Rui Oliveira
Universidade Nova de Lisboa, Chemistry REQUIMTE, Faculty Member
Background While functional genomics, focused on gene functions and gene-gene interactions, has become a very active field of research in molecular biology, equivalent methodologies embracing the environment and gene-environment... more
Background While functional genomics, focused on gene functions and gene-gene interactions, has become a very active field of research in molecular biology, equivalent methodologies embracing the environment and gene-environment interactions are relatively less developed. Understanding the function of environmental factors is, however, of paramount importance given the complex, interactive nature of environmental and genetic factors across multiple time scales. Results Here, we propose a systems biology framework, where the function of environmental factors is set at its core. We set forth a "reverse" functional analysis approach, whereby cellular functions are reconstructed from the analysis of dynamic envirome data. Our results show these data sets can be mapped to less than 20 core cellular functions in a typical mammalian cell culture, while explaining over 90% of flux data variance. A functional enviromics map can be created, which provides a template for manipulating...
Research Interests:
Research Interests:
Research Interests:
Research Interests:
This work proposes a hybrid modeling method that combines data-driven modeling, metabolic flux analysis, and bioreactor transport phenomena models. The main objective was to understand the time evolution of metabolism in fedbatch... more
This work proposes a hybrid modeling method that combines data-driven modeling, metabolic flux analysis, and bioreactor transport phenomena models. The main objective was to understand the time evolution of metabolism in fedbatch cultivations and to identify favorable conditions for product formation. The overall metabolic network is simplified using the elementary flux modes method. The hybrid modeling scheme was implemented in
Research Interests:
ABSTRACT
Research Interests:
This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant... more
This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry.
Research Interests:
Research Interests:
ABSTRACT Whereas bottom-up systems biology relies primarily on parametric mathematical models, which try to infer the system behavior from a priori specified mechanisms, top-down systems biology typically applies nonparametric techniques... more
ABSTRACT Whereas bottom-up systems biology relies primarily on parametric mathematical models, which try to infer the system behavior from a priori specified mechanisms, top-down systems biology typically applies nonparametric techniques for system identification based on extensive “omics” data sets. Merging bottom-up and top-down into middle-out strategies is confronted with the challenge of handling and integrating the two types of models efficiently. Hybrid semiparametric models are natural candidates since they combine parametric and nonparametric structures in the same model structure. They enable to blend mechanistic knowledge and data-based identification methods into models with improved performance and broader scope. This chapter aims at giving an overview on theoretical fundaments of hybrid modeling for middle-out systems biology and to provide practical examples of applications, which include hybrid metabolic flux analysis on ill-defined metabolic networks, hybrid dynamic models with unknown reaction kinetics, and hybrid dynamic models of biochemical systems with intrinsic time delays.
Research Interests:
Research Interests:
Research Interests:
A novel method for bioreactor hybrid modeling is presented that combines first principles models and modular artificial neural networks trained with the Expectation Maximization (EM) algorithm. The use of modular networks was motivated by... more
A novel method for bioreactor hybrid modeling is presented that combines first principles models and modular artificial neural networks trained with the Expectation Maximization (EM) algorithm. The use of modular networks was motivated by the nature of the ‘cells system’ that may be viewed as a highly complex network of metabolic reactions organised in modular pathways. The proposed hybrid modelling
Research Interests:
Research Interests:
The main aim of this study is to develop a bioprocess dynamic optimisation method that integrates bioreactor transport phenomena, with metabolic flux analysis (MFA). The central metabolic pathways of many biological systems with... more
The main aim of this study is to develop a bioprocess dynamic optimisation method that integrates bioreactor transport phenomena, with metabolic flux analysis (MFA). The central metabolic pathways of many biological systems with industrial interest are ...
Research Interests:
Research Interests:
Background Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional... more
Background Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions. Results Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome. Conclusions Overall, our results demonstrate that PLP slightly outperforms PLS in terms of p...
Research Interests:
In this study, fed-batch cultures of a Pichia pastoris strain constitutively expressing a single chain antibody fragment (scFv) under the control of the glyceraldehyde-3-phosphate dehydrogenase (GAP) promoter were performed in a pilot 50... more
In this study, fed-batch cultures of a Pichia pastoris strain constitutively expressing a single chain antibody fragment (scFv) under the control of the glyceraldehyde-3-phosphate dehydrogenase (GAP) promoter were performed in a pilot 50 L bioreactor. Due to the very high cell density achieved within the first 75 h, typically between 140 and 160 g-DCW/L of dry cell weight (DCW), most of the scFv is produced under hard oxygen transfer limitation. To improve scFv productivity, a direct adaptive dissolved oxygen (DO)-stat feeding controller that maximizes glycerol feeding under the constraint of available oxygen transfer capacity was developed and applied to this process. The developed adaptive controller enabled to maximize glycerol feeding through the regulation of DO concentration between 3 and 5 % of saturation, thereby improving process productivity. Set-point convergence dynamics are characterized by a fast response upon large perturbations to DO, followed by a slower but very robust convergence in the vicinity of the boundary with almost imperceptible overshoot. Such control performance enabled operating closer to the 0 % boundary for longer periods of time when compared to a traditional proportional-integral-derivative algorithm, which tends to destabilize with increasing cell density.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests: Engineering, Fluorescence Spectroscopy, Computational Biology, Chemometrics, Biotechnology, and 15 moreBiology, Medicine, Cell Culture, Biological Sciences, Animals, Method, Cell Proliferation, Multivariate Statistical Analysis, Cell Survival, Fluorometry, immunoglobulin G, Cell Growth, CHO cells, Cricetinae, and Chinese hamster ovary
Research Interests:
In this paper, a stochastic dynamical model for heterologous protein expression in the baculovirus/insect cells system is presented. The model describes foreign protein expression under the control of the polyhedrin promoter, which is the... more
In this paper, a stochastic dynamical model for heterologous protein expression in the baculovirus/insect cells system is presented. The model describes foreign protein expression under the control of the polyhedrin promoter, which is the most commonly used promoter in this system. The present study explores the hypothesis of gene size being the main factor affecting the rate of protein expression in the host cell. The infection process, prior to protein expression, is of stochastic nature. Thus, the combination of infection and intracellular dynamics results in a complex stochastic/structured dynamical model which is very challenging in a systems engineering perspective. Due to the randomness of virus binding to the host cells, the intracellular dynamics of individual cells show unique patterns, potentially leading to a very large scale stochastic problem. The size of the problem was reduced by considering a finite number of subpopulations of “equal” cells. A discrete time formulat...
