Bioprocess Modeling Research Articles

Heterogeneous Evolution of Breast Cancer Cells-An Endogenous Molecular-Cellular Network Study.

Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular-cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets.

Role of comammox bacteria in granular bioreactor for nitrogen removal via partial nitritation/anammox

In this study, two bioprocess models were first constructed with the newly-discovered comammox process described as one-step and two-step nitrification and evaluated against relevant experimental data. The validated models were then applied to reveal the potential effect of comammox bacteria on the granular bioreactor particularly suitable for undertaking partial nitritation/anammox (PN/A) under different operating conditions of bulk dissolved oxygen (DO) and influent NH4+. The results showed although comammox bacteria-based PN/A could achieve > 80.0 % total nitrogen (TN) removal over a relatively wider range of bulk DO and influent NH4+ (i.e., 0.25–0.40 g‐O2/m3 and 470–870 g‐N/m3, respectively) without significant nitrous oxide (N2O) production (< 0.1 %), the bulk DO should be finely controlled based on the influent NH4+ to avoid the undesired full nitrification by comammox bacteria. Comparatively, conventional ammonium‐oxidizing bacteria (AOB)-based PN/A not only required higher bulk DO to achieve > 80.0 % TN removal but also suffered from 1.7 %∼2.8 % N2O production.

Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation.

Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.

Combined bio-methanol and power production from bio-oil: Proposing a clean bio-process through waste heat recovery and environmental optimization

Traditional methanol production from natural gas is well-studied, but there is a lack of research on environmentally optimizing bio-based methanol production. This study focuses on optimizing the combined production of bio-methanol and power from bio-oil using a comprehensive bio-process model. The research includes a novel framework for environmental, technical, and economic optimization of bio-oil to methanol and power to determine the most eco-friendly and cost-effective approach. An organic Rankine cycle (ORC) is also integrated for waste heat recovery. The results of this study revealed that the bio-based process with ORC showed a 22.6 % reduction in global warming potential impact (GWP) per kilogram of methanol, compared to the bio-based process without ORC. Also, by implementing the most environmentally friendly process, GWP will be reduced by 70.6 % per kg methanol compared to base sub-optimal operation. The results showed that for the process with the addition of ORC, after performing multi-objective optimization, under the optimal conditions, the economic annual rate of return increased from 19.6 % to 34.7 %. Finally, it was revealed that if the considered technology is commercialized, for every percentage of the methanol market it gains, it will contribute to 2.176 million tons CO2-eq/year less emissions.

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.

Data-driven inference of bioprocess models: A low-rank matrix approximation approach

Following the recent advent of Process Analytical Technologies, dataset production has undergone significant leverage. In this new abundance of data, isolating meaningful, informative content is critical for process dynamic modeling. This paper proposes a data-driven algorithm based on low-rank matrix approximation, the so-called successive projection algorithm, to retrieve a minimal set of macroscopic reactions, the corresponding stoichiometry, and a consistent kinetic model structure from the measurements of the trajectories of the species concentrations during cultures in a bioreactor. The proposed method is successfully validated in simulation, considering a case study related to monoclonal antibody (MAb) production with hybridoma cell cultures.

Medium-sized anammox granules achieve the optimal synergism between AnAOB and HDB via nitrite cycle

