2016
DOI: 10.4995/msel.2016.5789
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Modelización de crecimientos microbianos en medios heterogéneos y de movilidad reducida

Abstract: En este trabajo se han analizado diversos modelos computacionales publicados en revistas científicas que abordan el estudio del crecimiento microbiano en entornos semisólidos y heterogeneos en superficie, para valorar el interés y utilidad que pueden tener estos modelos en el entorno académico.Postprint (published version

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Cited by 4 publications
(3 citation statements)
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“…For each of the 3,358 initial configurations, we ran 4-6 repetitions to ensure statistical sampling, which yields around 18,000 simulations in total. In these simulations, we fixed the values of six parameters to values generally accepted by the literature or based on our experiments: efficiency (McCarty, 2007), energy_ maintenance_pa (Gras et al, 2011), ammonium (Wushensky et al, 2018), total-length-world based on the experiments described above, and lastly, max-time-viability_pa and rep_pa based on preliminary tests following a similar application by Font Marques and Ginovart (2016). The remaining seven parameters, namely pmax, microorganism, depth, umax_pa, diffusion-coefficient, glucose, and min/steptime, were randomly varied within a pre-defined range.…”
Section: Agent-based Modeling Methodsmentioning
confidence: 99%
“…For each of the 3,358 initial configurations, we ran 4-6 repetitions to ensure statistical sampling, which yields around 18,000 simulations in total. In these simulations, we fixed the values of six parameters to values generally accepted by the literature or based on our experiments: efficiency (McCarty, 2007), energy_ maintenance_pa (Gras et al, 2011), ammonium (Wushensky et al, 2018), total-length-world based on the experiments described above, and lastly, max-time-viability_pa and rep_pa based on preliminary tests following a similar application by Font Marques and Ginovart (2016). The remaining seven parameters, namely pmax, microorganism, depth, umax_pa, diffusion-coefficient, glucose, and min/steptime, were randomly varied within a pre-defined range.…”
Section: Agent-based Modeling Methodsmentioning
confidence: 99%
“…The use of specific programming environments to implement these computational models facilitates their use [55,57], which along with computer processing tools and statistical analysis of data provides parameter estimation and the corresponding sensitivity analysis. These facilities make the methodology of discrete modelling based on the individual a valid and attractive option for study of microbial systems, increasing its presence in academic [58][59][60][61] and scientific fields [56,[62][63][64][65].…”
Section: Introductionmentioning
confidence: 99%
“…The use of specific programming environments to implement these computational models facilitates their use (Hellweger et al, 2016;Wilensky, 1999), which along with computer processing tools and statistical analysis of data provides parameter estimation and the corresponding sensitivity analysis. These facilities make the methodology of discrete modelling based on the individual a valid and attractive option for study of microbial systems, increasing its presence in academic (Font-Marques and Ginovart, 2016;Ginovart, 2014;Ginovart and Prats, 2012) and scientific fields (Thiele, 2014;Thiele et al, 2014Thiele et al, , 2012aThiele et al, , 2012bThiele and Grimm, 2010).…”
Section: The Individual-based Models In Microbiologymentioning
confidence: 99%