Metabolic Engineering

Metabolic sensors are microbial strains modified so that biomass formation correlates with the availability of specific metabolites. These sensors are essential for bioengineering (e.g. in growth-coupled designs) but creating them is often a time-consuming and low-throughput process that can potentially be streamlined by in silico analysis. Here, we present the systematic workflow of designing, implementing, and testing versatile Escherichia coli metabolic sensor strains. Glyoxylate, a key metabolite in (synthetic) CO2 fixation and carbon-conserving pathways, served as the test molecule. Through iterative screening of a compact metabolic model, we identified non-trivial growth-coupled designs that resulted in six metabolic sensors with a wide sensitivity range for glyoxylate, spanning three orders of magnitude in detected concentrations. We further adapted these E. coli strains for sensing glycolate and demonstrated their utility in both pathway engineering (testing a key metabolic module via glyoxylate) and applications in environmental monitoring (quantifying glycolate produced by photosynthetic microalgae). The versatility and ease of implementation of this workflow make it suitable for designing and building multiple metabolic sensors for diverse biotechnological applications. TeaserA streamlined workflow enables the rapid design of versatile E. coli metabolic sensors for detecting key metabolites in bioengineering and monitoring.

Escherichia coli, a key workhorse in industrial biotechnology, is commonly grown on glucose, which supports rapid growth but leads to acetate overflow, which instead inhibits growth, diverts carbon from the production pathway, and reduces productivity. Recent studies suggest that acetate may also have beneficial effects in glucose-grown E. coli, but its potential in bioprocesses remains unexplored. In this study, we systematically investigated acetates impact on bioproduction using a kinetic model of glucose and acetate metabolism in E. coli. The model predicts that acetate can enhance bioproduction in glucose-grown E. coli through three mechanisms: (i) by minimizing acetate overflow, thereby reducing carbon loss, (ii) by increasing acetyl-CoA levels, thereby boosting the biosynthetic flux of acetyl-CoA-derived compounds, and (iii) by promoting biomass accumulation, thus improving overall productivity. We experimentally validated the predictions of the model for mevalonate and 3-hydroxypropionate production, where acetate supplementation increased productivity by 117% and 34%, respectively. Our findings provide a valuable framework for optimizing E. coli-based bioprocesses and highlight acetates underutilized potential in biotechnology. By leveraging acetate from waste streams as a metabolic booster, this approach could contribute to more sustainable and environmentally friendly bioprocesses. Highlights- A kinetic model was used for rational optimization of E. coli-based bioprocesses - Acetate can enhance growth and production of acetyl-CoA-derived bioproducts - Model predictions were validated for mevalonate and 3-hydroxypropionate production - Acetate can be used as a metabolic booster for glucose-based bioproduction in E. coli

Cell-free protein expression has become a widely used research tool in systems and synthetic biology and a promising technology for protein biomanufacturing. Cell-free protein synthesis relies on in-vitro transcription and translation processes to produce a protein of interest. However, transcription and translation depend upon the operation of complex metabolic pathways for precursor and energy regeneration. Toward understanding the role of metabolism in a cell-free system, we developed a dynamic constraint-based simulation of protein production in the myTXTL E. coli cell-free system with and without electron transport chain inhibitors. Time-resolved absolute metabolite measurements for [M] = 63 metabolites, along with absolute concentration measurements of the mRNA and protein abundance and measurements of enzyme activity, were integrated with kinetic and enzyme abundance information to simulate the time evolution of metabolic flux and protein production with and without inhibitors. The metabolic flux distribution estimated by the model, along with the experimental metabolite and enzyme activity data, suggested that the myTXTL cell-free system has an active central carbon metabolism with glutamate powering the TCA cycle. Further, the electron transport chain inhibitor studies suggested the presence of oxidative phosphorylation activity in the myTXTL cell-free system; the oxidative phosphorylation inhibitors provided biochemical evidence that myTXTL relied, at least partially, on oxidative phosphorylation to generate the energy required to sustain transcription and translation for a 16-hour batch reaction.

