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A hybrid modeling approach, combining a stoichiometric model of the E. coli metabolic network and kinetic-based descriptions for the production of recombinant protein, cell growth and ppGpp synthesis, was applied to describe metabolic bottlenecks associated with recombinant processes. The model represents the triggering of the stringent response upon the deprivation of amino acids caused by the additional drainage of biosynthetic precursors for the production of recombinant proteins. The equation for ppGpp synthesis allows to estimate the accumulation of this molecule above its basal levels once amino acid shortages occur. The capability to predict these stress-responsive events might be crucial in the design of optimal cultivation strategies.

The field of Metabolic Engineering has been growing, sup- ported by the increase in the number of annotated genomes and genome- scale metabolic models. In silico strain optimization methods allow to create mutant strains able to overproduce certain metabolites of interest in Biotechnology. Thus, it is possible to reach (near-) optimal solutions, i.e. strains that provide the desired phenotype in computational pheno- type simulations. However, the validation of the results involves under- standing the strategies followed by these mutant strains to achieve the desired phenotype, studying the different use of reactions/ pathways by the mutants. This is quite complex given the size of the networks and the interactions between (sometimes distant) components. The manual verification and comparison of phenotypes is typically impossible. Here, automatic methods are proposed to analyse large sets of mutant strains, by taking the phenotypes of a large number of possible solutions and identifying shared patterns, using methods from network topology analysis. The topological comparison between the networks provided by the wild type and mutant strains highlights the major changes that lead to successful mutants. The methods are applied to a case study consider- ing E. coli and aiming at the production of succinate, optimizing the set of gene knockouts to apply to the wild type. Solutions provided by the use of Simulated Annealing and Evolutionary Algorithms are analyzed. The results show that these methods can help in the identification of the strategies leading to the overproduction of succinate., This work is supported by project PTDC/EIA-EIA/115176/2009, funded by Portuguese FCT and Programa COMPETE., José Pedro Pinto work is funded by a PhD grant from the Portuguese FCT (ref. SFRH/BD/41763/2007).

Many regulatory processes in the cell are based on the control of gene expression through the interaction of transcription factors. However, enzymatic regulation often overlays transcriptional regulation and even, in some metabolic pathways, enzymatic regulation prevails. The present study addresses the regulatory network of Escherichia coli and offers a global view of the regulation of its metabolic pathways. It identifies the regulatory mechanisms responsible for key metabolic activities and details the structures behind such mechanisms. This knowledge is considered of relevance to further studies on the bacteria’s system and its industrial application, namely for understanding the signal cascades comprised in the responses to various environmental stresses., FCT funded MIT-Portugal Program in Bioengineering (MIT-Pt/BSBB/ 0082/2008); PhD grants from FCT (ref. SFRH/BD/22863/2005 and SFRH/BD/41763/2007).

Publicado em "Advances in bioinformatics : 4th International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010)", ISBN 978-3-642-13213-1, Elementary Flux Modes (EFMs) have been claimed as one of the most promising approaches for pathway analysis. These are a set of vectors that emerge from the stoichiometric matrix of a biochemical network through the use of convex analysis. The computation of all EFMs of a given network is an NP-hard problem and existing algorithms do not scale well. Moreover, the analysis of results is difficult given the thousands or millions of possible modes generated. In this work, we propose a new plugin, running on top of the OptFlux Metabolic Engineering workbench, whose aims are to ease the analysis of these results and explore synergies among EFM analysis, phenotype simulation and strain optimization., The authors wish to thank the financial support of the Portuguese FCT for the Ph.D grant SFRH/BD/61465/2009 and the company Dupont under the scope of the Dupont European University Support Program Award.

The major objective of metabolic engineering is the construction of industrially relevant microbial strains with desired properties. From an engineering perspective, dynamic mathematical modeling to quantitatively assess intracellular metabolism and predict the complex behavior of living cells is one of the most successful tools to achieve that goal. In this work, we present an expansion of the original E. coli dynamic model [1], which links the acetate metabolism and tricarboxylic acid cycle (TCA) with the phosphotransferase systems, the pentose-phosphate pathway and the glycolysis system based on mechanistic enzymatic rate equations. The kinetic information is collected from available database and literature, and is used as an initial guess for the global fitting. The results of the numeric simulations were in good agreement with the experimental results. Thus, the results are sufficiently good to prompt us to seek further experimental data for comparison with the simulations.

In Biomedical research, the ability to retrieve the adequate information from the ever growing literature is an extremely important asset. This work provides an enhanced and general purpose approach to the process of document retrieval that enables the filtering of PubMed query results. The system is based on semantic indexing providing, for each set of retrieved documents, a network that links documents and relevant terms obtained by the annotation of biological entities (e.g. genes or proteins). This network provides distinct user perspectives and allows navigation over documents with similar terms and is also used to assess document relevance. A network learning procedure, based on previous work from e-mail spam filtering, is proposed, receiving as input a training set of manually classified documents.

One of the purposes of Systems Biology is the quantitative modeling of biochemical networks. In this effort, the use of dynamical mathematical models provides for powerful tools in the prediction of the phenotypical behavior of microorganisms under distinct environmental conditions or subject to genetic modifications. The purpose of the present study is to explore a computational environment where dynamical models are used to support simulation and optimization tasks. These will be used to study the effects of two distinct types of modifications over metabolic models: deleting a few reactions (knockouts) and changing the values of reaction kinetic parameters. In the former case, we aim to reach an optimal knockout set, under a defined objective function. In the latter, the same objective function is used, but the aim is to optimize the values of certain enzymatic kinetic coefficients. In both cases, we seek for the best model modifications that might lead to a desired impact on the concentration of chemical species in a metabolic pathway. This concept was tested by trying to maximize the production of dihydroxyacetone phosphate, using Evolutionary Computation approaches. As a case study, the central carbon metabolism of Escherichia coli is considered. A dynamical model based on ordinary differential equations is used to perform the simulations. The results validate the main features of the approach.

Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)", ISBN 978-3-540-85860-7, Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work, a set of computational tools are proposed, allowing researchers in Biotechnology to use ANNs for the modelling of fed-batch fermentation processes. The main task is to predict the values of kinetics parameters from the values of a set of state variables. The tools were validated with two case studies, showing the main functionalities of the application., This work was supported by the Portuguese FCT under project POSC/EIA/59899/2004.