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      Product-Form Stationary Distributions for Deficiency Zero Networks with Non-mass Action Kinetics

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          Abstract

          In many applications, for example when computing statistics of fast subsystems in a multiscale setting, we wish to find the stationary distributions of systems of continuous-time Markov chains. Here we present a class of models that appears naturally in certain averaging approaches whose stationary distributions can be computed explicitly. In particular, we study continuous-time Markov chain models for biochemical interaction systems with non-mass action kinetics whose network satisfies a certain constraint. Analogous with previous related results, the distributions can be written in product form.

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          The chemical Langevin equation

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            Complex balancing in general kinetic systems

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              Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies

              Background The WHO considers leishmaniasis as one of the six most important tropical diseases worldwide. It is caused by parasites of the genus Leishmania that are passed on to humans and animals by the phlebotomine sandfly. Despite all of the research, there is still a lack of understanding on the metabolism of the parasite and the progression of the disease. In this study, a mathematical model of disease progression was developed based on experimental data of clinical symptoms, immunological responses, and parasite load for Leishmania amazonensis in BALB/c mice. Results Four biologically significant variables were chosen to develop a differential equation model based on the GMA power-law formalism. Parameters were determined to minimize error in the model dynamics and time series experimental data. Subsequently, the model robustness was tested and the model predictions were verified by comparing them with experimental observations made in different experimental conditions. The model obtained helps to quantify relationships between the selected variables, leads to a better understanding of disease progression, and aids in the identification of crucial points for introducing therapeutic methods. Conclusions Our model can be used to identify the biological factors that must be changed to minimize parasite load in the host body, and contributes to the design of effective therapies.
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                Author and article information

                Contributors
                anderson@math.wisc.edu
                simon.cotter@manchester.ac.uk
                Journal
                Bull Math Biol
                Bull. Math. Biol
                Bulletin of Mathematical Biology
                Springer US (New York )
                0092-8240
                1522-9602
                27 October 2016
                27 October 2016
                2016
                : 78
                : 12
                : 2390-2407
                Affiliations
                [1 ]Department of Mathematics, University of Wisconsin, Madison, WI USA
                [2 ]Department of Mathematics, University of Manchester, Manchester, UK
                Author information
                http://orcid.org/0000-0001-5974-7393
                Article
                220
                10.1007/s11538-016-0220-y
                5104833
                27796722
                4f22bae3-cfb6-49f1-9384-1a54bdb904de
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 23 May 2016
                : 4 October 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/L023393/1
                Award ID: EP/K032208/1
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Society for Mathematical Biology 2016

                Quantitative & Systems biology
                product-form stationary distributions,deficiency zero,constrained averaging,stochastically modeled reaction network

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