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Paalvast Y, Zhou E, Rozendaal YJW, Wang Y, Gerding A, van Dijk TH, de Boer JF, Rensen PCN, van Dijk KW, Kuivenhoven JA, Bakker BM, van Riel NAW, Groen AK. A Systems Analysis of Phenotype Heterogeneity in APOE*3Leiden.CETP Mice Induced by Long-Term High-Fat High-Cholesterol Diet Feeding. Nutrients 2022; 14:nu14224936. [PMID: 36432620 PMCID: PMC9698005 DOI: 10.3390/nu14224936] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/23/2022] Open
Abstract
Within the human population, considerable variability exists between individuals in their susceptibility to develop obesity and dyslipidemia. In humans, this is thought to be caused by both genetic and environmental variation. APOE*3-Leiden.CETP mice, as part of an inbred mouse model in which mice develop the metabolic syndrome upon being fed a high-fat high-cholesterol diet, show large inter-individual variation in the parameters of the metabolic syndrome, despite a lack of genetic and environmental variation. In the present study, we set out to resolve what mechanisms could underlie this variation. We used measurements of glucose and lipid metabolism from a six-month longitudinal study on the development of the metabolic syndrome. Mice were classified as mice with either high plasma triglyceride (responders) or low plasma triglyceride (non-responders) at the baseline. Subsequently, we fitted the data to a dynamic computational model of whole-body glucose and lipid metabolism (MINGLeD) by making use of a hybrid modelling method called Adaptations in Parameter Trajectories (ADAPT). ADAPT integrates longitudinal data, and predicts how the parameters of the model must change through time in order to comply with the data and model constraints. To explain the phenotypic variation in plasma triglycerides, the ADAPT analysis suggested a decreased cholesterol absorption, higher energy expenditure and increased fecal fatty acid excretion in non-responders. While decreased cholesterol absorption and higher energy expenditure could not be confirmed, the experimental validation demonstrated that the non-responders were indeed characterized by increased fecal fatty acid excretion. Furthermore, the amount of fatty acids excreted strongly correlated with bile acid excretion, in particular deoxycholate. Since bile acids play an important role in the solubilization of lipids in the intestine, these results suggest that variation in bile acid homeostasis may in part drive the phenotypic variation in the APOE*3-Leiden.CETP mice.
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Affiliation(s)
- Yared Paalvast
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - Enchen Zhou
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Yvonne J. W. Rozendaal
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Yanan Wang
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Albert Gerding
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - Theo H. van Dijk
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - Jan Freark de Boer
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - Patrick C. N. Rensen
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Jan A. Kuivenhoven
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - Barbara M. Bakker
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Laboratory of Experimental Vascular Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef, 1105 AZ Amsterdam, The Netherlands
| | - Albert K. Groen
- Department of Pediatrics, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
- Laboratory of Experimental Vascular Medicine, University of Amsterdam, Amsterdam UMC, Meibergdreef, 1105 AZ Amsterdam, The Netherlands
- Correspondence:
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2
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van Riel NAW, Tiemann CA, Hilbers PAJ, Groen AK. Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis. Front Bioeng Biotechnol 2021; 8:536957. [PMID: 33665185 PMCID: PMC7921164 DOI: 10.3389/fbioe.2020.536957] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 12/16/2020] [Indexed: 11/23/2022] Open
Abstract
Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.
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Affiliation(s)
- Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Department of Vascular Medicine, Amsterdam UMC, Amsterdam, Netherlands.,Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | - Christian A Tiemann
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Peter A J Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Albert K Groen
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, Netherlands.,Department of Laboratory Medicine, University Medical Center Groningen, Groningen, Netherlands
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3
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Kinetic analysis via mathematical modeling for ferrous iron oxidation in a class of SBR-type system. Chin J Chem Eng 2019. [DOI: 10.1016/j.cjche.2019.04.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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4
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Rozendaal YJW, Wang Y, Hilbers PAJ, van Riel NAW. Computational modelling of energy balance in individuals with Metabolic Syndrome. BMC SYSTEMS BIOLOGY 2019; 13:24. [PMID: 30808366 PMCID: PMC6390597 DOI: 10.1186/s12918-019-0705-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 02/14/2019] [Indexed: 02/07/2023]
Abstract
Background A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations. Results We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified. In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment. Conclusion The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure – for instance by cold exposure to activate brown adipose tissue (BAT) – could resolve MetS symptoms. Electronic supplementary material The online version of this article (10.1186/s12918-019-0705-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yvonne J W Rozendaal
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Yanan Wang
- Department of Pediatrics, Section Molecular Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A J Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. .,Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.
