A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

Coarse-Grained Modeling Elucidates Differential Metabolism of Saccharomyces cerevisiae under Varied Nutrient Limitations

Microorganisms such as Saccharomyces cerevisiae have a native ability to adapt their metabolism to varying nutrient conditions. Understanding their responses to nutrient limitations is critical for decoding cellular physiology and designing strategies for metabolic engineering. While the influence of carbon availability on yeast metabolism has been extensively studied, the role of nitrogen availability remains relatively underexplored. In this study, we utilized a coarse-grained kinetic model to systematically analyze and compare the effects of carbon and nitrogen limitations on yeast metabolism. Our model successfully revealed the differential metabolic characteristics of S. cerevisiae under carbon- and nitrogen-limited chemostat conditions. It also highlighted the significance of protein activity regulation at varying carbon-to-nitrogen ratios, and elucidated distinct strategies employed to maintain ATP homeostasis. This study provides a computational tool for investigating yeast physiology under nutrient limitations and offers quantitative and mechanistic insights into yeast metabolism.

Metabolic Engineering of Yeast

Microbial cell factories have been developed to produce various compounds in a sustainable and economically viable manner. The yeast Saccharomyces cerevisiae has been used as a platform cell factory in industrial biotechnology with numerous advantages, including ease of operation, rapid growth, and tolerance for various industrial stressors. Advances in synthetic biology and metabolic models have accelerated the design-build-test-learn cycle in metabolic engineering, significantly facilitating the development of yeast strains with complex phenotypes, including the redirection of metabolic fluxes to desired products, the expansion of the spectrum of usable substrates, and the improvement of the physiological properties of strain. Strains with enhanced titer, rate, and yield are now competing with traditional petroleum-based industrial approaches. This review highlights recent advances and perspectives in the metabolic engineering of yeasts for the production of a variety of compounds, including fuels, chemicals, proteins, and peptides, as well as advancements in synthetic biology tools and mathematical modeling.

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite concentrations, metabolic fluxes and enzyme levels. Nevertheless, determining the kinetic parameters that underlie cellular physiology poses notable obstacles to the widespread use of these mathematical representations of metabolism. Here we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration of diverse omics data and other relevant information, including extracellular medium composition, physicochemical data and expertise of domain specialists, RENAISSANCE accurately characterizes intracellular metabolic states in Escherichia coli. It also estimates missing kinetic parameters and reconciles them with sparse experimental data, substantially reducing parameter uncertainty and improving accuracy. This framework will be valuable for researchers studying metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnology.

Construction of an alternative NADPH regeneration pathway improves ethanol production in Saccharomyces cerevisiae with xylose metabolic pathway

Full conversion of glucose and xylose from lignocellulosic hydrolysates is required for obtaining a high ethanol yield. However, glucose and xylose share flux in the pentose phosphate pathway (PPP) and glycolysis pathway (EMP), with glucose having a competitive advantage in the shared metabolic pathways. In this work, we knocked down ZWF1 to preclude glucose from entering the PPP. This reduced the [NADPH] level and disturbed growth on both glucose or xylose, confirming that the oxidative PPP, which begins with Zwf1p and ultimately leads to CO2 production, is the primary source of NADPH in both glucose and xylose. Upon glucose depletion, gluconeogenesis is necessary to generate glucose-6-phosphate, the substrate of Zwf1p. We re-established the NADPH regeneration pathway by replacing the endogenous NAD+-dependent glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene TDH3 with heterogenous NADP + -GAPDH genes GDH, gapB, and GDP1. Among the resulting strains, the strain BZP1 (zwf1Δ, tdh3::GDP1) exhibited a similar xylose consumption rate before glucose depletion, but a 1.6-fold increased xylose consumption rate following glucose depletion compared to the original strain BSGX001, and the ethanol yield for total consumed sugars of BZP1 was 13.5% higher than BSGX001. This suggested that using the EMP instead of PPP to generate NADPH reduces the wasteful metabolic cycle and excess CO2 release from oxidative PPP. Furthermore, we used a copper-repressing promoter to modulate the expression of ZWF1 and optimize the timing of turning off the ZWF1, therefore, to determine the competitive equilibrium between glucose-xylose co-metabolism. This strategy allowed fast growth in the early stage of fermentation and low waste in the following stages of fermentation.

