Metabolic Engineering

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

Mesoplasma florum is a fast-growing, near-minimal bacterium and an emerging model for systems and synthetic biology. However, its dependence on complex serum-containing media limits experimental control and complicates the interpretation of cellular phenotypes. Here, we developed CMRL-AT, a serum-free, quasi-defined medium that supports rapid growth comparable to the commonly used ATCC 1161 medium. Despite supporting similar biomass, CMRL-AT profoundly reshaped the M. florum transcriptome, with approximately one-third of the annotated protein-encoding genes being differentially expressed relative to ATCC 1161. These changes revealed distinct physiological programs associated with rapid growth in complex medium and higher nutrient acquisition in CMRL-AT, illustrating how medium composition alters the functional priorities of a near-minimal cell. Transcriptome profiling across six energy sources further uncovered distinct sugar-responsive expression programs. Combining these responses with transcription-unit organization, protein-domain predictions, and metabolic context resolved fructose- and sucrose-responsive modules, and allowed the assignment of previously ambiguous phosphotransferase system components to specific sugar-utilization pathways. CMRL-AT provides an experimental framework to help resolving gene functions, refining metabolic models, and designing reduced genomes adapted to defined environments.

Azetidine-2-carboxylic acid (Aze) is a toxic, non-proteogenic amino acid produced in restricted plants. It can enter the human food chain through direct or indirect dietary exposure and has been linked to neurological disorders due to its ability to misincorporate for its structural analog proline (Pro) during translation and lead to protein misfolding. Although several microorganisms have evolved mechanisms to detoxify Aze, these pathways have not previously been exploited to engineer Aze tolerance in plants. Here, we heterologously expressed a bacterial Aze hydrolase (HAS) in Arabidopsis thaliana to establish a metabolic strategy for Aze detoxification in plants. Transgenic plants exhibited markedly enhanced tolerance to Aze, as demonstrated by improved root growth, cotyledon development, and survival compared with wild-type plants under Aze stress. We further showed that HAS localized to the cytosol, where it converted Aze into the non-toxic metabolite 2-hydroxy-4-aminobutyrate (HAB). HAB formation was confirmed both in planta and in vitro, demonstrating that HAS remained catalytically active in plant cells and effectively detoxified Aze. Enzyme kinetics revealed that HAS has a high affinity for Aze and is stereospecific for L-Aze, with no activity toward D-Aze. Multi-omics analysis including transcriptomics and misincorporation proteomics show that the transgenic lines misincorporate Aze significantly less compared to wild-type leading to enhanced Aze tolerance. Our findings establish bacterial HAS as an effective metabolic detoxification system that confers enhanced Aze tolerance in plants. This study provides a promising strategy for engineering plant resistance to toxic non-proteogenic amino acids.

The valorisation of biological polymers requires microbial systems that can both access recalcitrant substrates and convert the resulting carbon into useful products. Although microbial genome and metagenome resources provide an expanding reservoir of candidate depolymerizing and modifying enzymes, most discovery workflows remain disconnected from enzyme optimisation and host adaptation. Here we present a coupled sequence-based enzyme discovery, enzyme evolution and synthetic yeast chassis adaptation strategy for microbial biopolymer valorisation. Focusing on laccases for the depolymerisation of lignin as a proof of concept, we combine sequence data mining for enzyme discovery, modular yeast surface display for functional screening, directed evolution for enzyme optimisation and synthetic yeast genome diversification for chassis improvement. In our study, surface display enabled functional benchmarking and recovery of improved laccase variants and synthetic-genome-enabled diversification provided a route to explore host configurations that influence display and enzyme performance. By integrating enzyme-level and chassis-level optimisation, this framework addresses a central bottleneck in converting microbial biodiversity by computational sequence repository mining into deployable biomanufacturing systems. Our results establish laccases as tractable entry points for oxidative biopolymer conversion and provide a generalizable platform for engineering yeast systems for sustainable carbon valorisation.

Engineered microbial consortia offer a promising strategy for chemical production by distributing specialized functions among microbial strains, reducing metabolic burden, facilitating modular pathway optimization, reducing toxicity, and increasing strain stability. However, differences in growth rates can destabilize population composition, compromising productivity and limiting their applicability. Here, we developed a multichromatic optogenetic Toxin-Antitoxin (optogeneticTA) platform for dynamic control of Escherichia coli consortia of up to four members using blue, red, and near-infrared light and darkness. By varying light intensities or pulses, we precisely program and dynamically modulate the population composition of two-, three-, and four-membered consortia. We further developed a modular mathematical framework that captures and predicts population dynamics of these optogenetically controlled co-cultures. Applying dynamic control to a two-membered engineered consortium increased phenol production by ~69% relative to unregulated consortia. These results establish a programmable platform for stabilizing and dynamically optimizing microbial consortia, with potential applications across microbial biomanufacturing.