Anaerobic ammonium oxidation (Anammox) is acknowledged as one of the most cost-efficient nitrogen removal biotechnologies from wastewater. Anammox bacteria (AnAOB) tend to self-aggregate to form the granules, which can harbor symbiotic heterotrophic denitrifying bacteria (HDB). However, the dynamics of microbial synergism between AnAOB and HDB in size-fractioned anammox granules are still unclear. This study systematically investigated the bioactivity and bioabundance of highly enriched anammox granules with different sizes by combining specific activity tests, DNA sequencing and bioprocess modeling. The medium granules of 1.0–2.0 mm were the most distributed size in the whole reactor with the volumetric percentage of 55.7 ± 7.5%. The medium-large granules held the highest total nitrogen removal rate of 1.10 ± 0.05 mmol N2/(g VSS·h), where AnAOB and HDB co-existed with anammox contributing 93.1 ± 0.5% to N2 production. Candidatus Kuenenia, Denitratisoma and Anaerolineaceae were the only three genera that could be highly detected, dominating 52.5–65.6% of microbial communities. Kuenenia (AnAOB) only held the positive correlation with Denitratisoma (HDB) by co-occurrence network analysis. Furthermore, AnAOB were proved to have a synergistic growth with HDB, form the nitrite cycle, and maximize at the medium size by joint analysis of denitrifying functional gene and steady-state simulation. Collectively, we proposed that the medium-sized anammox granules of 1.0–2.0 mm could reach the optimal synergism between AnAOB and HDB via nitrite cycle. This work could provide insights into the microbial synergism in anammox granules and benefit the development of size-control strategy for anammox operation.

Bioprocess Modelling, Intensification, and Control

&#x0D; &#x0D; &#x0D; Chemical bio-process intensification, driven by the need for sustainability and cost-effectiveness, has gained significant attention. This study explores dynamic intensification (DI), a strategy to enhance efficiency by modifying system dynamics, operation, and control. Two key approaches are investigated: intensification by design, involving deliberate changes to physical and chemical aspects, and intensification by control, optimizing process parameters and feedback loops.&#x0D; The research focuses on the Optimal Periodic Control (OPC) method, dating back to the 1960s but now revisited with modern technology. To investigate the feasibility and effectiveness of dynamic intensification, a comprehensive methodology encompassing modeling, optimization, linearization, dynamic intensification, and control was designed. This methodology ensures systematic and standardized testing of DI in various bioprocesses within the industry.&#x0D; Through rigorous case studies, such as Activated Sludge Model No. 1 (ASM1) and an antibiotic perfusion bioreactor, the study demonstrates the practical benefits of dynamic intensification, including enhanced process efficiency and control. Applying model predictive control (MPC) further improves process efficiency, and the intensified systems are thoroughly compared to their respective base cases.&#x0D; The insights gained from this research contribute to advancements in bioprocessing technologies across industries, paving the way for sustainable and efficient engineering practices. Engineers can leverage this knowledge and the developed methodology to optimize chemical bioprocesses through innovative intensification strategies, ultimately promoting more eco-friendly and economical bio-processes. The study's comprehensive methodology offers valuable insights and understanding for bioprocess engineers seeking to enhance system efficiency and contribute to a greener future.&#x0D; &#x0D; &#x0D;

Impacts of granular sludge properties on the bioreactor performing nitrate/nitrite-dependent anaerobic methane oxidation/anammox processes

In this work, a bioprocess model was applied to first determine the impacts of influent substrates conditions on the granular bioreactor performing nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) and anammox integrated processes and then investigate the roles of granular sludge properties in regulating the bioreactor performance and start-up process. The ideal influent substrates conditions were identified at NO2−-N/NH4+-N of 1:1 and dissolved CH4 concentration of 85 g COD m−3, which achieved 98.6% total nitrogen removal and 87.7% dissolved CH4 utilization. Under such ideal influent conditions, the initial properties of granular sludge didn’t significantly affect the granular bioreactor performance. However, inoculation of granular sludge with a relatively small granular sludge size and a high abundance of n-DAMO archaea or/and anammox bacteria could effectively shorten the bioreactor start-up. Meanwhile, reducing the diffusivity of solutes within granular sludge was also beneficial for expediting the start-up process and promoting dissolved CH4 utilization.

