Center for Biofilm Engineering (CBE)

Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/9334

At the Center for Biofilm Engineering (CBE), multidisciplinary research teams develop beneficial uses for microbial biofilms and find solutions to industrially relevant biofilm problems. The CBE was established at Montana State University, Bozeman, in 1990 as a National Science Foundation Engineering Research Center. As part of the MSU College of Engineering, the CBE gives students a chance to get a head start on their careers by working on research teams led by world-recognized leaders in the biofilm field.

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Author: search.filters.author.Parker, A.search.filters.applied.f.department: search.filters.department.Center for Biofilm Engineering.

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    Multiscale Flux-Based Modeling of Biofilm Communities
    (Society for Industrial & Applied Mathematics, 2020-01) Zhang, T.; Parker, A.; Carlson, R.P.; Stewart, Philip S.; Klapper, I.
    Models of microbial community dynamics generally rely on a subscale description for microbial metabolisms. In systems such as distributed multispecies communities like biofilms, where it may not be reasonable to simplify to a small number of limiting substrates, tracking the large number of active metabolites likely requires measurement or estimation of large numbers of kinetic and regulatory parameters. Alternatively, a largely kinetics-free framework is proposed combining cellular level constrained, steady state flux analysis of metabolism with macroscale microbial community models. This multiscale setup naturally allows coupling of macroscale information, including measurement data, with cell scale metabolism. Further, flexibility in methodology is stressed: choices at the microscale (e.g., flux balance analysis or elementary flux modes) and at the macroscale (e.g., physical-chemical influences relevant to biofilm or planktonic environments) are available to the user. Illustrative computations in the context of a biofilm, including comparisons of systemic and Nash equilibration as well as an example of coupling experimental data into predictions, are provided.
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