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

Rational strain design with minimal phenotype perturbation

Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.

In Vivo Sampling of Intracellular Heterogeneity of Pseudomonas putida Enables Multiobjective Optimization of Genetic Devices

The inner physicochemical heterogeneity of bacterial cells generates three-dimensional (3D)-dependent variations of resources for effective expression of given chromosomally located genes. This fact has been exploited for adjusting the most favorable parameters for implanting a complex device for optogenetic control of biofilm formation in the soil bacterium Pseudomonas putida. To this end, a DNA segment encoding a superactive variant of the Caulobacter crescendus diguanylate cyclase PleD expressed under the control of the cyanobacterial light-responsive CcaSR system was placed in a mini-Tn5 transposon vector and randomly inserted through the chromosome of wild-type and biofilm-deficient variants of P. putida lacking the wsp gene cluster. This operation delivered a collection of clones covering a whole range of biofilm-building capacities and dynamic ranges in response to green light. Since the phenotypic output of the device depends on a large number of parameters (multiple promoters, RNA stability, translational efficacy, metabolic precursors, protein folding, etc.), we argue that random chromosomal insertions enable sampling the intracellular milieu for an optimal set of resources that deliver a preset phenotypic specification. Results thus support the notion that the context dependency can be exploited as a tool for multiobjective optimization, rather than a foe to be suppressed in Synthetic Biology constructs.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by ¹³C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae

Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes

Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli

Background

Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems.

Results

We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes.

Conclusions

We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04066-y.

Building kinetic models for metabolic engineering

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

Transcription Analysis of Recombinant Trichoderma reesei HJ-48 to Compare the Molecular Basis for Fermentation of Glucose and Xylose

Profiling the transcriptome changes involved in xylose metabolism by the fungus Trichoderma reesei allows for the identification of potential targets for ethanol production processing. In the present study, the transcriptome of T. reesei HJ-48 grown on xylose versus glucose was analyzed using nextgeneration sequencing technology. During xylose fermentation, numerous genes related to central metabolic pathways, including xylose reductase (XR) and xylitol dehydrogenase (XDH), were expressed at higher levels in T. reesei HJ-48. Notably, growth on xylose did not fully repress the genes encoding enzymes of the tricarboxylic acid and respiratory pathways. In addition, increased expression of several sugar transporters was observed during xylose fermentation. This study provides a valuable dataset for further investigation of xylose fermentation and provides a deeper insight into the various genes involved in this process.

Promoting the Synthesis of Precursor Substances by Overexpressing Hexokinase (Hxk) and Hydroxymethylglutaryl-CoA Synthase (Erg13) to Elevate β-Carotene Production in Engineered Yarrowia lipolytica

As a valuable carotenoid, β-carotene is commercially used in food, cosmetics, animal feeds, and other industries. Metabolic engineering of microorganisms has been widely explored to improve the production of β-carotene. Compared with the traditional genetic modifications mainly focused on the pathways of mevalonate (MVA) and β-carotene biosynthesis, this study aims to increase the β-carotene production through promoting the synthesis of precursor substances by overexpressing hexokinase and hydroxymethylglutaryl-CoA synthase in an engineered Yarrowia lipolytica. In this study, we investigated the effect of the unique hexokinase gene (Hxk) overexpression on β-carotene accumulation and glucose consumption. The Hxk gene was introduced into a β-carotene producing strain Y.L-1 to generate strain Y.L-2, and this increased the β-carotene content by 98%. Overexpression of the Hxk gene led to increasing in hexokinase activity (329% higher), glucose-6-phosphate content (92% higher), and improvement of the transcriptional level of Hxk (315% higher) compared to the control Y.L-1 strain. Moreover, Hxk overexpression accelerated the utilization rate of glucose. The gene erg13 encoding hydroxymethylglutaryl-CoA synthase was also overexpressed to increase the precursor supply for β-carotene biosynthesis. Recombinant Y.L-4 harboring two copies of erg13 produced 8.41 mg/g dry cell weight (DCW) of β-carotene, which was 259% higher than Y.L-1. The β-carotene content of 9.56 mg/g DCW was achieved in strain Y.L-6 by integrating erg13 into the chromosome and Hxk overexpression. The 3-Hydroxy-3-Methylglutaryl-CoA content in the cells was increased by overexpressing two copies of the erg13 gene. Finally, the titer of β-carotene reached 2.4 g/L using a 50 L bioreactor by the engineered strain, and the fermentation cycle was shortened from 144 to 120 h. Overall, overexpression of Hxk and erg13 could improve β-carotene production and successfully overcoming the bottleneck of precursor generation to support a more efficient pathway for the production of the target product. Our results revealed a novel strategy to engineer the pathway of β-carotene synthesis.

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

BACKGROUND

Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.

RESULTS

In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.

CONCLUSIONS

The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis

Summary

pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.

Motivation

High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions.

Results

We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli., and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA.

Availability and implementation

Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Galactose to tagatose isomerization at moderate temperatures with high conversion and productivity

There are many industrially-relevant enzymes that while active, are severely limited by thermodynamic, kinetic, or stability issues (isomerases, lyases, transglycosidases). In this work, we study Lactobacillus sakeil-arabinose isomerase (LsLAI) for d-galactose to d-tagatose isomerization—that is limited by all three reaction parameters. The enzyme demonstrates low catalytic efficiency, low thermostability at temperatures > 40 °C, and equilibrium conversion < 50%. After exploring several strategies to overcome these limitations, we show that encapsulating LsLAI in gram-positive Lactobacillus plantarum that is chemically permeabilized enables reactions at high rates, high conversions, and elevated temperatures. In a batch process, this system enables ~ 50% conversion in 4 h starting with 300 mM galactose (an average productivity of 37 mM h⁻¹), and 85% conversion in 48 h. We suggest that such an approach may be invaluable for other enzymatic processes that are similarly kinetically-, thermodynamically-, and/or stability-limited.

Production of tagatose, a sugar substitute, by isomerization of galactose suffers from unfavorable enzymatic kinetics, low enzyme stability, and low equilibrium constant. Here, the authors simultaneously overcome these limitations by encapsulating l-arabinose isomerase in permeabilized Lactobacillus plantarum.

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.

Kinetic models are the most promising tool for understanding the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and thus we need large-scale models for dependable in silico analyses of metabolism. However, uncertainty in kinetic parameters impedes the development of kinetic models, and uncertainty levels increase with the model size. Tools that will address the issues with parameter uncertainty and that will be able to reduce the uncertainty propagation through the system are therefore needed. In this work, we applied a method called iSCHRUNK that combines parameter sampling and machine learning techniques to characterize the uncertainties and uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify a small number of parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.

Defining informative priors for ensemble modeling in systems biology

Ensemble modeling in molecular systems biology requires the reproducible translation of kinetic parameter data into informative probability distributions (priors), as well as approaches that sample parameters from these distributions without violating the thermodynamic consistency of the overall model. Although a number of pioneering frameworks for ensemble modeling have been published, the issue of generating informative priors has not yet been addressed. Here, we present a protocol that aims to fill this gap. This protocol discusses the collection of parameter values from a diverse range of sources (literature, databases and experiments), assessment of their plausibility, and creation of log-normal probability distributions that can be used as informative priors in ensemble modeling. Furthermore, the protocol enables sampling from the generated distributions while maintaining thermodynamic consistency. Once all parameter values have been retrieved from literature and databases, the protocol can be implemented within ~5-10 min per parameter. The aim of this protocol is to facilitate the design and use of informative distributions for ensemble modeling, especially in fields such as synthetic biology and systems medicine.

Laboratory evolution for forced glucose-xylose co-consumption enables identification of mutations that improve mixed-sugar fermentation by xylose-fermenting Saccharomyces cerevisiae

