Examining organic acid production potential and growth-coupled strategies in Issatchenkia orientalis using constraint-based modeling

Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.

A Prototype for Modular Cell Engineering

When aiming to produce a target chemical at high yield, titer, and productivity, various combinations of genetic parts available to build the target pathway can generate a large number of strains for characterization. This engineering approach will become increasingly laborious and expensive when seeking to develop desirable strains for optimal production of a large space of biochemicals due to extensive screening. Our recent theoretical development of modular cell (MODCELL) design principles can offer a promising solution for rapid generation of optimal strains by coupling a modular cell with exchangeable production modules in a plug-and-play fashion. In this study, we experimentally validated some design properties of MODCELL by demonstrating the following: (i) a modular (chassis) cell is required to couple with a production module, a heterologous ethanol pathway, as a testbed, (ii) degree of coupling between the modular cell and production modules can be modulated to enhance growth and product synthesis, (iii) a modular cell can be used as a host to select an optimal pyruvate decarboxylase (PDC) of the ethanol production module and to help identify a hypothetical PDC protein, and (iv) adaptive laboratory evolution based on growth selection of the modular cell can enhance growth and product synthesis rates. We envision that the MODCELL design provides a powerful prototype for modular cell engineering to rapidly create optimal strains for synthesis of a large space of biochemicals.

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

BACKGROUND

Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes.

RESULTS

In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances, and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis-Menten kinetic parameters.

CONCLUSIONS

The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.

Development of a Framework for Metabolic Pathway Analysis-Driven Strain Optimization Methods

Genome-scale metabolic models (GSMMs) have become important assets for rational design of compound overproduction using microbial cell factories. Most computational strain optimization methods (CSOM) using GSMMs, while useful in metabolic engineering, rely on the definition of questionable cell objectives, leading to some bias. Metabolic pathway analysis approaches do not require an objective function. Though their use brings immediate advantages, it has mostly been restricted to small scale models due to computational demands. Additionally, their complex parameterization and lack of intuitive tools pose an important challenge towards making these widely available to the community. Recently, MCSEnumerator has extended the scale of these methods, namely regarding enumeration of minimal cut sets, now able to handle GSMMs. This work proposes a tool implementing this method as a Java library and a plugin within the OptFlux metabolic engineering platform providing a friendly user interface. A standard enumeration problem and pipeline applicable to GSMMs is proposed, making use by the community simpler. To highlight the potential of these approaches, we devised a case study for overproduction of succinate, providing a phenotype analysis of a selected strategy and comparing robustness with a selected solution from a bi-level CSOM.

Double Dutch: A Tool for Designing Combinatorial Libraries of Biological Systems

Recently, semirational approaches that rely on combinatorial assembly of characterized DNA components have been used to engineer biosynthetic pathways. In practice, however, it is not practical to assemble and test millions of pathway variants in order to elucidate how different DNA components affect the behavior of a pathway. To address this challenge, we apply a rigorous mathematical approach known as design of experiments (DOE) that can be used to construct empirical models of system behavior without testing all variants. To support this approach, we have developed a tool named Double Dutch, which uses a formal grammar and heuristic algorithms to automate the process of DOE library design. Compared to designing by hand, Double Dutch enables users to more efficiently and scalably design libraries of pathway variants that can be used in a DOE framework and uniquely provides a means to flexibly balance design considerations of statistical analysis, construction cost, and risk of homologous recombination, thereby demonstrating the utility of automating decision making when faced with complex design trade-offs.

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

Rational design of efficient modular cells

The modular cell design principle is formulated to devise modular (chassis) cells. These cells can be assembled with exchangeable production modules in a plug-and-play fashion to build microbial cell factories for efficient combinatorial biosynthesis of novel molecules, requiring minimal iterative strain optimization steps. A modular cell is designed to be auxotrophic, containing core metabolic pathways that are necessary but insufficient to support cell growth and maintenance. To be functional, it must tightly couple with an exchangeable production module containing auxiliary metabolic pathways that not only complement cell growth but also enhance production of targeted molecules. We developed a MODCELL (modular cell) framework based on metabolic pathway analysis to implement the modular cell design principle. MODCELL identifies genetic modifications and requirements to construct modular cell candidates and their associated exchangeable production modules. By defining the degree of similarity and coupling metrics, MODCELL can evaluate which exchangeable production module(s) can be tightly coupled with a modular cell candidate. We first demonstrated how MODCELL works in a step-by-step manner for example metabolic networks, and then applied it to design modular Escherichia coli cells for efficient combinatorial biosynthesis of five alcohols (ethanol, propanol, isopropanol, butanol and isobutanol) and five butyrate esters (ethyl butyrate, propyl butyrate, isopropyl butyrate, butyl butyrate and isobutyl butyrate) from pentose sugars (arabinose and xylose) and hexose sugars (glucose, mannose, and galactose) under anaerobic conditions. We identified three modular cells, MODCELL1, MODCELL2 and MODCELL3, that can couple well with Group 1 of modules (ethanol, isobutanol, butanol, ethyl butyrate, isobutyl butyrate, butyl butyrate), Group 2 (isopropanol, isopropyl butyrate), and Group 3 (propanol, isopropanol), respectively. We validated the design of MODCELL1 for anaerobic production of ethanol, butanol, and ethyl butyrate using experimental data available in literature.