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5
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Álvarez-Buylla Roces ME, Martínez-García JC, Dávila-Velderrain J, Domínguez-Hüttinger E, Martínez-Sánchez ME. Medical Systems Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1069:1-33. [PMID: 30076565 DOI: 10.1007/978-3-319-89354-9_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.
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Abstract
Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.
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Rozendaal YJW, Wang Y, Paalvast Y, Tambyrajah LL, Li Z, Willems van Dijk K, Rensen PCN, Kuivenhoven JA, Groen AK, Hilbers PAJ, van Riel NAW. In vivo and in silico dynamics of the development of Metabolic Syndrome. PLoS Comput Biol 2018; 14:e1006145. [PMID: 29879115 PMCID: PMC5991635 DOI: 10.1371/journal.pcbi.1006145] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 04/13/2018] [Indexed: 12/16/2022] Open
Abstract
The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.
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Affiliation(s)
- Yvonne J. W. Rozendaal
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Yanan Wang
- Department of Pediatrics, Section Molecular Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Yared Paalvast
- Department of Pediatrics, Section Molecular Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Lauren L. Tambyrajah
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zhuang Li
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ko Willems van Dijk
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Patrick C. N. Rensen
- Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan A. Kuivenhoven
- Department of Pediatrics, Section Molecular Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Albert K. Groen
- Department of Pediatrics, Section Molecular Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Amsterdam Diabetes Center, Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter A. J. Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Amsterdam Diabetes Center, Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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8
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Oguz C, Watson LT, Baumann WT, Tyson JJ. Predicting network modules of cell cycle regulators using relative protein abundance statistics. BMC SYSTEMS BIOLOGY 2017; 11:30. [PMID: 28241833 PMCID: PMC5329933 DOI: 10.1186/s12918-017-0409-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Accepted: 02/17/2017] [Indexed: 11/10/2022]
Abstract
BACKGROUND Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of "feasible" parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network. RESULTS Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in "regulation of cell size" and "regulation of G1/S transition" contribute most to predictive variability, whereas proteins involved in "positive regulation of transcription involved in exit from mitosis," "mitotic spindle assembly checkpoint" and "negative regulation of cyclin-dependent protein kinase by cyclin degradation" contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50. CONCLUSIONS By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.
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Affiliation(s)
- Cihan Oguz
- Department of Biological Sciences, Virginia Tech, Blacksburg VA, 24061, USA.
| | - Layne T Watson
- Department of Computer Science, Virginia Tech, Blacksburg VA, 24061, USA.,Department of Mathematics, Virginia Tech, Blacksburg VA, 24061, USA.,Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg VA, 24061, USA
| | - William T Baumann
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg VA, 24061, USA
| | - John J Tyson
- Department of Biological Sciences, Virginia Tech, Blacksburg VA, 24061, USA
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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: 2.3] [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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10
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Evaluating computational models of cholesterol metabolism. Biochim Biophys Acta Mol Cell Biol Lipids 2015; 1851:1360-76. [DOI: 10.1016/j.bbalip.2015.05.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 05/08/2015] [Accepted: 05/26/2015] [Indexed: 02/02/2023]
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11
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Haanstra JR, Bakker BM. Drug target identification through systems biology. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 15:17-22. [PMID: 26464086 DOI: 10.1016/j.ddtec.2015.06.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Revised: 05/18/2015] [Accepted: 06/12/2015] [Indexed: 06/05/2023]
Abstract
To rationalise drug target selection, we should understand the role of putative targets in biological pathways quantitatively. We review here how experimental and computational network-based approaches can aid more rational drug target selection and illustrate this with results obtained for microbes and for cancer. Comparison of the drug response of biochemical networks in target cells and (healthy) host cells can reveal network-selective targets.
