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INDISIM, An Individual-based Discrete Simulation Model to Study Bacterial Cultures
Open accessAbstract
An individual-based model has been developed and designed to simulate the growth and behaviour of bacterial colonies. The simulator is called INDISIM, which stands for INDividual DIScrete SIMulations. INDISIM is discrete in space and time, and controls a group of bacterial cells at each time step, using a set of random, time-dependent variables for each bacterium. These variables are used to characterize its position in space, biomass, state in the cellular reproduction cycle as well as other individual properties. The space where the bacterial colony evolves is also discrete. A physical lattice is introduced, subject to the appropriate boundary conditions. The lattice is subdivided into spatial cells, also defined by a set of random, time-dependent variables. These variables may include concentrations of different types of particles, nutrients, reaction products and residual products. Random variables are used to characterize the individual bacterium and the individual particle, as well as the updating of individual rules. Thus, the simulations are stochastic rather than deterministic. The whole set of variables, those that characterize the bacterial population and the environment where they evolve, enables the simulator to study the behaviour of each microorganism-such as its motion, uptake, metabolism, and viability-according to given rules suited for the system under study. These rules require the input of only a few parameters. Once this information is inputted, INDISIM…
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In IBM research using the INDISIM methodology as a core model, it is essential to know the metabolic reactions carried out by microorganisms to increase or decrease their own biomass.
“In the research field on IBMs, using the INDISIM methodology as a core model, for instance [24], [25], [26], [52], [53], [54], we realized that is essential to know the metabolic reactions carried out by the micro-organisms to increase or decrease their own biomass.”
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Araujo Granda, P., Gras, A., & Ginovart, M. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.08.001 · Cited by 9
Metadata
- Authors
- GINOVART, M., LÓPEZ, D., & VALLS, J.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 2002-01
- Volume
- 214
- Issue
- 2
- Pages
- 305-319
- ISSN
- 0022-5193 (ISSN-L)
- OpenAlex
- W2079531646
- Cited by
- 90
- References
- 0
- Citations by year
- 2025: 4 · 2023: 2 · 2021: 4 · 2020: 1 · 2019: 3 · 2018: 5 · 2017: 4 · 2016: 8 · 2015: 5 · 2014: 6 · 2013: 2 · 2012: 1
- FWCI
- 3.25
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- public-domain
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Keywords
- agent-based modeling, discrete simulation, bacterial colony growth, stochastic simulation, lattice model, biomass allocation, nutrient concentration, temperature effects, metabolic oscillations, batch culture, bacterial metabolism, cell viability
- MeSH terms
- Bacteria, Computer Simulation, Culture Media, Microbiological Techniques, Models, Biological, Nutritional Physiological Phenomena, Biomass
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