Simultaneous fermentation of glucose and xylose can contribute to improved productivity and robustness of yeast-based processes for bioethanol production from lignocellulosic hydrolysates. This study explores a novel laboratory evolution strategy for identifying mutations that contribute to simultaneous utilisation of these sugars in batch cultures of Saccharomyces cerevisiae. To force simultaneous utilisation of xylose and glucose, the genes encoding glucose-6-phosphate isomerase (PGI1) and ribulose-5-phosphate epimerase (RPE1) were deleted in a xylose-isomerase-based xylose-fermenting strain with a modified oxidative pentose-phosphate pathway. Laboratory evolution of this strain in serial batch cultures on glucose-xylose mixtures yielded mutants that rapidly co-consumed the two sugars. Whole-genome sequencing of evolved strains identified mutations in HXK2, RSP5 and GAL83, whose introduction into a non-evolved xylose-fermenting S. cerevisiae strain improved co-consumption of xylose and glucose under aerobic and anaerobic conditions. Combined deletion of HXK2 and introduction of a GAL83G673T allele yielded a strain with a 2.5-fold higher xylose and glucose co-consumption ratio than its xylose-fermenting parental strain. These two modifications decreased the time required for full sugar conversion in anaerobic bioreactor batch cultures, grown on 20 g L-1 glucose and 10 g L-1 xylose, by over 24 h. This study demonstrates that laboratory evolution and genome resequencing of microbial strains engineered for forced co-consumption is a powerful approach for studying and improving simultaneous conversion of mixed substrates.

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.

Association of improved oxidative stress tolerance and alleviation of glucose repression with superior xylose-utilization capability by a natural isolate of Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae wild strains generally have poor xylose-utilization capability, which is a major barrier for efficient bioconversion of lignocellulosic biomass. Laboratory adaption is commonly used to enhance xylose utilization of recombinant S. cerevisiae. Apparently, yeast cells could remodel the metabolic network for xylose metabolism. However, it still remains unclear why natural isolates of S. cerevisiae poorly utilize xylose. Here, we analyzed a unique S. cerevisiae natural isolate YB-2625 which has superior xylose metabolism capability in the presence of mixed-sugar. Comparative transcriptomic analysis was performed using S. cerevisiae YB-2625 grown in a mixture of glucose and xylose, and the model yeast strain S288C served as a control. Global gene transcription was compared at both the early mixed-sugar utilization stage and the latter xylose-utilization stage.

RESULTS

Genes involved in endogenous xylose-assimilation (XYL2 and XKS1), gluconeogenesis, and TCA cycle showed higher transcription levels in S. cerevisiae YB-2625 at the xylose-utilization stage, when compared to the reference strain. On the other hand, transcription factor encoding genes involved in regulation of glucose repression (MIG1, MIG2, and MIG3) as well as HXK2 displayed decreased transcriptional levels in YB-2625, suggesting the alleviation of glucose repression of S. cerevisiae YB-2625. Notably, genes encoding antioxidant enzymes (CTT1, CTA1, SOD2, and PRX1) showed higher transcription levels in S. cerevisiae YB-2625 in the xylose-utilization stage than that of the reference strain. Consistently, catalase activity of YB-2625 was 1.9-fold higher than that of S. cerevisiae S288C during the xylose-utilization stage. As a result, intracellular reactive oxygen species levels of S. cerevisiae YB-2625 were 43.3 and 58.6% lower than that of S288C at both sugar utilization stages. Overexpression of CTT1 and PRX1 in the recombinant strain S. cerevisiae YRH396 deriving from S. cerevisiae YB-2625 increased cell growth when xylose was used as the sole carbon source, leading to 13.5 and 18.1%, respectively, more xylose consumption.

CONCLUSIONS

Enhanced oxidative stress tolerance and relief of glucose repression are proposed to be two major mechanisms for superior xylose utilization by S. cerevisiae YB-2625. The present study provides insights into the innate regulatory mechanisms underlying xylose utilization in wild-type S. cerevisiae, which benefits the rapid development of robust yeast strains for lignocellulosic biorefineries.

Strategies to Improve Saccharomyces cerevisiae: Technological Advancements and Evolutionary Engineering

Bakery industries are thriving to augment the diverse properties of Saccharomyces cerevisiae to increase its flavor, texture and nutritional parameters to attract the more consumers. The improved technologies adopted for quality improvement of baker's yeast are attracting the attention of industry and it is playing a pivotal role in redesigning the quality parameters. Modern yeast strain improvement tactics revolve around the use of several advanced technologies such as evolutionary engineering, systems biology, metabolic engineering, genome editing. The review mainly deals with the technologies for improving S. cerevisiae, with the objective of broadening the range of its industrial applications.