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Genome complexity versus metabolic constrains
by
Pietro Lio, Giuseppe Nicosia, Jole Costanza, Claudio Angione

Bacteria are intriguing living machines; the optimisation of their
metabolism depends also on their genome structure. Genomic and
metabolic constraints should also accommodate each other. Metabolic
engineering algorithms provide means to optimize a single biological
process leading to the improvement of a biotechnological important
molecule. The next step is to identify the best conditions to optimize
both gene arrangements, replication constraints (such as the GC skew)
to accommodate multiple biochemical yields, or to switch from a single
multiple set of biological functions (environment 1) to a diff erent
set (environment 2). Two novel coupled algorithms (GDMO, Genetic
Design through Multi-Objective and PoSA, Pathway-oriented Sensitivity
Analysis) optimize multiple biological functions and perform global
sensitivity analysis. They combine the exploration of gene positions,
species, reactions, pathways and knockouts parameter spaces with the
Pareto optimality principle. They also provide theoretical and
practical guidelines for design automation. The statistical cross
comparison of our new optimization procedures, carried out with
respect to currently widely used algorithms for important bacteria
(e.g., Escherichia coli, Geobacter, Yersinia pestis) over di fferent
multiple functions, opens new directions for producing a variety of
biotechnological products.
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