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Lao-Martil D, Verhagen KJA, Schmitz JPJ, Teusink B, Wahl SA, van Riel NAW. Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities. Metabolites 2022; 12:74. [PMID: 35050196 PMCID: PMC8779790 DOI: 10.3390/metabo12010074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/23/2022] Open
Abstract
Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.
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Affiliation(s)
- David Lao-Martil
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands;
| | - Koen J. A. Verhagen
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany; (K.J.A.V.); (S.A.W.)
| | - Joep P. J. Schmitz
- DSM Biotechnology Center, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands;
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands;
| | - S. Aljoscha Wahl
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany; (K.J.A.V.); (S.A.W.)
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands;
- Amsterdam University Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
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2
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O’Donovan SD, Lenz M, Vink RG, Roumans NJT, de Kok TMCM, Mariman ECM, Peeters RLM, van Riel NAW, van Baak MA, Arts ICW. A computational model of postprandial adipose tissue lipid metabolism derived using human arteriovenous stable isotope tracer data. PLoS Comput Biol 2019; 15:e1007400. [PMID: 31581241 PMCID: PMC6890259 DOI: 10.1371/journal.pcbi.1007400] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 12/03/2019] [Accepted: 09/13/2019] [Indexed: 12/16/2022] Open
Abstract
Given the association of disturbances in non-esterified fatty acid (NEFA) metabolism with the development of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, computational models of glucose-insulin dynamics have been extended to account for the interplay with NEFA. In this study, we use arteriovenous measurement across the subcutaneous adipose tissue during a mixed meal challenge test to evaluate the performance and underlying assumptions of three existing models of adipose tissue metabolism and construct a new, refined model of adipose tissue metabolism. Our model introduces new terms, explicitly accounting for the conversion of glucose to glyceraldehye-3-phosphate, the postprandial influx of glycerol into the adipose tissue, and several physiologically relevant delays in insulin signalling in order to better describe the measured adipose tissues fluxes. We then applied our refined model to human adipose tissue flux data collected before and after a diet intervention as part of the Yoyo study, to quantify the effects of caloric restriction on postprandial adipose tissue metabolism. Significant increases were observed in the model parameters describing the rate of uptake and release of both glycerol and NEFA. Additionally, decreases in the model's delay in insulin signalling parameters indicates there is an improvement in adipose tissue insulin sensitivity following caloric restriction.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Division of Human Health and Nurtrition, Wageningen University, Wageningen, The Netherlands
- * E-mail:
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine - Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Roel G. Vink
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Nadia J. T. Roumans
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Edwin C. M. Mariman
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marleen A. van Baak
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Ilja C. W. Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Epidemiology, CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, The Netherlands
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3
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Zechendorf E, Vaßen P, Zhang J, Hallawa A, Martincuks A, Krenkel O, Müller-Newen G, Schuerholz T, Simon TP, Marx G, Ascheid G, Schmeink A, Dartmann G, Thiemermann C, Martin L. Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical- In silico Approach Combining In vitro Experiments and Machine Learning. Front Immunol 2018; 9:393. [PMID: 29616016 PMCID: PMC5869260 DOI: 10.3389/fimmu.2018.00393] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 02/12/2018] [Indexed: 11/21/2022] Open
Abstract
Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the underling causative factors are still debatable. Heparan sulfate (HS) fragments belong to the class of danger/damage-associated molecular patterns liberated from endothelial-bound proteoglycans by heparanase during tissue injury associated with trauma or sepsis. We hypothesized that HS induces apoptosis or necroptosis in murine cardiomyocytes. By using a novel Medical-In silico approach that combines conventional cell culture experiments with machine learning algorithms, we aimed to reduce a significant part of the expensive and time-consuming cell culture experiments and data generation by using computational intelligence (refinement and replacement). Cardiomyocytes exposed to HS showed an activation of the intrinsic apoptosis signal pathway via cytochrome C and the activation of caspase 3 (both p < 0.001). Notably, the exposure of HS resulted in the induction of necroptosis by tumor necrosis factor α and receptor interaction protein 3 (p < 0.05; p < 0.01) and, hence, an increased level of necrotic cardiomyocytes. In conclusion, using this novel Medical-In silico approach, our data suggest (i) that HS induces necroptosis in cardiomyocytes by phosphorylation (activation) of receptor-interacting protein 3, (ii) that HS is a therapeutic target in trauma- or sepsis-associated cardiomyopathy, and (iii) indicate that this proof-of-concept is a first step toward simulating the extent of activated components in the pro-apoptotic pathway induced by HS with only a small data set gained from the in vitro experiments by using machine learning algorithms.
