Process characterization for dynamic design space development: An intensified design of experiment method

In this contribution, an intensified Design of Experiment (iDoE) methodology will be introduced. The iDoE approach is based on the idea that the values of certain factors do not need to be kept constant throughout the experiments. Instead the value of the factors can be changed during the experiments, e.g. after a specified time a step-change from 23 to 30 (°C) can be applied in temperature. In this way a classical Design of Experiment plan can in principle be executed using less experiments. The iDoE method is applied to industrial and simulated E.coli fed-batch fermentations. A dynamic hybrid modeling method is adopted for the analysis of the data, since the analysis cannot be accomplished with the traditional static statistical methods. The process understanding gathered from the iDoE is compared to DoE results. The results suggest that the number of experiments can be reduced by factor of three to two, meaning less than half of the experiments of a classical DoE are required with the iDoE method. In addition, the understanding of the process dynamics is much improved, which is of particular importance to assess the impact of temporal deviations in the factors on the process response

Model-based estimation of reaction rates in stirred tank bioreactors

In this paper an adaptive model-based algorithm is proposed for the on-line estimation of reaction rates in stirred tank bioreactors. The main design condition imposes that the observation errors reflecting the mismatch between the estimated parameters and the 'true' values follow second-order dynamics of convergence. The gain matrices are shown to be functions of the state and of user-defined damping coefficients and natural periods of oscillation for second-order trajectories. The application of the algorithm is illustrated with a simple case-study involving the estimation of the specific reaction rate for a single substrate, single product scheme.Junta Nacional de Investigação Científica e Tecnológica (JNICT)

Stability, dynamics of convergence and tuning of observer-based kinetics estimators

This work discusses issues concerning stability, tuning and dynamics of convergence of observer-based kinetics estimators. The analysis focuses on both continuous and discrete time formulations of the estimation algorithms. Concerning the former, it is shown that, with proper tuning, stability can be guaranteed, while simultaneously imposing a desired quasi-time invariant second order time response for the convergence of estimates to true values. Concerning the latter, an algorithm is presented, based on a forward Euler discretisation, whose error system is shown to be linear time-invariant. Furthermore, stability conditions were derived, which define the stable domain for the discretisation period as function of the tuning parameters. The theory is illustrated with a case-study of Baker’s yeast fermentation. Results clearly confirm the theoretical developments. In particular, results concerning the stability domain for the Euler-based discrete formulation of the estimator are shown to have relevant practical implications

A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard

This work was supported by the Associate Laboratory for Green Chemistry—LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 870292 (BioICEP project). J.P. acknowledges a PhD grant (SFRD/BD14610472019), Fundação para a Ciência e Tecnologia (FCT) and R.S.C. the contract CEECIND/01399/2017In this paper, a computational framework is proposed that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models that are currently stored in public databases in SBML. With the proposed framework, existing SBML models may be redesigned into hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and uploaded in model databases, where they may be further analyzed as regular SBML models. This approach is illustrated with three well-known case studies: the Escherichia coli threonine synthesis model, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.publishersversionpublishe

A study on the convergence of observer-based kinetics estimators in stirred tank bioreactors

This paper is devoted to the tuning problem of the "observer-based kinetics estimator" in stirred tank bioreactors. This algorithm estimates the reaction kinetics from the on-line knowledge of the state variables (either from measurement or by means of state observer), when the yield coefficients are known. The relation between the dynamics of convergence and the tuning procedure is explored. The method proposed imposes a second-order dynamics to the convergence of the estimator. This approach will be shown to compare favourably with a pole placement based technique, in an application to a baker's yeast fed-batch fermentation.Junta Nacional de Investigação Científica e Tecnológica (JNICT) - contract numbers BIC/636/92, BD/224/90-IF, BD/1476/91-RM

Advances and Future Perspectives

Agharafeie , R., Ramos, J. R. C., Mendes, J. M., & Oliveira, R. M. F. (2023). From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives. Fermentation, 9(10), 1-22. [922]. https://doi.org/10.20944/preprints202310.0107.v1, https://doi.org/10.3390/fermentation9100922--- This work was supported by the Associate Laboratory for Green Chemistry - LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). This work received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement no. 101099487- BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer)Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.publishersversionpublishe

Computer-aided teaching of process engineering : VI - studies on bioprocess identification and control through a process simulator

