Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction

JP acknowledges the PhD grant SFRD/BD14610472019, Fundação para a Ciência e Tecnologia (FCT).Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition.publishersversionpublishe

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis

Funding Information: This work was sponsored by GlaxoSmithKline Biologicals SA whereby the NOVA University Lisbon was engaged under an Agreement for R and D Services. All authors were involved in the conception and design of the study. PD’s lab performed the experiments/acquired the data. JR, GO, RO analyzed and interpreted the data. All authors were involved in drafting the manuscript or critically revising it for important intellectual content. All authors had full access to the data and approved the manuscript before it was submitted by the corresponding author. Publisher Copyright: © 2022, The Author(s).Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.publishersversionpublishe

combining first-principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, Fundação para a Ciência e Tecnologia (FCT).Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on threelayered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power.publishersversionpublishe

Soft culture substrates favor stem-like cellular phenotype and facilitate reprogramming of human mesenchymal stem/stromal cells (hMSCs) through mechanotransduction

Fundação para a Ciência e a Tecnologia) - FCT - grant FCT-UID/NEU/04539/2019. European Regional Development Fund (ERDF/FEDER) through the Operational Program Competitiveness Factors (Programa Operacional Factores de Competitividade) - COMPETE - funding through Project 'Stem cell based platforms for Regenerative and Therapeutic Medicine', Centro-07-ST24-FEDER-002008. M.G. acknowledges funding by the ERDF/FEDER through COMPETE and by national funds by FCT through grant FCOMP-01-0124-FEDER-021150 - PTDC/SAU-889 ENB/119292/2010 and grant POCI-01-0145-FEDER-029516, co-financed by the ERDF/FEDER under the framework Competitiveness and Internationalization Operational Program (Programa Operacional Competitividade e Internacionalizacao -POCI), national funds through FCT/'Ministerio da Ciencia, Tecnologia e Ensino Superior' (FCT/MCTES) through the Portuguese State Budget. Grant PTDC/SAU-ENB/113696/2009 was attributed to R.P.N. R.D.M.T. and J.C. thank the support of FEDER funds through COMPETE and by national funds by FCT under the strategic project UID/FIS/04564/2016 and under POCI-01-0145-FEDER-031743 - PTDC/BIA-CEL/31743/2017. R.D.M.T. acknowledges FCT's support through the FCT Researcher Program.Biophysical cues influence many aspects of cell behavior. Stiffness of the extracellular matrix is probed by cells and transduced into biochemical signals through mechanotransduction protein networks, strongly influencing stem cell behavior. Cellular stemness is intimately related with mechanical properties of the cell, like intracellular contractility and stiffness, which in turn are influenced by the microenvironment. Pluripotency is associated with soft and low-contractility cells. Hence, we postulated that soft cell culture substrates, presumably inducing low cellular contractility and stiffness, increase the reprogramming efficiency of mesenchymal stem/stromal cells (MSCs) into induced pluripotent stem cells (iPSCs). We demonstrate that soft substrates (1.5 or 15 kPa polydimethylsiloxane – PDMS) caused modulation of several cellular features of MSCs into a phenotype closer to pluripotent stem cells (PSCs). MSCs cultured on soft substrates presented more relaxed nuclei, lower maturation of focal adhesions and F-actin assembling, more euchromatic and less heterochromatic nuclear DNA regions, and increased expression of pluripotency-related genes. These changes correlate with the reprogramming of MSCs, with a positive impact on the kinetics, robustness of colony formation and reprogramming efficiency. Additionally, substrate stiffness influences several phenotypic features of iPS cells and colonies, and data indicates that soft substrates favor full iPSC reprogramming.publishersversionpublishe

Conceiving a Digital Twin for a Flexible Manufacturing System

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.Thanks are due to Elkartek 2022 project LANVERSO, and in some sections (simulations) to Basque government university group IT 1573-22

Forage Intake and Nitrogen Retention in Wethers Fed Ryegrass Haylage Supplemented with Maize Silage

Many decision support tools have been developed to predict herbage intake with herbivore ruminants indoors (Faverdin 1992) or at grazing, both using short-term (Baumont et al. 2004) or daily scale input variables (Heard et al. 2004; Delagarde et al. 2011). However, the ingestive and digestive interactions when diets with more than one type of forage are used have not been sufficiently studied. The aim of this study was to assess the effects of maize silage supplementation to wethers receiving ryegrass haylage on OM intake, OM digestibility, microbial protein synthesis and N retention

Magnetoliposomes based on manganese ferrite nanoparticles for guided transport of antitumor drugs

Publicado em "RICI6 abstract book"In this work, manganese ferrite nanoparticles with size distribution of 46 ± 17 nm and superparamagnetic behavior were synthesized by coprecipitation method. These magnetic nanoparticles were either entrapped in liposomes, originating aqueous magnetoliposomes (AMLs), or covered with a lipid bilayer, forming solid magnetoliposomes (SMLs).MAP-Fis PhD Programme, FEDER, COMPETE/QREN/EU for financial support to CFUM (PEst-C/FIS/UI0607/2013) and FCT and POPH/QREN for PhD grant (SFRH/BD/90949/2012)

