Biology subjects

Kontoravdi, C.

Publications and source records attributed to Kontoravdi, C..

13 recordsLinked to original sources

bioRxiv2026

Metabolism-Informed Bioreactor Model for Transferring Knowledge from Fed-batch to Continuous Biomanufacturing

Chinese hamster ovary (CHO) cells remain the dominant platform for monoclonal antibody (mAb) manufacturing, and rising demand is pushing the industry from fed-batch toward continuous operation. Continuous cultures promise sustained high cell densities and volumetric productivities, but their operation depends on reliable real-time monitoring, prediction and control. Limited intracellular observability and sparse extracellular measurements hinder this, leaving a persistent gap between mechanistic understanding and predictive deployment. Here we describe a metabolism-informed model for mAb production that embeds reactor- and intracellular-level mass balances directly within a learning architecture, while deliberately avoiding predefined kinetic rate laws and explicit steady-state assumptions. Because conservation relationships remain invariant across operating modes, this formulation provides a principled structural prior for cross-regime generalisation, positioning the model as a soft sensor for continuous bioprocesses: inferring unmeasured intracellular fluxes and one-step ahead forecasting extracellular concentrations to support process monitoring and control. Benchmarking two metabolic network sizes and three metabolic optimisation strategies, concentration predictions are indistinguishable across network sizes. Exchange flux fidelity is agnostic to solver choice, with network size mattering only for intracellular interpretability. Transferring the developed model from fed-batch to continuous operation with fine-tuning outperforming other transfer learning variants. This is consistent with the metabolic structure being invariant to mode of operation. Predicted fluxes reveal growth rate in fed-batch, rather than operating mode, as the dominant axis of metabolic variation, and identify potential regime-shift signals to guide experimental design for continuous culture characterisation. These results chart a route towards digital models sufficiently grounded in first-principles knowledge, yet adaptive for deployment under regime shifts.

systems biology↗

bioRxiv2026

Soft Sensing of Intracellular States for CHO Cell Bioprocessing with Ensemble Kalman Filters

In biotherapeutic manufacturing, product quality such as glycosylation profile is typically assessed only after harvest, limiting opportunities for corrective action during cell culture operation. Intracellular nucleotide sugar donors (NSD) directly determine glycosylation outcomes but are rarely measured, even offline, due to analytical complexity and process disruption. As a result, quality-related decisions remain constrained to fixed operating strategies. This work introduces a model-based soft sensing framework to infer NSD concentrations from readily available extracellular measurements. A Bayesian state estimation approach based on the Ensemble Kalman Filter (EnKF) is developed to reconstruct unmeasured intracellular states during CHO cell culture. An imperfect kinetic process model is combined with noisy extracellular measurements, explicitly accounting for process variability and measurement uncertainty through ensemble-based propagation and updates. The framework is validated using four independent experiments with distinct feeding perturbations that are not used for model calibration. Although the open-loop model exhibited substantial mismatch for both extracellular metabolites and intracellular NSDs, EnKF assimilation of extracellular measurements corrected key metabolic profiles. Building on these corrected extracellular dynamics, the EnKF demonstrated robust estimation of a growth-determining amino acid, asparagine, from correlated extracellular states. Based on the improved extracellular and amino acid estimates, the framework further enabled reliable inference of intracellular NSDs across all experiments.

bioengineering↗

bioRxiv2025

Model-Enabled Knowledge Transfer across cell lines, culture scales and conditions

Mechanistic models are central to quantitative understanding and optimisation of Chinese hamster ovary (CHO) cell culture processes, but their utility is often restricted by parameter sets calibrated for specific cell lines, scales, or operating conditions. In this study, we present the application of the Ensemble Kalman Filter (EnKF) to bioprocessing, introducing an ensemble-based framework for dual state and parameter estimation that enables mechanistic model adaptation across distinct systems. The EnKF recursively assimilates process measurements to update uncertain kinetic parameters and predict system states, allowing a model developed for one system to be transferred to a new one without reparametrisation and using only a single experimental dataset. The evolving parameter ensembles provide a time-resolved sensitivity analysis that identifies which parameters have dominant influence under new process conditions and when their effects become significant. The framework was evaluated across six CHO cell experimental datasets differing in scale, cell line, temperature, and feeding strategy, demonstrating accurate reconstruction of system dynamics and progressive improvement in long-term predictions as new data became available. By maintaining full mechanistic transparency while flexibly adapting to new data, the EnKF offers a practical route for knowledge transfer across systems, strengthening the role of mechanistic modelling in data-informed bioprocess understanding and control.

systems biology↗

bioRxiv2025

STAMPS: Signal-peptide Transformer for Augmenting Mammalian Protein Secretion

While secretion plays a key role in the diverse applications of cell engineering, to date only a handful of mammalian signal peptides have been characterised in depth and systematic efforts to build novel variants remain sporadic. We present STAMPS (Signal-peptide Transformer for Augmenting Mammalian Protein Secretion), a generative, autoregressive transformer fine-tuned on [~]6,000 mammalian signal peptides to design de novo sequences that modulate and enhance secretion across proteins and hosts. We show that STAMPS can be used to identify candidate signal peptides that outperform the widely used IgG {kappa} light-chain leader (IgKL) when used to secrete EGFP in HEK293T and CHO cells, and hEPO in HEK293T cells. When incorporated in an industrial cell line development framework, STAMPS leads to the generation of signal peptides that yield [~]2.3-fold gain in secretion of a VHH-Fc compared to the internal industrial benchmark in CHO G22 cells, with the same candidates ranking highly in both CHO and HEK293T hosts. Sequence-to-function analysis highlights a longer, strongly hydrophobic core and a tightly positioned cleavage site as drivers of this strong performance. Together, these results establish data-driven, generative design of mammalian signal peptides as a practical route to tune and improve secretion for bioproduction and cell engineering applications.