As one of the top value-added chemicals, succinic acid has been the focus of numerous metabolic engineering campaigns since the 1990s. However, microbial production of succinic acid at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation. Here we describe the metabolic engineering of Issatchenkia orientalis, a non-conventional yeast with superior tolerance to highly acidic conditions, for cost-effective succinic acid production. Through deletion of byproduct pathways, transport engineering, and expanding the substrate scope, the resulting strains could produce succinic acid at the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations using bench-top reactors, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further performed batch fermentation in a pilot-scale fermenter with a scaling factor of 300x, achieving 63.1 g/L of succinic acid using sugarcane juice medium. A downstream processing comprising of two-stage vacuum distillation and crystallization enabled direct recovery of succinic acid, without further acidification of fermentation broth, with an overall yield of 64.0%. Finally, we simulated an end-to-end low-pH succinic acid production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce life cycle greenhouse gas emissions by 34-90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for the production of a wide variety of organic acids.

3-Hydroxy fatty acids (3-HFAs) are versatile intermediates for bio-based polymers, fuels, and surfactants, and are advancing circular economy manufacturing. We built an acyl- CoA ligase-deficient chassis of Pseudomonas putida KT2440, thereby blocking 3-HFA activation and preventing its use as carbon source. This strategy decoupled synthesis from catabolism and enabled 3-HFA accumulation. We then compared two different routes for modifying the free 3-HFA composition. 1. In a direct route, overexpressing native PhaG produced C8, C10, C12, and C14 3-HFAs, achieving a total titer of 0.73 g/L in shake flasks. Functional analyses under our tested conditions support a re-assignment of PhaG: rather than acting primarily as a 3-hydroxyacyl-ACP:CoA transacylase, it functions mainly as a thioesterase, liberating free 3-HFAs from hydroxyacyl-ACP. 2. In an indirect route, overexpressing RhlA variants generated hydroxyalkanoyl-alkanoates (HAAs) that were converted to free 3-HFAs by endogenous esterase(s): RhlA from Pseudomonas aeruginosa PAO1 favored C8-C12 and yielded 0.47 g/L 3-HFAs, whereas RhlA from Burkholderia plantarii PG1 favored C10-C14 with 0.14 g/L, of which 80% was C14. Finally, we demonstrated process feasibility by up-scaling the PhaG pathway in a stirred-tank reactor. These results establish modular, stable, chassis-compatible routes for tailoring 3-HFA chain-length distributions, thereby providing a foundation for scalable, bio-based monomer supply in a circular economy.

Biomanufacturing can play a pivotal role in the transition away from fossil fuel dependence for the production of chemicals and fuels. There is growing interest in alternative bioproduction feedstocks to conventional sugars that do not compete for land use with food production. Ethylene glycol, a C2 compound that can be recovered from plastic waste or derived from CO2 with increasing efficiency, is gaining attention as a carbon source for microbial processes. Here we review the natural and synthetic metabolic pathways currently available for ethylene glycol assimilation. The pathways are compared in terms of their maximum theoretical yields for biomass and value-added products, thermodynamic favourability, minimum enzyme costs, and orthogonality to central carbon metabolism. We find that synthetic pathways outperform their natural counterparts in terms of higher thermodynamic driving forces, reduced enzyme costs, and higher theoretical yields for the majority of bioproducts analyzed as well as for biomass. However, natural assimilation pathways are equally or even more orthogonal to growth-associated reactions than synthetic pathways. Given these tradeoffs, the optimal EG assimilation pathway may depend on product and process choice.

The Warburg effect, the preferential conversion of glucose-derived pyruvate to lactate despite available oxygen, is a key feature of Chinese hamster ovary (CHO) cell culture. Lactate accumulation in recombinant protein-producing cell culture is an inefficient usage of glucose, as well as being deleterious to cells. Lactate accumulation lowers culture pH, requiring base addition to maintain bioreactor pH setpoint, which subsequently leads to hyperosmolarity, adversely impacting cell growth, productivity and product quality. A key driver for the Warburg effect, and hence lactate accumulation, is the need to regenerate NAD+ consumed during glycolysis. Since oxidative phosphorylation (OXPHOS) has limited capacity to recycle NADH back to NAD+ at high glycolytic fluxes, cells rely on lactate dehydrogenase (LDH) to convert pyruvate to lactate, simultaneously regenerating NAD+ and sustaining glycolysis. Thus, providing the cells capacity to generate more NAD+ would decrease the reliance on the Warburg effect. In this study, feeding the NAD+ precursor nicotinamide (NAM) leads to reversal of the Warburg effect, inducing the "lactate shift" three days earlier in cell culture and reducing peak lactate concentration by 40%. Transcriptomic analysis further confirms this metabolic shift, with an upregulation of key mitochondrial electron transport chain genes. These results identify NAD+/NADH balance as a key regulator of the Warburg effect and demonstrate NAM supplementation as a simple, cost-effective strategy to mitigate lactate accumulation and improve metabolic efficiency in CHO cell cultures.