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Affiliation(s)
- Elisabeth Zechendorf
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Phillip Vaßen
- Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Aachen, Germany
| | - Jieyi Zhang
- Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Aachen, Germany
| | - Ahmed Hallawa
- Chair for Integrated Signal Processing Systems, RWTH Aachen University, Aachen, Germany
| | - Antons Martincuks
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Aachen, Germany
| | - Oliver Krenkel
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany.,Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Gerhard Müller-Newen
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, Aachen, Germany
| | - Tobias Schuerholz
- Department of Anesthesia and Intensive Care, University Hospital Rostock, Rostock, Germany
| | - Tim-Philipp Simon
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Gernot Marx
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany
| | - Gerd Ascheid
- Chair for Integrated Signal Processing Systems, RWTH Aachen University, Aachen, Germany
| | - Anke Schmeink
- Research Area Information Theory and Systematic Design of Communication Systems, RWTH Aachen University, Aachen, Germany
| | - Guido Dartmann
- Research Area Distributed Systems, Trier University of Applied Sciences, Trier, Germany
| | - Christoph Thiemermann
- William Harvey Research Institute, Queen Mary University London, London, United Kingdom
| | - Lukas Martin
- Department of Intensive Care and Intermediate Care, University Hospital RWTH Aachen, Aachen, Germany.,William Harvey Research Institute, Queen Mary University London, London, United Kingdom
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4
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Kim S. Parameter Estimation Using Divide-and-Conquer Methods for Differential Equation Models. JOURNAL OF BIOMETRICS & BIOSTATISTICS 2016; 7. [PMID: 27489746 PMCID: PMC4968870 DOI: 10.4172/2155-6180.1000305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Seongho Kim
- Biostatistics Core, Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, USA
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5
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Nyman E, Rozendaal YJW, Helmlinger G, Hamrén B, Kjellsson MC, Strålfors P, van Riel NAW, Gennemark P, Cedersund G. Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes. Interface Focus 2016; 6:20150075. [PMID: 27051506 DOI: 10.1098/rsfs.2015.0075] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.
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Affiliation(s)
- Elin Nyman
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden
| | - Yvonne J W Rozendaal
- Department of Biomedical Engineering , Eindhoven University of Technology , Eindhoven , The Netherlands
| | - Gabriel Helmlinger
- Quantitative Clinical Pharmacology, AstraZeneca , Pharmaceuticals LP, Waltham, MA , USA
| | - Bengt Hamrén
- Quantitative Clinical Pharmacology , AstraZeneca , Gothenburg , Sweden
| | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences , Uppsala University , Uppsala , Sweden
| | - Peter Strålfors
- Department of Clinical and Experimental Medicine , Linköping University , Linköping , Sweden
| | - Natal A W van Riel
- Department of Biomedical Engineering , Eindhoven University of Technology , Eindhoven , The Netherlands
| | | | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
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6
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Kurata H, Maeda K, Matsuoka Y. Dynamic Modeling of Metabolic and Gene Regulatory Systems toward Developing Virtual Microbes. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2014. [DOI: 10.1252/jcej.13we152] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
- Biomedical Informatics R&D Center, Kyushu Institute of Technology
| | - Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
| | - Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
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7
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Streif S, Kim KKK, Rumschinski P, Kishida M, Shen DE, Findeisen R, Braatz RD. Robustness Analysis, Prediction and Estimation for Uncertain Biochemical Networks. ACTA ACUST UNITED AC 2013. [DOI: 10.3182/20131218-3-in-2045.00190] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Gu X, Reid D, Higham DJ, Gilbert D. Mathematical modelling of polyamine metabolism in bloodstream-form Trypanosoma brucei: an application to drug target identification. PLoS One 2013; 8:e53734. [PMID: 23372667 PMCID: PMC3553166 DOI: 10.1371/journal.pone.0053734] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Accepted: 12/04/2012] [Indexed: 11/23/2022] Open
Abstract