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

Motivation: Retrobiosynthesis tools use different reaction rules, search strategies and biochemical priors, so each can recover transformations missed by others. Integrating their outputs could broaden pathway coverage, but ensembling can also worsen ranking. Results: SynBioCrow runs independent retrosynthesis engines, normalizes predictions into a provenance-preserving reaction graph, and separates route generation from biochemical evidence, sequence design and bounded learning. On the frozen 65-path Galaxy-SynBioCAD literature benchmark used for SynBioCrow 2.3, connectivity-level partial recovery was 19/65 for the ensemble, 13/65 for RetroBioCat2, 9/65 for RetroPath and 0/65 for DORAnet; the ensemble preserved two exact RetroPath routes but worsened their ranks. SynBioCrow 2.4 subsequently repaired structured-reaction parsing and target-connected Candidate generation and prospectively locked two new external targets, 1,3-propanediol and 1,4-butanediol, before pathway truth was examined. RetroBioCat2 produced 3 and 2 backend-internal SOLVED search outcomes, respectively, whereas DORAnet produced none; however, neither backend recovered any predeclared explicit literature intermediate by exact structure. The negative prospective result was frozen without post-reveal tuning. Production ranking in 2.4 is therefore NONE and the release remains Candidate-stage; validated complete-pathway recovery is not claimed. Availability: SynBioCrow 2.4.0 is archived at DOI 10.5281/zenodo.23173173 with source code and frozen supporting evidence.

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

Plasmid copy number (PCN) is a central determinant of recombinant gene expression levels and metabolic burden in bacterial cell factories. However, dynamic real-time control of PCN during microbial cultivation remains largely unexplored. In the ColE1/pUC19 replication system, PCN is governed by an antisense RNAI/RNAII circuit, but this native mechanism provides no external handle for process-level intervention. Here, we engineer and characterize a dual-layer control architecture that combines two orthogonal perturbations to direct plasmid replication in Escherichia coli populations i.e., temperature shifts, which modulate the secondary structure of RNAII and, consequently, its binding to RNAI and its inhibitory potency (ex vivo control), and arabinose-inducible overexpression of a supplementary RNAI cassette, integrated either on a secondary low-copy plasmid or directly on the high-copy target plasmid (in vivo control). Using a superfolder GFP validated fluorescent reporter and automated flow cytometry in continuous cultures, we track PCN dynamics at the population level in real time under chemostat conditions. Preliminary screening in microplates high-throughput cultivation device across three strains, two carbon sources, and three temperatures (30, 37, and 42C) reveals that the two control layers act synergistically. Neither temperature alone nor inducible RNAI alone achieves the dynamic range obtained by their combination. This synergistic effect is sustained during continuous cultivation at a fixed dilution rate of 0.1/h, with carbon source and temperature transitions producing predictable and reproducible shifts in population-level PCN. These results establish a scalable, dual-input framework for on-demand PCN tuning in continuous bioprocesses, with direct relevance to biopharmaceutical plasmid production and synthetic biology applications.

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

Lipid droplets (LDs) play a central role in cellular carbon and energy homeostasis, in particular through the roles played by numerous LD-associated proteins. A long-standing question concerns how these proteins are molecularly organized at the seemingly overcrowded LD surface. Delayed in TAG Hydrolysis 1 (DTH1) has been identified as a key protein for efficient LD degradation in Chlamydomonas reinhardtii, however the underlying molecular mechanism remains unknown. DTH1 contains several structural features including a coiled-coil domain, a phosphatidylethanolamine (PE) specific lipid-binding domain, intrinsically disordered regions, and an alpha-helical C-terminal domain, that make it a compelling candidate for acting as a molecular scaffold in protein recruitment and/or organelle tethering. In this study, we used the Chlamydomonas knock-out mutant dth1 as an entry point to begin dissecting the molecular organization of proteins at the LD surface. Comparative lipidomic and proteomic analyses of isolated LDs from both WT and dth1 mutants revealed several key features associated with LD degradation during nutrient recovery. First, PE emerged as an important lipid species involved in LD degradation. Second, active communication occurs among multiple subcellular organelles particularly between LDs, peroxisomes and mitochondria. Third, DTH1 was found to play a critical role in remodeling both the LD proteome and lipidome. Furthermore, detailed analyses of LD degradation during nitrogen recovery uncovered the formation of micro-LDs, a phenomenon not yet reported in microalgae. Collectively, these results highlight the tight coordination of metabolic activities across subcellular compartments and emphasize the importance of intracellular carbon trafficking during stress recovery.

