Bevan Kai-Sheng Chung

Software applications for flux balance analysis

Flux balance analysis (FBA) is a widely used computational method for characterizing and engineering intrinsic cellular metabolism. The increasing number of its successful applications and growing popularity are possibly attributable to the availability of specific software tools for FBA. Each tool has its unique features and limitations with respect to operational environment, user-interface and supported analysis algorithms. Presented herein is an in-depth evaluation of currently available FBA applications, focusing mainly on usability, functionality, graphical representation and inter-operability. Overall, most of the applications are able to perform basic features of model creation and FBA simulation. COBRA toolbox, OptFlux and FASIMU are versatile to support advanced in silico algorithms to identify environmental and genetic targets for strain design. SurreyFBA, WEbcoli, Acorn, FAME, GEMSiRV and MetaFluxNet are the distinct tools which provide the user friendly interfaces in model handling. In terms of software architecture, FBA-SimVis and OptFlux have the flexible environments as they enable the plug-in/add-on feature to aid prospective functional extensions. Notably, an increasing trend towards the implementation of more tailored e-services such as central model repository and assistance to collaborative efforts was observed among the web-based applications with the help of advanced web-technologies. Furthermore, most recent applications such as the Model SEED, FAME, MetaFlux and MicrobesFlux have even included several routines to facilitate the reconstruction of genome-scale metabolic models. Finally, a brief discussion on the future directions of FBA applications was made for the benefit of potential tool developers.

Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

BackgroundConstraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.ResultsPresented herein is a metabolite-centric approach of analyzing the metabolic network by including the turnover rate of metabolite, known as flux-sum, as key descriptive variable within the model formulation. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. From the results, we could classify various metabolite types based on the flux-sum profile. Using the i AF1260 in silico metabolic model of Escherichia coli, we demonstrated that this novel concept complements the conventional reaction-centric analysis.ConclusionsMetabolite flux-sum analysis elucidates the roles of metabolites in the network. In addition, this metabolite perturbation analysis identifies the key metabolites, implicating practical application which is achievable through metabolite flux-sum manipulation in the areas of biotechnology and biomedical research.

Computational codon optimization of synthetic gene for protein expression.

BackgroundThe construction of customized nucleic acid sequences allows us to have greater flexibility in gene design for recombinant protein expression. Among the various parameters considered for such DNA sequence design, individual codon usage (ICU) has been implicated as one of the most crucial factors affecting mRNA translational efficiency. However, previous works have also reported the significant influence of codon pair usage, also known as codon context (CC), on the level of protein expression.ResultsIn this study, we have developed novel computational procedures for evaluating the relative importance of optimizing ICU and CC for enhancing protein expression. By formulating appropriate mathematical expressions to quantify the ICU and CC fitness of a coding sequence, optimization procedures based on genetic algorithm were employed to maximize its ICU and/or CC fitness. Surprisingly, the in silico validation of the resultant optimized DNA sequences for Escherichia coli, Lactococcus lactis, Pichia pastoris and Saccharomyces cerevisiae suggests that CC is a more relevant design criterion than the commonly considered ICU.ConclusionsThe proposed CC optimization framework can complement and enhance the capabilities of current gene design tools, with potential applications to heterologous protein production and even vaccine development in synthetic biotechnology.

Codon Optimization OnLine (COOL): a web-based multi-objective optimization platform for synthetic gene design

SUMMARY Codon optimization has been widely used for designing synthetic genes to improve their expression in heterologous host organisms. However, most of the existing codon optimization tools consider a single design criterion and/or implement a rather rigid user interface to yield only one optimal sequence, which may not be the best solution. Hence, we have developed Codon Optimization OnLine (COOL), which is the first web tool that provides the multi-objective codon optimization functionality to aid systematic synthetic gene design. COOL supports a simple and flexible interface for customizing various codon optimization parameters such as codon adaptation index, individual codon usage and codon pairing. In addition, users can visualize and compare the optimal synthetic sequences with respect to various fitness measures. User-defined DNA sequences can also be compared against the COOL optimized sequences to show the extent by which the users sequences can be further improved. AVAILABILITY AND IMPLEMENTATION COOL is free to academic and non-commercial users and licensed to others for a fee by the National University of Singapore. Accessible at http://bioinfo.bti.a-star.edu.sg/COOL/ CONTACT: cheld@nus.edu.sg SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Enhanced expression of codon optimized interferon gamma in CHO cells

