Trends in Biotechnology
Volume 28, Issue 8, August 2010, Pages 391-397
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Opinion
Production of biofuels and biochemicals: in need of an ORACLE

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The engineering of cells for the production of fuels and chemicals involves simultaneous optimization of multiple objectives, such as specific productivity, extended substrate range and improved tolerance – all under a great degree of uncertainty. The achievement of these objectives under physiological and process constraints will be impossible without the use of mathematical modeling. However, the limited information and the uncertainty in the available information require new methods for modeling and simulation that will characterize the uncertainty and will quantify, in a statistical sense, the expectations of success of alternative metabolic engineering strategies. We discuss these considerations toward developing a framework for the Optimization and Risk Analysis of Complex Living Entities (ORACLE) – a computational method that integrates available information into a mathematical structure to calculate control coefficients.

Section snippets

Biofuels and biochemicals: a multi-objective optimization problem

Nearly 20 years ago, Tong and Cameron classified the applications of metabolic engineering into five main areas: (i) improved production and utilization of chemicals already produced/used by the host; (ii) extended substrate range for growth and production; (iii) addition of new catabolic activities for the degradation of toxic chemicals; (iv) production of chemicals new to the host; and (v) modification of the cell [1]. In the development of microorganisms for fuels and chemicals, one must

Guidance for metabolic engineering

Metabolic engineering design requires identification of rate-limiting steps in metabolic pathways. A large amount of effort has been invested in the quantification of the metabolic fluxes within cells, using methods such as metabolic flux analysis 8, 9, 10. However, one of the limitations of these methods is that they provide only a snapshot of the fluxes and do not quantify the responses of metabolic networks to the changes in the metabolic parameters or the process parameters, such as oxygen

Uncertainty in biological systems

A predominant issue in the development of kinetic models of metabolic networks is the lack of available information and the uncertainty associated with such information, such as metabolic fluxes and kinetic properties of enzymes. The uncertainty in the study of metabolic pathways can be classified in two types: structural and quantitative (Table 1). ‘Structural uncertainty’ deals with the lack of knowledge concerning the stoichiometry and the kinetic laws of the enzymes in the pathways. For

Optimization and Risk Analysis of Complex Living Entities (ORACLE)

Wang and Hatzimanikatis 40, 41, 42 have proposed an approach based on MCA frameworks that uses uncertainty analysis methods for the study of metabolic pathways and address most of the previously mentioned issues. The method, called ORACLE (optimization and risk analysis of complex living entities), is based on a sampling computational procedure and involves several steps where the available information is integrated into a mathematical structure and the control coefficients are calculated.

Prediction versus expectation

The results from the application of the ORACLE or similar methods should not be viewed as predictions in the strict engineering sense; instead, they are predictions in a statistical manner, as expectations of success of the metabolic engineering targets they identify. Rather than providing a single solution, the analyses offer a set of alternative solutions that are evaluated with respect to their uncertainty, which itself is the propagation of the uncertainty in the available information. One

A case study: ethanol production in yeast

ORACLE has been used for the analysis of ethanol production by S. cerevisiae41, 42. The only experimental information used in this analysis was the pathway stoichiometry and the distribution of the metabolic fluxes [10]. Standard kinetic expressions (not kinetic parameters) were used for the enzymes, and metabolite concentrations were sampled within typical physiological ranges. In these studies, a mathematical model of the central carbon pathways in yeast was constructed, comprising 55

Concluding remarks

Engineering complex metabolic pathways for the efficient production of biofuels and biochemicals is a challenging task, and the partial knowledge and uncertainty associated with the components of these pathways makes it difficult to advance the field. Although it will be hard to find some prophets and deities to help us with these complex problems, hopefully ORACLE and similar risk analysis methods will provide some much-needed ‘prophetic’ guidance.

The development of industrial microorganisms

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