Combining mathematical models and statistical methods to understand and predict the dynamics of antibiotic-sensitive mutants in a population of resistant bacteria during experimental evolution.
about
Sociobiological control of plasmid copy number in bacteriaResource competition may lead to effective treatment of antibiotic resistant infectionsMathematical modeling of bacterial kinetics to predict the impact of antibiotic colonic exposure and treatment duration on the amount of resistant enterobacteria excretedMultidrug evolutionary strategies to reverse antibiotic resistance.Emerging patterns of plasmid-host coevolution that stabilize antibiotic resistanceSelection pressure required for long-term persistence of blaCMY-2-positive IncA/C plasmids.Dynamics of soil bacterial communities in response to repeated application of manure containing sulfadiazineThe population biology of bacterial plasmids: a hidden Markov model approach.The role of clonal interference in the evolutionary dynamics of plasmid-host adaptationAdaptive plasmid evolution results in host-range expansion of a broad-host-range plasmid.Evolutionary Paths That Expand Plasmid Host-Range: Implications for Spread of Antibiotic Resistance.Genomics of IncP-1 antibiotic resistance plasmids isolated from wastewater treatment plants provides evidence for a widely accessible drug resistance gene pool.The rising impact of mathematical modelling in epidemiology: antibiotic resistance research as a case study.A modeling framework for the evolution and spread of antibiotic resistance: literature review and model categorization.Resistance Gene Replacement in the mosquito Culex pipiens: fitness estimation from long-term cline series.Identification of bacterial plasmids based on mobility and plasmid population biology.Plasmid stability analysis based on a new theoretical model employing stochastic simulations.System for determining the relative fitness of multiple bacterial populations without using selective markers.Genomic analysis by deep sequencing of the probiotic Lactobacillus brevis KB290 harboring nine plasmids reveals genomic stability.The persistence of parasitic plasmidsCRISPR-Cas systems preferentially target the leading regions of MOBF conjugative plasmids.Escherichia coli adapts to tetracycline resistance plasmid (pBR322) by mutating endogenous potassium transport: in silico hypothesis testing.Evolutionary loss of the rdar morphotype in Salmonella as a result of high mutation rates during laboratory passage.Human dissemination of genes and microorganisms in Earth's Critical Zone.Mathematical modelling of the antibiotic-induced morphological transition of Pseudomonas aeruginosa.Reversing resistance: different routes and common themes across pathogens.Mutational neighbourhood and mutation supply rate constrain adaptation in Pseudomonas aeruginosaAntimicrobial-Resistant Indicator Bacteria in Manure and the Tracking of Indicator Resistance GenesThe ABCs of Experimental Evolution
P2860
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P2860
Combining mathematical models and statistical methods to understand and predict the dynamics of antibiotic-sensitive mutants in a population of resistant bacteria during experimental evolution.
description
2004 nî lūn-bûn
@nan
2004 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
Combining mathematical models ...... during experimental evolution.
@ast
Combining mathematical models ...... during experimental evolution.
@en
Combining mathematical models ...... during experimental evolution.
@nl
type
label
Combining mathematical models ...... during experimental evolution.
@ast
Combining mathematical models ...... during experimental evolution.
@en
Combining mathematical models ...... during experimental evolution.
@nl
prefLabel
Combining mathematical models ...... during experimental evolution.
@ast
Combining mathematical models ...... during experimental evolution.
@en
Combining mathematical models ...... during experimental evolution.
@nl
P2093
P2860
P1433
P1476
Combining mathematical models ...... during experimental evolution.
@en
P2093
José M Ponciano
Larry J Forney
Leen De Gelder
Paul Joyce
P2860
P304
P356
10.1534/GENETICS.104.033431
P407
P577
2004-11-01T00:00:00Z