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Affiliation(s)
- Jurgen R Haanstra
- Department of Pediatrics and Systems Biology Centre for Energy Metabolism and Ageing, University of Groningen, University Medical Center Groningen, The Netherlands; Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, The Netherlands.
| | - Barbara M Bakker
- Department of Pediatrics and Systems Biology Centre for Energy Metabolism and Ageing, University of Groningen, University Medical Center Groningen, The Netherlands; Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, The Netherlands
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12
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Hijmans BS, Tiemann CA, Grefhorst A, Boesjes M, van Dijk TH, Tietge UJF, Kuipers F, van Riel NAW, Groen AK, Oosterveer MH. A systems biology approach reveals the physiological origin of hepatic steatosis induced by liver X receptor activation. FASEB J 2014; 29:1153-64. [PMID: 25477282 DOI: 10.1096/fj.14-254656] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 11/05/2014] [Indexed: 12/12/2022]
Abstract
Liver X receptor (LXR) agonists exert potent antiatherosclerotic actions but simultaneously induce excessive triglyceride (TG) accumulation in the liver. To obtain a detailed insight into the underlying mechanism of hepatic TG accumulation, we used a novel computational modeling approach called analysis of dynamic adaptations in parameter trajectories (ADAPT). We revealed that both input and output fluxes to hepatic TG content are considerably induced on LXR activation and that in the early phase of LXR agonism, hepatic steatosis results from only a minor imbalance between the two. It is generally believed that LXR-induced hepatic steatosis results from increased de novo lipogenesis (DNL). In contrast, ADAPT predicted that the hepatic influx of free fatty acids is the major contributor to hepatic TG accumulation in the early phase of LXR activation. Qualitative validation of this prediction showed a 5-fold increase in the contribution of plasma palmitate to hepatic monounsaturated fatty acids on acute LXR activation, whereas DNL was not yet significantly increased. This study illustrates that complex effects of pharmacological intervention can be translated into distinct patterns of metabolic regulation through state-of-the-art mathematical modeling.
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Affiliation(s)
- Brenda S Hijmans
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Christian A Tiemann
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Aldo Grefhorst
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Marije Boesjes
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Theo H van Dijk
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Uwe J F Tietge
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Folkert Kuipers
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Natal A W van Riel
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Albert K Groen
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
| | - Maaike H Oosterveer
- Departments of *Pediatrics and Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; and Groningen Centre for Systems Biology, University of Groningen, Groningen, The Netherlands
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13
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Sips FLP, Tiemann CA, Oosterveer MH, Groen AK, Hilbers PAJ, van Riel NAW. A computational model for the analysis of lipoprotein distributions in the mouse: translating FPLC profiles to lipoprotein metabolism. PLoS Comput Biol 2014; 10:e1003579. [PMID: 24784354 PMCID: PMC4006703 DOI: 10.1371/journal.pcbi.1003579] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Accepted: 03/11/2014] [Indexed: 12/27/2022] Open
Abstract
Disturbances of lipoprotein metabolism are recognized as indicators of cardiometabolic disease risk. Lipoprotein size and composition, measured in a lipoprotein profile, are considered to be disease risk markers. However, the measured profile is a collective result of complex metabolic interactions, which complicates the identification of changes in metabolism. In this study we aim to develop a method which quantitatively relates murine lipoprotein size, composition and concentration to the molecular mechanisms underlying lipoprotein metabolism. We introduce a computational framework which incorporates a novel kinetic model of murine lipoprotein metabolism. The model is applied to compute a distribution of plasma lipoproteins, which is then related to experimental lipoprotein profiles through the generation of an in silico lipoprotein profile. The model was first applied to profiles obtained from wild-type C57Bl/6J mice. The results provided insight into the interplay of lipoprotein production, remodelling and catabolism. Moreover, the concentration and metabolism of unmeasured lipoprotein components could be determined. The model was validated through the prediction of lipoprotein profiles of several transgenic mouse models commonly used in cardiovascular research. Finally, the framework was employed for longitudinal analysis of the profiles of C57Bl/6J mice following a pharmaceutical intervention with a liver X receptor (LXR) agonist. The multifaceted regulatory response to the administration of the compound is incompletely understood. The results explain the characteristic changes of the observed lipoprotein profile in terms of the underlying metabolic perturbation and resultant modifications of lipid fluxes in the body. The Murine Lipoprotein Profiler (MuLiP) presented here is thus a valuable tool to assess the metabolic origin of altered murine lipoprotein profiles and can be applied in preclinical research performed in mice for analysis of lipid fluxes and lipoprotein composition.
