Extracting kinetic information from literature with KineticRE

To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.The work was funded by National Funds through the FCT (Portuguese Foundation for ScienceandTechnology)withinprojectref. PTDC/QUI-BIQ/119657/2010 “Finding the naturally evolved design principles of prevalent metabolic circuits”. The authors would like to thank the FCT Strategic Project PEst-OE/EQB/ LA0023/2013 and the Projects “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE07-0124-FEDER-000028 and “PEM Metabolic Engineering Platform”, project number 23060, both co-funded by the Programa Operacional Regional do Norte (ON.2 ONovoNorte),QREN, FEDER

A text mining approach for the extraction of kinetic information from literature

Systems biology has fostered interest in the use of kinetic models to better understand the dynamic behavior of metabolic networks in a wide variety of conditions. Unfortunately, in most cases, data available in different databases are not sufficient for the development of such models, since a significant part of the relevant information is still scattered in the literature. Thus, it becomes essential to develop specific and powerful text mining tools towards this aim. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The pipeline proposed integrates the development of a novel plug-in over the text mining tool @Note2. Overall, the results validate the developed approach

OptimModels: a framework for strain optimization using kinetic models

Book of Abstracts of CEB Annual Meeting 2017Mathematical models have been applied to represent the complexity of cellular metabolism over the last decades. Two types of mathematical models are used for this purpose: kinetic and stoichiometric models. The reconstruction of kinetic metabolic models, however, is a complex task due to the difficulty in obtaining detailed information of enzyme kinetics. Despite the early stage of their development, when compared with the stoichiometric metabolic models, kinetic models have already proven their capability to improve phenotype predictions and consequently more precise in silico strain design approaches [1]. One of the goals of Metabolic Engineering is the identification of genetic manipulations that will result in a microbial strain with a high yield/productivity of the desirable compound. This task can be reached using optimization algorithms based on metaheuristic approaches, such as Evolutionary Algorithms [2]. Although they do not guarantee the convergence to the best solution, these algorithms require relatively low computational time and provide a family of optimal or sub-optimal solutions that can be further inspected to select the most promising ones. Moreover, they allow the implementation of flexible objective functions and multi-objective design, and are easily parallelizable. In this work, we developed a python package, named optimModels, which implements strain design methods based on Evolutionary Algorithms, using large-scale kinetic models as input. Our case study uses two of the published kinetic metabolic models for Escherichia coli, proposed by Chassagnole and co-workers in 2002, and Jahan and co-workers in 2016. We selected the maximization of serine and succinate production as objective functions and applied two different approaches, knockouts and under/over expression of enzymes, for strain design. Preliminary results show that the framework presented here can be used for in silico strain design with kinetic metabolic models by finding the combination of genes to be knockout or/and their optimal levels of up/down-regulation.info:eu-repo/semantics/publishedVersio

Evolutionary algorithms for offline and online optimization of fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) were used to control a recombinant bacterial fed-batch fermentation process that aims to produce a biopharmaceutical product. Initially, a novel EA, based on real-valued representations and that makes use of individuals with variable sized chromosomes, was used to optimize the process, prior to its run (offline optimization), by simultaneously adjusting the feeding trajectory, the duration of the fermentation and the initial conditions of the process2. A white box mathematical model derived from literature1 and fine tuned by practice was used in the fitness function, based on differential equations and kinetic algebraic equations. Outstanding productivity levels were obtained and the results are validated by practice. Finally, online optimization is proposed, where the EA is running simultaneously with the fermentation process, receiving information regarding the process, updating its internal model and reaching new solutions that will be used to online control. Results obtained by simulation of the system show that without online optimization minor changes cause the process to reach sub-optimal levels in the long run. On the other hand, when online optimization is performed, minor changes are corrected and the behaviour of the system is near optimal

Evolutionary algorithms for static and dynamic optimization of fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) are used to control a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. In a first stage, a novel EA is used to optimize the process, prior to its start, by simultaneously adjusting the feeding trajectory, the duration of the fermentation and the initial conditions of the process. In a second stage, dynamic optimization is proposed, where the EA is running simultaneously with the fermentation process, receiving information regarding from the process, updating its internal model, reaching new solutions that will be used for online control

Valuation of fresh produce at Local Markets, consumer´sperspectives

[resumo][abstract

A Global optimization stochastic algorithm for head motion stabilization during quadruped robot locomotion

Visually-guided locomotion is important for autonomous robotics. However, there are several di culties, for instance, the robot locomotion induces head shaking that constraints stable image acquisition and the possibility to rely on that information to act accordingly. In this work, we propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a genetic algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows to explicitly specify parameters such as ampli- tude, o set and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. Thus, in order to achieve the desired head movement, opposed to the one induced by locomotion, it is necessary to appropriately tune the CPG parameters. Since this is a non-linear and non-convex optimization problem, the tuning of CPG parameters is achieved by using a global optimization method. The genetic algorithm searches for the best set of parameters that generates the head movement in order to reduce the head shaking caused by locomotion. Optimization is done o ine according to the head movement induced by the locomotion when no stabilization procedure was performed. In order to evaluate the resulting head movement, a tness function based on the Euclidian norm is investigated. Moreover, a constraint handling technique based on tournament selection was im- plemented. Experimental results on a simulated AIBO robot demonstrate that the proposed approach generates head movement that reduces signi cantly the one induced by locomotion

ProPythia, an automated platform for the classification of peptides/proteins using machine learning

