Literature DB >> 31568526

Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths.

James A R Marshall1, Andreagiovanni Reina1, Thomas Bose1.   

Abstract

Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.

Entities:  

Year:  2019        PMID: 31568526      PMCID: PMC6768458          DOI: 10.1371/journal.pone.0222906

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  19 in total

1.  Emergence of scaling in random networks

Authors: 
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

2.  Efficient formulations for exact stochastic simulation of chemical systems.

Authors:  Sean Mauch; Mark Stalzer
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2011 Jan-Mar       Impact factor: 3.710

3.  Dizzy: stochastic simulation of large-scale genetic regulatory networks.

Authors:  Stephen Ramsey; David Orrell; Hamid Bolouri
Journal:  J Bioinform Comput Biol       Date:  2005-04       Impact factor: 1.122

4.  Computational modeling of biochemical networks using COPASI.

Authors:  Pedro Mendes; Stefan Hoops; Sven Sahle; Ralph Gauges; Joseph Dada; Ursula Kummer
Journal:  Methods Mol Biol       Date:  2009

5.  StochKit2: software for discrete stochastic simulation of biochemical systems with events.

Authors:  Kevin R Sanft; Sheng Wu; Min Roh; Jin Fu; Rone Kwei Lim; Linda R Petzold
Journal:  Bioinformatics       Date:  2011-07-04       Impact factor: 6.937

6.  Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

Authors:  David Adalsteinsson; David McMillen; Timothy C Elston
Journal:  BMC Bioinformatics       Date:  2004-03-08       Impact factor: 3.169

7.  A Design Pattern for Decentralised Decision Making.

Authors:  Andreagiovanni Reina; Gabriele Valentini; Cristian Fernández-Oto; Marco Dorigo; Vito Trianni
Journal:  PLoS One       Date:  2015-10-23       Impact factor: 3.240

8.  A mechanism for value-sensitive decision-making.

Authors:  Darren Pais; Patrick M Hogan; Thomas Schlegel; Nigel R Franks; Naomi E Leonard; James A R Marshall
Journal:  PLoS One       Date:  2013-09-02       Impact factor: 3.240

9.  StochPy: a comprehensive, user-friendly tool for simulating stochastic biological processes.

Authors:  Timo R Maarleveld; Brett G Olivier; Frank J Bruggeman
Journal:  PLoS One       Date:  2013-11-18       Impact factor: 3.240

10.  Tellurium notebooks-An environment for reproducible dynamical modeling in systems biology.

Authors:  J Kyle Medley; Kiri Choi; Matthias König; Lucian Smith; Stanley Gu; Joseph Hellerstein; Stuart C Sealfon; Herbert M Sauro
Journal:  PLoS Comput Biol       Date:  2018-06-15       Impact factor: 4.475

View more
  2 in total

1.  Noise-induced effects in collective dynamics and inferring local interactions from data.

Authors:  Jitesh Jhawar; Vishwesha Guttal
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

2.  Negative feedback may suppress variation to improve collective foraging performance.

Authors:  Andreagiovanni Reina; James A R Marshall
Journal:  PLoS Comput Biol       Date:  2022-05-18       Impact factor: 4.779

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.