Seung Bum Sohn

Recent advances in reconstruction and applications of genome-scale metabolic models.

In the last decade, reconstruction and applications of genome-scale metabolic models have greatly influenced the field of systems biology by providing a platform on which high-throughput computational analysis of metabolic networks can be performed. The last two years have seen an increase in volume of more than 33% in the number of published genome-scale metabolic models, signifying a high demand for these metabolic models in studying specific organisms. The diversity in modeling different types of cells, from photosynthetic microorganisms to human cell types, also demonstrates their growing influence in biology. Here we review the recent advances and current state of genome-scale metabolic models, the methods employed towards ensuring high quality models, their biotechnological applications, and the progress towards the automated reconstruction of genome-scale metabolic models.

Genome‐scale metabolic model of methylotrophic yeast Pichia pastoris and its use for in silico analysis of heterologous protein production

The methylotrophic yeast Pichia pastoris has gained much attention during the last decade as a platform for producing heterologous recombinant proteins of pharmaceutical importance, due to its ability to reproduce post-translational modification similar to higher eukaryotes. With the recent release of the full genome sequence for P. pastoris, in-depth study of its functions has become feasible. Here we present the first reconstruction of the genome-scale metabolic model of the eukaryote P. pastoris type strain DSMZ 70382, PpaMBEL1254, consisting of 1254 metabolic reactions and 1147 metabolites compartmentalized into eight different regions to represent organelles. Additionally, equations describing the production of two heterologous proteins, human serum albumin and human superoxide dismutase, were incorporated. The protein-producing model versions of PpaMBEL1254 were then analyzed to examine the impact on oxygen limitation on protein production.

Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production

The production of recombinant proteins is frequently enhanced at the levels of transcription, codon usage, protein folding and secretion. Overproduction of heterologous proteins, however, also directly affects the primary metabolism of the producing cells. By incorporation of the production of a heterologous protein into a genome scale metabolic model of the yeast Pichia pastoris, the effects of overproduction were simulated and gene targets for deletion or overexpression for enhanced productivity were predicted. Overexpression targets were localized in the pentose phosphate pathway and the TCA cycle, while knockout targets were found in several branch points of glycolysis. Five out of 9 tested targets led to an enhanced production of cytosolic human superoxide dismutase (hSOD). Expression of bacterial β-glucuronidase could be enhanced as well by most of the same genetic modifications. Beneficial mutations were mainly related to reduction of the NADP/H pool and the deletion of fermentative pathways. Overexpression of the hSOD gene itself had a strong impact on intracellular fluxes, most of which changed in the same direction as predicted by the model. In vivo fluxes changed in the same direction as predicted to improve hSOD production. Genome scale metabolic modeling is shown to predict overexpression and deletion mutants which enhance recombinant protein production with high accuracy.

In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival.

Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli.

Strategies for systems-level metabolic engineering.

Bio‐based production of chemicals, fuels and materials is becoming more and more important due to the increasing environmental problems and sharply increasing oil price. To make these biobased processes economically competitive, the biotechnology industry explores new ways to improve the performance of microbial strains in fermentation processes. In contrast to the random mutagenesis and/or intuitive local metabolic engineering practiced in the past, we are now moving towards global‐scale metabolic engineering, aided by various experimental and computational tools. This has recently led to some remarkable achievements for the overproduction of valueadded products. In this review, we highlight several relevant gene manipulation tools and computational tools using genome‐scale stoichiometric models, and provide useful strategies for successful metabolic engineering along with selected exemplary studies.

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome‐scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome‐scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome‐scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organisms metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome‐scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.

Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth.

BackgroundOver the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.ResultsHere we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).ConclusionThis study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data.

Genome-Scale Network Modeling

Genome-scale models have garnered considerable interest for their ability to elucidate cellular characteristics and lead to a better understanding of biological systems. Metabolic models in particular have been widely used to study complex metabolic pathways in order to better understand microbial systems and to design strategies for engineering various biotechnological applications. Similar to metabolic networks, transcriptional and signaling network models have also been reconstructed to elucidate regulatory interactions and to further understand the response of systems to various environmental stimuli. However, a true genome-scale model that integrates all these characteristics into one comprehensive model has not yet been constructed. For the time being, the existing network models have individually contributed to the knowledge of their respective fields and to our understanding of biological systems. In selected cases they have provided design strategies for systems wide engineering of metabolism. There have been several attempts to integrate these networks to realize the full potential of a complete cellular network model, although there is still room for further development. Here, we review the different network types and highlight their contributions to biotechnological applications via illustrative examples.

Computational Methods for Strain Design

Synthetic biology aims to design and modify cellular functions to engineer cells to perform specific and well-defined functions. This control over the capabilities of the cell is dependent on the knowledge of cellular behavior and its regulatory control. Towards this end, synthetic biology has resulted in the creation of genetic circuits to help visualize and standardize the various modules employed to modify cellular functions. However, the complexity of biological systems has made it difficult to directly modify the target cellular component, due to unanticipated effects on other cellular functions and the overall physiology. Therefore, computational tools have been developed to aid in the prediction and analysis of possible synthetic biology strategies for modifying the cell. In this chapter, computational tools developed for designing genetic components and strategies are presented. Furthermore, the need for a systems-level analysis and the application for engineering cells using these computational synthetic biology tools are discussed.

2.61 – Metabolic Control

Understanding metabolic control is an important facet of manipulating metabolic pathways. It has been well documented that simple removal of competing pathways and regulatory effects results in limited success. More often than not, such modifications result in no change or an effect that is less beneficial than predicted. Control analysis provides a quantitative analysis of the system allowing us to better understand how systems respond to various perturbations. Consequently, knowledge of the systems response to perturbation allows us to target specific elements for modification so that the desired response is obtained. This article will focus on the various approaches developed for metabolic control as well as experimental approaches developed to obtain data for computational analysis. With full understanding of the dynamics of metabolic control, a successful manipulation of the metabolic flux and metabolite concentrations can be achieved.