An integrated framework for strain optimization

The identification of genetic modifications leading to mutant strains able to overproduce compounds of industrial interest is a challenging task in Metabolic Engineering (ME). Several methods have been proposed but, to some extent, none of them is suitable for all the specificities of each particular strain optimization problem. This work proposes an integrated framework that allows its users to configure and fine tune all the various steps involved in a strain optimization strategy, including the loading of models in distinct formats, the definition of a suitable phenotype simulation method and the choice and configuration of the strain optimization engine. Moreover, it is designed to suit the needs of users skilled at programming, as well as less advanced users. The framework includes a GUI implemented as the strain optimization plug-in for the OptFlux workbench (version 3), a reference platform for ME (http://www.optflux.org). All the code is distributed under the GPLv3 licence and it is fully available (http://sourceforge.net/projects/optflux/).This work is partially funded by ERDF- European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP- 01-0124-FEDER-015079 and PTDC/EBB-EBI/104235/2008. This work is also funded by National Funds through the FCT within project PEst-OE/EEI/UI0752/2011. The work of PM was supported by the FCT through the Ph.D. grant SFRH/BD/61465/2009

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials

Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and `ground truth'. The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel value-gradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strength-normalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources---i.e. reduced order models---based on different homogenization assumptions---isostrain, isostress and isowork---are used to efficiently and optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table

Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation

Background Outer membrane proteins (OMPs) of Pasteurella multocida have various functions related to virulence and pathogenesis and represent important targets for vaccine development. Various bioinformatic algorithms can predict outer membrane localization and discriminate OMPs by structure or function. The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome. Results In the present study, we used 10 different predictors classified into three groups (subcellular localization, transmembrane β-barrel protein and lipoprotein predictors) to identify putative OMPs from two available P. multocida genomes: those of avian strain Pm70 and porcine non-toxigenic strain 3480. Predicted proteins in each group were filtered by optimized criteria for consensus prediction: at least two positive predictions for the subcellular localization predictors, three for the transmembrane β-barrel protein predictors and one for the lipoprotein predictors. The consensus predicted proteins were integrated from each group into a single list of proteins. We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction. As a result, we were able to confidently predict 98 putative OMPs from the avian strain genome and 107 OMPs from the porcine strain genome with 83% overlap between the two genomes. Conclusions The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence. Our approach can be applied to investigate the outer membrane proteomes of other Gram-negative bacteria

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy