Literature DB >> 14580574

Dynamic modeling of microbial cell populations.

Michael A Henson1.   

Abstract

Microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins and other constituents. Recent advances have been made in cell population modeling, which allow the effects of cell heterogeneity on culture dynamics and metabolite production to be predicted. If the intracellular state can be captured with a few variables, the population balance equation framework is a viable modeling approach. The cell ensemble modeling technique is better suited for the development of population models that include more detailed descriptions of cellular metabolism and/or cell-cycle progression.

Mesh:

Year:  2003        PMID: 14580574     DOI: 10.1016/s0958-1669(03)00104-6

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  16 in total

Review 1.  Estimation methods for heterogeneous cell population models in systems biology.

Authors:  Steffen Waldherr
Journal:  J R Soc Interface       Date:  2018-10-31       Impact factor: 4.118

2.  Numerical rate function determination in partial differential equations modeling cell population dynamics.

Authors:  Andreas Groh; Holger Kohr; Alfred K Louis
Journal:  J Math Biol       Date:  2016-06-13       Impact factor: 2.259

3.  A mathematical and computational approach for integrating the major sources of cell population heterogeneity.

Authors:  Michail Stamatakis; Kyriacos Zygourakis
Journal:  J Theor Biol       Date:  2010-06-08       Impact factor: 2.691

4.  A population balance equation model of aggregation dynamics in Taxus suspension cell cultures.

Authors:  Martin E Kolewe; Susan C Roberts; Michael A Henson
Journal:  Biotechnol Bioeng       Date:  2011-09-09       Impact factor: 4.530

Review 5.  Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges.

Authors:  Robert S Parker; Gilles Clermont
Journal:  J R Soc Interface       Date:  2010-02-10       Impact factor: 4.118

6.  A computational framework for finding parameter sets associated with chaotic dynamics.

Authors:  S Koshy-Chenthittayil; E Dimitrova; E W Jenkins; B C Dean
Journal:  In Silico Biol       Date:  2021

7.  Identification of models of heterogeneous cell populations from population snapshot data.

Authors:  Jan Hasenauer; Steffen Waldherr; Malgorzata Doszczak; Nicole Radde; Peter Scheurich; Frank Allgöwer
Journal:  BMC Bioinformatics       Date:  2011-04-28       Impact factor: 3.169

8.  Temperature affects the silicate morphology in a diatom.

Authors:  N Javaheri; R Dries; A Burson; L J Stal; P M A Sloot; J A Kaandorp
Journal:  Sci Rep       Date:  2015-06-26       Impact factor: 4.379

9.  In Silico Modeling in Infectious Disease.

Authors:  Silvia Daun; Gilles Clermont
Journal:  Drug Discov Today Dis Models       Date:  2007-10-01

10.  Understanding the sub-cellular dynamics of silicon transportation and synthesis in diatoms using population-level data and computational optimization.

Authors:  Narjes Javaheri; Roland Dries; Jaap Kaandorp
Journal:  PLoS Comput Biol       Date:  2014-06-19       Impact factor: 4.475

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