Estimation of biomass composition from genomic and transcriptomic information

Given the great potential impact of the growing number of complete genome-scale metabolic network reconstructions of microorganisms, bioinformatics tools are needed to simplify and accelerate the course of knowledge in this field. One essential component of a genome-scale metabolic model is its biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information, providing reliable data without the need of performing lab experiments. In this work a java tool is proposed that estimates microbial biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in the csv format. This application allows to obtain the results rapidly and is also a user-friendly tool for users with any or little background in informatics (http://darwin.di.uminho.pt/biomass/). The results obtained using this tool are fairly close to experimental data, showing that the estimation of amino acid and nucleotide compositions from genome information and from transcriptomic data is a good alternative when no experimental data is available.The authors thank the project DeYeastLibrary - Designer yeast strain library optimized for metabolic engineering applications, Ref. ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES. This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTec Norte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used

Towards metabolic optimization of CHO cells: in silico improvement of culture medium

The emergence of omics tools and bioinformatics potentiated the development of new strategies to optimize several expression platforms, in particular mammalian cell lines, being CHO cells one of the most commonly used cell line for the production of recombinant proteins. Foremost, computational modelling combined with CHO cell omics data can help optimizing growth parameters, as well as improving the final product yield. In this context, CHO genome scale metabolic model (GSSM) was used in order to study the metabolic behavior of the cells in response to variations in environmental constraints, such as amino acids levels, targeting the development of a novel chemically defined culture medium formulation for CHO cells. To study this influence, GSSM combined with an in-house developed algorithm was employed to determine the minimal medium formulation to sustain growth for non-recombinant as well as for recombinant CHO cells lines. Optflux tool was used to predict metabolic behavior of the cells in response to the environmental constraints tested. Based on in silico predictions, growth yield value was improved 2.8 times and 1.8 times, respectively, for non-recombinant and recombinant CHO cells lines comparing to previously reported data. Furthermore, toxic by-products such as ammonium were decreased to their lowest levels. In silico-based approaches for medium optimization are powerful tools for predicting the metabolic interconnexion in the cell and for selecting potential experimental conditions for further validation in bioreactor systems.info:eu-repo/semantics/publishedVersio

Inferring optimal minimal medium on genome-scale metabolic models using evolutionary algorithms

Metabolic Pathway Analysis 2018Genome-scale metabolic models (GSMMs) are a valuable tool for the study of metabolic systems biology through biomedical to industrial research and are becoming available for an increasing number of single organisms and more recently also for microbial communities. One of the most promising features for the use of GSMMs is the rational design of microorganisms in isolation or in communities that could turn them capable of producing desired compounds in industrially relevant amount. The metabolic engineering or design problem can be simply formulated as the maximization of the production of a target compound by manipulating either environmental conditions, performing genetic manipulations or even, in the case of a microbial community, manipulate microbial composition in terms of species. In this work, it has been implemented and validated an optimization framework that allows to find an optimal minimal medium composition for a given objective function, such as maximizing growth, or the production of a given target compound. This framework was fully implemented in Python language and the workflow of the optimization process uses Evolutionary Algorithms (EA). The code, installation files and documentation are available at the GitHub repository (https://github.com/BioSystemsUM/optimModels). For the validation of this framework it was used published GSMMs of single prokaryotic organisms and natural and synthetic microbial communities. All results were compared and validated with experimental data in literature. Overall, the results obtained for minimal medium composition using the developed tool showed biological significance, correctly predicting the minimal medium in aerobic/anerobic and light/dark conditions, as required by the specific organisms involved.info:eu-repo/semantics/publishedVersio

