It is absolutely critical that the entire suite of sugars produced from all types of biomass be effectively converted to ethanol (or other products) by the fermentative microorganism — the ethanologen. A particular concern is the conversion of five-carbon sugars, primarily xylose and arabinose from grasses and hard woods. Desired characteristics for the ideal ethanologen include the following: it ferments all biomass sugars equally well (glucose, xylose, arabinose, galactose, mannose, and even sucrose); it resists toxic compounds produced during pre­treatment (furfural, hydroxymethyl furfural, acetic acid, and soluble phenolics); it ferments high concentrations of sugars likely to be produced from high-solids pretreatments; and it produces fermentation beers with byproducts credits intact (9).

Realizing the potential of cellulosic biofuels may be facilitated by applying a new gener­ation of genomic research tools. Metabolic engineering is now used routinely to develop microbial biocatalysts. Key approaches are the targeted manipulation of their metabolic pathways, or the introduction of new ones, with the goal of improving cellular properties or directing the synthesis of metabolic products with commercial value.

There are many examples now where metabolic engineering has improved the conver­sion yield, productivity, product concentration, and economic feasibility of an industrial bioprocess (10). One particularly relevant example is the success in extending the sub­strate utilization range of yeast and bacteria to include the pentose sugars derived from the hemicellulose fraction of biomass for conversion to fuel ethanol. Other examples include the introduction of genes that permit microorganisms to metabolize cellulose, starch, xylan, lactose, cellobiose, and sucrose. Other work has improved microbial growth rates and yields, nutrient uptake, and strain stability, or has reduced the overflow metabolism that causes the accumulation of inhibitory organic acid byproducts.

These efforts have provided a greater understanding of microbial physiology and the com­plexity of the interactions between metabolic pathways and their regulatory networks. A key discovery to emerge from these studies is that flux control is often distributed over several reactions in a pathway, rather than at a single “rate-limiting” step. Consequently, simulta­neous and coordinated overexpression of all the genes encoding a metabolic pathway may be necessary to increase metabolic flux without the detrimental accumulation of metabolic intermediates. Sophisticated in-silico models of complex metabolic networks are now used to define the minimal set of genes needed to optimize growth or product formation under particular conditions (11).

The “genomics revolution” has opened a whole new dimension to metabolic engineering. More than 800 microbial genomes have been sequenced thus far, representing enormous metabolic potential as a source of novel genes for strain development. Not too surprisingly, many enzymes catalyzing the same reaction in different microorganisms show widely vary­ing kinetic properties. Furthermore, in vitro enzyme kinetics may not predict the in vivo activities of a complex pathway, making rational selection of best-pathway genes difficult. Combinatorial assembly of divergent homologs, coupled with strain selection and evolu­tionary adaptation, can overcome many of the limitations with rational gene selection.

The emerging field of synthetic biology now makes it possible to synthesize and assemble DNA fragments into modular cassettes that encode an entire metabolic pathway, synthetic chromosomes, and even whole genomes (12). The transplantation of a whole genome from one species of bacteria into another has recently been demonstrated and represents a major step toward developing customized microbial biofactories (13).

Microbial strain development historically relied almost exclusively on mutagenesis and selection to identify strains with superior traits, and the success of this approach is still evident today in the commercial production of amino acids, antibiotics, solvents, and vita­mins. However, a systematic integration of the data generated by genomics, gene expression profiles, proteomics, and metabolomics offers the promise that we may develop a cohe­sive understanding of cellular metabolism sufficient to guide rational strain design. The new methods of synthetic biology now provide us with the means to introduce vast ge­netic diversity into a microbial host. And when combined with selection, high-throughput screening, and evolutionary adaptation, synthetic biology will allow us to identify those combinations of genes that optimize bioprocesses.