OptStrain: a computational framework for redesign of microbial production systems

Towards multidimensional genome annotation

Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.

Evolutionary programming as a platform for in silico metabolic engineering

BACKGROUND

Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms.

RESULTS

In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed.

CONCLUSION

We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

Insights on biology and evolution from microbial genome sequencing

No field of research has embraced and applied genomic technology more than the field of microbiology. Comparative analysis of nearly 300 microbial species has demonstrated that the microbial genome is a dynamic entity shaped by multiple forces. Microbial genomics has provided a foundation for a broad range of applications, from understanding basic biological processes, host-pathogen interactions, and protein-protein interactions, to discovering DNA variations that can be used in genotyping or forensic analyses, the design of novel antimicrobial compounds and vaccines, and the engineering of microbes for industrial applications. Most recently, metagenomics approaches are allowing us to begin to probe complex microbial communities for the first time, and they hold great promise in helping to unravel the relationships between microbial species.

An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems

We introduce a computational framework termed OptReg that determines the optimal reaction activations/inhibitions and eliminations for targeted biochemical production. A reaction is deemed up- or downregulated if it is constrained to assume flux values significantly above or below its steady-state before the genetic manipulations. The developed framework is demonstrated by studying the overproduction of ethanol in Escherichia coli. Computational results reveal the existence of synergism between reaction deletions and modulations implying that the simultaneous application of both types of genetic manipulations yields the most promising results. For example, the downregulation of phosphoglucomutase in conjunction with the deletion of oxygen uptake and pyruvate formate lyase yields 99.8% of the maximum theoretical ethanol yield. Conceptually, the proposed strategies redirect both the carbon flux as well as the cofactors to enhance ethanol production in the network. The OptReg framework is a versatile tool for strain design which allows for a broad array of genetic manipulations.

All systems go: launching cell simulation fueled by integrated experimental biology data

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.