Figure - available from: Biotechnology and Bioengineering
This content is subject to copyright. Terms and conditions apply.
Source publication
Genome‐scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole‐cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. He...
Similar publications
Niemann-Pick type C (NP-C) disease is a rare lysosomal storage disease caused by mutations in NPC1 (95% cases) or NPC2 (5% cases). These proteins function together in cholesterol egress from the lysosome, whereby upon mutation, cholesterol and other lipids accumulate causing major pathologies. However, it is not fully understood how cholesterol is...
Glycolaldehyde (GA), the smallest sugar, has significant potential as a biomass-derived platform chemical and is a key metabolite in several synthetic pathways for one-carbon metabolism and new-to-nature photorespiration. This study introduces two metabolic schemes for engineering Escherichia coli into GA biosensors. Through creating GA-dependent a...
Formaldehyde is a key metabolite in natural and synthetic one‐carbon metabolism. To facilitate the engineering of formaldehyde‐producing enzymes, the development of sensitive, user‐friendly, and cost‐effective detection methods is required. In this study, we engineered Escherichia coli to serve as a cellular biosensor capable of detecting a broad r...
Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic n...
The apple is a significant global fruit cultivated extensively worldwide. Fire blight, caused by Erwinia amylovora (Ea), poses a significant threat to global apple production. To control this disease, characterizing the virulence mechanisms/factors is imperative. Carbamoyl phosphate synthase is an essential enzyme in the biosynthesis of arginine an...
Citations
... Building upon this foundation, three subsequent GEMs (iCHO2291, iCHO2048, and iCHO2101) were published (Fouladiha et al. 2021;Gutierrez et al. 2020;Yeo et al. 2020). The most recent and comprehensive CHO GEM, iCHO2441, incorporates updated elements and has been systematically evaluated against all the previous models (Strain et al. 2023). These GEMs serve as platforms for constraint-based modeling, allowing researchers to tailor models to specific experimental conditions and generate personalised metabolic predictions. ...
... The marginal flux distribution can be summarised for each reaction as a histogram and can also be investigated for flux correlations (Herrmann et al. 2019). The coordinate hit-and-run with rounding algorithm has been determined to be the most efficient flux sampling algorithm (Haraldsdóttir et al. 2017;Herrmann et al. 2019), and previously, flux sampling has been used to evaluate the accuracy of the CHO GEM, iCHO2441 (Strain et al. 2023) and to predict uptake and secretion behavior across distinct culture phases (Gopalakrishnan et al. 2024). However, due to the computational intensiveness of flux sampling, it is used much less often than FBA and FVA, highlighting the need for new workflows employing the methodology. ...
... Furthermore, due to its unbiased nature, flux sampling represents the ideal analysis method for studying the metabolic plasticity of CHO cells. Utilising the iCHO2441 model as a framework (Strain et al. 2023), we aimed to constrain this CHO GEM with transcriptomics data to generate three separate culture phase-specific models. By comparing these models, we sought to identify metabolic features associated with improved mAb production and identify potential targets for directed media and feed optimisation. ...
Chinese hamster ovary (CHO) cells remain the industry standard for producing numerous therapeutic proteins, particularly monoclonal antibodies (mAbs). However, achieving higher recombinant protein titers remains an ongoing challenge and a fundamental understanding of the cellular mechanism driving improved bioprocess performance remains elusive. To directly address these challenges and achieve substantial improvements, a more in‐depth understanding of cellular function within a bioprocess environment may be required. Over the past decade, significant advancements have been made in the building of genome‐scale metabolic models (GEMs) for CHO cells, bridging the gap between high information content 'omics data and the ability to perform in silico phenotypic predictions. Here, time‐course transcriptomics has been employed to constrain culture phase‐specific GEMs, representing the early exponential, late exponential, and stationary/death phases of CHO cell fed‐batch bioreactor culture. Temporal bioprocess data, including metabolite uptake and secretion rates, as well as growth and productivity, has been used to validate flux sampling results. Additionally, high mAb‐producing solutions have been identified and the metabolic signatures associated with improved mAb production have been hypothesized. Finally, constraint‐based modeling has been utilized to infer specific amino acids, cysteine, histidine, leucine, isoleucine, asparagine, and serine, which could drive increased mAb production and guide optimal media and feed formulations.
... When a cell line is auxotrophic for a nutrient, this cell line is not able to grow in a medium lacking this element. Thus, CHO cells are auxotrophic for the 9 amino acids that are essential in mammalian cells: His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val [43][44][45]. ...
