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

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      PLoS ONE
      Public Library of Science

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          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.

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          Most cited references23

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.

            The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.
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              SymPy: symbolic computing in Python

              SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                30 September 2019
                : 14
                : 9
                : e0222906
                Affiliations
                [001] Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
                Brandeis University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-1506-167X
                http://orcid.org/0000-0003-4745-992X
                Article
                PONE-D-19-18541
                10.1371/journal.pone.0222906
                6768458
                31568526
                562f8035-a6d5-4642-980e-2e6a002e0f2b
                © 2019 Marshall et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 July 2019
                : 10 September 2019
                Page count
                Figures: 6, Tables: 0, Pages: 16
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 647704
                Award Recipient :
                JARM acknowledges funding by the European Research Council (ERC - https://erc.europa.eu) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 647704). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Chemical Reactions
                Reactants
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Bees
                Honey Bees
                Computer and Information Sciences
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Systems Science
                Dynamical Systems
                Physical Sciences
                Chemistry
                Physical Chemistry
                Reaction Dynamics
                Reaction Kinetics
                Computer and Information Sciences
                Software Engineering
                Software Tools
                Engineering and Technology
                Software Engineering
                Software Tools
                Biology and Life Sciences
                Psychology
                Collective Human Behavior
                Social Sciences
                Psychology
                Collective Human Behavior
                Biology and Life Sciences
                Psychology
                Behavior
                Animal Behavior
                Collective Animal Behavior
                Social Sciences
                Psychology
                Behavior
                Animal Behavior
                Collective Animal Behavior
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Collective Animal Behavior
                Research and Analysis Methods
                Simulation and Modeling
                Custom metadata
                All software is available from GitHub ( https://github.com/DiODeProject/MuMoT).

                Uncategorized
                Uncategorized

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