Systems biotechnology for strain improvement

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Various high-throughput experimental techniques are routinely used for generating large amounts of omics data. In parallel, in silico modelling and simulation approaches are being developed for quantitatively analyzing cellular metabolism at the systems level. Thus informative high-throughput analysis and predictive computational modelling or simulation can be combined to generate new knowledge through iterative modification of an in silico model and experimental design. On the basis of such global cellular information we can design cells that have improved metabolic properties for industrial applications. This article highlights the recent developments in these systems approaches, which we call systems biotechnology, and discusses future prospects.

Introduction

The indispensable role of biotechnology is increasing in nearly every industry, including the healthcare, pharmaceutical, chemical, food and agricultural industries. Biotechnological production of small-volume high-value drugs, chemicals and bioproducts is well justified economically. However, production of large-volume low-value bioproducts requires the development of lower-cost and higher-yield processes. Towards this goal, improved microorganisms have traditionally been developed through random mutagenesis followed by intelligent screening processes [1]. Rational metabolic and cellular engineering approaches have also been successful in improving strain performance in several cases; however, such attempts were limited to the manipulation of only a handful of genes encoding enzymes and regulatory proteins selected using available information and research experience.

Recent advances in high-throughput experimental techniques supported by bioinformatics have resulted in rapid accumulation of a wide range of omics data at various levels (Figure 1), thus providing a foundation for in-depth understanding of biological processes 2, 3, 4. Even though our ability to analyze these x-omic (see Glossary) data in a truly integrated manner is currently limited, new targets for strain improvement can be identified from these global data 5, 6. More recently, several examples of combined analysis of these x-omic data towards the development of improved strains have been reported [7]. Along with these high-throughput experimental techniques, in silico modelling and simulation are providing powerful solutions for deciphering the functions and characteristics of biological systems 8, 9, 10, 11. These in silico experiments would elevate our capability for understanding and predicting the cellular behaviour of microorganisms under any perturbations (e.g. genetic modifications and/or environmental changes) on a global scale [12].

Consequently, systems-level engineering of microorganisms can be achieved by integrating high-throughput experiments and in silico experiments (Figure 2). The results of genomic, transcriptomic, proteomic, metabolomic and fluxomic studies, the data available in databases, and those predicted by computational modelling and simulation, are considered together within the global context of the metabolic system. This gives rise to new knowledge that can facilitate development of strains that are efficient and productive enough to be suitable for industrial applications.

Section snippets

High-throughput x-omic analyses for strain improvement

As DNA sequencing has become faster and cheaper the genome sequences of many microorganisms have been completed and many more are in progress. With the complete genome sequences in our hands, post-genomic research (the ‘omics’ fields) is increasing rapidly. Transcriptomics allows massively parallel analysis of mRNA expression levels using DNA microarrays. Proteomics allows analysis of the protein complement of the cell or its parts by using two-dimensional gel electrophoresis (2DGE) or

In silico modelling and simulation

In addition to high-throughput experimental analysis, in silico modelling and simulation are important aspects of systems biotechnology. The effects of genetic and/or environmental perturbations on cellular metabolism can be predicted by various in silico modelling and simulation approaches. The results of in silico analysis can then be used to design strategies for strain improvement. Detailed reviews on modelling and simulation of metabolic pathways are available elsewhere 11, 25.

Systemic and integrative strategy for developing improved strains

The strategic foundation of systems biotechnology is largely based on the systemic integration of high-throughput x-omic analysis and in silico modelling or simulation. Figure 3 outlines the conceptual procedure for systems biotechnological research. At the outset, the computational model describing the metabolic system is constructed. This model can be used to analyze and/or predict the system's behaviour for a particular experimental situation under systematic perturbation (e.g. gene deletion

Future prospects

Systems biotechnology is now in its early stages of development and presents a variety of technical challenges. The central task of systems biotechnology is to comprehensively collect global cellular information, such as omics data, and to combine these data through metabolic, signaling and regulatory networks to generate predictive computational models of the biological system. Because each x-ome alone is not enough to understand cellular physiology and regulatory mechanisms, combined analysis

Acknowledgements

Our work described in this paper was supported by the Korean Systems Biology Research Program (M10309020000–03B5002–00000) of the Ministry of Science and Technology and by the BK21 project. Further support from the LG Chem Chair Professorship, KOSEF and the IBM-SUR program are greatly appreciated.

Glossary

Bilevel optimization:
A traditional mathematical programming problem maximizes a single objective function over a set of feasible solutions. Bilevel optimization seeks to maximize two objective functions simultaneously over a set of feasible solutions.
Flux:
The production or consumption of mass (metabolite) per unit area per unit time. It is, however, often used on the basis of unit cell mass rather than unit area in metabolic flux analysis.
High cell density culture:
High cell density culture is

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