We present the first computational kinetic model of polyamine metabolism in bloodstream-form Trypanosoma brucei, the causative agent of human African trypanosomiasis. We systematically extracted the polyamine pathway from the complete metabolic network while still maintaining the predictive capability of the pathway. The kinetic model is constructed on the basis of information gleaned from the experimental biology literature and defined as a set of ordinary differential equations. We applied Michaelis-Menten kinetics featuring regulatory factors to describe enzymatic activities that are well defined. Uncharacterised enzyme kinetics were approximated and justified with available physiological properties of the system. Optimisation-based dynamic simulations were performed to train the model with experimental data and inconsistent predictions prompted an iterative procedure of model refinement. Good agreement between simulation results and measured data reported in various experimental conditions shows that the model has good applicability in spite of there being gaps in the required data. With this kinetic model, the relative importance of the individual pathway enzymes was assessed. We observed that, at low-to-moderate levels of inhibition, enzymes catalysing reactions of de novo AdoMet (MAT) and ornithine production (OrnPt) have more efficient inhibitory effect on total trypanothione content in comparison to other enzymes in the pathway. In our model, prozyme and TSHSyn (the production catalyst of total trypanothione) were also found to exhibit potent control on total trypanothione content but only when they were strongly inhibited. Different chemotherapeutic strategies against T. brucei were investigated using this model and interruption of polyamine synthesis via joint inhibition of MAT or OrnPt together with other polyamine enzymes was identified as an optimal therapeutic strategy.
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Affiliation(s)
- Xu Gu
- Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom.
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Chakrabarty A, Buzzard GT, Rundell AE. Model-based design of experiments for cellular processes. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2013; 5:181-203. [PMID: 23293047 DOI: 10.1002/wsbm.1204] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Ankush Chakrabarty
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
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10
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Maeda K, Minamida H, Yoshida K, Kurata H. Flux module decomposition for parameter estimation in a multiple-feedback loop model of biochemical networks. Bioprocess Biosyst Eng 2012; 36:333-44. [DOI: 10.1007/s00449-012-0789-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Accepted: 07/05/2012] [Indexed: 11/30/2022]
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Sridharan GV, Hassoun S, Lee K. Identification of biochemical network modules based on shortest retroactive distances. PLoS Comput Biol 2011; 7:e1002262. [PMID: 22102800 PMCID: PMC3213171 DOI: 10.1371/journal.pcbi.1002262] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2011] [Accepted: 09/21/2011] [Indexed: 12/21/2022] Open
Abstract
Modularity analysis offers a route to better understand the organization of cellular biochemical networks as well as to derive practically useful, simplified models of these complex systems. While there is general agreement regarding the qualitative properties of a biochemical module, there is no clear consensus on the quantitative criteria that may be used to systematically derive these modules. In this work, we investigate cyclical interactions as the defining characteristic of a biochemical module. We utilize a round trip distance metric, termed Shortest Retroactive Distance (ShReD), to characterize the retroactive connectivity between any two reactions in a biochemical network and to group together network components that mutually influence each other. We evaluate the metric on two types of networks that feature feedback interactions: (i) epidermal growth factor receptor (EGFR) signaling and (ii) liver metabolism supporting drug transformation. For both networks, the ShReD partitions found hierarchically arranged modules that confirm biological intuition. In addition, the partitions also revealed modules that are less intuitive. In particular, ShReD-based partition of the metabolic network identified a ‘redox’ module that couples reactions of glucose, pyruvate, lipid and drug metabolism through shared production and consumption of NADPH. Our results suggest that retroactive interactions arising from feedback loops and metabolic cycles significantly contribute to the modularity of biochemical networks. For metabolic networks, cofactors play an important role as allosteric effectors that mediate the retroactive interactions. Mathematical models are powerful tools to understand and predict the behavior of complex systems. However, the complexity presents many challenges in developing such models. In the case of a biological cell, a fully detailed and comprehensive model of a major function such as signaling and metabolism remains out of reach, due to the very large number of interdependent biochemical reactions that are required to carry out the function. In this regard, one practical approach is to develop simplified models that nevertheless preserve the essential features of the cell as a complex system by better understanding the chemical organization of the cell, or the layout of the biochemical network. In this work, we describe a computational method to systematically identify closely interacting groups of biochemical reactions by recognizing the modular hierarchy inherent in biochemical networks. We focus on cyclical interactions based on the rationale that reactions that mutually influence each other belong in the same group. We demonstrate our method on a signaling and metabolic network and show that the results confirm biological intuition as well as provide new insights into the coordination of biochemical pathways. Prospectively, our modularization method could be used to systematically derive simplified and practically useful models of complex biological networks.