... However, several studies suggest that many CHO cell lines are also auxotrophic for proline (Pro) [47,48] and arginine (Arg) [49]. This limitation of the iCHOv1 genome-scale model has been reported in [44,50]. If a cell-line specific model were used instead, it would predict a different optimized medium to accommodate the additional auxotrophies. ...
Antibodies are therapeutic proteins with many applications in medicine, such as treating viral infections, different types of cancer, and common diseases such as psoriasis and multiple sclerosis. Chinese Hamster Ovary (CHO) cells are the most widely used cells for antibody production due to their well-established use and favorable features. However, the current design of antibody production systems often relies on a “trial and error” approach to manipulate CHO cells. This approach is time-consuming and costly, and can lead to suboptimal process performance. The use of mathematical models has the potential to greatly accelerate and improve the design and optimization of antibody production. Starting from a systematic and formal approach, the aim is to achieve an automatic design of the whole process that allows optimal productivity to be reached. To this end, we develop mathematical models and methods for the design and optimization of antibody manufacturing systems. The mathematical models are based on Flexible Nets (FNs), a modeling formalism that accommodates uncertain parameters and nonlinear dynamics. FNs enable the development of comprehensive models that encompass both
the metabolic network of CHO cells and the dynamics of the bioreactor in which the cells are cultured. Thus, by integrating macroscopic variables (e.g. dilution rate, substrate concentration, cell density, etc.) with microscopic variables (such as intracellular metabolic fluxes), our model represents a multi-scale system and facilitates global optimization.
... We have built GEMs (iGBM, iAST and iODG) for the three glioma subtypes based on the 2021 WHO classification and have predicted new repurposable, single drugs and combinations. While the sensitivity of essential gene predictions of the metabolic models remains low, the specificity is high [52,53]. The low sensitivity is often attributed to the fact that in silico single gene KOs only capture essentiality related to metabolism and most specifically to the optimization function and fail capturing essentiality to other processes such as regulatory processes. ...
... The low sensitivity is often attributed to the fact that in silico single gene KOs only capture essentiality related to metabolism and most specifically to the optimization function and fail capturing essentiality to other processes such as regulatory processes. The large efforts invested in the curation and standardization of genome-scale metabolic reconstruction, notably MEMOTE [54] and MetaNetX [55] as well as the benchmarking of contextspecific model algorithms [56][57][58], and the curation of GPR rules [59] and improvement of biomass formulation [53] are likely to further improve the accuracy of the predictions [52,60]. Furthermore, unlike some other computational drug repurposing approaches, metabolic modeling allowed understanding the effect of a KD or KO on a system level and confronting it to the earlier knowledge. ...
Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype–specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.
Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present i CHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, i CHO3K 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 i CHO3K 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 i CHO3K’s 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, i CHO3K 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.
Mammalian cell culture processes are widely utilized for biotherapeutics production, disease diagnostics, and biosensors, and hence, should be optimized to support robust cell growth and viability. However, toxic by-products accumulate in cultures due to inefficiencies in metabolic activities and nutrient utilization. In this study, we applied comprehensive ¹³ C stable-isotope tracing of amino acids and glucose to two Immunoglobulin G (IgG) producing Chinese Hamster Ovary (CHO) cell lines to identify secreted by-products and trace their origins. CHO cells were cultured in media formulations missing a single amino acid or glucose supplemented with a ¹³ C-tracer of the missing substrate, followed by gas chromatography-mass spectrometry (GC-MS) analysis to track labeled carbon flows and identify by-products. We tracked the sources of all secreted by-products and verified the identity of 45 by-products, majority of which were derived from glucose, leucine, isoleucine, valine, tyrosine, tryptophan, methionine, and phenylalanine. In addition to by-products identified previously, we identified several metabolites including 2-hydroxyisovaleric acid, 2-aminobutyric acid, L-alloisoleucine, ketoisoleucine, 2-hydroxy-3-methylvaleric acid, desmeninol, and 2-aminobutyric acid. When added to CHO cell cultures at different concentrations, certain metabolites inhibited cell growth while others including 2-hydroxy acids, surprisingly, reduced lactate accumulation. In vitro enzymatic analysis indicated that 2-hydroxy acids were metabolized by lactate dehydrogenase suggesting a possible mechanism for lowered lactate accumulation, e.g., competitive substrate inhibition. The ¹³ C-labeling assisted metabolomics pipeline developed and the metabolites identified will serve as a springboard to reduce undesirable by-products accumulation and alleviate inefficient substrate utilization in mammalian cultures used for biomanufacturing and other applications through altered media formulations and pathway engineering strategies.
Mammalian cells produce up to 80 % of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.
