Computational Methods to Assess the Production Potential of Bio-Based Chemicals.

Elevated costs and long implementation times of bio-based processes for producing chemicals represent a bottleneck for moving to a bio-based economy. A prospective analysis able to elucidate economically and technically feasible product targets at early research phases is mandatory. Computational tools can be implemented to explore the biological and technical spectrum of feasibility, while constraining the operational space for desired chemicals. In this chapter, two different computational tools for assessing potential for bio-based production of chemicals from different perspectives are described in detail. The first tool is GEM-Path: an algorithm to compute all structurally possible pathways from one target molecule to the host metabolome. The second tool is a framework for Modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes, and economic impact assessment. Integrating GEM-Path and MuSIC will play a vital role in supporting early phases of research efforts and guide the policy makers with decisions, as we progress toward planning a sustainable chemical industry.

Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes.

Escherichia coli strains are widely used in academic research and biotechnology. New technologies for quantifying strain-specific differences and their underlying contributing factors promise greater understanding of how these differences significantly impact physiology, synthetic biology, metabolic engineering, and process design. Here, we quantified strain-specific differences in seven widely used strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics, and genome-scale modeling. Metabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titer. These differences could be linked to differential regulatory structure. Analyzing high-flux reactions and expression of encoding genes resulted in a correlated and quantitative link between these sets, with strain-specific caveats. Integrated modeling revealed that certain strains are better suited to produce given compounds or express desired constructs considering native expression states of pathways that enable high-production phenotypes. This study yields a framework for quantitatively comparing strains in a species with implications for strain selection.

Acidithiobacillus ferrooxidans's comprehensive model driven analysis of the electron transfer metabolism and synthetic strain design for biomining applications.

Acidithiobacillus ferrooxidans is a gram-negative chemolithoautotrophic γ-proteobacterium. It typically grows at an external pH of 2 using the oxidation of ferrous ions by oxygen, producing ferric ions and water, while fixing carbon dioxide from the environment. A. ferrooxidans is of great interest for biomining and environmental applications, as it can process mineral ores and alleviate the negative environmental consequences derived from the mining processes. In this study, the first genome-scale metabolic reconstruction of A. ferrooxidans ATCC 23270 was generated (iMC507). A total of 587 metabolic and transport/exchange reactions, 507 genes and 573 metabolites organized in over 42 subsystems were incorporated into the model. Based on a new genetic algorithm approach, that integrates flux balance analysis, chemiosmotic theory, and physiological data, the proton translocation stoichiometry for a number of enzymes and maintenance parameters under aerobic chemolithoautotrophic conditions using three different electron donors were estimated. Furthermore, a detailed electron transfer and carbon flux distributions during chemolithoautotrophic growth using ferrous ion, tetrathionate and thiosulfate were determined and reported. Finally, 134 growth-coupled designs were calculated that enables Extracellular Polysaccharide production. iMC507 serves as a knowledgebase for summarizing and categorizing the information currently available for A. ferrooxidans and enables the understanding and engineering of Acidithiobacillus and similar species from a comprehensive model-driven perspective for biomining applications.

Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path.

The production of 75% of the current drug molecules and 35% of all chemicals could be achieved through bioprocessing (Arundel and Sawaya, 2009). To accelerate the transition from a petroleum-based chemical industry to a sustainable bio-based industry, systems metabolic engineering has emerged to computationally design metabolic pathways for chemical production. Although algorithms able to provide specific metabolic interventions and heterologous production pathways are available, a systematic analysis for all possible production routes to commodity chemicals in Escherichia coli is lacking. Furthermore, a pathway prediction algorithm that combines direct integration of genome-scale models at each step of the search to reduce the search space does not exist. Previous work (Feist et al., 2010) performed a model-driven evaluation of the growth-coupled production potential for E. coli to produce multiple native compounds from different feedstocks. In this study, we extended this analysis for non-native compounds by using an integrated approach through heterologous pathway integration and growth-coupled metabolite production design. In addition to integration with genome-scale model integration, the GEM-Path algorithm developed in this work also contains a novel approach to address reaction promiscuity. In total, 245 unique synthetic pathways for 20 large volume compounds were predicted. Host metabolism with these synthetic pathways was then analyzed for feasible growth-coupled production and designs could be identified for 1271 of the 6615 conditions evaluated. This study characterizes the potential for E. coli to produce commodity chemicals, and outlines a generic strain design workflow to design production strains.