Data-driven soft sensors can estimate fermentation product concentrations from routinely recorded process variables, but strong performance during normal operation does not establish reliability during process deviations. This study evaluated current-time penicillin-concentration estimation using 100 simulated IndPenSim batches comprising 90 normal-operation batches and ten documented deviation batches, with 113,935 observations in total. Initial normal-trained models and five follow-up experiments examined complete-batch validation, dependence on batch-progress features, phase-specific error, model-family comparisons, early out-of-distribution warnings and empirical prediction ranges. These analyses motivated a matched comparison between normal-only and fault-inclusive HistGradientBoosting regressors using 36 current and causal history-based inputs. Normal performance was evaluated in five regime-balanced complete-batch folds, and deviation performance by leave-one-fault-batch-out evaluation; every tested deviation batch was excluded from its own model fit. Batch-balanced sample weights were used, with a factor of three assigned to permitted deviation batches in fault-inclusive fitting. On held-out deviation batches, pooled RMSE decreased from 3.195 to 2.564 g/L, a reduction of 19.73%, while MAE decreased from 2.076 to 1.441 g/L and R2 increased from 0.8580 to 0.9085. Normal-operation RMSE was nearly unchanged at 1.981 and 1.983 g/L. Eight of ten deviation batches improved. The mean paired fault-batch RMSE difference was -0.7063 g/L, with a descriptive 95% batch-bootstrap interval of -1.2844 to -0.2194 g/L. Improvement was largest within the first-to-last recorded fault-reference window, but late-stage errors persisted. Batch 100 remained poorly predicted, with fault-inclusive RMSE of 6.631 g/L and R2 of -2.4837. A fault-risk classifier, Isolation Forest OOD detector and empirical error ranges offered incomplete reliability information. Fault-inclusive training therefore improved this benchmark on average, but did not establish generalization to unseen fault mechanisms, calibrated safety warnings or deployment in physical fermentation.

L-Threonine is an essential amino acid widely used in the feed, food, and pharmaceutical industries, and microbial fermentation is now the dominant route for its production. Further increases in producer titers depend on random mutagenesis coupled with high-throughput screening (HTS), but progress has been limited by the lack of a biosensor with a broad operating range, high sensitivity, and target specificity. Here, we show that L-threonine induces the tryptophan-repressed trp promoter of Escherichia coli, a response that, to our knowledge, has not been described previously. Deletion analysis showed that both the trpO operator and the trpL attenuation element contribute to induction, and that the lowest basal expression occurs only when both elements are present. By optimizing the ribosome-binding site, the promoter -10 region, and TrpR expression, we constructed an L-threonine biosensor on a pACYC backbone. The sensor responded linearly to L-threonine from 0.1 to 30 g/L (R2 = 0.9895), with an induction ratio of up to 51.7-fold. Among the 20 proteinogenic amino acids tested, only L-threonine produced a detectable response. The biosensor distinguished strains with different L-threonine production capacities and guided the directed evolution of homoserine kinase (ThrB) at a conserved motif, yielding variants with brighter reporter fluorescence and increased L-threonine production. This work provides a high-performance biosensor based on native E. coli regulatory elements for screening L-threonine overproducers.

Metabolic profiles are used to quantify disturbance, classify disease and infer biochemical function, yet these endpoints retain different information. We combine secondary analysis of 386 metabolites in six Arabidopsis thaliana mutation accumulation lines, known-disease classification in 129 public human metabolic-disease profiles, and an analytically solvable kinetic model. A conditional occupancy model separates annotation-bin breadth from hit count and measured bin sizes: the plant low-versus-high fitness comparison changes from a two-sided exact permutation p = 0.10 to p = 0.30 after calibration. Unsigned metabolite profiles achieve mean macro recall of 0.911 and 0.895 in separate EDTA and heparin subsets, compared with 0.624 and 0.382 for a burden baseline. However, controlled loss of 50% of held-out measurements reverses representation ranking in heparin, giving signed profiles a recall advantage of 0.108 with a conditional bootstrap interval of 0.063-0.155. A spectral decomposition identifies the within-pathway contrasts attenuated by smoothing. The kinetic model then shows that lower concentration and faster relaxation can coexist with reduced desired product flux, while a null-space criterion states when observations identify a metabolic task. The results connect assay coverage, diagnostic information and functional identifiability. Human performance is internally validated, and the kinetic predictions remain uncalibrated to patients.

