conserved cycles when compared with other similar software tools. Biochemical simulators such as Jarnac and COPASI are successful at extracting only a subset of the conservation laws that our algorithm can. This is illustrated with examples for some large networks which show the advantages of our method.
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Snoep JL, Bruggeman F, Olivier BG, Westerhoff HV. Towards building the silicon cell: a modular approach. Biosystems 2005; 83:207-16. [PMID: 16242236 DOI: 10.1016/j.biosystems.2005.07.006] [Citation(s) in RCA: 77] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2005] [Revised: 06/27/2005] [Accepted: 07/12/2005] [Indexed: 10/25/2022]
Abstract
Systems Biology aims to understand quantitatively how properties of biological systems can be understood as functions of the characteristics of, and interactions between their macromolecular components. Whereas, traditional biochemistry focused on isolation and characterization of cellular components, the challenge for Systems Biology lies in integration of this knowledge and the knowledge about molecular interactions. Computer models play an important role in this integration. We here discuss an approach with which we aim to link kinetic models on small parts of metabolism together, so as to form detailed kinetic models of larger chunks of metabolism, and ultimately of the entire living cell. Specifically, we will discuss techniques that can be used to model a sub-network in isolation of a larger network of which it is a part, while still maintaining the dynamics of the larger complete network. We will start by outlining the JWS online system, the silicon cell project, and the type of models we propose. JWS online is a model repository, which can be used for the storage, simulation and analysis of kinetic models. We advocate to integrate a top-down approach, where measurements on the complete system are used to derive fluxes in a detailed structural model, with a bottom-up approach, consisting of the integration of molecular mechanism-based detailed kinetic models into the structural model.
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Affiliation(s)
- Jacky L Snoep
- Department of Biochemistry, University of Stellenbosch, Triple-J Group for Molecular Cell Physiology, Private Bag X1, Matieland 7602, South Africa.
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