The fundamental Embden–Meyerhoff–Paranas (EMP) pathway for sugar catabolism, anabolism, and energy metabolism has been reconstituted with non-oxidative glycolysis (NOG). Although carbon conservation was achieved via NOG, the energy metabolism was significantly limited. Herein, we showed the construction of a hybrid EMP that replaced the first phase of the EMP in Corynebacterium glutamicum with NOG and revealed a metabolic link of carbon and phosphorus metabolism. In accordance with synthetic glucose kinase activity and phosphoketolase on the hybrid EMP, cell growth was completely recovered in the C. glutamicum pfkA mutant strain where the first phase of EMP was eliminated. Notably, we have revealed a phosphate-replenishing pathway that involved trehalose biosynthesis for the generation of inorganic phosphate (Pi) sources in the hybrid EMP when external Pi supply was limited. Thus, the re-designed hybrid EMP pathway with balanced carbon and phosphorus states provides an efficient microbial platform for biochemical production.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane), and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Digital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is challenged by the nonlinear dynamics and metabolic complexity of these systems. We present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct predictive digital twins for Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies. The framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates. Applied to 23 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data. Cross-validation analyses demonstrated strong generalization across process variations, highlighting the frameworks capacity to capture both biochemical constraints and adaptive cellular behavior. This hybrid modeling approach provides a mechanistically interpretable yet data-adaptive foundation for constructing bioprocess digital twins. By bridging statistical, mechanistic, and machine learning methodologies, it advances the computational representation of CHO cell culture systems and offers a generalizable strategy for predictive modeling in complex biological production processes.

Robust strain stability is essential for industrial-scale fatty alcohol production, as metabolic burden and toxicity not only constrain productivity but also create selective pressure for low-producing or non-producing subpopulations. This genetic instability leads to genetic heterogeneity and compromises strain performance during large-scale fermentation. In this study, we investigated the production stability of fatty alcohol-producing Yarrowia lipolytica strains and developed systematic strategies to improve the genetic stability of engineered strains for scale-up production. In a mock fermentation scale-up, a fatty acyl-CoA reductase (FAR)-expressing strain lost fatty alcohol production after five consecutive passages. To address this, we fused FAR to phosphoglycerate kinase I (PGK1), a gene essential to cell growth, to promote the stability of FAR expression and prevent production loss. This strategy extended fatty alcohol production by one passage. Additionally, FAR was fused to GFP and extended production stability by four additional passages. In parallel, competitive co-culture experiments, in which producing strains were cultured alongside non-producing mutants, revealed that when non-producers emerged with a frequency of 10-5, they dominated the fermentation population within six passages; at 10%, they took only two passages. Furthermore, deep sequencing of strains that demonstrate different levels of stability and fatty alcohol productivity identified mutation patterns that contribute to strain instability. These findings provide insights into engineering Y. lipolytica with enhanced genetic stability for scale-up fatty alcohol production.

Monoterpene indole alkaloids (MIAs) from Mitragyna speciosa ("kratom"), such as mitragynine and speciogynine, are promising novel scaffolds for opioid receptor ligands for treatment of pain, addiction, and depression. While kratom leaves have been used for centuries in South-East Asia as stimulant and pain management substance, the biosynthetic pathway of these psychoactives have only recently been partially elucidated. Here, we demonstrate the de novo production of mitragynine and speciogynine in Saccharomyces cerevisiae through the reconstruction of a five-step synthetic pathway from common MIA precursor strictosidine comprising fungal tryptamine 4-monooxygenase to bypass an unknown kratom hydroxylase. Upon optimizing cultivation conditions, a titer of [~]290 {micro}g/L kratom MIAs from glucose was achieved. Untargeted metabolomics analysis of lead production strains led to the identification of numerous shunt products derived from the activity of strictosidine synthase (STR) and dihydrocorynantheine synthase (DCS), highlighting them as candidates for enzyme engineering to further improve kratom MIAs production in yeast. Finally, by feeding fluorinated tryptamine and expressing a human tailoring enzyme, we further demonstrate production of fluorinated and hydroxylated mitragynine derivatives with potential applications in drug discovery campaigns. Altogether, this study introduces a yeast cell factory platform for the biomanufacturing of complex natural and new-to-nature kratom MIAs derivatives with therapeutic potential.

