Dataset Information

Twycross2010_Auxin_Transport

ABSTRACT: This is a model with 20 cells - 20 cytplasms and 21 apoplasts - as described in the article: Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study. by Twycross J, Band LR, Bennett MJ, King JR, Krasnogor N. BMC Syst Biol. 2010 Mar 26;4:34. PubmedID: 20346112 ; DOI: 10.1186/1752-0509-4-34 Abstract: Background: Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks. Results: In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system. Conclusions: Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). 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.

SUBMITTER: Jamie Twycross

PROVIDER: MODEL1005200000 | BioModels | 2005-01-01

REPOSITORIES: BioModels

ACCESS DATA

Publications

Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study.

Twycross Jamie J Band Leah R LR Bennett Malcolm J MJ King John R JR Krasnogor Natalio N

BMC systems biology 20100326

<h4>Background</h4>Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks.<h4>Results</h4>I ...[more]

PMID: 20346112

Similar Datasets

Project description:This is the single cell model for analysis of hormonal crosstalk in Arabidopsis described in the article: Modelling and experimental analysis of hormonal crosstalk in Arabidopsis. Liu J, Mehdi S, Topping J, Tarkowski P and Lindsey K. Mol Syst Biol. 2010 Jun 8;6:373; PmID: 20531403 , DOI: 10.1038/msb.2010.26 Abstract: An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS-ethylene pathway interactions is developed, and used to make quantitative predictions about PLS-hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose-response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants. This model was originally created using Copasi and taken from the supplementary materials of the MSB article. It uses equation 5 for the auxin biosynthesis and was altered to also contain the reactions for ACC, IAA and cytokinine import. Different from the supplementary material, the parameters for the auxin synthesis, v2, are set to k2c = 0.01 uM and k2=0.2 uM_per_sec and for the WT PLS transcription k6=0.3 . To obtain the model described in the first table of the supplementary materials, set k2c=k2=0 and k6=0.9 . For the pls and PLSox mutants, k6 should be set to 0 and 0.45, respectively. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team. For more information see the terms of use . To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Project description:Restif2006 - Whooping cough This model is described in the article: Integrating life history and cross-immunity into the evolutionary dynamics of pathogens. Restif O, Grenfell BT. Proc. Biol. Sci. 2006 Feb; 273(1585): 409-416 Abstract: Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), interfere through a given level of cross-immunity. We used whooping cough as a potential example, but the framework proposed here could be applied to other acute infectious diseases. Specifically, we analysed the effects of these parameters on the invasion dynamics of one strain into a population, where the second strain is endemic. Whereas the deterministic version of the model converges towards stable coexistence of the two strains in most cases, stochastic simulations showed that transient epidemic dynamics can cause the extinction of either strain. Thus ecological dynamics, modulated by the immune parameters, eventually determine the adaptive value of different pathogen genotypes. We advocate an integrative view of pathogen dynamics at the crossroads of immunology, epidemiology and evolution, as a way towards efficient control of infectious diseases. This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size N = 1. The model reproduces the deterministic time courses. Stochastic interpretation with Copasi UI gave results similar to the article, but was not extensively tested. The initial conditions for competition simulations can be derived by equilibrating the system for one pathogen and then adding a starting concentration for the other. Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1) This model is hosted on BioModels Database and identified by: BIOMD0000000249. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. 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.

Dataset Information

Twycross2010_Auxin_Transport

Publications

Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure