ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels are biofuels produced from a renewable carbon source using engineered microorganisms. Two biofuels, ethanol and biodiesel, have been made inroads to displacing petroleum-based fuels, but their penetration has been limited by the amounts that can be used in conventional engines and by cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure, but have had limited penetration due to costs. In this review, we will discuss the advances in engineering microbial metabolism to produce advanced biofuels and prospects for reducing their costs

Development of the biotechnological production of (+)-zizaene : enzymology, metabolic engineering and in situ product recovery

The sesquiterpene (+)-zizaene is the immediate precursor of khusimol, the main compound of the vetiver essential oil from the vetiver grass, which grants its characteristic woody scent. Among its distinct applications, this oil is relevant for the formulation of cosmetics and used in approximately 20% of all men’s perfumery. The traditional supply of the vetiver essential oil had suffered shortages due to natural disasters. Consequently, the biotechnological production of khusimol is an alternative towards a more reliable supply. In this study, we provide new insights towards the microbial production of khusimol by characterizing the zizaene synthase, engineering the metabolic pathway of (+)-zizaene in Escherichia coli and analyzing the in situ recovery of (+)-zizaene from fermentation. In the first chapter, the zizaene synthase, the critical enzyme for khusimol biosynthesis, was characterized. A SUMO-fused zizaene synthase variant was overexpressed in E. coli, and in vitro reactions yielded 90% (+)-zizaene. Furthermore, enzyme characterization comprised enzyme kinetics, optimal reaction conditions, substrate specificity and reaction mechanisms. The in vitro reactions showed high stability through varying pH and temperature values. By in silico docking model, this was explained due to the hydrophobicity of the surrounding loops, which stabilized the closed conformation of the active site. The second chapter addressed the metabolic engineering of the (+)-zizaene biosynthetic pathway in E. coli. A systematic strategy was applied by modulating the substrate FDP and the zizaene synthase to improve the zizaene titers. The optimal (+)-zizaene production was reached by engineering the mevalonate pathway and two copies of the zizaene synthase into a multi-plasmid strain. Optimization of the fermentation conditions such as IPTG, media, pH and temperature improved the production further, achieving a (+)-zizaene titer of 25 mg L‒1. In the third chapter, the in situ recovery of (+)-zizaene from fermentation was analyzed. Initially, liquid-liquid phase partitioning cultivation improved the (+)-zizaene recovery at shake flask scale. Subsequently, solid-liquid phase partitioning cultivation was evaluated by screening polymeric adsorbers, where Diaion HP20 obtained the highest recovery ratio. The bioprocess was scaled up to 2 L fed-batch bioreactors by integrating in situ recovery and fermentation. External and internal (with and without gas stripping) recovery configurations were tested, where the internal configuration obtained the highest (+)-zizaene recovery of all, achieving a (+)-zizaene titer of 211.13 mg L−1 and a productivity of 3.2 mg L−1 h−1

A systems biology understanding of protein constraints in the metabolism of budding yeasts

Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts. In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts. On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society

METHANOGEN METABOLIC FLEXIBILITY

Methanogens are obligately anaerobic archaea which produce methane as a byproduct of their respiration. They are found across a wide diversity of environments and play an important role in cycling carbon in anaerobic spaces and the removal of harmful fermentation byproducts which would otherwise inhibit other organisms. Methanogens subsist on low-energy substrates which requires them to utilize a highly efficient central metabolism which greatly favors respiratory byproducts over biomass. This metabolic strategy creates high substrate:product conversion ratios which is industrially relevant for the production of biomethane, but may also allow for the production of value-added commodities. Particularly of interest are terpene compounds, as methanogen membranes are composed of isoprenoid lipids resulting in a higher flux through isoprenoid biosynthetic pathways compared to Eukarya and Bacteria. To assess the metabolic plasticity of methanogens, our laboratory has engineered the methanogen Methanosarcina acetivorans to produce the hemiterpene isoprene. We hypothesized that isoprene producing strains would result in a decreased growth phenotype corresponding to a depletion of metabolic precursors needed for isoprenoid membrane production. We found that the engineered methanogens responded well to the modification, directing up to 4% of total towards isoprene production and increasing overall biomass despite the additional metabolic burden. Using flux balance analysis and RNA sequencing we investigated how the engineered strains respond to isoprene production and how production can be enhanced. Advisor: Nicole R. Bua