A new antitumoral Heteroarylaminothieno[3,2-b]pyridine derivative : its incorporation into liposomes and interaction with proteins monitored by fluorescence

Advance Article. 2014 Oct 16 [Epub ahead of print]The fluorescence properties of the new potent antitumoral methyl 3-amino-6-(benzo[d]thiazol-2-ylamino)thieno[3,2-b]pyridine-2-carboxylate in solution and when encapsulated in several different nanoliposome formulations were investigated. The compound exhibits very reasonable fluorescence quantum yields and a solvent sensitive emission in several polar and non-polar media, despite not being fluorescent in protic solvents. Fluorescence anisotropy measurements of the compound incorporated in liposomes revealed that this thienopyridine derivative can be carried in the hydrophobic region of the lipid membrane. Liposome formulations including this antitumor compound are nanometric in size, with a diameter lower than 130 nm and generally low polydispersity, being promising for future drug delivery developments. The interaction of the compound with bovine serum albumin (BSA) and the multidrug resistance protein MDR1 was monitored by FRET, the compound acting as energy acceptor. It was observed a lower interaction with MDR1 protein than with the native form of BSA, which is an important result regarding applications of this antitumoral drug.Foundation for the Science and Technology (FCT, Portugal), FEDER/COMPETE and QREN for financial support to the Research Centres, CFUM [Strategic Project PEst- C/FIS/UI0607/2013 (FCOMP-01-0124-FEDER-022711) and CQ/UM [Strategic Project PEst-C/QUI/UI0686/2013 (FCOMP-01-0124-FEDER-022716)] and n-STeP Project NORTE-07-0124-FEDER-000039 supported by the Operational Regional Programme of North of Portugal (ON.2). FCT and POPH/QREN/FSE are acknowledged for the Post- Doc. grant of R.C.C. (SFRH/BPD/68344/2010)

Cintilografia de perfusão miocárdica na detecção da isquemia silenciosa em pacientes diabéticos assintomáticos

OBJECTIVE: This study was aimed to evaluate myocardial perfusion in asymptomatic patients with type 1 (DM1) and type 2 diabetes mellitus (DM2) without previous diagnoses of coronary artery disease (CAD) or cerebral infarction. MATERIALS AND METHODS: Fifty-nine consecutive asymptomatic patients (16 DM1, 43 DM2) underwent myocardial perfusion scintigraphy with 99mTc-sestamibi (MPS). They were evaluated for body mass index, metabolic control of DM, type of therapy, systemic arterial hypertension, dyslipidemia, nephropathy, retinopathy, peripheral neuropathy, smoking, and familial history of CAD. RESULTS: MPS was abnormal in 15 patients (25.4%): 12 (20.3%) with perfusion abnormalities, and 3 with isolated left ventricular dysfunction. The strongest predictors for abnormal myocardial perfusion were: age 60 years and above (p = 0.017; odds ratio [OR] = 6.0), peripheral neuropathy (p = 0.028; OR = 6.1), nephropathy (p = 0.031; OR = 5.6), and stress ECG positive for ischemia (p = 0.049; OR = 4.08). CONCLUSION: Silent myocardial ischemia occurs in more than one in five asymptomatic diabetic patients. The strongest predictors of ischemia in this study were: patient age, peripheral neuropathy, nephropathy, retinopathy and a stress ECG positive for ischemia.OBJETIVO: Este estudo teve por finalidade avaliar a perfusão miocárdica de pacientes com diabetes mellitus tipo 1 (DM1) e tipo 2 (DM2) assintomáticos, sem diagnóstico prévio de doença arterial coronariana (DAC) ou acidente vascular cerebral. MATERIAIS E MÉTODOS: Cinquenta e nove pacientes consecutivos (16 DM1, 43 DM2) foram submetidos a cintilografia de perfusão miocárdica com sestamibi-99mTc (CPM). Foram avaliados quanto ao índice de massa corpórea, controle metabólico do diabetes, dislipidemia, terapia para o diabetes, hipertensão arterial sistêmica, nefropatia, retinopatia, neuropatia periférica, tabagismo e história familiar de DAC. RESULTADOS: CPM foi anormal em 25,4%: 12 (20,3%) com alterações de perfusão e 3 com disfunção ventricular esquerda isolada. Os mais fortes preditores de perfusão miocárdica anormal foram: idade igual ou maior a 60 anos (p = 0,017, odds ratio [OR] = 6,0), neuropatia periférica (p = 0,028, OR = 6,1), nefropatia (p = 0,031, OR = 5,6) e ECG de esforço positivo para isquemia (p = 0,049, OR = 4,08). CONCLUSÃO: A isquemia miocárdica silenciosa ocorre em mais de um em cada cinco diabéticos assintomáticos. Os mais fortes preditores de isquemia foram: idade avançada, neuropatia periférica, nefropatia, retinopatia e ECG de esforço positivo para isquemia.714Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES