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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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.

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