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Agency: Cordis | Branch: H2020 | Program: RIA | Phase: BIOTEC-2-2015 | Award Amount: 7.09M | Year: 2016

Omics data is not leveraged effectively in the biotechnology industry due to lack of tools to rapidly access public and private data and to design cellular manipulations or interventions based on the data. With this project we aim to make a broad spectrum of omics data useful to the biotechnology industry covering application areas ranging from industrial biotechnology to human health. We will develop novel approaches for integrative model-based omics data analysis to enable 1) Identification of novel enzymes and pathways by mining metagenomic data, 2) Data-driven design of cell factories for the production of chemicals and proteins, and 3) Analysis and design of microbial communities relevant to human health, industrial biotechnology and agriculture. All research efforts will be integrated in an interactive web-based platform that will be available for the industrial and academic research and development communities, in particular enhancing the competitiveness of biotech SMEs by economizing resources and reducing time-to-market within their respective focus areas. The platform will be composed of standardized and interoperable components that service-oriented bioinformatics SMEs involved in the project can reuse in their own products. An important aspect of the platform will be implementation of different access levels to data and software tools allowing controlling access to proprietary data and analysis tools. Two end-user companies will be involved in practical testing of the platform built within the project using proprietary omics data generated at the companies.


Vieira V.,University of Minho | Maia P.,SilicoLife | Rocha I.,University of Minho | Rocha M.,University of Minho
Advances in Intelligent Systems and Computing | Year: 2016

Under the realm of in silico Metabolic Engineering, pathway analysis approaches to strain optimization have shown a large potential as tools capable of providing an unbiased view over metabolic models. Most of these methods were difficult or impossible to use due to their heavy computational needs, since they are based in the calculation of elementary modes/minimal cut sets in large networks. However, a recent method (MCSEnumerator) has enabled the application of these approaches to genome-scale metabolic models. This work proposes a new software tool where this method is implemented in a novel Java library, that provides support for a plugin for the OptFlux metabolic engineering platform. Together, these tools implement the routines necessary for the calculation of minimal cut sets and their use to provide strain optimization methods. The aim is to provide an open-source software tool that includes an intuitive graphical user interface, thus facilitating its use by the community. © Springer International Publishing Switzerland 2016. Source


Vasilakou E.,Technical University of Delft | Machado D.,University of Minho | Theorell A.,Julich Research Center | Rocha I.,University of Minho | And 5 more authors.
Current Opinion in Microbiology | Year: 2016

While the stoichiometry of metabolism is probably the best studied cellular level, the dynamics in metabolism can still not be well described, predicted and, thus, engineered. Unknowns in the metabolic flux behavior arise from kinetic interactions, especially allosteric control mechanisms. While the stoichiometry of enzymes is preserved in vitro, their activity and kinetic behavior differs from the in vivo situation. Next to this challenge, it is infeasible to test the interaction of each enzyme with each intracellular metabolite in vitro exhaustively. As a consequence, the whole interacting metabolome has to be studied in vivo to identify the relevant enzymes properties. In this review we discuss current approaches for in vivo perturbation experiments, that is, stimulus response experiments using different setups and quantitative analytical approaches, including dynamic carbon tracing. Next to reliable and informative data, advanced modeling approaches and computational tools are required to identify kinetic mechanisms and their parameters. © 2016 Source


Liu F.,University of Minho | Vilaca P.,University of Minho | Vilaca P.,SilicoLife | Rocha I.,University of Minho | Rocha M.,University of Minho
Computer Methods and Programs in Biomedicine | Year: 2015

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modeling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cerevisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation. © 2014 Elsevier Ireland Ltd. Source


Noronha A.,University of Minho | Vilaca P.,University of Minho | Vilaca P.,SilicoLife | Rocha M.,University of Minho
BMC Bioinformatics | Year: 2014

Background: Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. Results: In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. Conclusions: The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies. © Noronha et al. Source

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