Isabel Rocha

Universidade Nova de Lisboa

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E. C. Ferreira

Universidade Federal Rural de Pernambuco

166

Miguel Rocha

University of Minho

122

Sónia Carneiro

SilicoLife (Portugal)

ÓD

Óscar Dias

University of Minho

Paulo Maia

SilicoLife (Portugal)

Anália Lourenço

University of Minho

Kiran Raosaheb Patil

University of Cambridge

Paulo Vilaça

SilicoLife (Portugal)

Rafael S. Costa

Universidade Nova de Lisboa

Rafael Carreira

SilicoLife (Portugal)

Cooperative Institutions

John Wiley & Sons (United States)

108

Hudson Institute

105

University of Minho

102

Institute for Biotechnology and Bioengineering

University of Lisbon

Universidade Nova de Lisboa

Universidade do Porto

Technical University of Denmark

Instituto de Biologia Experimental e Tecnológica

Universidade de São Paulo

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Modeling formalisms in Systems Biology

AMB Express (2011)

Daniel Machado Rafael S. Costa Miguel Rocha E. C. Ferreira Bruce Tidor

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.

Modelling biological systems

Gene regulatory network

Synthetic Biology

10.1186/2191-0855-1-45

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Citations (176)

Yeast chassis design for production of dicarboxylic acids

New Biotechnology (2018)

Filipa Pereira Helena Lopes Paulo Maia Britta Meyer Dimitrios Konstantinidis

Methylobacterium

10.1016/j.nbt.2018.05.181

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Citations (3)

Metabolomic approaches for the characterization of metabolic bottlenecks in recombinant protein production processes

Sónia Carneiro E. C. Ferreira Isabel Rocha

Book of abstracts of the Meeting of the Institute for Biotechnology and Bioengineering, 2, Braga, Portugal, 2010

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Modeling and simulation of fructo-oligosaccharides production

Orlando Rocha Ana Dominguez Clarisse Nobre Duarte Torres L. R. Rodrigues

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A study on the effects of using gene-reaction rules on in silico strain optimization

Paulo Vilaça Paulo Maia Pedro Evangelista E. C. Ferreira Miguel Rocha

To identify a set of genetic manipulations that will result in a microbial strain with improved production capabilities of a metabolite / product of industrial interest, is one of the greatest challenges in Metabolic Engineering. This problem represents a complex combination between the development of accurate metabolic and regulatory models / networks, plus the need for appropriate simulation and optimization tools. To achieve this end, Evolutionary Algorithms (EAs) and Simulation Annealing (SA) have been previously proposed as tools to perform in silico Metabolic Engineering [1]. These methods are used to identify sets of reaction deletions, towards the maximization of a desired physiological objective function. In order to simulate the cell phenotype for each mutant strain, including its growth and the by-products secretion, the Flux-Balance Analysis approach is used, assuming that microorganisms have maximized their growth along evolution. Currently, the available optimization algorithms work only with reaction deletions, i.e. their result is a set of reactions that have to be removed from the metabolic model. Biologically, it is possible to knockout genes, not reactions. In this work, the transcriptional information is added to the underlying models using gene-reaction rules based on a boolean logic representation. So, for each reaction we have a Boolean expression, where the variables are the encoding genes and including the logical AND and OR operators. The aim is to find the optimal / near-optimal set of gene knockouts necessary to reach a given productivity goal. The results obtained are compared with the ones using the deletion of reactions. A set of computational experiments were performed, using four case studies and the production of succinate and lactic acid as the metabolite to maximize and E. coli as the selected organism. Genome-scale models including both reactions and gene-reaction rules [2] are used to conduct the necessary FBA simulations. The results show that several of the results from reaction deletion optimizations are not feasible using the provided gene-reaction rules, i.e. the genes that would need to be removed in order to delete the reaction also lead to the removal of other reactions causing side e!ects that make the strain unviable. Nevertheless, basing the optimization algorithms on gene knockouts, we were able to reach solutions where the production of the desired compounds is similar to the ones using reaction deletions.

Flux Balance Analysis

Gene knockout

Metabolic Engineering

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OPTFERM - a computational platform for the optimization of fermentation processes

Orlando Rocha Paulo Maia Isabel Rocha Miguel Rocha

Numerous products such as antibiotics, proteins, amino-acids and other chemicals are produced using fermentation processes. These systems are affected by biochemical and chemical phenomena as well as environmental conditions. Consequently, several computational tools have been designed and implemented for modeling, simulation and optimization, sharing a common purpose: increase the production yield of the final product. We present OptFerm, a computational platform for the simulation and optimization of fermentation processes. The aim of this project is to offer a platform-independent, user-friendly, open-source and extensible environment for the improvement of Bioengineering processes. This tool is focused in optimizing a feeding trajectory to be fed into a fed-batch bioreactor and to calculate the best concentration of nutrients to initiate the fermentation. Furthermore, a module for the estimation of kinetic and yield parameters has been developed, allowing the use of experimental data obtained from batch or fed-batch fermentations to reach the best possible model setup. The features present in this tool allow the users to analyze the robustness of a fed-batch model, compare simulated with experimental data, determine unknown parameters and optimize feeding profiles. The software was built using a component-based modular development methodology, using Java as the programming language. AIBench, a Model-View-Control based Java application framework was used as the basis to implement the different data objects and operations, as well as their graphical user interfaces. Moreover, this allows the tool to be easily extended with new modules, which are currently being developed.

Robustness

Graphical user interface

Batch processing

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MODEL-BASED ADAPTIVE CONTROL OF ACETATE CONCENTRATION DURING THE PRODUCTION OF RECOMBINANT PROTEINS WITH E. COLI

IFAC Proceedings Volumes (2002)

Isabel Rocha E. C. Ferreira

Industrial fermentation

Flow injection analysis

10.3182/20020721-6-es-1901.01386

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Citations (7)

The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber

PLoS Computational Biology (2023)

Emanuel Cunha Miguel Silva Inês Chaves Hüseyin Demirci Davide Lagoa

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

Quercus suber

Suberin

Metabolic pathway

Metabolic network

Plant metabolism

Secondary metabolism

10.1371/journal.pcbi.1011499

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Citations (9)

Uncovering the metabolic capacities of H. pylori 26695 using 13C labeling experiments

Daniela M. Correia Rafael Carreira Nuno F. Azevedo Isabel Rocha

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