Desenvolvimento e caracterização de geleia mista de maracujá e acerola

É de conhecimento geral que as frutas tropicais são altamente perecíveis, deteriorando-se em poucos dias, o que dificulta sua comercialização na forma in natura. A produção de geleias constitui uma importante alternativa para o processamento destas frutas, visto que há uma grande produção na região Nordeste do Brasil das espécies frutíferas tropicais, dentre elas o maracujá e a acerola. O objetivo do presente trabalho foi desenvolver e caracterizar duas formulações de geleia mista de maracujá (Passiflora edulis flavicarpa) e acerola (Malpighia punicifolia Linn), utilizando a polpa destas frutas e aproveitando a casca do maracujá como fonte de pectina para alcançar a consistência final. As duas formulações testadas foram: Formulação A: 70% de polpa de maracujá e 30% de polpa de acerola; Formulação B: 30% de polpa de maracujá e 70% de polpa de acerola. Para a caracterização das amostras foram realizadas análises de pH, °Brix, acidez titulável (ATT), relação °Brix/ATT, vitamina C, coliformes totais e termotolerantes, bolores e leveduras. Além disto, as amostras foram submetidas a teste de aceitação sensorial com 50 provadores não treinados para verificar a opinião dos mesmos em relação aos atributos sabor, doçura e consistência dos produtos elaborados. Os resultados mostraram que os teores de sólidos solúveis (ºBrix) das geleias formuladas atingiram valores entre 68-69°Brix, os quais foram suficientes para a formação do gel. Em relação às análises físico-químicas, apenas o pH, a ATT e a relação ºBrix/ATT apresentaram diferença significativa entre as formulações A e B e foram semelhantes a trabalhos realizados com geleias de outras frutas. Quanto ao conteúdo de vitamina C, o presente trabalho demonstrou que as formulações A e B não apresentaram diferença significativa entre si, embora a formulação A tenha apresentado média levemente maior que a formulação B. As análises microbiológicas de ambas as formulações apresentaram valores abaixo do limite estabelecido pela legislação vigente, indicando boas condições sanitárias na produção da geleia. Com relação à análise sensorial as duas formulações não obtiveram diferença significativa na aceitação dos atributos sabor e consistência. Entretanto para o atributo doçura a formulação B, que continha maior proporção de suco integral de acerola, obteve maior aceitação. Estes resultados mostram que a geleia produzida a partir do suco integral de maracujá e acerola pode ser uma alternativa para a indústria processadora de frutas, visto que o produto foi bem aceito, apresentou características semelhantes às geleias comerciais e contribuiu na diminuição da geração de resíduos pela utilização da casca do maracujá

Development of computational methods for the determination of biomass composition and evaluation of its impact in genome-scale models predictions

Dissertação de mestrado em BioinformáticaThe use of genome-scale metabolic models is rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation, since this reaction will be used as the objective function in most simulation approaches. In order to obtain a reliable metabolic model, the biomass precursors and their coefficients must be as precise as possible. Ideally, the determination of the biomass composition would be performed experimentally, but due to technical limitations in cellular components quantification, budget restraints and time limitations, this is often established by approximation to closely related organisms. Computational methods however, can extract some information from the genome, such as amino acid and nucleotide compositions. One main objective in this study was to evaluate how biomass precursor coefficients computationally determined, affected the predictability of several genome-scale metabolic models by comparison with experimental data. Sensitivity analysis studies were performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rate and flux distribution. Several metabolic models, whose biomass composition had been experimentally determined, were used to evaluate the impact of biomass coefficients on growth rates and flux distributions. In this study, biomass precursor coefficients were changed based on data obtained from computational methods and from closely related organisms. The results obtained from these two changes were then compared to the results obtained from the model using the experimentally determined biomass composition. Finally, analytical methods were established for macromolecule quantification and protein, DNA and RNA content of Helicobacter pylori biomass were experimentally determined. The results obtained suggest that small modifications (around 1×10-2) in biomass precursor coefficients have no significant impact on the computed specific growth rate and flux distributions. We also observed that, despite computationally determined biomass coefficients present differences to those experimentally determined, the growth rate and flux distributions have similar results (differences below 1,5 %). Surprisingly, specific growth rates and flux distributions were more distant from experimental data when adopting biomass precursor coefficients from closely related organisms.O uso de modelos metabólicos à escala genómica tem grande importância áreas, tais como a engenharia metabólica. A equação da biomassa é uma das reações fundamentais nestes modelos, uma vez que esta reacção é usada como função objectivo na maioria das abordagens de simulação. Para se obter um modelo à escala genómica coerente, os percursores da biomassa devem ser o mais precisos possível. A composição da biomassa deveria ser determinada experimentalmente; contudo, devido a limitações técnicas de quantificação, limitações de material biológico e tempo, muitos modelos metabólicos adoptam a composição da biomassa de organismos similares. No entanto, alguns métodos computacionais conseguem estimar coeficientes de aminoácidos e nucleótidos, a partir de informação do genoma. Neste trabalho, pretende-se avaliar o impacto que os coeficientes estimados a partir da informação do genoma, têm na previsão destes modelos à escala genómica, comparando-os com dados experimentais. Realizou-se uma análise de sensibilidade aos coeficientes da composição da biomassa do modelo à escala genómica da Escherichia coli iAF1260, comparando valores de taxa específica de crescimento e distribuição de fluxos. Foram também usados outros modelos à escala genómica, que possuem composição da biomassa com dados experimentais, de modo a avaliar o impacto da alteração da composição da biomassa na taxa específica de crescimento e distribuição de fluxos. Neste estudo fez-se a alteração da composição da biomassa com valores estimados in silico e com valores experimentais de organismos similares. Os valores de taxa específica de crescimento e de distribuição de fluxos obtidos para cada composição de biomassa foram comparados com os respectivos valores da composição da biomassa experimental. Por fim, procedeu-se também à implementação de métodos para análise da composição da biomassa em macromoléculas e determinou-se experimentalmente a composição de proteína, DNA e RNA total para o organismo Helicobacter pylori. Os resultados obtidos sugerem que pequenas alterações (na ordem de 1×10-2) nos coeficientes da composição da biomassa não afectam os valores das taxas específicas de crescimento e distribuição de fluxos. Observa-se também que os coeficientes da biomassa estimados a partir da composição do genoma, apesar de não serem muito semelhantes aos determinados experimentalmente, produzem resultados de taxa específica de crescimento e distribuição de fluxos muito semelhantes (diferenças menores que 1,5%). Estas diferenças são menores do que quando se adopta composições de biomassa de organismos semelhantes.Fundação para a Ciência e a Tecnologia (FCT) - Project FCOMP-01-0124-FEDER-009707 (HeliSysBio-molecular Systems Biology in Helicobacter pylori).ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness)

