Braga, Portugal
Braga, Portugal
Time filter
Source Type

Maia P.,SilicoLife | Rocha I.,University of Minho | Rocha M.,University of Minho
GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion | Year: 2017

The past two decades have witnessed great advances in the computational modeling and systems biology fields. Soon after the first models of metabolism were developed, methods for phenotype prediction were put forward, as well as strain optimization methods, within the field of Metabolic Engineering. Evolutionary computation has been on the front line, with the proposal of bilevel metaheuristics, where EC works over phenotype simulation, selecting the most promising solutions for bioengineering tasks. Recently, Schuetz and co-workers proposed that the metabolism of bacteria operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Albeit multi-objective strain optimization approaches focused on bioengineering objectives have been proposed, none tackles the multiobjective nature of the cellular objectives. In this work, we propose multi-objective evolutionary algorithms for strain optimization, where objective functions are defined based on distinct phenotype prediction methods, showing that those can lead to more robust designs, allowing to find solutions in more complex scenarios. © 2017 ACM.

Maia P.,University of Minho | Maia P.,SilicoLife | Rocha M.,University of Minho | Rochaa I.,University of Minho
Microbiology and Molecular Biology Reviews | Year: 2016

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraintbased strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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.

Agency: European Commission | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2015-ETN | Award Amount: 3.81M | Year: 2015

Mathematical, computational models are central in biomedical and biological systems engineering; models enable (i) mechanistically justifying experimental results via current knowledge and (ii) generating new testable hypotheses or novel intervention methods. SyMBioSys is a joint academic/industrial training initiative supporting the convergence of engineering, biological and computational sciences. The consortiums mutual goal is developing a new generation of innovative and entrepreneurial early-stage researchers (ESRs) to develop and exploit cutting-edge dynamic (kinetic) mathematical models for biomedical and biotechnological applications. SyMBioSys integrates: (i) six academic beneficiaries with a strong record in biomedical and biological systems engineering research, these include four universities and two research centres; (ii) four industrial beneficiaries including key players in developing simulation software for process systems engineering, metabolic engineering and industrial biotechnology; (iii) three partner organisations from pharmaceutical, biotechnological and entrepreneurial sectors. SyMBioSys is committed to supporting the establishment of a Biological Systems Engineering research community by stimulating programme coordination via joint activities. The main objectives of this initiative are: * Developing new algorithms and methods for reverse engineering and identifying dynamic models of biosystems and bioprocesses * Developing new model-based optimization algorithms for exploiting dynamic models of biological systems (e.g. predicting behavior in biological networks, identifying design principles and selecting optimal treatment intervention) * Developing software tools, implementing the preceding novel algorithms, using state-of-the-art software engineering practices to ensure usability in biological systems engineering research and practice * Applying the new algorithms and software tools to biomedical and biological test cases.

Agency: European Commission | 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.

Agency: European Commission | Branch: FP7 | Program: CP-TP | Phase: KBBE.2012.3.4-02 | Award Amount: 8.00M | Year: 2012

BRIGIT aims to develop a cost-competitive and environmentally friendly continuous process to produce biopolymers (polyhydroxybutyrate, PHB, and succinate-based biopolyesters, PBS-Poly-Butylene-Succinate) from waste-derived lignocelullosic sugar feedstock liquor of wood sulphite pulping process based on in-situ fermentation process and new fermentation culture technology without alteration of the quality of current lignosulphonates (they have a high market demand as additive). Other non-wood plant waste, used nowadays in the pulp production, will be also considered as alternative sugar source in this project. In comparison with previous projects to obtain biopolymers from different sources, the main innovation in BRIGIT is the use of an existing sugar-rich waste stream and the process integration with the existing industrial operation, that will permit an overall reduction in resource consumption and in greenhouse gas emissions and a dramatic reduction of operational costs due to the use of non-sterile steps, without the need of intermediate discontinuous bioreactors and avoiding waste transport. BRIGIT aims to develop bio-based composites for high-tech fire-resistant applications. The use of these biopolymers in combination with natural fabrics (flax, hemp,...) will be mainly in the passenger and goods transport sector (aeronautics, train, buses, shipping, trucks,..) as an alternative to 3D sandwich panels made from thermoset resins reinforced with continuous glass fibres with high fire resistance. The new panels will be recyclable, lighter, with a broad processing windows, high production capacity (using a continuous compression moulding process) and low embodied energy in comparison with current panels that are heavy, non-recyclable, have narrow processing windows, low production capacity, dirty process with high production of waste and based on materials with high embodied energy.

Agency: European Commission | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2016 | Award Amount: 3.99M | Year: 2016

The world economy is dependent on fossil resources: oil, gas and coal. The fossil resources are finite and their consumption causes catastrophic environmental changes. Therefore we need to move towards sustainable economy using renewable resources for energy and chemicals production. Via metabolic engineering approach, novel microbial cells can be created that can convert biomass and waste into fuels and chemicals. Metabolic engineering however distinguishes itself from other engineering disciplines by low predictability of the design and long turnover times for the cell factory construction and screening. Therefore there is a need for scientists, who can address these challenges. European Training Network on Predictable and Accelerated Metabolic Engineering Networks (PAcMEN) will be established at 5 renowned European universities and 2 SMEs with participation of 5 industrial and 1 academic partner organizations. In this program 16 PhD students (of which 15 funded by EU contribution) will learn to conduct state-of-the-art research on metabolic engineering of microbial cell factories and learn to commercialize innovations. This will be achieved via collaborative research projects under supervision of top scientists from academia and industry, network training, secondments with network partners, training on innovation and entrepreneurship, and individual career coaching. Altogether, PAcMEN training programme will provide young scientists with the ideal combination of scientific, technological, industrial and management skills to prepare them for their role as breakthrough pioneers in the establishment of tomorrows biorefineries. The PAcMEN project will have an overall positive impact by strengthening the research networks in the area of metabolic engineering, establishing long-term collaborations between the universities and industry, and by creating a framework for future interdisciplinary training programs.

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.

Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-2013-ITN | Award Amount: 3.45M | Year: 2013

Protozoan parasites and helminths are the cause of some of the most devastating diseases worldwide and a major effort is needed to be able to control or eliminate these diseases. Glycoconjugates are abundant and ubiquitous on the surface of many parasites and they are frequently involved in their survival strategies by forming a protective barrier against host defences. A common feature of the parasites cell surface architecture is the presence of an elaborate and often highly decorated glycocalyx that allows it to interact and respond to the external environment. Therefore, the study of the glycobiology of these organisms offers unique opportunities to devise novel strategies to tackle parasitic-caused diseases. However, the exquisite diversity of these glycoconjugates and of their biosynthetic machineries, the difficulties related with their structural analysis and the complexity associated with their synthesis in the laboratory, poses a tremendous challenge for the scientific community. To address these challenges GlycoPar proposes to establish a European based training programme in a world-class collaborative research environment steered by some of the world leaders in the fast evolving field of parasite glycobiology, in close association with European industrial enterprises. The researchers recruited through this initiative will be exposed, both at the local and network-wide level, to a multicultural and highly multidisciplinary PhD training. This programme will acquaint them with a complete range of state-of-the-art glycobiology methodologies, alongside with valuable transferable and entrepreneurial skills. All together the aim is to create a PhD-level trained generation of young scientists capable of tackling the challenges that parasite glycobiology implies with improved career prospects and employability as well as preparing them to become future leaders in research institutions and industry.

Loading SilicoLife collaborators
Loading SilicoLife collaborators