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Affiliation(s)
- Fianne L P Sips
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
| | - Christian A Tiemann
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
| | - Maaike H Oosterveer
- Department of Pediatrics, University Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Albert K Groen
- Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands; Department of Pediatrics, University Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Laboratory Medicine, University Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Peter A J Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
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14
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Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW. Optimal experiment design for model selection in biochemical networks. BMC SYSTEMS BIOLOGY 2014; 8:20. [PMID: 24555498 PMCID: PMC3946009 DOI: 10.1186/1752-0509-8-20] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Accepted: 02/13/2014] [Indexed: 01/06/2023]
Abstract
Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
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Affiliation(s)
- Joep Vanlier
- Eindhoven University of Technology, Department of Biomedical Engineering, PO Box 513, Eindhoven, 5600 MB, The Netherlands.
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15
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Parameter trajectory analysis to identify treatment effects of pharmacological interventions. PLoS Comput Biol 2013; 9:e1003166. [PMID: 23935478 PMCID: PMC3731221 DOI: 10.1371/journal.pcbi.1003166] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2012] [Accepted: 06/18/2013] [Indexed: 11/29/2022] Open
Abstract
The field of medical systems biology aims to advance understanding of molecular mechanisms that drive disease progression and to translate this knowledge into therapies to effectively treat diseases. A challenging task is the investigation of long-term effects of a (pharmacological) treatment, to establish its applicability and to identify potential side effects. We present a new modeling approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT), to analyze the long-term effects of a pharmacological intervention. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of molecular adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The trajectories provide insight in the affected underlying biological systems and identify the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects caused by interactions with the proteome and transcriptome levels, which are often less well understood, can be captured by the time-dependent descriptions of the parameters. ADAPT was employed to identify metabolic adaptations induced upon pharmacological activation of the liver X receptor (LXR), a potential drug target to treat or prevent atherosclerosis. The trajectories were investigated to study the cascade of adaptations. This provided a counter-intuitive insight concerning the function of scavenger receptor class B1 (SR-B1), a receptor that facilitates the hepatic uptake of cholesterol. Although activation of LXR promotes cholesterol efflux and -excretion, our computational analysis showed that the hepatic capacity to clear cholesterol was reduced upon prolonged treatment. This prediction was confirmed experimentally by immunoblotting measurements of SR-B1 in hepatic membranes. Next to the identification of potential unwanted side effects, we demonstrate how ADAPT can be used to design new target interventions to prevent these. A driving ambition of medical systems biology is to advance our understanding of molecular processes that drive the progression of complex diseases such as Type 2 Diabetes and cardiovascular disease. This insight is essential to enable the development of therapies to effectively treat diseases. A challenging task is to investigate the long-term effects of a treatment, in order to establish its applicability and to identify potential side effects. As such, there is a growing need for novel approaches to support this research. Here, we present a new computational approach to identify treatment effects. We make use of a computational model of the biological system. The model is used to describe the experimental data obtained during different stages of the treatment. To incorporate the long-term/progressive adaptations in the system, induced by changes in gene and protein expression, the model is iteratively updated. The approach was employed to identify metabolic adaptations induced by a potential anti-atherosclerotic and anti-diabetic drug target. Our approach identifies the molecular events that should be studied in more detail to establish the mechanistic basis of treatment outcome. New biological insight was obtained concerning the metabolism of cholesterol, which was in turn experimentally validated.
