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Predicting the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes

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Du Lac, Melchior, Scarpelli, Andrew H., Younger, Andrew K. D., Bates, Declan and Leonard, Joshua N. (2017) Predicting the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. ACS Synthetic Biology, 6 (7). pp. 1131-1139. doi:10.1021/acssynbio.5b00217

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Official URL: http://dx.doi.org/10.1021/acssynbio.5b00217

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Abstract

For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as E. coli, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions.

Item Type: Journal Article
Subjects: Q Science > QC Physics
Q Science > QP Physiology
Q Science > QR Microbiology
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): DNA replication, Genomes, Bacteria -- Growth
Journal or Publication Title: ACS Synthetic Biology
Publisher: American Chemical Society
ISSN: 2161-5063
Official Date: 2017
Dates:
DateEvent
2017Published
30 September 2016Available
14 September 2016Accepted
Volume: 6
Number: 7
Page Range: pp. 1131-1139
DOI: 10.1021/acssynbio.5b00217
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
PHY-0943390National Science Foundationhttp://dx.doi.org/10.13039/100000001
MCB-134141National Science Foundationhttp://dx.doi.org/10.13039/100000001
NAKFI-SB6National Academies of Sciences, Engineering, and Medicinehttp://dx.doi.org/10.13039/100009643
EP/H019154/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/K034359/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
BB/K003240/1[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
NCI CA060553Department of Molecular Biosciences, Northwestern Universityhttp://dx.doi.org/10.13039/100011176

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