Synthetic allopolyploidization is a powerful strategy for generating strains with novel industrially relevant traits. During the design of select, hybridize, evolved and learn cycle, the mitochondrial inheritance is considered neutral and most studies overcome their phenotypic impact. Here, we generated 168 synthetic allotetraploid strains representing all pairwise parental combinations of eight wild Saccharomyces species, and systematically controlled mitochondrial inheritance in these crosses. These allopolyploids were phenotyped across 31 environmental conditions relevant to biotechnology. In ~50% of all condition-strain combinations conditions, allotetraploids exhibited mid-parent heterosis. In approximately 25% of conditions, we found crosses with hybrid vigor, particularly in combinations involving Saccharomyces arboricola x Saccharomyces mikatae . However, allotetraploids were highly unstable and rapidly sporulated. Our analyses revealed that environmental conditions accounted for the largest effects, follow by its interaction with mitotype. However, mitotype alone showed small effects but significantly affected 42% of the tested conditions, underscoring the importance of mitochondrial inheritance as a key determinant of allopolyploid performance. These findings demonstrate that mitochondrial inheritance is a key, environment-dependent determinant of allopolyploid fitness and should be considered alongside nuclear genome composition in the rational design of industrial allopolyploid strains

Influenza A virus (IAV) defective interfering particles (DIPs) are regarded as promising broad-spectrum antivirals due to their high efficacy demonstrated in animal trials. Previously, an OP7 chimera DIP was generated that showed markedly enhanced antiviral efficacy compared to conventional IAV DIPs. In addition, a high-yield cell culture-based production process for OP7 chimera DIPs was established in perfusion mode. However, due to the technical complexity of this technology, we aimed to combine the advantages of perfusion in terms of yield with that of a simple and less complex batch process. In the present work, we established a fed-batch production process for OP7 chimera DIPs that allows a more straightforward implementation into existing production facilities. Using a feeding regime that was able to overcome the cell density effect, we achieved productive virus infection in stirred tank reactors until a cell density of 10E+6 cells/mL. Furthermore, we obtained a high virus titer of 3.8 log10(HAU/100 uL), corresponding to a 3.4-fold increase compared to a reference batch production. Moreover, the produced material demonstrated a significantly increased interfering bioactivity shown by an in vitro potency assay. Interestingly, this efficacy was similarly high as compared to OP7 chimera DIP material previously produced in perfusion mode, despite twofold lower virus titers. Finally, we demonstrate robustness of OP7 chimera DIP seed virus trains, which is significant step towards later pharmaceutical production. Further, corresponding GMP MDCK-PB2(sus) cell and OP7 chimera virus banks are now available for later biomanufacturing further use. In conclusion, the established fed-batch process offers a valid n additional option for pharmaceutical grade OP7 chimera DIP GMP manufacturing production under GMP conditions required for subsequent clinical testing and for later large-scale manufacturing.

Human pluripotent stem cells (hPSCs) are increasingly expanded in 3D suspension culture to support scalable cell culturing, yet how the transition from 2D to 3D cultures affects hPSCs remains poorly understood. Here, we compare hPSCs in 2D and 3D cultures using transcriptomics, proteomics, spent medium analysis, and stable-isotope tracing. hPSCs maintained pluripotency in both formats, although suspension culture showed altered growth dynamics and increased initial cell loss. Multi-omics analysis identified culture format as the dominant driver of molecular variation and revealed changes in adhesion, cytoskeletal organization, glycolysis, and oxidative phosphorylation. Functional analyses showed increased lactate production from glucose and stable-isotope tracing revealed increased glucose-derived pyruvate formation and altered partitioning of glucose-derived carbon within the TCA cycle in 3D suspension cultures. Glutamine contribution to the TCA cycle was reduced, whereas relative reductive carboxylation was increased in 3D suspension cultures. Together, these findings show that transition from 2D to 3D suspension culture induces metabolic rewiring in hPSCs.

Genome-scale metabolic models guide rational strain engineering, while high-throughput insertional mutagenesis enables systematic investigation of gene function and condition-dependent growth requirements. However, a platform integrating high-density transposon insertion sequencing with matched stoichiometric, thermodynamic, enzyme-constrained and combined metabolic models has not, to our knowledge, been reported in Aspergillus. Here, we establish a unified experimental and computational platform in Aspergillus niger ATCC1015 by combining curated metabolic reconstruction, Ac/Ds transposon mutagenesis and machine-learning-based essentiality prediction. Our stoichiometric reconstruction, FBA1015, expands reaction coverage and compartmental resolution beyond four published reconstructions. From FBA1015, we developed three models sharing its biochemical network: GECKO light with enzyme-capacity constraints, TFA with thermodynamic constraints and ecTFA combining both. FBA1015 and its derivatives performed competitively in phenotype benchmarks, with added constraints improving agreement with measured growth and gas exchange. Insertion libraries grown in rich medium, glucose or xylose achieved genome-wide densities of one distinct site per 21--112bp across individual libraries. A machine-learning classifier integrated insertion patterns and gene structure to predict essentiality across 11,111 genes. Applying a stringent consensus criterion identified 709 genes predicted essential on both glucose and xylose as sole carbon sources, and 91 predicted essential on one but nonessential on the other. Integration with simulated gene deletions prioritizes candidate genes and testable pathway hypotheses, connecting high-throughput genetic screening with mechanistic analysis for fungal functional investigation and strain engineering.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.