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16
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van der Wulp MYM, Verkade HJ, Groen AK. Regulation of cholesterol homeostasis. Mol Cell Endocrinol 2013; 368:1-16. [PMID: 22721653 DOI: 10.1016/j.mce.2012.06.007] [Citation(s) in RCA: 129] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Revised: 05/17/2012] [Accepted: 06/11/2012] [Indexed: 12/28/2022]
Abstract
Hypercholesterolemia is an important risk factor for cardiovascular disease. It is caused by a disturbed balance between cholesterol secretion into the blood versus uptake. The pathways involved are regulated via a complex interplay of enzymes, transport proteins, transcription factors and non-coding RNA's. The last two decades insight into underlying mechanisms has increased vastly but there are still a lot of unknowns, particularly regarding intracellular cholesterol transport. After decades of concentration on the liver, in recent years the intestine has come into focus as an important control point in cholesterol homeostasis. This review will discuss current knowledge of cholesterol physiology, with emphasis on cholesterol absorption, cholesterol synthesis and fecal excretion, and new (possible) therapeutic options for hypercholesterolemia.
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17
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van Riel NAW, Tiemann CA, Vanlier J, Hilbers PAJ. Applications of analysis of dynamic adaptations in parameter trajectories. Interface Focus 2013; 3:20120084. [PMID: 23853705 PMCID: PMC3638482 DOI: 10.1098/rsfs.2012.0084] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Metabolic profiling in combination with pathway-based analyses and computational modelling are becoming increasingly important in clinical and preclinical research. Modelling multi-factorial, progressive diseases requires the integration of molecular data at the metabolome, proteome and transcriptome levels. Also the dynamic interaction of organs and tissues needs to be considered. The processes involved cover time scales that are several orders of magnitude different. We report applications of a computational approach to bridge the scales and different levels of biological detail. Analysis of dynamic adaptations in parameter trajectories (ADAPTs) aims to investigate phenotype transitions during disease development and after a therapeutic intervention. ADAPT is based on a time-dependent evolution of model parameters to describe the dynamics of metabolic adaptations. The progression of metabolic adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages. To get a better understanding of the concept, the ADAPT approach is illustrated in a theoretical study. Its application in research on progressive changes in lipoprotein metabolism is also discussed.
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Affiliation(s)
- Natal A W van Riel
- Department of Biomedical Engineering , Eindhoven University of Technology , Den Dolech 2, Eindhoven, 5612 AZ , The Netherlands ; Institute for Complex Molecular Systems , Eindhoven University of Technology , Den Dolech 2, Eindhoven, 5612 AZ , The Netherlands ; Netherlands Consortium for Systems Biology , University of Amsterdam , Science Park 904, Amsterdam, 1098 XH , The Netherlands
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18
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Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW. Parameter uncertainty in biochemical models described by ordinary differential equations. Math Biosci 2013; 246:305-14. [PMID: 23535194 DOI: 10.1016/j.mbs.2013.03.006] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Revised: 03/07/2013] [Accepted: 03/12/2013] [Indexed: 12/21/2022]
Abstract
Improved mechanistic understanding of biochemical networks is one of the driving ambitions of Systems Biology. Computational modeling allows the integration of various sources of experimental data in order to put this conceptual understanding to the test in a quantitative manner. The aim of computational modeling is to obtain both predictive as well as explanatory models for complex phenomena, hereby providing useful approximations of reality with varying levels of detail. As the complexity required to describe different system increases, so does the need for determining how well such predictions can be made. Despite efforts to make tools for uncertainty analysis available to the field, these methods have not yet found widespread use in the field of Systems Biology. Additionally, the suitability of the different methods strongly depends on the problem and system under investigation. This review provides an introduction to some of the techniques available as well as gives an overview of the state-of-the-art methods for parameter uncertainty analysis.
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Affiliation(s)
- J Vanlier
- Eindhoven University of Technology, Department of Biomedical Engineering, Eindhoven, The Netherlands; Netherlands Consortium for Systems Biology, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands.
