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1.  Growth Temperature and Genome Size in Bacteria Are Negatively Correlated, Suggesting Genomic Streamlining During Thermal Adaptation 
Genome Biology and Evolution  2013;5(5):966-977.
Prokaryotic genomes are small and compact. Either this feature is caused by neutral evolution or by natural selection favoring small genomes—genome streamlining. Three separate prior lines of evidence argue against streamlining for most prokaryotes. We find that the same three lines of evidence argue for streamlining in the genomes of thermophile bacteria. Specifically, with increasing habitat temperature and decreasing genome size, the proportion of genomic DNA in intergenic regions decreases. Furthermore, with increasing habitat temperature, generation time decreases. Genome-wide selective constraints do not decrease as in the reduced genomes of host-associated species. Reduced habitat variability is not a likely explanation for the smaller genomes of thermophiles. Genome size may be an indirect target of selection due to its association with cell volume. We use metabolic modeling to demonstrate that known changes in cell structure and physiology at high temperature can provide a selective advantage to reduce cell volume at high temperatures.
doi:10.1093/gbe/evt050
PMCID: PMC3673621  PMID: 23563968
streamlining; genome evolution; thermophilic bacteria
2.  A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli 
Background
A prerequisite for a successful design and discovery of an antibacterial drug is the identification of essential targets as well as potent inhibitors that adversely affect the survival of bacteria. In order to understand how intracellular perturbations occur due to inhibition of essential metabolic pathways, we have built, through the use of ordinary differential equations, a mathematical model of 8 major Escherichia coli pathways.
Results
Individual in vitro enzyme kinetic parameters published in the literature were used to build the network of pathways in such a way that the flux distribution matched that reported from whole cells. Gene regulation at the transcription level as well as feedback regulation of enzyme activity was incorporated as reported in the literature. The unknown kinetic parameters were estimated by trial and error through simulations by observing network stability. Metabolites, whose biosynthetic pathways were not represented in this platform, were provided at a fixed concentration. Unutilized products were maintained at a fixed concentration by removing excess quantities from the platform. This approach enabled us to achieve steady state levels of all the metabolites in the cell. The output of various simulations correlated well with those previously published.
Conclusion
Such a virtual platform can be exploited for target identification through assessment of their vulnerability, desirable mode of target enzyme inhibition, and metabolite profiling to ascribe mechanism of action following a specific target inhibition. Vulnerability of targets in the biosynthetic pathway of coenzyme A was evaluated using this platform. In addition, we also report the utility of this platform in understanding the impact of a physiologically relevant carbon source, glucose versus acetate, on metabolite profiles of bacterial pathogens.
doi:10.2147/AABC.S14368
PMCID: PMC3170011  PMID: 21918631
antibacterial drug; mathematical model; kinetic platform; metabolic dynamics; Escherichia coli

Results 1-2 (2)