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Affiliation(s)
- Gautham Vivek Sridharan
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts, United States of America
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America
| | - Kyongbum Lee
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts, United States of America
- * E-mail:
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12
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Ropers D, Baldazzi V, de Jong H. Model reduction using piecewise-linear approximations preserves dynamic properties of the carbon starvation response in Escherichia coli. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011; 8:166-181. [PMID: 21071805 DOI: 10.1109/tcbb.2009.49] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The adaptation of the bacterium Escherichia coli to carbon starvation is controlled by a large network of biochemical reactions involving genes, mRNAs, proteins, and signalling molecules. The dynamics of these networks is difficult to analyze, notably due to a lack of quantitative information on parameter values. To overcome these limitations, model reduction approaches based on quasi-steady-state (QSS) and piecewise-linear (PL) approximations have been proposed, resulting in models that are easier to handle mathematically and computationally. These approximations are not supposed to affect the capability of the model to account for essential dynamical properties of the system, but the validity of this assumption has not been systematically tested. In this paper, we carry out such a study by evaluating a large and complex PL model of the carbon starvation response in E. coli using an ensemble approach. The results show that, in comparison with conventional nonlinear models, the PL approximations generally preserve the dynamics of the carbon starvation response network, although with some deviations concerning notably the quantitative precision of the model predictions. This encourages the application of PL models to the qualitative analysis of bacterial regulatory networks, in situations where the reference time scale is that of protein synthesis and degradation.
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13
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An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks. Bioprocess Biosyst Eng 2010; 34:433-46. [DOI: 10.1007/s00449-010-0486-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2010] [Accepted: 11/04/2010] [Indexed: 11/27/2022]
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14
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Yan H, Zhang B, Li S, Zhao Q. A formal model for analyzing drug combination effects and its application in TNF-alpha-induced NFkappaB pathway. BMC SYSTEMS BIOLOGY 2010; 4:50. [PMID: 20416113 PMCID: PMC2873319 DOI: 10.1186/1752-0509-4-50] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2009] [Accepted: 04/25/2010] [Indexed: 01/29/2023]
Abstract
Background Drug combination therapy is commonly used in clinical practice. Many methods including Bliss independence method have been proposed for drug combination design based on simulations models or experiments. Although Bliss independence method can help to solve the drug combination design problem when there are only a small number of combinations, as the number of combinations increases, it may not be scalable. Exploration of system structure becomes important to reduce the complexity of the design problem. Results In this paper, we deduced a mathematical model which can simplify the serial structure and parallel structure of biological pathway for synergy evaluation of drug combinations. We demonstrated in steady state the sign of the synergism assessment factor derivative of the original system can be predicted by the sign of its simplified system. In addition, we analyzed the influence of feedback structure on survival ratio of the serial structure. We provided a sufficient condition under which the combination effect could be maintained. Furthermore, we applied our method to find three synergistic drug combinations on tumor necrosis factor α-induced NFκB pathway and subsequently verified by the cell experiment. Conclusions We identified several structural properties underlying the Bliss independence criterion, and developed a systematic simplification framework for drug combiation desgin by combining simulation and system reaction network topology analysis. We hope that this work can provide insights to tackle the challenging problem of assessment of combinational drug therapy effect in a large scale signaling pathway. And hopefully in the future our method could be expanded to more general criteria.
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Affiliation(s)
- Han Yan
- Department of Automation and TNList, Tsinghua University, Beijing, 100084, China
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15
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Daigle BJ, Srinivasan BS, Flannick JA, Novak AF, Batzoglou S. Current Progress in Static and Dynamic Modeling of Biological Networks. SYSTEMS BIOLOGY FOR SIGNALING NETWORKS 2010. [DOI: 10.1007/978-1-4419-5797-9_2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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16
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Porreca R, Drulhe S, de Jong H, Ferrari-Trecate G. Structural identification of piecewise-linear models of genetic regulatory networks. J Comput Biol 2009; 15:1365-80. [PMID: 19040369 DOI: 10.1089/cmb.2008.0109] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
We present a method for the structural identification of genetic regulatory networks (GRNs), based on the use of a class of Piecewise-Linear (PL) models. These models consist of a set of decoupled linear models describing the different modes of operation of the GRN and discrete switches between the modes accounting for the nonlinear character of gene regulation. They thus form a compromise between the mathematical simplicity of linear models and the biological expressiveness of nonlinear models. The input of the PL identification method consists of time-series measurements of concentrations of gene products. As output it produces estimates of the modes of operation of the GRN, as well as all possible minimal combinations of threshold concentrations of the gene products accounting for switches between the modes of operation. The applicability of the PL identification method has been evaluated using simulated data obtained from a model of the carbon starvation response in the bacterium Escherichia coli. This has allowed us to systematically test the performance of the method under different data characteristics, notably variations in the noise level and the sampling density.