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Randolph-Gips M, Srinivasan P. Modeling autism: a systems biology approach. J Clin Bioinforma 2012; 2:17. [PMID: 23043674 PMCID: PMC3507704 DOI: 10.1186/2043-9113-2-17] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2012] [Accepted: 08/09/2012] [Indexed: 12/13/2022] Open
Abstract
Autism is the fastest growing developmental disorder in the world today. The prevalence of autism in the US has risen from 1 in 2500 in 1970 to 1 in 88 children today. People with autism present with repetitive movements and with social and communication impairments. These impairments can range from mild to profound. The estimated total lifetime societal cost of caring for one individual with autism is $3.2 million US dollars. With the rapid growth in this disorder and the great expense of caring for those with autism, it is imperative for both individuals and society that techniques be developed to model and understand autism. There is increasing evidence that those individuals diagnosed with autism present with highly diverse set of abnormalities affecting multiple systems of the body. To this date, little to no work has been done using a whole body systems biology approach to model the characteristics of this disorder. Identification and modelling of these systems might lead to new and improved treatment protocols, better diagnosis and treatment of the affected systems, which might lead to improved quality of life by themselves, and, in addition, might also help the core symptoms of autism due to the potential interconnections between the brain and nervous system with all these other systems being modeled. This paper first reviews research which shows that autism impacts many systems in the body, including the metabolic, mitochondrial, immunological, gastrointestinal and the neurological. These systems interact in complex and highly interdependent ways. Many of these disturbances have effects in most of the systems of the body. In particular, clinical evidence exists for increased oxidative stress, inflammation, and immune and mitochondrial dysfunction which can affect almost every cell in the body. Three promising research areas are discussed, hierarchical, subgroup analysis and modeling over time. This paper reviews some of the systems disturbed in autism and suggests several systems biology research areas. Autism poses a rich test bed for systems biology modeling techniques.
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Affiliation(s)
- Mary Randolph-Gips
- Systems Engineering and Computer Engineering, University of Houston - Clear Lake, 2700 Bay Area Bvd, Houston, TX, 77058, USA.
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van de Pas NCA, Woutersen RA, van Ommen B, Rietjens IMCM, de Graaf AA. A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans. J Lipid Res 2012; 53:2734-46. [PMID: 23024287 DOI: 10.1194/jlr.m031930] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Increased plasma cholesterol concentration is associated with increased risk of cardiovascular disease. This study describes the development, validation, and analysis of a physiologically based kinetic (PBK) model for the prediction of plasma cholesterol concentrations in humans. This model was directly adapted from a PBK model for mice by incorporation of the reaction catalyzed by cholesterol ester transfer protein and contained 21 biochemical reactions and eight different cholesterol pools. The model was calibrated using published data for humans and validated by comparing model predictions on plasma cholesterol levels of subjects with 10 different genetic mutations (including familial hypercholesterolemia and Smith-Lemli-Opitz syndrome) with experimental data. Average model predictions on total cholesterol were accurate within 36% of the experimental data, which was within the experimental margin. Sensitivity analysis of the model indicated that the HDL cholesterol (HDL-C) concentration was mainly dependent on hepatic transport of cholesterol to HDL, cholesterol ester transfer from HDL to non-HDL, and hepatic uptake of cholesterol from non-HDL-C. Thus, the presented PBK model is a valid tool to predict the effect of genetic mutations on cholesterol concentrations, opening the way for future studies on the effect of different drugs on cholesterol levels in various subpopulations in silico.
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Affiliation(s)
- Niek C A van de Pas
- The Netherlands Organization for Applied Scientific Research, 3700 AJ Zeist, The Netherlands
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Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW. A Bayesian approach to targeted experiment design. Bioinformatics 2012; 28:1136-42. [PMID: 22368245 PMCID: PMC3324513 DOI: 10.1093/bioinformatics/bts092] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Motivation: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity. Results: We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions. Availability and implementation: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html Contact:j.vanlier@tue.nl; N.A.W.v.Riel@tue.nl Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- J Vanlier
- Department of BioMedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
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