Malonyl-CoA is the major precursor for the biosynthesis of diverse industrially valuable products such as fatty acids/alcohols, flavonoids, and polyketides. However, its intracellular availability is limited in most microbial hosts, hampering the biological synthesis of such chemicals. To address this limitation, we present a multi-level optimization workflow using modern metabolic engineer-ing technologies to systematically increase the malonyl-CoA levels in Pseudomonas putida. The workflow involves the identification of gene downregulations, chassis selection, and optimization of the acetyl-CoA carboxylase complex through ribosome binding site engineering. Computa-tional tools and high-throughput screening with a malonyl-CoA biosensor enabled the rapid eval-uation of numerous genetic targets. Combining the most beneficial targets led to a 5.8-fold en-hancement in the production titer of the valuable polyketide phloroglucinol. This study demon-strates the effective integration of computational and genetic technologies for engineering P. putida, opening new avenues for the development of industrially relevant strains and the investi-gation of fundamental biological questions.

Gene expression data of cell cultures is commonly measured in biological and medical studies to understand cellular decision-making in various conditions. Metabolism, affected but not solely determined by the expression, is much more difficult to measure experimentally. Thus, finding a reliable method to predict cell metabolism for given expression data will greatly benefit model-aided metabolic engineering. We have developed such a pipeline that can explore cellular fluxomics from expression data, using only a high-quality genome-scale metabolic model. This is done through two main steps: first, construct a protein-constrained metabolic model by integrating protein and enzyme information into the metabolic model. Secondly, overlay the expression data onto the modified model using a new two-step non-convex and convex optimization formulation, resulting in context-specific models with optionally calibrated rate constants. The resulting model computes proteomes and intracellular flux states that are consistent with the measured transcriptomes. Therefore, it provides detailed cellular insights that are difficult to glean individually from the omic data or metabolic models alone. As a case study, we apply the pipeline to interpret triacylglycerol (TAG) overproduction by Chlamydomonas reinhardtii, using time-course RNA-Seq data. The pipeline allows us to compute C. reinhardtii metabolism under nitrogen deprivation and metabolic shifts after an acetate boost. We also suggest a list of possible bottlenecking proteins that need to be overexpressed to increase the TAG accumulation rate, as well as discussing other TAG-overproduction strategies.

Monoterpenes are commonly known for their role in the flavors and fragrances industry and are also gaining attention for other uses like insect repellant and as potential renewable fuels for aviation. Corynebacterium glutamicum, a Generally Recognized as Safe microbe, has been a choice organism in industry for the annual million ton-scale bioproduction of amino acids for more than 50 years; however, efforts to produce monoterpenes in C. glutamicum have remained relatively limited. In this study, we report a further expansion of the C. glutamicum biosynthetic repertoire through the development and optimization of a mevalonate-based monoterpene platform. In the course of our plasmid design iterations, we increased flux through the mevalonate-based bypass pathway, measuring isoprenol production as a proxy for monoterpene precursor abundance and demonstrating the highest reported titers in C. glutamicum to date at nearly 1500 mg/L. Our designs also evaluated the effects of backbone, promoter, and GPP synthase homolog origin on monoterpene product titers. Monoterpene production was further improved by disrupting competing pathways for isoprenoid precursor supply and by implementing a biphasic production system to prevent volatilization. With this platform, we achieved 321.1 mg/L of geranoids, 723.6 mg/L of 1,8-cineole, and 227.8 mg/L of linalool. Furthermore, we determined that C. glutamicum first oxidizes geraniol through an aldehyde intermediate before it is asymmetrically reduced to citronellol. Additionally, we demonstrate that the aldehyde reductase, AdhC, possesses additional substrate promiscuity for acyclic monoterpene aldehydes. HighlightsO_LIDesign of a mevalonate-based monoterpene production platform in C. glutamicum C_LIO_LIHighest production titers of geranoids, eucalyptol, and linalool reported in C. glutamicum to date C_LIO_LIIdentification of citronellal as an intermediate in the reduction of geraniol to citronellol by C. glutamicum C_LI

