OptFlux: an open-source software platform for in silico metabolic engineering

<p>Abstract</p> <p>Background</p> <p>Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications.</p> <p>Results</p> <p><it>OptFlux </it>is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes.</p> <p><it>OptFlux </it>also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms.</p> <p>The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. <it>OptFlux </it>has a visualization module that allows the analysis of the model structure that is compatible with the layout information of <it>Cell Designer</it>, allowing the superimposition of simulation results with the model graph.</p> <p>Conclusions</p> <p>The <it>OptFlux </it>software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community.</p> <p>Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.</p

Correlation of cell growth and heterologous protein production by Saccharomyces cerevisiae

With the increasing demand for biopharmaceutical proteins and industrial enzymes, it is necessary to optimize the production by microbial fermentation or cell cultures. Yeasts are well established for the production of a wide range of recombinant proteins, but there are also some limitations; e.g., metabolic and cellular stresses have a strong impact on recombinant protein production. In this work, we investigated the effect of the specific growth rate on the production of two different recombinant proteins. Our results show that human insulin precursor is produced in a growth-associated manner, whereas alpha-amylase tends to have a higher yield on substrate at low specific growth rates. Based on transcriptional analysis, we found that the difference in the production of the two proteins as function of the specific growth rate is mainly due to differences in endoplasmic reticulum processing, protein turnover, cell cycle, and global stress response. We also found that there is a shift at a specific growth rate of 0.1 h(-1) that influences protein production. Thus, for lower specific growth rates, the alpha-amylase and insulin precursor-producing strains present similar cell responses and phenotypes, whereas for higher specific growth rates, the two strains respond differently to changes in the specific growth rate

Economic Impact of Dengue Illness and the Cost-Effectiveness of Future Vaccination Programs in Singapore

Dengue illness is a tropical disease transmitted by mosquitoes that threatens more than one third of the worldwide population. Dengue has important economic consequences because of the burden to hospitals, work absenteeism and risk of death of symptomatic cases. Governments attempt to reduce the disease burden using costly mosquito control strategies such as habitat reduction and spraying insecticide. Despite such efforts, the number of cases remains high. Dengue vaccines are expected to be available in the near future and there is an urgent need to evaluate their cost-effectiveness, i.e. whether their cost will be justified by the reduction in disease burden they bring. For such an evaluation, we estimated the economic impacts of dengue in Singapore and the expected vaccine costs for different prices. In this way we estimated price thresholds for which vaccination is not cost-effective. This research provides useful estimates that will contribute to informed decisions regarding the adoption of dengue vaccination programs

Optimization in computational systems biology

Optimization aims to make a system or design as effective or functional as possible. Mathematical optimization methods are widely used in engineering, economics and science. This commentary is focused on applications of mathematical optimization in computational systems biology. Examples are given where optimization methods are used for topics ranging from model building and optimal experimental design to metabolic engineering and synthetic biology. Finally, several perspectives for future research are outlined

Anaerobic α-Amylase Production and Secretion with Fumarate as the Final Electron Acceptor in Saccharomyces cerevisiae

In this study, we focus on production of heterologous alpha-amylase in the yeast Saccharomyces cerevisiae under anaerobic conditions. We compare the metabolic fluxes and transcriptional regulation under aerobic and anaerobic conditions, with the objective of identifying the final electron acceptor for protein folding under anaerobic conditions. We find that yeast produces more amylase under anaerobic conditions than under aerobic conditions, and we propose a model for electron transfer under anaerobic conditions. According to our model, during protein folding the electrons from the endoplasmic reticulum are transferred to fumarate as the final electron acceptor. This model is supported by findings that the addition of fumarate under anaerobic (but not aerobic) conditions improves cell growth, specifically in the alpha-amylase-producing strain, in which it is not used as a carbon source. Our results provide a model for the molecular mechanism of anaerobic protein secretion using fumarate as the final electron acceptor, which may allow for further engineering of yeast for improved protein secretion under anaerobic growth conditions

Heat shock response improves heterologous protein secretion in Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is a widely used platform for the production of heterologous proteins of medical or industrial interest. However, heterologous protein productivity is often low due to limitations of the host strain. Heat shock response (HSR) is an inducible, global, cellular stress response, which facilitates the cell recovery from many forms of stress, e.g., heat stress. In S. cerevisiae, HSR is regulated mainly by the transcription factor heat shock factor (Hsf1p) and many of its targets are genes coding for molecular chaperones that promote protein folding and prevent the accumulation of mis-folded or aggregated proteins. In this work, we over-expressed a mutant HSF1 gene HSF1-R206S which can constitutively activate HSR, so the heat shock response was induced at different levels, and we studied the impact of HSR on heterologous protein secretion. We found that moderate and high level over-expression of HSF1-R206S increased heterologous alpha-amylase yield 25 and 70 % when glucose was fully consumed, and 37 and 62 % at the end of the ethanol phase, respectively. Moderate and high level over-expression also improved endogenous invertase yield 118 and 94 %, respectively. However, human insulin precursor was only improved slightly and this only by high level over-expression of HSF1-R206S, supporting our previous findings that the production of this protein in S. cerevisiae is not limited by secretion. Our results provide an effective strategy to improve protein secretion and demonstrated an approach that can induce ER and cytosolic chaperones simultaneously

Combinatorial Engineering of 1-Deoxy-D-Xylulose 5-Phosphate Pathway Using Cross-Lapping In Vitro Assembly (CLIVA) Method

The ability to assemble multiple fragments of DNA into a plasmid in a single step is invaluable to studies in metabolic engineering and synthetic biology. Using phosphorothioate chemistry for high efficiency and site specific cleavage of sequences, a novel ligase independent cloning method (cross-lapping in vitro assembly, CLIVA) was systematically and rationally optimized in E. coli. A series of 16 constructs combinatorially expressing genes encoding enzymes in the 1-deoxy-D-xylulose 5-phosphate (DXP) pathway were assembled using multiple DNA modules. A plasmid (21.6 kb) containing 16 pathway genes, was successfully assembled from 7 modules with high efficiency (2.0 x 10[superscript 3] cfu/ µg input DNA) within 2 days. Overexpressions of these constructs revealed the unanticipated inhibitory effects of certain combinations of genes on the production of amorphadiene. Interestingly, the inhibitory effects were correlated to the increase in the accumulation of intracellular methylerythritol cyclodiphosphate (MEC), an intermediate metabolite in the DXP pathway. The overexpression of the iron sulfur cluster operon was found to modestly increase the production of amorphadiene. This study demonstrated the utility of CLIVA in the assembly of multiple fragments of DNA into a plasmid which enabled the rapid exploration of biological pathways.Singapore-MIT Allianc