Systems Biology and Metabolic Engineering in Bacteria - Systems Biology of Metabolic and Signaling Networks - page 354

may therefore prove to be the most effective strategy to strain optimization (Yadav
and Stephanopoulos 2010 ).
In addition to fine-tuning the expression of individual genes, modifying the
global gene expression machinery can prove even more efficient in optimizing
product yield. In a proof of concept study, Alper and Stephanopoulos showed that
mutations in the major sigma factor of E. coli ,

70 , outperform traditional metabolic
engineering approaches for improving ethanol tolerance or lycopene production
(Alper and Stephanopoulos 2007 ). Our recent results in E. coli confirm the impor-
tance of global versus gene-specific regulation (Berthoumieux et al. 2013 ). We
have measured the relative importance of global versus gene-specific factors for the
regulation of promoter activities at growth transitions. Contrary to the commonly
accepted paradigm that attributes a major importance to gene-specific regulations,
we find that even for global regulators these interactions serve “only” to fine-tune
the transcription of the target gene. These results emphasize the need for developing
global, integrated models of gene expression and metabolism.

σ

13.7 Conclusions and a Prospective

The pace of research in systems biology of microbial metabolism has tremendously
accelerated since the availability of genome sequences and the deluge of -omics
data (metabol-, prote-, transcript-omics). The different modeling approaches,
top-down and bottom-up, are progressing rapidly and both ends will meet in the
near future. Whole-cell models of an organism, including metabolism and all levels
of regulation, have already become a reality. The advances made with small
bacteria, such as M. genitalium , will have to be transposed to larger and more
experimentally accessible bacteria, such as E. coli . These conceptual and experi-
mental advances have already led the way to new fundamental discoveries about
the functioning of microorganisms and the concepts and techniques are being
exploited in biotechnological and industrial applications. The last decade of
research has clearly demonstrated that a true understanding of a biological system
necessarily involves mathematical modeling. Many modeling and experimental
tools are available and are continuously improved. We can now ask many important
questions about the functioning of an organism and obtain the answers.
The promise of combining synthetic biology and metabolic engineering is the
design and construction of microorganisms that transform a starting chemical into a
desired product: microorganisms will serve as “chemical factories.” The tools for
analyzing metabolic networks and calculating potential flux distributions are avail-
able (FBA, etc.). The database of metabolic networks of microorganisms is rapidly
expanding. However, predictive, integrated models of metabolism and genetic
regulation are still scarce. The major challenge for the future consists in developing
such nonlinear models for systems of biotechnological interest. However, reliable
models require good parameter estimates, and obtaining parameters for all cellular
reactions remains a tantamount endeavor. The task is simplified by the development