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Affiliation(s)
- Riccardo Porreca
- Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy
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17
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Zhang Y, Shankaran H, Opresko L, Resat H. System theoretical investigation of human epidermal growth factor receptor-mediated signalling. IET Syst Biol 2009; 2:273-84. [PMID: 19045822 DOI: 10.1049/iet-syb:20080116] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The partitioning of biological networks into coupled-functional modules is being increasingly applied for developing predictive models of biological systems. This approach has the advantage that predicting a system-level response does not require a mechanistic description of the internal dynamics of each module. Identification of the input-output characteristics of the network modules and the connectivity between the modules provide the necessary quantitative representation of system dynamics. However, the determination of the input-output relationships of the modules is not trivial; it requires the controlled perturbation of module inputs and systematic analysis of experimental data. In this report, the authors apply a system theoretical analysis approach to derive the time-dependent input-output relationships of the functional module for the human epidermal growth factor receptor (HER) mediated Erk and Akt signalling pathways. Using a library of cell lines expressing endogenous levels of epidermal growth factor receptor (EGFR) and varying levels of HER2, the authors show that a transfer function-based representation can be successfully applied to quantitatively characterise information transfer in this system.
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Affiliation(s)
- Y Zhang
- Pacific Northwest National Laboratory, Computational Biology and Bioinformatics Group, Richland, WA 99352, USA
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18
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Vries D, Verheijen PJ, den Dekker AJ. Identification. Hybrid system modeling and identification of cell biology systems: perspectives and challenges. ACTA ACUST UNITED AC 2009. [DOI: 10.3182/20090706-3-fr-2004.00038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Sheppard AR, Swicord ML, Balzano Q. Quantitative evaluations of mechanisms of radiofrequency interactions with biological molecules and processes. HEALTH PHYSICS 2008; 95:365-396. [PMID: 18784511 DOI: 10.1097/01.hp.0000319903.20660.37] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The complexity of interactions of electromagnetic fields up to 10(12) Hz with the ions, atoms, and molecules of biological systems has given rise to a large number of established and proposed biophysical mechanisms applicable over a wide range of time and distance scales, field amplitudes, frequencies, and waveforms. This review focuses on the physical principles that guide quantitative assessment of mechanisms applicable for exposures at or below the level of endogenous electric fields associated with development, wound healing, and excitation of muscles and the nervous system (generally, 1 to 10(2) V m(-1)), with emphasis on conditions where temperature increases are insignificant (<<1 K). Experiment and theory demonstrate possible demodulation at membrane barriers for frequencies < or =10 MHz, but not at higher frequencies. Although signal levels somewhat below system noise can be detected, signal-to-noise ratios substantially less than 0.1 cannot be overcome by cooperativity, signal averaging, coherent detection, or by nonlinear dynamical systems. Sensory systems and possible effects on biological magnetite suggest paradigms for extreme sensitivity at lower frequencies, but there are no known radiofrequency (RF) analogues. At the molecular level, vibrational modes are so overdamped by water molecules that excitation of molecular modes below the far infrared cannot occur. Two RF mechanisms plausibly may affect biological matter under common exposure conditions. For frequencies below approximately 150 MHz, shifts in the rate of chemical reactions can be mediated by radical pairs and, at all frequencies, dielectric and resistive heating can raise temperature and increase the entropy of the affected biological system.