One of the most challenging problems in bioinformatics is to computationally characterize sequences, structures and functions of proteins. Sequence-derived structural and physicochemical properties of proteins have been used in the development of machine learning models in protein related problems. However, tools and platforms to calculate features and perform Machine learning (ML) with proteins are scarce and have their limitations in terms of effectiveness, user-friendliness and applicability. Here, a generic modular automated ML-based platform for the classification of proteins based on their physicochemical properties is proposed. ProPythia, developed as a Python package, facilitates the major tasks of ML and includes modules to read and alter sequences, calculate protein features, pre-process datasets, execute feature reduction and selection, perform clustering, train and optimize ML models and make predictions. This platform was validated by testing its ability to classify anticancer and antimicrobial peptides and further used to explore viral fusion peptides. Membrane-interacting peptides play a crucial role in several biological processes. Fusion peptides are a subclass found in enveloped viruses, that are particularly relevant for membrane fusion. Determining what are the properties that characterize fusion peptides and distinguishing them from other proteins is a very relevant scientific question with important technological implications. Using three different datasets composed by well annotated sequences, different feature extraction techniques and feature selection methods, ML models were trained, tested and used to predict the location of a known fusion peptide in a protein sequence from the Dengue virus. Feature importance was also analysed. The models obtained will be useful in future research, also providing a biological insight into the distinctive physicochemical characteristics of fusion peptides. This work presents a freely available tool to perform ML-based protein classification and the first global analysis and prediction of viral fusion peptides using ML, reinforcing the usability and importance of ML in protein classification problems.info:eu-repo/semantics/publishedVersio

Costo económico del tratamiento de las úlceras por presión : una aproximación teórica

O presente artigo consiste numa abordagem teórica sobre a problemática dos custos económicos das úlceras por pressão. Parte-se do conhecimento do problema, numa perspetiva conceptual, para, de seguida, apresentar resultados de estudos de prevalência, a partir dos quais foram delineados estudos de impacto económico. O objectivo deste artigo é o de reflectir sobre os custos económicos associados às úlceras por pressão, quer numa perspetiva global, considerando a repercussão financeira, quer numa vertente personalista, atendendo aos custos intangíveis. Relativamente ao impacto económico das úlceras por pressão, foi efectuada uma estimativa ao nível da Região Autónoma dos Açores do custo total do tratamento por ambiente de cuidados. Nos cuidados domiciliários o custo com o tratamento de todas as categorias é calculado em 7.086.415 euros; nos cuidados hospitalares, em 1.723.509 euros, e nos cuidados prestados em lares de idosos, em 1.002.562 euros. Nos Açores, a estimativa do custo total do tratamento das úlceras por pressão, considerando todas as suas categorias, ronda os 9.812.486 euros. Quanto ao impacto emocional associado, este tem elevados custos para pessoa e para os familiares, nomeadamente pelo sofrimento gerado. De facto, as úlceras por pressão acarretam elevados custos económicos associados ao tratamento, bem como custos intangíveis pelo sofrimento vivenciado por pessoas e cuidadores.RESUMEN: El presente artículo consiste en una reflexión teórica sobre el problema de los costos económicos de las úlceras por presión. Se empieza por el conocimiento del problema, desde una perspectiva conceptual, y, a continuación, se presentan los resultados de estudios de prevalencia, a partir de los cuales se diseñaron estudios de impacto económico. El objetivo del artículo es reflexionar sobre los costos económicos asociados a las úlceras por presión tanto en una perspectiva global, considerando la repercusión financiera, como en una vertiente personalista, de acuerdo a los costos intangibles. En cuanto al impacto económico de las úlceras por presión, se realizó una estimación de la Región Autónoma de Açores del costo total del tratamiento por ámbito de atención. En la atención domiciliaria el costo con el tratamiento de todas las categorías se estima en € 7.086.415, en la atención hospitalaria, se estima € 1.723.509 y en la atención en los asilos se estima en €1.002.562. En Açores, el costo total estimado del tratamiento de las úlceras por presión en todas las categorías, es de alrededor de € 9.812.486. En cuanto al impacto emocional asociado, éste tiene elevados costos para la persona y para los familiares, principalmente, por el sufrimiento causado. De hecho, las úlceras por presión implican altos costos económicos asociados con el tratamiento, así como, costos intangibles generados por el sufrimiento experimentado por los individuos y los cuidadores.ABSTRACT: The present study consisted of a theoretical approach to the problem posed by the economic costs associated with pressure ulcers (PUs). The initial aim was to assess the target problem from a conceptual perspective and then to report the results of prevalence studies that formed the basis for investigations of the disease’s economic impact. The purpose of the present article is to discuss the economic costs associated with PUs from both the global point of view (appraising their financial repercussion) and the individual point of view (addressing the intangible costs). Regarding the economic impact of the costs associated with PUs, the total cost of treatment per healthcare setting was estimated relative to the Autonomous Community of Azores. The total cost of all the PU categories was EUR 7,086,415 in the homecare setting, EUR 1,723,509 in the hospital setting, and EUR 1,002,562 in older people’s homes. Therefore, the estimated total treatment cost of all the PU categories was approximately EUR 9,812,486 in Azores. However, the emotional impact of this disease imposes high costs on patients and their relatives as a function of the resultant suffering. Indeed, PUs impose high costs not only related to the treatment but also related to the intangible costs of the suffering caused to patients and their caregivers.Project ICE 2 – Investigação Científica em Enfermagem – Estudo do “Custo Económico das Úlceras por Pressão na Macaronésia” (MAC/1/A029) de Iniciativa Comunitária – Programa de Cooperação Transnacional Madeira-Açores-Canárias 2007-2013info:eu-repo/semantics/publishedVersio