Cofactor imbalance obstructs the productivities of metabolically engineered cells. Herein, we employed a minimally perturbing system, xylose reductase and lactose (XR/lactose), to increase levels of a pool of sugar-phosphates which are connected to the biosynthesis of NAD(P)H, FAD, FMN and ATP in Escherichia coli. The XR/lactose system could increase the amounts of the precursors of these cofactors and was tested with three different metabolically engineered cell systems (fatty alcohol biosynthesis, bioluminescence light generation and alkane biosynthesis) with different cofactor demands. Productivities of these cells were increased 2-4-fold by the XR/lactose system. Untargeted metabolomic analysis revealed different metabolite patterns among these cells; demonstrating that only metabolites involved in relevant cofactor biosynthesis were altered. The results were also confirmed by transcriptomic analysis. Another sugar reducing system (glucose dehydrogenase, GDH) could also be used to increase fatty alcohol production but resulted in less yield enhancement than XR. This work demonstrates that the approach of increasing cellular sugar phosphates can be a generic tool to increase in vivo cofactor generation upon cellular demand for synthetic biology. TeaserUse of sugar and sugar reductase to increase sugar phosphates for enhancing in situ synthesis of cofactors upon cellular demand for synthetic biology.

The rational design of microbial cell factories for high bioproduction remains a key challenge in metabolic engineering. While advanced modelling frameworks incorporating protein resource allocation, such as enzyme-constrained models (ecGEMs) and Expression and Thermodynamic Flux (ETFL), provide superior predictive power, their application is limited by a lack of user-friendly computational tools. Here, we present strainOptimizer, a comprehensive computational platform for rational strain design that systematically evaluates key resource allocation principles: the coupling of gene expression with metabolism, subcellular compartmentalization, and enzyme capacity limitations. Our benchmark analyses demonstrate that each principle offers distinct advantages: models coupling metabolism and expression (like ETFL) enable the identification of non-metabolic targets, organelle-level proteomic constraints improve precision for high-protein-cost products, and protein-usage-based objectives consistently outperformed traditional flux-based approaches. To demonstrate its practical utility, we applied strainOptimizer to an engineered sclareol-overproducing Saccharomyces cerevisiae strain. The platform identified novel targets, and experimental validation confirmed a 67% success rate, increasing the final sclareol titer by 14-26% and productivity by up to 45%. StrainOptimizer bridges the gap between resource allocation theory and applied engineering, providing a powerful, validated tool to accelerate the development of high-performance cell factories.