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Min Lee J, Gianchandani EP, Eddy JA, Papin JA. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol 2008; 4:e1000086. [PMID: 18483615 PMCID: PMC2377155 DOI: 10.1371/journal.pcbi.1000086] [Citation(s) in RCA: 156] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2007] [Accepted: 04/15/2008] [Indexed: 01/30/2023] Open
Abstract
Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)–based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for “fast” reactions and incorporates “slow” reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporating kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here. Cellular systems comprise many diverse components and component interactions spanning signal transduction, transcriptional regulation, and metabolism. Although signaling, metabolic, and regulatory activities are often investigated independently of one another, there is growing evidence that considerable interplay occurs among them, and that the malfunctioning of this interplay is associated with disease. The computational analysis of integrated networks has been challenging because of the varying time scales involved as well as the sheer magnitude of such systems (e.g., the numbers of rate constants involved). To this end, we developed a novel computational framework called integrated dynamic flux balance analysis (idFBA) that generates quantitative, dynamic predictions of species concentrations spanning signaling, regulatory, and metabolic processes. idFBA extends an existing approach called flux balance analysis (FBA) in that it couples “fast” and “slow” reactions, thereby facilitating the study of whole-cell phenotypes and not just sub-cellular network properties. We applied this framework to a prototypic integrated system derived from literature as well as a representative integrated yeast module (the high-osmolarity glycerol [HOG] pathway) and generated time-course predictions that matched with available experimental data. By extending this framework to larger-scale systems, phenotypic profiles of whole-cell systems could be attained expeditiously.
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Affiliation(s)
- Jong Min Lee
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, United States of America
| | - Erwin P. Gianchandani
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, United States of America
| | - James A. Eddy
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, United States of America
- * E-mail:
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Quach M, Brunel N, d'Alché-Buc F. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics 2007; 23:3209-16. [PMID: 18042557 DOI: 10.1093/bioinformatics/btm510] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Minh Quach
- IBISC FRE CNRS 2873, University of Evry and Genopole 523, place des terrasses 91025 Evry, France
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22
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Luan D, Zai M, Varner JD. Computationally derived points of fragility of a human cascade are consistent with current therapeutic strategies. PLoS Comput Biol 2007; 3:e142. [PMID: 17658944 PMCID: PMC1924874 DOI: 10.1371/journal.pcbi.0030142] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2007] [Accepted: 06/05/2007] [Indexed: 01/29/2023] Open
Abstract
The role that mechanistic mathematical modeling and systems biology will play in molecular medicine and clinical development remains uncertain. In this study, mathematical modeling and sensitivity analysis were used to explore the working hypothesis that mechanistic models of human cascades, despite model uncertainty, can be computationally screened for points of fragility, and that these sensitive mechanisms could serve as therapeutic targets. We tested our working hypothesis by screening a model of the well-studied coagulation cascade, developed and validated from literature. The predicted sensitive mechanisms were then compared with the treatment literature. The model, composed of 92 proteins and 148 protein-protein interactions, was validated using 21 published datasets generated from two different quiescent in vitro coagulation models. Simulated platelet activation and thrombin generation profiles in the presence and absence of natural anticoagulants were consistent with measured values, with a mean correlation of 0.87 across all trials. Overall state sensitivity coefficients, which measure the robustness or fragility of a given mechanism, were calculated using a Monte Carlo strategy. In the absence of anticoagulants, fluid and surface phase factor X/activated factor X (fX/FXa) activity and thrombin-mediated platelet activation were found to be fragile, while fIX/FIXa and fVIII/FVIIIa activation and activity were robust. Both anti-fX/FXa and direct thrombin inhibitors are important classes of anticoagulants; for example, anti-fX/FXa inhibitors have FDA approval for the prevention of venous thromboembolism following surgical intervention and as an initial treatment for deep venous thrombosis and pulmonary embolism. Both in vitro and in vivo experimental evidence is reviewed supporting the prediction that fIX/FIXa activity is robust. When taken together, these results support our working hypothesis that computationally derived points of fragility of human relevant cascades could be used as a rational basis for target selection despite model uncertainty.
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Affiliation(s)
- Deyan Luan
- Department of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Michael Zai
- Department of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
| | - Jeffrey D Varner
- Department of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America
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Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006; 8:1195-203. [PMID: 17060902 DOI: 10.1038/ncb1497] [Citation(s) in RCA: 376] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Physicochemical modelling of signal transduction links fundamental chemical and physical principles, prior knowledge about regulatory pathways, and experimental data of various types to create powerful tools for formalizing and extending traditional molecular and cellular biology.
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Affiliation(s)
- Bree B Aldridge
- Center for Cell Decision Processes, Department Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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