Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation. Constraint-based modeling has become one of the most successful approaches for modeling large-scale biochemical networks. There are nowadays hundreds of genome-scale reconstructions of metabolic networks available for a wide variety of organisms ranging from bacteria to human cells. One of the limitations of the constraint-based approach is that it describes the cellular phenotype simply in terms of biochemical reaction rates, in a way that is disconnected from other biological processes such as genetic regulation. In order to overcome this limitation, different approaches for integration of gene expression data into constraint-based models have been developed during the past few years. However, all the methods developed so far have only been tested using isolated case studies. In this work, we elaborate a detailed survey of these methods, and perform a critical and quantitative evaluation of a selected subset of methods, using experimental datasets that include different organisms and conditions. This study highlights some of the current limitations in many of these methods, and reveals that no method published so far systematically outperforms the others.

In the future, oil- and gas-derived polymers may be replaced with bio-based polymers, produced from renewable feedstocks using engineered cell factories. Acrylic acid and acrylic esters with an estimated world annual production of approximately 6 million tons by 2017 can be derived from 3-hydroxypropionic acid (3HP), which can be produced by microbial fermentation. For an economically viable process 3HP must be produced at high titer, rate and yield and preferably at low pH to minimize downstream processing costs. Here we describe the metabolic engineering of baker’s yeast Saccharomyces cerevisiae for biosynthesis of 3HP via a malonyl-CoA reductase (MCR)-dependent pathway. Integration of multiple copies of MCR from Chloroflexus aurantiacus and of phosphorylation-deficient acetyl-CoA carboxylase ACC1 genes into the genome of yeast increased 3HP titer fivefold in comparison with single integration. Furthermore we optimized the supply of acetyl-CoA by overexpressing native pyruvate decarboxylase PDC1, aldehyde dehydrogenase ALD6, and acetyl-CoA synthase from Salmonella enterica SEacs L641P . Finally we engineered the cofactor specificity of the glyceraldehyde-3-phosphate dehydrogenase to increase the intracellular production of NADPH at the expense of NADH and thus improve 3HP production and reduce formation of glycerol as by-product. The final strain produced 9.8 ± 0.4 g L−1 3HP with a yield of 13 % C-mol C-mol−1 glucose after 100 h in carbon-limited fed-batch cultivation at pH 5. The 3HP-producing strain was characterized by 13C metabolic flux analysis and by transcriptome analysis, which revealed some unexpected consequences of the undertaken metabolic engineering strategy, and based on this data, future metabolic engineering directions are proposed. In this study, S. cerevisiae was engineered for high-level production of 3HP by increasing the copy numbers of biosynthetic genes and improving flux towards precursors and redox cofactors. This strain represents a good platform for further optimization of 3HP production and hence an important step towards potential commercial bio-based production of 3HP.

Biochemical production faces economic and environmental challenges that need to be overcome to enable a viable and sustainable bioeconomy. We propose an assessment framework that consistently combines environmental and economic indicators to support optimized biochemical production at early development stages. We define internally consistent system boundaries and a comprehensive set of quantitative indicators from life cycle assessment (LCA) and techno-economic assessment (TEA) to combine environmental and economic performance in a single score. Our framework enables the identification of trade-offs across environmental and economic aspects over the entire biochemical life cycle. This approach provides input for the optimization of future biochemicals in terms of overall sustainability, to overcome prevailing obstacles in the development of biochemical production processes.