Continuous production of succinic acid from glycerol: A complete experimental and computational study

In this work, a bioprocess for the fermentation of A. succinogenes for the production of succinic acid from glycerol was developed, employing a continuous bioreactor with recycle. Moreover, a new bioprocess model was constructed, based on an existing double substrate limitation model, which was validated with experimental results for a range of operating parameters. The model was used to successfully predict the dynamics of the continuous fermentation process and was subsequently employed in optimisation studies to compute the optimal conditions, dilution rate, reflux rate and feed glycerol concentration, that maximise the productivity of bio-succinic acid. In addition, a Pareto front for optimal volumetric productivity and glycerol conversion combinations was computed. Maximum volumetric productivity of 0.518 g/L/h, was achieved at the optimal computed conditions, which were experimentally validated. This is the highest bio-succinic acid productivity reported so far, for such a continuous bioprocess.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges.

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Model-based development of strategies enabling effective enrichment and application of comammox bacteria in floccular sludge under mainstream conditions

The discovery of complete ammonium oxidation (comammox) has redefined the perception of the nitrification process which plays a vital part in biological nitrogen removal (BNR) from wastewater. Despite the reported detection or cultivation of comammox bacteria in biofilm or granular sludge reactors, limited attempts have been made to enrich or assess comammox bacteria in floccular sludge reactors with suspended growth of microbes, which are most extensively applied at wastewater treatment plants. Therefore, through making use of a comammox-inclusive bioprocess model reliably evaluated using batch experimental data with joint contributions of different nitrifying guilds, this work probed into the proliferation and functioning of comammox bacteria in two commonly-used floccular sludge reactor configurations, i.e., continuous stirred tank reactor (CSTR) and sequencing batch reactor (SBR), under mainstream conditions. The results indicated that compared with the studied SBR, the CSTR was observed to favor the enrichment of comammox bacteria through maintaining a sufficient sludge retention time (40–100 d) while avoiding an extremely low DO level (e.g., 0.05 g-O2/m3), irrespective of the varied influent NH4+-N of 10–100 g-N/m3. Meanwhile, the inoculum sludge was found to greatly influence the start-up process of the studied CSTR. By inoculating the CSTR with a sufficient amount of sludge, finally enriched floccular sludge with a high abundance of comammox bacteria (up to 70.5 %) could be rapidly obtained. These results not only benefitted further investigation and application of comammox-inclusive sustainable BNR technologies but also explained, to some extent, the discrepancy in the reported presence and abundance of comammox bacteria at wastewater treatment plants adopting floccular sludge-based BNR technologies.

Achieving stable anaerobic mono-digestion of concentrated waste activated sludge without any pretreatment

Sludge digesters are generally designed using empirical thresholds that were defined several decades ago, typically leading to large digesters displaying low organic loading rates (1–2.5 kgVS.m−3.d−1). However, the state of the art has significantly evolved since these rules were set, especially regarding bioprocess modelling and ammonia inhibition. This study demonstrates that digesters can be safely operated at high sludge concentration and total ammonia concentration up to 3.5 gN.L−1, without any sludge pretreatment. The possibility of operating sludge digesters at organic loading rates of 4 kgVS.m−3.d−1 by feeding concentrated sludge was identified through modelling and experimentally confirmed. Based on these results, the present work proposes a new mechanistic digester sizing strategy based on microbial growth and ammonia-related inhibition in lieu of historical empirical methods. Applying such method to sludge digester sizing could lead to very significant volume reduction (25–55%), which would result in reduced process footprint and more competitive building costs.

Practical data-driven modeling and robust predictive control of mammalian cell fed-batch process

Even if the performances of bioprocesses can be significantly improved by model-based control, there often remains a tradeoff between model complexity and control robustness. This paper proposes an original data-driven strategy for fast design of dynamic bioprocess models with minimal complexity (i.e., minimal number of bioreactions). Maximum likelihood principal component analysis (MLPCA) is applied to infer the minimal reaction scheme from a 25-state mammalian cell culture database. Then, a systematic algorithm is used to provide a continuous kinetic model formulation assuming all rates to occur simultaneously, which may be far from true cell metabolic conditions sometimes presenting discontinuous metabolic switches. A robust model predictive formulation is therefore adopted to reduce the impact of model structural uncertainty on the process performances. Additional numerical results show that the proposed strategy presents excellent performances in presence of unexpected metabolic switches.