Research Interests:
Enterobacter sp. was grown on glycerol byproduct from the biodiesel industry for the production of a value-added exopolysaccharide (EPS). The culture broth was characterized in terms of its morphological and rheological properties... more
Enterobacter sp. was grown on glycerol byproduct from the biodiesel industry for the production of a value-added exopolysaccharide (EPS). The culture broth was characterized in terms of its morphological and rheological properties throughout the cultivation run. Microscopic observations revealed the formation of cell aggregates surrounded by the EPS at the beginning of the cultivation run, while, at the end, aggregates were reduced and an EPS matrix with the cells embedded in it was observed. The apparent viscosity of the culture broth increased over time, which was attributed to the increase of the EPS concentration in the first period of the cultivation run. However, in the final stage, the creation of new polymer interactions within the complex culture broth was likely the reason for the viscosity increase observed, since there was not a significant variation of the EPS concentration, average molecular weight or chemical composition. The broth presented a Newtonian behavior at th...
Research Interests:
Research Interests:
In this work we study the optimization of a polyhydroxyalkanoates (PHA) production process by mixed cultures based on a detailed hybrid metabolic model. The metabolic network under consideration was first decomposed into its fundamental... more
In this work we study the optimization of a polyhydroxyalkanoates (PHA) production process by mixed cultures based on a detailed hybrid metabolic model. The metabolic network under consideration was first decomposed into its fundamental pathways using the elementary ...
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests: Engineering, Technology, Kinetics, Biotechnology, DNA replication, and 13 moreMathematical Modelling, Gene expression, Protein synthesis, Biological Sciences, Cell line, Model validation, Animals, Rotavirus, Protein Expression, Spodoptera Frugiperda, Large Scale, Recombinant Proteins, and Multiplicity of Infection (MOI)
Composite structures integrity is sensible to service life. To insure their integrity NDE evaluations are required. Acoustic emission is a nondestructive technique that allows detection in real-time of defects under evolution. For... more
Composite structures integrity is sensible to service life. To insure their integrity NDE evaluations are required. Acoustic emission is a nondestructive technique that allows detection in real-time of defects under evolution. For cross-ply laminates and for uniaxial loading, the early stage of damage is dominated by transverse matrix cracking in 90° plies. Transverse matrix cracking consists on the rupture of
Research Interests:
In this work we used the model organism Saccharomyces cerevisiae to characterise the biological activity and the mechanism of action of phytochemicals. One of the goals is to use mutant strains affected in basic mechanisms of oxidative... more
In this work we used the model organism Saccharomyces cerevisiae to characterise the biological activity and the mechanism of action of phytochemicals. One of the goals is to use mutant strains affected in basic mechanisms of oxidative stress response and DNA repair ...
Research Interests:
ABSTRACT A neural network based methodology for the modelling of a sequencing batch reactor (SBR) for producing Polyhydroxybutyrate (PHB) with Mixed Microbial Cultures (MMC) is proposed. The advantages of applying MMC for more effective... more
ABSTRACT A neural network based methodology for the modelling of a sequencing batch reactor (SBR) for producing Polyhydroxybutyrate (PHB) with Mixed Microbial Cultures (MMC) is proposed. The advantages of applying MMC for more effective production of PHB have already been documented and mechanistic models were developed, however, the lack of good understanding and the ability to describe phenomena involved in the complex nature of the bioprocess led to unsuccessful release of reliable and accurate mechanistic models. In order to perform successful process control and optimisation, empirical models developed from process operational data should be capitalised. Bootstrap aggregated neural networks are used in this study to enhance model accuracy and reliability. In the case of PHB production through SBR using MMC, the two feeding substrates of acetate and ammonia were found to play dominant roles in PHB production trajectory and different process operation regimes exist depending on the concentrations of these substrates. This paper proposes a method for the classification of such operation regimes and building neural network models corresponding to these regimes using bootstrap aggregated neural networks.
Research Interests:
Research Interests:
Research Interests:
In this paper a methodology is proposed for bioreactor batch-to-batch optimization based on hybrid first principles/artificial neural network models. The method does not require the knowledge of the kinetics, which are modelled with... more
In this paper a methodology is proposed for bioreactor batch-to-batch optimization based on hybrid first principles/artificial neural network models. The method does not require the knowledge of the kinetics, which are modelled with neural networks. The reliability of the neural network components is monitored with a cluster-based technique during the optimization procedure. The optimization is realized numerically with a genetic