Co-evolution of strain design methods based on flux balance and elementary mode analysis

More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last 10 years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications.

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Computational strain design methods are divided into two main families.

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We trace the evolutionary history of these two families.

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Surveyed successful cases of model-guided strain design for industrial applications.

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Most proposed methods have not yet been tested in real applications.

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Agreement between in silico and in vivo results shows potential of tested methods.

Genome-scale strain designs based on regulatory minimal cut sets

MOTIVATION

Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype.

RESULTS

We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. Because of vastly enlarged search spaces in genome-scale networks, we developed strategies to (optionally) preselect suitable candidates for flux regulation and novel algorithmic techniques to further enhance efficiency and speed of cMCSs calculation. We illustrate the cRegMCSs approach by a simple example network and apply it then by identifying strain designs for ethanol production in a genome-scale metabolic model of Escherichia coli. The results clearly show that cRegMCSs combining reaction deletions and flux regulations provide a much larger number of suitable strain designs, many of which are significantly smaller relative to cMCSs involving only knockouts. Furthermore, with cRegMCSs, one may also enable the fine tuning of desired behaviours in a narrower range. The new cRegMCSs approach may thus accelerate the implementation of model-based strain designs for the bio-based production of fuels and chemicals.

AVAILABILITY AND IMPLEMENTATION

MATLAB code and the examples can be downloaded at http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html.

CONTACT

krishna.mahadevan@utoronto.ca or klamt@mpi-magdeburg.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.

Synergistic Synthetic Biology: Units in Concert

Synthetic biology aims at translating the methods and strategies from engineering into biology in order to streamline the design and construction of biological devices through standardized parts. Modular synthetic biology devices are designed by means of an adequate elimination of cross-talk that makes circuits orthogonal and specific. To that end, synthetic constructs need to be adequately optimized through in silico modeling by choosing the right complement of genetic parts and by experimental tuning through directed evolution and craftsmanship. In this review, we consider an additional and complementary tool available to the synthetic biologist for innovative design and successful construction of desired circuit functionalities: biological synergies. Synergy is a prevalent emergent property in biological systems that arises from the concerted action of multiple factors producing an amplification or cancelation effect compared with individual actions alone. Synergies appear in domains as diverse as those involved in chemical and protein activity, polypharmacology, and metabolic pathway complementarity. In conventional synthetic biology designs, synergistic cross-talk between parts and modules is generally attenuated in order to verify their orthogonality. Synergistic interactions, however, can induce emergent behavior that might prove useful for synthetic biology applications, like in functional circuit design, multi-drug treatment, or in sensing and delivery devices. Synergistic design principles are therefore complementary to those coming from orthogonal design and may provide added value to synthetic biology applications. The appropriate modeling, characterization, and design of synergies between biological parts and units will allow the discovery of yet unforeseeable, novel synthetic biology applications.

Bridging the gap between systems biology and synthetic biology

Systems biology is an inter-disciplinary science that studies the complex interactions and the collective behavior of a cell or an organism. Synthetic biology, as a technological subject, combines biological science and engineering, allowing the design and manipulation of a system for certain applications. Both systems and synthetic biology have played important roles in the recent development of microbial platforms for energy, materials, and environmental applications. More importantly, systems biology provides the knowledge necessary for the development of synthetic biology tools, which in turn facilitates the manipulation and understanding of complex biological systems. Thus, the combination of systems and synthetic biology has huge potential for studying and engineering microbes, especially to perform advanced tasks, such as producing biofuels. Although there have been very few studies in integrating systems and synthetic biology, existing examples have demonstrated great power in extending microbiological capabilities. This review focuses on recent efforts in microbiological genomics, transcriptomics, proteomics, and metabolomics, aiming to fill the gap between systems and synthetic biology.

Editorial: how multiplexed tools and approaches speed up the progress of metabolic engineering

Systems metabolic engineering is becoming a widely-evoked paradigm for industrial strain design and optimization. Specifically, systems wide experimental and computational analyses of cells and their environments enable guide metabolic engineers to quickly parse the genome and creating desirable overproduction phenotypes.