KRAS-mutant colorectal cancer (CRC) is characterized by metabolic reprogramming that can lead to tumor progression and drug resistance. The tumor microenvironment (TME) plays a pivotal role in modulating these metabolic adaptations. In particular, cancer-associated fibroblasts (CAFs), which make up a large portion of the TME, have been shown to strongly contribute to metabolic reprogramming in CRC. This study applies flux sampling, a computational method that explores the full range of feasible metabolic states, combined with representation learning and hierarchical clustering, to a computational model of central carbon metabolism to understand how CAFs influence metabolic adaptations of KRAS-mutant CRC cells following targeted enzyme knockdowns. Focusing on twelve key enzymes involved in glycolysis and the pentose phosphate pathway, knockdowns were simulated under both normal CRC media and CAF-conditioned media (CCM) conditions. Analysis revealed that CCM induces greater metabolic heterogeneity, with knockdown models exhibiting more variable and distinct metabolic states compared to those cultured in normal CRC media. While some enzyme knockdowns produced similar metabolic states, this overlap was less frequent in CCM, indicating that CAF-derived factors diversify the metabolic responses of CRC cells to enzyme perturbations. Pathway-level flux analysis demonstrated media-specific shifts in central carbon metabolism pathways. Importantly, the predicted biomass flux showed that enzyme knockdowns reduced growth across both conditions, but models in the CCM condition indicated CAFs could offer a protective effect against metabolic perturbation. Overall, this study reveals that CCM significantly influences the metabolic state and adaptability of KRAS-mutant CRC cells to enzyme perturbations, emphasizing the importance of including TME components in metabolic modeling and therapeutic development. These findings provide valuable insights into the metabolic adaptability of CRC and suggest that targeting tumor-CAF metabolic interactions may improve treatment strategies. Graphical Abstract Overview of computational workflowModels of interest represent simulated enzyme knockdowns in central carbon metabolism. Flux sampling searches the entire metabolic solution space and results in a distribution of flux values for each reaction within each model. Samples can be organized by knockdown and condition into matrices for input into representation learning. Representation learning is applied to sampling data to identify shared and independent metabolic states. Metabolic states indicate a heterogeneous response to enzyme knockdowns. Overlap of dark and light blue flux distributions, sampling clusters, and metabolic responses exemplify a shared metabolic state separate from to the gray unperturbed state. This workflow provides a low-dimensional representation of metabolic state that captures both the pathway- and reaction-level differences that describe each simulated knockdown. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/679631v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@cb6226org.highwire.dtl.DTLVardef@98d94eorg.highwire.dtl.DTLVardef@e2b20aorg.highwire.dtl.DTLVardef@116cfcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Understanding Chinese hamster ovary (CHO) cell metabolism through mathematical models is essential for optimizing culture media and biomanufacturing processes. Current mechanistic models rely primarily on either flux balance analysis (FBA), estimating intracellular fluxes while assuming steady state, or kinetic modeling, capturing dynamic behavior but typically for a limited number of reactions. Dynamic FBA (dFBA) integrates both approaches in a hybrid framework, but challenges remain in integrating the two formats to describe bioprocesses. In this study, we first enhanced an existing dynamic CHO-metabolism model by incorporating 13C-labeled data to refine kinetic expressions and stoichiometric constraints of amino acid pathways, including the asparagine-aspartate network and serine biosynthesis. We next evaluated the impact of prioritizing either stoichiometry, through the pseudo steady state assumption (PSSA), or the kinetic expressions of fluxes. Comparing error and predictive performance for both models for two industrially relevant fed-batch CHO culture conditions involving varying initial concentrations of nutrients and three feed streams, demonstrated that the kinetic-oriented model (KOM) yielded superior predictions for viable cell density (VCD), antibody production, and a range of amino acids and metabolites compared to the stoichiometric oriented model (SOM). Indeed, the KOM was able to predict production-to-consumption shifts of lactate and alanine, fluctuating levels of ammonia based on reversible kinetic expressions, and amino acids like asparagine and the serine-glycine pool. The KOM also provided better predictions for a third case including lactate-supplemented (LS) feed; however, slight parameter adjustments helped to improve model fidelity, likely due to the impact of high lactate on kinetic expressions of antibody (directly) and VCD (indirectly). In summary, our findings demonstrate that hybrid models emphasizing empirical kinetics over strict pseudo-steady-state constraints capture biologically realistic dynamics such as transient shifts for key metabolites like lactate, alanine, and ammonia, and also produce parameters useful across varying conditions, making them a practical and powerful tool for characterizing CHO cell culture performance in the future.

The methylotrophic yeast Pichia pastoris is frequently used for heterologous protein production and it assimilates methanol efficiently via the xylulose-5-phosphate pathway. This pathway is entirely localized in the peroxisomes and has striking similarities to the Calvin-Benson-Bassham (CBB) cycle, which is used by a plethora of organisms like plants to assimilate CO2 and is likewise compartmentalized in chloroplasts. By metabolic engineering the methanol assimilation pathway of P. pastoris was re-wired to a CO2 fixation pathway resembling the CBB cycle. This new yeast strain efficiently assimilates CO2 into biomass and utilizes it as its sole carbon source, which changes the lifestyle from heterotrophic to autotrophic. In total eight genes, including genes encoding for RuBisCO and phosphoribulokinase, were integrated into the genome of P. pastoris, while three endogenous genes were deleted to block methanol assimilation. The enzymes necessary for the synthetic CBB cycle were targeted to the peroxisome. Methanol oxidation, which yields NADH, is employed for energy generation defining the lifestyle as chemoorganoautotrophic. This work demonstrates that the lifestyle of an organism can be changed from chemoorganoheterotrophic to chemoorganoautotrophic by metabolic engineering. The resulting strain can grow exponentially and perform multiple cell doublings on CO2 as sole carbon source with a {micro}max of 0.008 h-1. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/862599v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@c13591org.highwire.dtl.DTLVardef@a3e4d4org.highwire.dtl.DTLVardef@427f5org.highwire.dtl.DTLVardef@db8675_HPS_FORMAT_FIGEXP M_FIG C_FIG