In this paper we present a simple computer-based laboratory set-up for experiments concerning real-time system identification. The set-up is constituted by a 'Process Simulator' computer and a 'Control' computer, communicating via serial RS-232 protocol. The former can simulate multivariable non-linear systems in real-time, in this specific experiment the fed-batch fermentation of baker's yeast. The latter performs the tasks of system identification and control, viz - (i) identification is performed employing a methodology based on a general non-linear deterministic model representation of fed-batch fermentations; (ii) control is performed employing an adaptive linearizing scheme.Junta Nacional de Investigação Científica e Tecnológica (JNICT) - contract nr. B/D 224/90-IF

Ten-year survival of patients undergoing coronary angioplasty with first-generation sirolimus-eluting stents and bare-metal stents

Introduction: Compared to bare-metal stents (BMS), drug-eluting stents reduce stent restenosis and improve subsequent revascularization rates. The impact on patients’ survival has been the subject of debate. Objective: To assess the long-term (10-year) survival of patients undergoing percutaneous coronary intervention (PCI) with first-generation sirolimus-eluting stents (SES) in comparison with BMS. Methods: In a single-center registry, 600 consecutive patients who underwent successful PCI with SES between April 2002 and February 2003 were compared to 594 patients who underwent PCI with BMS between January 2002 and April 2002, just before the introduction of SES. Clinical and procedural data were collected at the time of intervention and 10-year survival status was assessed via the national life status database. Results: All baseline characteristics were similar between groups except for smaller stent diameter (2.84±0.38 vs. 3.19±0.49 mm; p<0.001), greater stent length (18.50±8.2 vs. 15.96±6.10 mm; p<0.001) and higher number of stents per patient (1.95 vs. 1.46, p<0.001) in the SES group. Overall five- and 10-year all-cause mortality was 9.6% (n=110) and 22.7% (n=272), respectively. The adjusted HR for 10-year mortality in patients undergoing PCI with SES was 0.74 (95% CI 0.58-0.94; p=0.013), corresponding to a relative risk reduction of 19.8%. Other than PCI with BMS, older age, chronic kidney disease, chronic obstructive pulmonary disease and lower ejection fraction were independent predictors of 10-year mortality. Conclusion: To date, this is the longest follow-up study ever showing a potential survival benefit of first-generation sirolimus-eluting stents versus bare-metal stents, supporting prior observations on their sustained efficacy and safety relative to contemporary BMS.publishersversionpublishe

Nationwide access to endovascular treatment for acute ischemic stroke in portugal

Publisher Copyright: Copyright Ordem dos M dicos 2021.Introduction: Since the publication of endovascular treatment trials and European Stroke Guidelines, Portugal has re-organized stroke healthcare. The nine centers performing endovascular treatment are not equally distributed within the country, which may lead to differential access to endovascular treatment. Our main aim was to perform a descriptive analysis of the main treatment metrics regarding endovascular treatment in mainland Portugal and its administrative districts. Material and Methods: A retrospective national multicentric cohort study was conducted, including all ischemic stroke patients treated with endovascular treatment in mainland Portugal over two years (July 2015 to June 2017). All endovascular treatment centers contributed to an anonymized database. Demographic, stroke-related and procedure-related variables were collected. Crude endovascular treatment rates were calculated per 100 000 inhabitants for mainland Portugal, and each district and endovascular treatment standardized ratios (indirect age-sex standardization) were also calculated. Patient time metrics were computed as the median time between stroke onset, first-door, and puncture. Results: A total of 1625 endovascular treatment procedures were registered. The endovascular treatment rate was 8.27/100 000 inhabitants/year. We found regional heterogeneity in endovascular treatment rates (1.58 to 16.53/100 000/year), with higher rates in districts closer to endovascular treatment centers. When analyzed by district, the median time from stroke onset to puncture ranged from 212 to 432 minutes, reflecting regional heterogeneity. Discussion: Overall endovascular treatment rates and procedural times in Portugal are comparable to other international registries. We found geographic heterogeneity, with lower endovascular treatment rates and longer onset-to-puncture time in southern and inner regions. Conclusion: The overall national rate of EVT in the first two years after the organization of EVT-capable centers is one of the highest among European countries, however, significant regional disparities were documented. Moreover, stroke-onset-to-first-door times and in-hospital procedural times in the EVT centers were comparable to those reported in the randomized controlled trials performed in high-volume tertiary hospitalspublishersversionpublishe