Dataset Information

Benson2017 - Systems Pharmacology Multidrug (cholesterol biosynthesis pathway)

ABSTRACT: BensonWattersonetal_SystemsPharmacology_Multidrug This model is described in the article: Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway. Benson H, Watterson S, Sharman J, Mpamhanga C, Parton A, Southan C, Harmar A, Ghazal P. Br. J. Pharmacol. 2017 Sep; : Abstract: An ever-growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single-drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimisation to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi-drug treatments with high efficacy and minimal off-target effects.We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology work-flow for the refinement of data and the generation of future therapeutic hypotheses. This model is hosted on BioModels Database and identified by: MODEL1506220000. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Steven Watterson

PROVIDER: MODEL1506220000 | BioModels | 2017-09-26

REPOSITORIES: BioModels

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Publications

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway.

Benson Helen E HE Watterson Steven S Sharman Joanna L JL Mpamhanga Chido P CP Parton Andrew A Southan Christopher C Harmar Anthony J AJ Ghazal Peter P

British journal of pharmacology 20171126 23

<h4>Background and purpose</h4>An ever-growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single-drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis ...[more]

PMID: 28910500

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Project description:Model of cholesterol regulation (with Boolean Formulae) (2008) This model is described in Dynamical modeling of the cholesterol regulatory pathway with Boolean networks ; Gwenael Kervizic and Laurent Corcos, BMC Systems Biology 2008, 2:99 doi: 10.1186/1752-0509-2-99 Abstract: Background: Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now. Results: We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs), as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway. Conclusion: We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico experiments and confront the resulting properties with published and experimental data. The model of the cholesterol pathway and its regulation, along with Boolean formulae used for simulation are available on our web site http://Bioinformaticsu613.free.fr . Graphical results of the simulation are also shown online. The SBML model is available in the BioModels database (http://www.ebi.ac.uk/biomodels/). Curators comment: To make this model valid SBML, we had to put the BooleanLaws tags in the reactions' annotations into an ancillary namespace called http://kervizic/BooleanLaws . This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:This model is from the article: A model of flux regulation in the cholesterol biosynthesis pathway: Immune mediated graduated flux reduction versus statin-like led stepped flux reduction. Watterson S, Guerriero ML, Blanc M, Mazein A, Loewe L, Robertson KA, Gibbs H, Shui G, Wenk MR, Hillston J, Ghazal P. Biochimie. 2012 Jun 1. 22664637 , Abstract: The cholesterol biosynthesis pathway has recently been shown to play an important role in the innate immune response to viral infection with host protection occurring through a coordinate down regulation of the enzymes catalysing each metabolic step. In contrast, statin based drugs, which form the principle pharmaceutical agents for decreasing the activity of this pathway, target a single enzyme. Here, we build an ordinary differential equation model of the cholesterol biosynthesis pathway in order to investigate how the two regulatory strategies impact upon the behaviour of the pathway. We employ a modest set of assumptions: that the pathway operates away from saturation, that each metabolite is involved in multiple cellular interactions and that mRNA levels reflect enzyme concentrations. Using data taken from primary bone marrow derived macrophage cells infected with murine cytomegalovirus or treated with IFNγ, we show that, under these assumptions, coordinate down-regulation of enzyme activity imparts a graduated reduction in flux along the pathway. In contrast, modelling a statin-like treatment that achieves the same degree of down-regulation in cholesterol production, we show that this delivers a step change in flux along the pathway. The graduated reduction mediated by physiological coordinate regulation of multiple enzymes supports a mechanism that allows a greater level of specificity, altering cholesterol levels with less impact upon interactions branching from the pathway, than pharmacological step reductions. We argue that coordinate regulation is likely to show a long-term evolutionary advantage over single enzyme regulation. Finally, the results from our models have implications for future pharmaceutical therapies intended to target cholesterol production with greater specificity and fewer off target effects, suggesting that this can be achieved by mimicking the coordinated down-regulation observed in immunological responses.

Project description:An integrated systems approach was used with bioinformatics tools to understand the the role of aldehyde dehydrogenase 1A1 (ALDH1A1) in human colon cancer cells. We combined transcriptomics, proteomics and untargeted metabolomics to gain insight into the mechanisms affected by the suppression of the ALDH1A1 gene in COLO320 cells. RNA-seq and proteomics analyses revealed 1747 transcripts and 336 proteins that were differentially expressed in cells in which the ALDH1A1 had been suppressed by shRNA transfection. Transcriptomics pathway analysis showed significant signaling pathways, such as Wnt/β-catenin (p=3.47E-6), molecular mechanisms of cancer (7.41E-5) and others relevant to lipid metabolism, such as cholesterol biosynthesis I, II and III (p=1.82E-4). Proteomics pathway analysis identified oxidative phosphorylation to be the most highly significant pathway (p=5.01E-31). Pathway analysis of the genes common to the transcriptomics and proteomics analyses revealed the asparagine biosynthesis (p=3.16E-3) and cholesterol biosynthesis the most statistically significant (p=3.36E-3). Univariate analysis of the untargeted metabolomics dataset revealed 859 ions statistically significant. Network analysis showed alterations in pathways linked to energy and lipid metabolism, such carnitine shuttle (p=5.3E-4) and fatty acid oxidation (p=2.9E-2). A systems biology approach was used to integrate all three datasets and a total of 61 pathways were generated. A direct association between the suppression of ALDH1A1 and the Vitamin A (retinol) metabolism pathway and down-regulation of retinol and the UGT2B17, UGT2A3 and PRDX6 genes was shown. In conclusion, the present results confirm the role of ALDH1A1 in retinol metabolism and shows for the first time the influence of this gene on lipid metabolism pathways that may be crucial for cholesterol synthesis in human colon cancer cells. In addition, they demonstrate how an integrated systems approach using unbiased bioinformatics tools can be used to understand the interplay of cellular pathways.

Dataset Information

Benson2017 - Systems Pharmacology Multidrug (cholesterol biosynthesis pathway)

Publications

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway.

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