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1.  Historical contingency and the gradual evolution of metabolic properties in central carbon and genome-scale metabolisms 
BMC Systems Biology  2014;8:48.
Background
A metabolism can evolve through changes in its biochemical reactions that are caused by processes such as horizontal gene transfer and gene deletion. While such changes need to preserve an organism’s viability in its environment, they can modify other important properties, such as a metabolism’s maximal biomass synthesis rate and its robustness to genetic and environmental change. Whether such properties can be modulated in evolution depends on whether all or most viable metabolisms – those that can synthesize all essential biomass precursors – are connected in a space of all possible metabolisms. Connectedness means that any two viable metabolisms can be converted into one another through a sequence of single reaction changes that leave viability intact. If the set of viable metabolisms is disconnected and highly fragmented, then historical contingency becomes important and restricts the alteration of metabolic properties, as well as the number of novel metabolic phenotypes accessible in evolution.
Results
We here computationally explore two vast spaces of possible metabolisms to ask whether viable metabolisms are connected. We find that for all but the simplest metabolisms, most viable metabolisms can be transformed into one another by single viability-preserving reaction changes. Where this is not the case, alternative essential metabolic pathways consisting of multiple reactions are responsible, but such pathways are not common.
Conclusions
Metabolism is thus highly evolvable, in the sense that its properties could be fine-tuned by successively altering individual reactions. Historical contingency does not strongly restrict the origin of novel metabolic phenotypes.
doi:10.1186/1752-0509-8-48
PMCID: PMC4022055  PMID: 24758311
Genome scale metabolism; Central carbon metabolism; Genotype; Phenotype; Connectedness of genotype networks; Historical contingency
2.  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
3.  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 1-3 (3)