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      Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology

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      Author(s): , * , ,
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary: Morpheus is a modeling environment for the simulation and integration of cell-based models with ordinary differential equations and reaction-diffusion systems. It allows rapid development of multiscale models in biological terms and mathematical expressions rather than programming code. Its graphical user interface supports the entire workflow from model construction and simulation to visualization, archiving and batch processing.

          Availability and implementation: Binary packages are available at http://imc.zih.tu-dresden.de/wiki/morpheus for Linux, Mac OSX and MS Windows.

          Contact: walter.deback@ 123456tu-dresden.de

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Characterization of diverse natural variants of CYP102A1 found within a species of Bacillus megaterium

          Ji-Yeon Kang, So Young Kim, Dooil Kim … (2011)
          An extreme diversity of substrates and catalytic reactions of cytochrome P450 (P450) enzymes is considered to be the consequence of evolutionary adaptation driven by different metabolic or environmental demands. Here we report the presence of numerous natural variants of P450 BM3 (CYP102A1) within a species of Bacillus megaterium. Extensive amino acid substitutions (up to 5% of the total 1049 amino acid residues) were identified from the variants. Phylogenetic analyses suggest that this P450 gene evolve more rapidly than the rRNA gene locus. It was found that key catalytic residues in the substrate channel and active site are retained. Although there were no apparent variations in hydroxylation activity towards myristic acid (C14) and palmitic acid (C16), the hydroxylation rates of lauric acid (C12) by the variants varied in the range of >25-fold. Interestingly, catalytic activities of the variants are promiscuous towards non-natural substrates including human P450 substrates. It can be suggested that CYP102A1 variants can acquire new catalytic activities through site-specific mutations distal to the active site.
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            Integrating Intracellular Dynamics Using CompuCell3D and Bionetsolver: Applications to Multiscale Modelling of Cancer Cell Growth and Invasion

            Vivi Andasari, Ryan Roper, Maciej H. Swat … (2012)
            In this paper we present a multiscale, individual-based simulation environment that integrates CompuCell3D for lattice-based modelling on the cellular level and Bionetsolver for intracellular modelling. CompuCell3D or CC3D provides an implementation of the lattice-based Cellular Potts Model or CPM (also known as the Glazier-Graner-Hogeweg or GGH model) and a Monte Carlo method based on the metropolis algorithm for system evolution. The integration of CC3D for cellular systems with Bionetsolver for subcellular systems enables us to develop a multiscale mathematical model and to study the evolution of cell behaviour due to the dynamics inside of the cells, capturing aspects of cell behaviour and interaction that is not possible using continuum approaches. We then apply this multiscale modelling technique to a model of cancer growth and invasion, based on a previously published model of Ramis-Conde et al. (2008) where individual cell behaviour is driven by a molecular network describing the dynamics of E-cadherin and -catenin. In this model, which we refer to as the centre-based model, an alternative individual-based modelling technique was used, namely, a lattice-free approach. In many respects, the GGH or CPM methodology and the approach of the centre-based model have the same overall goal, that is to mimic behaviours and interactions of biological cells. Although the mathematical foundations and computational implementations of the two approaches are very different, the results of the presented simulations are compatible with each other, suggesting that by using individual-based approaches we can formulate a natural way of describing complex multi-cell, multiscale models. The ability to easily reproduce results of one modelling approach using an alternative approach is also essential from a model cross-validation standpoint and also helps to identify any modelling artefacts specific to a given computational approach.
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              Early Embryonic Vascular Patterning by Matrix-Mediated Paracrine Signalling: A Mathematical Model Study

              Alvaro Köhn-Luque, Walter de Back, Jörn Starruß … (2011)
              During embryonic vasculogenesis, endothelial precursor cells of mesodermal origin known as angioblasts assemble into a characteristic network pattern. Although a considerable amount of markers and signals involved in this process have been identified, the mechanisms underlying the coalescence of angioblasts into this reticular pattern remain unclear. Various recent studies hypothesize that autocrine regulation of the chemoattractant vascular endothelial growth factor (VEGF) is responsible for the formation of vascular networks in vitro. However, the autocrine regulation hypothesis does not fit well with reported data on in vivo early vascular development. In this study, we propose a mathematical model based on the alternative assumption that endodermal VEGF signalling activity, having a paracrine effect on adjacent angioblasts, is mediated by its binding to the extracellular matrix (ECM). Detailed morphometric analysis of simulated networks and images obtained from in vivo quail embryos reveals the model mimics the vascular patterns with high accuracy. These results show that paracrine signalling can result in the formation of fine-grained cellular networks when mediated by angioblast-produced ECM. This lends additional support to the theory that patterning during early vascular development in the vertebrate embryo is regulated by paracrine signalling.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 1 May 2014
                Publication date (Electronic): 17 January 2014
                Publication date PMC-release: 17 January 2014
                Volume: 30
                Issue: 9
                Pages: 1331-1332
                Affiliations
                Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Martin Bishop

                †The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Article
                Publisher ID: btt772
                DOI: 10.1093/bioinformatics/btt772
                PMC ID: 3998129
                PubMed ID: 24443380
                SO-VID: 104cb907-7e02-4190-a97d-0d7119132677
                Copyright © © The Author 2014. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 2 August 2013
                Date revision received : 12 December 2013
                Date accepted : 28 December 2013
                Page count
                Pages: 2
                Categories
                Subject: Applications Notes
                Subject: Systems Biology

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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