Enhancing the Industrial Potential of Filamentous Cyanobacteria

The objectives of this project were to improve the industrial potential of filamentous N2-fixing cyanobacteria by increasing its biofuel tolerance, and to evaluate the economic feasibility and environmental impacts of a theoretical, cyanobacteria-based biofuel production facility. To develop a method to quantify filamentous cyanobacteria in dilute culture media, a dual-stained fluorescence assay was evaluated. While the viable cell stain (SYTO® 9) was accurate, the non-viable cell stain (propidium iodide) also bound to viable cells. Additional non-viable cell stains were evaluated, but none were accurate at quantifying viability. Thus we concluded that the viable cell stain SYTO® 9 is a reliable assay and can be used in high-throughput assays. To develop cyanobacteria strains with increased tolerance to biofuels, directed evolution under the pressure of higher biofuel concentrations was used. As these biofuels are highly volatile, it was necessary to conduct experiments in sealed test-tubes. Thus, cyanobacteria growth in a sealed environment was optimized using BG11 as the basal medium supplemented with 0.5 g/L NaHCO3 as the carbon source. Subsequent directed evolution trials yielded 3 confirmed mutants with increased biofuel tolerance: Nostoc punctiforme ATCC 29133 with a 20% improvement in linalool tolerance, Anabaena variabilis ATCC 29413 with a 60% improvement in linalool tolerance, and Anabaena sp. PCC 7120 with a 220% improvement in farnesene tolerance. To determine the optimal nitrogen source, dinitrogen (N2 gas) was compared to various fixed nitrogen sources. Ammonium chloride was determined to be the preferred nitrogen source for large scale cyanobacteria cultivation based on growth rate and environmental impacts. Finally, an economic feasibility and a life cycle analysis were conducted on a theoretical limonene production facility that used a genetically engineered filamentous cyanobacteria strain. The facility was not economically feasible at current limonene productivity rates, but would be feasible if productivity can be increased 56.7-fold. The life cycle analysis showed that increasing limonene productivity worsens the environmental profile of the facility. While using filamentous N2-fixing cyanobacteria as industrial microorganisms is currently in its infancy, there is a great deal of potential for this microbe to become a significant contributor to renewable biofuels and high-value chemicals

Systems Biology – A Guide for Understanding and Developing Improved Strains of Lactic Acid Bacteria

Lactic Acid Bacteria (LAB) are extensively employed in the production of various fermented foods, due to their safe status, ability to affect texture and flavor and finally due to the beneficial effect they have on shelf-life. More recently, LAB have also gained interest as production hosts for various useful compounds, particularly compounds with sensitive applications, such as food ingredients and therapeutics. As for all industrial microorganisms, it is important to have a good understanding of the physiology and metabolism of LAB in order to fully exploit their potential, and for this purpose, many systems biology approaches are available. Systems metabolic engineering, an approach that combines optimization of metabolic enzymes/pathways at the systems level, synthetic biology as well as in silico model simulation, has been used to build microbial cell factories for production of biofuels, food ingredients and biochemicals. When developing LAB for use in foods, genetic engineering is in general not an accepted approach. An alternative is to screen mutant libraries for candidates with desirable traits using high-throughput screening technologies or to use adaptive laboratory evolution to select for mutants with special properties. In both cases, by using omics data and data-driven technologies to scrutinize these, it is possible to find the underlying cause for the desired attributes of such mutants. This review aims to describe how systems biology tools can be used for obtaining both engineered as well as non-engineered LAB with novel and desired properties