Systems biology is the quantitative characterization of genetic, transcription, protein, metabolic, signaling and other informational pathway responses to
a clearly defined perturbation of a biological system. More specifically, the perturbation may take the form of a genetic, biochemical, or environmental stimulus. At the core of systems biology is the transformation of quantita­tive, typically large-scale data sets, into in silico models that provide both interpretation and prediction. Systems biology has emerged as a tool applied in different fields, including metabolic engineering, to what many consider to be an independent discipline of study and research [57]. Table 1 provides an overview of commonly used industrial biotechnology strategies, focused on metabolic engineering with specific examples taken from applications

Table 1 Overview of commonly used industrial biotechnology strategies

Industrial biotechnology strategy Examples of application to

bioethanol production

A case study considering the co-production of ethanol and succinic acid suggests significant cost reduction (sales price of ethanol decreases from $0.51 to $0.42/gal.). Pilot plant confirmation pending [115,116].

In silico aided metabolic engineering of S. cerevisiae lead to a 40% reduction in glycerol formation and 3% increase in ethanol yield in vivo [154].

Natural ethanol producing bacterium Zymomonas mobilis metabolically engineered to ferment xylose and arabinose as preferred carbon sources via introduction/expression of E. coli pathway genes [6,155].

Xylose (C5H10O5, significant fraction of lignocelluloses) utilization by S. cerevisiae investigated and optimized via introduction of a Piromyces sp. xylose isomerase (XylA). Further xylose metabolic structural genes were overexpressed. Xylose consumption of

0.

9-1.1 g g-biomass-1 h-1, demonstrated in vivo [156-159].

to bioethanol production. The examples cited exploit toolboxes developed within systems biology.

Therefore, we refer here to industrial systems biology, defined as the appli­cation of experimental or numerical methods developed from systems biol­ogy to improve bioprocess development in terms of final product titer, yield, or productivity, or process robustness and efficiency. In most cases, indus­trial systems biology has been product — or process-specific; however, there are emerging examples of successful commercialization of stand-alone systems biology tools and products for broad application [58].

Recent advances in high-throughput experimental techniques have re­sulted in rapid accumulation of a wide range of x-omics data of various forms (Fig. 3), providing a foundation for in-depth understanding of biolog­ical processes [59-62]. How to integrate, interpret, and apply these data is an area of active research. Bioinformatics has become a well-established and recognized interdisciplinary field. To date, large data sets of transcriptomes, metabolomes, and to lesser degrees proteomes and fluxomes, for multiple organisms have been acquired. Resources are being applied to integrating the various data sets for in silico simulations and creating relevant models that represent in vivo physiological conditions of host cells responding to environmental stimuli. Even though our ability to analyze these x-omic (see “Glossary”) data in a truly integrated manner is limited, new targets for strain improvement can be identified from these global data [63-69].

A sampling of recent developments and applications in the field of sys­tems biology will be discussed in relation to improving the productivity of bioethanol. Examples will be provided on single x-ome approaches and com­bined analysis of these x-ome data for the development of improved strains and enhancement of metabolic engineering strategies.

3.1