synthetic biology↗

bioRxiv2025

Inferring the Metabolic Objectives of Mammalian Cells via Inverse Modeling of Fluxomics and Metabolomics

Metabolism reflects evolutionary priorities that govern how cells allocate resources. In mammalian cells, metabolic objectives are layered and context-dependent, making it difficult to pinpoint the priorities that underlie observed phenotypes. Here, we introduce ObjFind-M, an inverse optimization framework that infers reaction-level metabolic objectives in mammalian cells directly from fluxomic and metabolomic data. Using Chinese hamster ovary (CHO) cells as a data-rich mammalian cell system, ObjFind-M consistently identifies mitochondrial ATP synthase as the central metabolic driver, supported by key TCA cycle and electron transport chain nodes. Priorities adjust with cellular state, favoring glycolysis-TCA coupling in the growth phase and shifting toward oxidative phosphorylation and redox balance when proliferative activity slows. High recombinant protein producing CHO cells emphasize citrate shuttling and beta-oxidation, linking energy supply with biosynthetic capacity for protein secretion. Benchmarking against conventional objectives demonstrates that maximizing ATP production most accurately reproduces experimental fluxes. By quantifying metabolic objectives directly from data rather than assuming them a priori, ObjFind-M provides a framework for identifying reaction-level strategies that shape cellular decision making. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/677837v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@5c507aorg.highwire.dtl.DTLVardef@a7b544org.highwire.dtl.DTLVardef@6db0d4org.highwire.dtl.DTLVardef@17464e2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIObjFind-M, an inverse FBA framework, infers reaction-level metabolic objectives from fluxomic and metabolomic data. C_LIO_LIATP synthase (Complex V) emerges as the top reaction, supported by NADH-generating TCA reactions such as the -ketoglutarate dehydrogenase complex. C_LIO_LICHO cells suppress formation of toxic or wasteful byproducts while emphasizing energy-efficient flux routing. C_LIO_LIGrowth vs. non-growth phases reveal shifts between biosynthesis and oxidative phosphorylation. C_LIO_LIHigh recombinant protein producers emphasize citrate shuttling and beta-oxidation, linking energy with secretory capacity. C_LIO_LIATP demand maximization provides the most accurate generalizable FBA objective C_LI

systems biology↗

bioRxiv2025

Nicotinamide reverses the Warburg effect in Chinese hamster ovary cell culture

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.

bioengineering↗

bioRxiv2025

A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present iCHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, iCHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used iCHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging iCHO3Ks structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, iCHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

systems biology↗

bioRxiv2024

Degradation Bottlenecks and Resource Competition in Transiently and Stably Engineered Mammalian Cells

Degradation tags, otherwise known as degrons, are portable sequences that can be used to alter protein stability. Here, we report that degron-tagged proteins compete for cellular degradation resources in engineered mammalian cells leading to coupling of the degradation rates of otherwise independently expressed proteins when constitutively targeted human degrons are adopted. By adopting inducible bacterial and plant degrons we also highlight how orthogonality and uncoupling of synthetic construct degradation from the native machinery can be achieved. We show the effect of this competition to be dependent on the context of the degrons where C-terminal degradation appears to impact competition the most across our tested settings. We then build a genomically integrated capacity monitor tagged with different degrons and confirm resource competition between genomic and transiently expressed DNA constructs. This work expands the characterisation of resource competition in engineered mammalian cells to degradation also including integrated systems, providing a framework for the optimisation of heterologous expression systems to advance applications in fundamental and applied biological research.

synthetic biology↗

bioRxiv2023

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of the production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity but cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic characterization approach combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product production in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

systems biology↗

bioRxiv2023

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

systems biology↗

bioRxiv2022

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organisms metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organisms phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

systems biology↗

bioRxiv2022

Immobilised enzyme cascade for targeted glycosylation

Glycosylation is a critical post-translational modification of proteins, improving properties such as folding, half-life and functionality. However, glycosylation is a non-templated and heterogeneous process because of the promiscuity of the enzymes involved. Here we describe a platform for sequential glycosylation reactions for tailored sugar structures (SUGAR-TARGET) that allows bespoke, controlled N-linked glycosylation in vitro. This novel proof-of-concept system is enabled by immobilised enzymes produced with a "one-step immobilisation/purification" method to express, biotinylate in vivo and immobilise glycosyltransferases. The immobilised enzymes are used in a reaction cascade mimicking a human-like N-linked glycosylation pathway where promiscuity naturally exists. The enzyme cascade is applied to free glycans, and a monomeric Fc domain expressed in glycoengineered Pichia pastoris, yielding near homogeneous glycoforms (>95% conversion). Finally, immobilised {beta}-1,4 galactosyltransferase is used to enhance the galactosylation profile of three different IgGs yielding 80.2 - 96.3 % terminal galactosylation. Enzyme recycling was further demonstrated for 7 cycles, with a combined reaction time greater than 140 hours. The novel SUGAR-TARGET platform is easy to implement, modular and reusable, and therefore can lead to the development of homogeneous glycan structures for functional and clinical evaluation. The use of immobilised enzymes enables the economical modification of cell-based material supporting applications at a large industrial scale.

bioengineering↗

bioRxiv2021

CHOmpact: a reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

Metabolic modelling has emerged as a key tool for the characterisation of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimise the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are, therefore, difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy multi-objective optimisation and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and, with further development, will serve as a platform to develop dynamic metabolic models for the control and optimisation of biopharmaceutical cell culture processes.

systems biology↗