The human interferon-gamma (IFN-γ) is a potential drug candidate for treating various diseases due to its immunomodulatory properties. The efficient production of this protein can be achieved through a popular industrial host, Chinese hamster ovary (CHO) cells. However, recombinant expression of foreign proteins is typically suboptimal possibly due to the usage of non-native codon patterns within the coding sequence. Therefore, we demonstrated the application of a recently developed codon optimization approach to design synthetic IFN-γ coding sequences for enhanced heterologous expression in CHO cells. For codon optimization, earlier studies suggested to establish the target usage distribution pattern in terms of selected design parameters such as individual codon usage (ICU) and codon context (CC), mainly based on the hosts highly expressed genes. However, our RNA-Seq based transcriptome profiling indicated that the ICU and CC distribution patterns of different gene expression classes in CHO cell are relatively similar, unlike other microbial expression hosts, Escherichia coli and Saccharomyces cerevisiae. This finding was further corroborated through the in vivo expression of various ICU and CC optimized IFN-γ in CHO cells. Interestingly, the CC-optimized genes exhibited at least 13-fold increase in expression level compared to the wild-type IFN-γ while a maximum of 10-fold increase was observed for the ICU-optimized genes. Although design criteria based on individual codons, such as ICU, have been widely used for gene optimization, our experimental results suggested that codon context is relatively more effective parameter for improving recombinant IFN-γ expression in CHO cells.

In silico analyses for the discovery of tuberculosis drug targets

Antibacterial drug discovery is moving from largely unproductive high-throughput screening of isolated targets in the past decade to revisiting old, clinically validated targets and drugs, and to classical black-box whole-cell screens. At the same time, due to the application of existing methods and the emergence of new high-throughput biology methods, we observe the generation of unprecedented qualities and quantities of genomic and other omics data on bacteria and their physiology. Tuberculosis (TB) drug discovery and biology follow the same pattern. There is a clear need to reconnect antibacterial drug discovery with modern, genome-based biology to enable the identification of new targets with high confidence for the rational discovery of new drugs. To exploit the increasing amount of bacterial biology information, a variety of in silico methods have been developed and applied to large-scale biological models to identify candidate antibacterial targets. Here, we review key concepts in network analysis for target discovery in tuberculosis and provide a summary of potential TB drug targets identified by the individual methods. We also discuss current developments and future prospects for the application of systems biology in the field of TB target discovery.

Cofactor modification analysis: a computational framework to identify cofactor specificity engineering targets for strain improvement.

Cofactors, such as NAD(H) and NADP(H), play important roles in energy transfer within the cells by providing the necessary redox carriers for a myriad of metabolic reactions, both anabolic and catabolic. Thus, it is crucial to establish the overall cellular redox balance for achieving the desired cellular physiology. Of several methods to manipulate the intracellular cofactor regeneration rates, altering the cofactor specificity of a particular enzyme is a promising one. However, the identification of relevant enzyme targets for such cofactor specificity engineering (CSE) is often very difficult and labor intensive. Therefore, it is necessary to develop more systematic approaches to find the cofactor engineering targets for strain improvement. Presented herein is a novel mathematical framework, cofactor modification analysis (CMA), developed based on the well-established constraints-based flux analysis, for the systematic identification of suitable CSE targets while exploring the global metabolic effects. The CMA algorithm was applied to E. coli using its genome-scale metabolic model, iJO1366, thereby identifying the growth-coupled cofactor engineering targets for overproducing four of its native products: acetate, formate, ethanol, and lactate, and three non-native products: 1-butanol, 1,4-butanediol, and 1,3-propanediol. Notably, among several target candidates for cofactor engineering, glyceraldehyde-3-phosphate dehydrogenase (GAPD) is the most promising enzyme; its cofactor modification enhanced both the desired product and biomass yields significantly. Finally, given the identified target, we further discussed potential mutational strategies for modifying cofactor specificity of GAPD in E. coli as suggested by in silico protein docking experiments.

NADPH-dependent pgi-gene knockout Escherichia coli metabolism producing shikimate on different carbon sources.

We explored the physiological and metabolic effects of different carbon sources (glucose, fructose, and glucose/fructose mixture) in phosphoglucose isomerase (pgi) knockout Escherichia coli mutant producing shikimic acid (SA). It was observed that the pgi(-) mutant grown on glucose exhibited significantly lower cell growth compared with the pgi(+) strain and its mixed glucose/fructose fermentation grew well. Interestingly, when fructose was used as a carbon source, the pgi(-) mutant showed the enhanced SA production compared with the pgi(+) strain. In silico analysis of a genome-scale E. coli model was then conducted to characterize the cellular metabolism and quantify NAPDH regeneration, which allowed us to understand such experimentally observed attenuated cell growth and enhanced SA production in glucose- and fructose-consuming pgi(-) mutant, respectively with respect to cofactor regeneration.

Flux-sum analysis identifies metabolite targets for strain improvement

BackgroundRational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.ResultsIn this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology.ConclusionsThe application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts’ metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.