Functional guild dynamics in a single-sludge shortcut nitrogen and phosphorus removal reactor: a modeling study

A mathematical process model was set up to evaluate functional guild dynamics in an integrated N and P removal process.

NyctiDB: A non-relational bioprocesses modeling database supported by an ontology

Strategies to exploit and enable the digitalization of industrial processes are on course to become game-changers in optimizing (bio)chemical facilities. To achieve this, these industries face an increasing need for process models and, as importantly, an efficient way to store the models and data/information. Therefore, this work proposes developing an online information storage system that can facilitate the reuse and expansion of process models and make them available to the digitalization cycle. This system is namedNyctiDB, and it is a novel non-relational database coupled with a bioprocess ontology. The ontology supports the selection and classification of bioprocess models focused information, while the database is in charge of the online storage of said information. Through a series of online collections,NyctiDBcontains essential knowledge for the design, monitoring, control, and optimization of a bioprocess based on its mathematical model. OnceNyctiDBhas been implemented, its applicability and usefulness are demonstrated through two applications. Application A shows howNyctiDBis integrated inside the software architecture of an online educational bioprocess simulator. This implies thatNyctiDBprovides the information for the visualization of different bioprocess behaviours and the modifications of the models in the software. Moreover, the information related to the parameters and conditions of each model is used to support the users’ understanding of the process. Additionally, application B illustrates thatNyctiDBcan be used as AI enabler to further the research in this field through open-source and reliable data. This can, in fact, be used as the information source for the AI frameworks when developing, for example, hybrid models or smart expert systems for bioprocesses. Henceforth, this work aims to provide a blueprint on how to collect bioprocess modeling information and connect it to facilitate and empower the Internet-of-Things paradigm and the digitalization of the biomanufacturing industries.

Investigating ‘greyness’ of hybrid model for bioprocess predictive modelling

Hybrid modelling combines data-driven and mechanistic modelling, providing a cost-effective solution to modelling complex biochemical reaction kinetics when the underlying mechanisms are not fully understood. However, the question of how much kinetic information to incorporate into a hybrid bioprocess model has not been systematically addressed. Therefore, this work built three hybrid models for predicting the temperature-dependent rates of biomass growth, glucose consumption and γ-linolenic acid accumulation during fermentation of the fungus Cunninghamella echinulata. Each hybrid model incorporated different amounts of kinetic information from a pre-existing complex kinetic model, representing three levels of hybrid model ‘greyness’, then embedded a Gaussian Process (GP) to simulate the unknown kinetics inferred from experimental measurements. Although all three hybrid models fitted well, incorporating more specific kinetic information increased the risk of incorporating incorrect inductive bias that hindered rather than enhanced hybrid model performance. This observation also held when using the hybrid models to predict the temperature-shift dynamics for the upscaled 5 L bioreactor. Nonetheless, the hybrid models demonstrated much improved predictive confidence with similar predictive accuracy to the original kinetic model, demonstrating the proficiency of hybrid modelling for accelerating the construction of confident bioprocess models for robust process optimisation and real-time monitoring.

A Multiobservers Approach for a Class of Bidimensional Nonuniformly Observable Systems

We consider the observation problem for a particular class of bidimensional systems with scalar output, which requires the consideration of an embedding in a higher dimension for the usual high-gain observer synthesis. We propose a new approach that does not require any coordinates transformation. This approach is based on the design of a set of estimators running in parallel in the same dimension than the original system. Each estimator uses the knowledge of the first two derivatives of the output, and the further derivatives up to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> th one (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the observability index over an invariant domain) are used to discriminate at any time among the different estimators. We give two examples showing the applicability of this approach with measurement noise. Biological systems used in batch bioprocess models are of particular motivation for this article.

Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths.

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification.

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.