Mariano Nicolas Cruz Bournazou

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Cruz Bournazou

First Name

Mariano Nicolas

ORCID

0000-0001-9461-4414

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Publications 1 - 10 of 11

Bioprocessing in the Digital Age: The Role of Process Models
Narayanan, Harini; Luna, Martin F.; von Stosch, Moritz; et al. (2020)
Biotechnology Journal
Suspension of a point-mass-loaded filament in non-uniform flows: Passive dynamics of a ballooning spider
Cho, Moonsung; Cruz Bournazou, Mariano Nicolas; Park, Suhyeon; et al. (2024)
AIP Advances
Spiders utilize their fine silk fibers for their aerial dispersal, known as ballooning. With this method, spiders can disperse hundreds of kilometers, reaching as high as 4.5 km. However, the passive dynamics of a ballooning model (a highly flexible filament and a spider body at the end of it) are not well understood. Here, we introduce a bead-spring model that takes into account the anisotropic drag of a fiber to investigate the passive dynamics by the various non-uniform flows: (i) a shear flow, (ii) a periodic vortex flow field, and (iii) a homogeneous turbulent flow. For the analysis of the wide range of parameters, we defined a dimensionless parameter, which is called “a ballooning number.” The ballooning number is defined as the ratio of Stokes’ fluid-dynamic force on a fiber by the non-uniform flow field to the gravitational force of a body. Our simulations show that the present model in a homogeneous turbulent flow exhibits the biased characteristic of slow settling with increasing turbulence. Upon investigating this phenomenon for a shear flows, it was found that the drag anisotropy of the filament structure is the main cause of the slow settling. Particularly, the cause of slow settling speed lies not only in the deformed geometrical shape but also in its generation of fluid-dynamic force in a non-uniform flow. Additionally, we found that the ballooning structure could become trapped in a vortex flow. These results help deepen our understanding of the passive dynamics of spiders ballooning in the atmospheric boundary layer.
Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations
Hans, Sebastian; Haby, Benjamin; Krausch, Niels; et al. (2020)
Bioengineering
In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.
Accelerated Bioprocess Development of Endopolygalacturonase-Production with Saccharomyces cerevisiae Using Multivariate Prediction in a 48 Mini-Bioreactor Automated Platform
Sawatzki, Annina; Hans, Sebastian; Narayanan, Harini; et al. (2018)
Bioengineering
Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations are a promising alternative to accelerate and optimize bioprocess development allowing for sophisticated cultivation experiments in high throughput. These include fed-batch and continuous cultivations with multiple options of process control and sample analysis which deliver valuable screening tools for industrial production. However, the model-based methods needed to operate these robotic facilities efficiently considering the complexity of biological processes are missing. We present an automated experiment facility that integrates online data handling, visualization and treatment using multivariate analysis approaches to design and operate dynamical experimental campaigns in up to 48 mini-bioreactors (8–12 mL) in parallel. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed, running and comparing 16 experimental conditions in triplicate. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable to different strains and cultivation strategies, and suitable for automatized process development reducing the experimental times and costs.
Dynamic Modelling of Phosphorolytic Cleavage Catalyzed by Pyrimidine-Nucleoside Phosphorylase
Giessmann, Robert T.; Krausch, Niels; Kaspar, Felix; et al. (2019)
Processes
Pyrimidine-nucleoside phosphorylases (Py-NPases) have a significant potential to contribute to the economic and ecological production of modified nucleosides. These can be produced via pentose-1-phosphates, an interesting but mostly labile and expensive precursor. Thus far, no dynamic model exists for the production process of pentose-1-phosphates, which involves the equilibrium state of the Py-NPase catalyzed reversible reaction. Previously developed enzymological models are based on the understanding of the structural principles of the enzyme and focus on the description of initial rates only. The model generation is further complicated, as Py-NPases accept two substrates which they convert to two products. To create a well-balanced model from accurate experimental data, we utilized an improved high-throughput spectroscopic assay to monitor reactions over the whole time course until equilibrium was reached. We examined the conversion of deoxythymidine and phosphate to deoxyribose-1-phosphate and thymine by a thermophilic Py-NPase from Geobacillus thermoglucosidasius. The developed process model described the reactant concentrations in excellent agreement with the experimental data. Our model is built from ordinary differential equations and structured in such a way that integration with other models is possible in the future. These could be the kinetics of other enzymes for enzymatic cascade reactions or reactor descriptions to generate integrated process models.
Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions
Narayanan, Harini; Cruz Bournazou, Mariano Nicolas; Guillén Gosálbez, Gonzalo; et al. (2022)
Chemical Engineering Journal
Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledgeor black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these has been largely based on conventional machine learning algorithms (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain experts. In this work, we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of the Functional-Hybrid model and its interpretability, focusing on applying chemical reaction kinetic principles to classical chemical reactions, biochemistry, ecology, physiology, and a bioreactor. Furthermore, we demonstrate that during interpolation, the Functional-Hybrid model performs similarly to a Hybrid-ANN hybrid model implementing a conventional ANN. However, it provides the advantage of being –to some extent– interpretable, unlike the conventional Hybrid-ANN model. Additionally, it is shown that the Functional-Hybrid model outperforms the Hybrid-ANN model for a very low number of experiments, making it more suitable when data is scarce. Finally, the Functional-Hybrid models show superior extrapolation capabilities compared to the Hybrid-ANN model. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.
A model‐based framework for parallel scale‐down fed‐batch cultivations in mini‐bioreactors for accelerated phenotyping
Anane, Emmanuel; García, Ángel Córcoles; Haby, Benjamin; et al. (2019)
Biotechnology and Bioengineering
Concentration gradients that occur in large industrial‐scale bioreactors due to mass transfer limitations have significant effects on process efficiency. Hence, it is desirable to investigate the response of strains to such heterogeneities to reduce the risk of failure during process scale‐up. Although there are various scale‐down techniques to study these effects, scale‐down strategies are rarely applied in the early developmental phases of a bioprocess, as they have not yet been implemented on small‐scale parallel cultivation devices. In this study, we combine mechanistic growth models with a parallel mini‐bioreactor system to create a high‐throughput platform for studying the response of Escherichia coli strains to concentration gradients. As a scaled‐down approach, a model‐based glucose pulse feeding scheme is applied and compared with a continuous feed profile to study the influence of glucose and dissolved oxygen gradients on both cell physiology and incorporation of noncanonical amino acids into recombinant proinsulin. The results show a significant increase in the incorporation of the noncanonical amino acid norvaline in the soluble intracellular extract and in the recombinant product in cultures with glucose/oxygen oscillations. Interestingly, the amount of norvaline depends on the pulse frequency and is negligible with continuous feeding, confirming observations from large‐scale cultivations. Most importantly, the results also show that a larger number of the model parameters are significantly affected by the scale‐down scheme, compared with the reference cultivations. In this example, it was possible to describe the effects of oscillations in a single parallel experiment. The platform offers the opportunity to combine strain screening with scale‐down studies to select the most robust strains for bioprocess scale‐up.
Single-Stage and Two-Stage Anaerobic Digestion of Food Waste: Effect of the Organic Loading Rate on the Methane Production and Volatile Fatty Acids
Parra-Orobio, Brayan A.; Cruz Bournazou, Mariano Nicolas; Torres-Lozada, Patricia (2021)
Water, Air, & Soil Pollution
The high organic content of food waste (FW), which represents the largest proportion of municipal solid waste (MSW) (in Latinamerican countries 50–75%), makes its treatment increasingly common trough technologies such as anaerobic digestion (AD), to obtain value-added by-products, such as renewable energy in the form of methane, digestates and other by-products of biotechnological applications such as long-chain fatty acids. In this study, the influence of semi-continuous reactors in single-stage (R1) and two-stage (R2: acidogenic and R3: methanogenic reactors) configurations on the AD-FW was evaluated (including parameters related to process monitoring, organic matter conversion and process reactions) with the following organic loading rate (OLR: kgVS m-3 d-1) values: i. R1: 0.7, 1.5, 3.0 and 6.0; ii. R2: 3.0, 4.0, 9.0 and 15.0; and R3: 1.0, 2.0, 4.0 and 7.0. The two-stage configuration showed a better performance in terms of: i. the OLRs: 35% higher than that in the single-stage configuration, with chemical oxygen demand (CODtotal) and volatile solid (VS) removal efficiencies > 80%; ii. the best performance in terms of methane production, with statistically significant differences (p<0.05) in the quantity and quality of biogas and iii. obtaining other by-products with high added value, such as behenic and caproic acid, which are useful in biotechnological applications. Additionally, it was found that total reducing sugars (TRS) are an important parameter in the monitoring and conversion of matter organic, mainly in two-stage configuration.
Functional-Hybrid Modeling through automated adaptive symbolic regression for interpretable mathematical expressions
Narayanan, Harini; Cruz Bournazou, Mariano Nicolas; Guillén Gosálbez, Gonzalo; et al. (2021)
bioRxiv
Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.
Monitoring parallel robotic cultivations with online multivariate analysis
Hans, Sebastian; Ulmer, Christian; Narayanan, Harini; et al. (2020)
Processes
In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and automated fault detection of 24 parallel running experiments using recombinant E. coli cells expressing three different fluorescence proteins as the model organism. This approach allowed for capturing events like stirrer failures and blockage of the aeration system and provided a good signal to noise ratio. The developed application can be easily integrated in existing data- and device-infrastructures, allowing automated and remote monitoring of parallel bioreactor systems.

Publications 1 - 10 of 11

Mariano Nicolas Cruz Bournazou

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