An Application of the Walkability Index for Elderly Health-WIEH. The Case of the UNESCO Historic Centre of Porto, Portugal

This work provides a follow-up to the article Walkability Index for Elderly Health: A Proposal, published in 2020. Previous research linked the quality of public spaces, walkability characteristics relevant to older people, and the direct health benefits of walking for the same target group. The present article, on the other hand, aims to validate the conceptual design of the walkability index for elderly health (WIEH), developed by the authors in the previous study, by applying it to a study area located in the historic center of Porto, Portugal. Therefore, public spaces and the pedestrian network are analyzed according to their suitability for older people's walkability. Presented in a visual format, the results show that only a few paths within the study area were strongly suited to older people, and emphasize the impact of existing steep slopes on the quality of the pedestrian network

Uso de técnicas de gamificação na capacitação de colaboradores : um estudo de caso

Monografia (graduação)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Departamento de Administração, 2015.O presente trabalho aborda os principais conceitos e técnicas da gamificação, buscando analisar o uso de técnicas de gamificação na capacitação de colaboradores. Após proceder à revisão de literatura sobre o tema, o trabalho apresentou um estudo de caso baseado em um evento de capacitação, no qual foram utilizadas técnicas de jogos. Os dados foram coletados por meio de um survey junto aos participantes e de observações da autora, que participou do evento. Os dados analisados por meio de estatística descritiva e análise de conteúdo evidenciaram resultados que apoiam abordagens de gamificação e motivação. Ao final, são levantadas algumas recomendações de linhas de pesquisa a realizar

MIYeastK: The Metabolic Integrated Yeast Knowledgebase

The yeast Saccharomyces cerevisiae is one of the most widely used cell factories in industrial biotechnology. However, the development of optimized yeast strains for the production of novel compounds is a time-consuming process and represents a significant cost/time burden. Currently, genome-scale metabolic models play an important role to reduce cost and time in order to develop improved strains. Nevertheless, the existence of various genome-scale metabolic models for S. cerevisiae, with different metabolic information and predictability capabilities, increases the complexity of metabolic engineering studies. The MIYeastK is a web-accessible metabolic integrated knowledgebase (http://193.137.11.210/yeast/) that integrates the metabolic information of 10 genome-scale metabolic models of S. cerevisiae, not only between each other but also with external databases, such as KEGG and MetaCyc. An enhancement of the annotation of individual metabolites, reactions, genes and gene rules included in the models was also performed. Moreover, the gene information in the models is integrated with the myriad of information contained in SGD (Saccharomyces Genome Database) simplifying phenotype analysis. Hence, MIYeastK is valuable tool for users to compare and implement metabolic engineering strategies using yeast metabolic models

Metabolic Profiling in Blastocoel Fluid and Blood Plasma of Diabetic Rabbits

Metabolic disorders of the mother adversely affect early embryo development, causing changes in maternal metabolism and consequent alterations in the embryo environment in the uterus. The goal of this study was to analyse the biochemical profiles of embryonic fluids and blood plasma of rabbits with and without insulin-dependent diabetes mellitus (DT1), to identify metabolic changes associated with maternal diabetes mellitus in early pregnancy. Insulin-dependent diabetes was induced by alloxan treatment in female rabbits 10 days before mating. On day 6 post-coitum, plasma and blastocoel fluid (BF) were analysed by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) (Metabolon Inc. Durham, NC, USA). Metabolic datasets comprised a total of 284 and 597 compounds of known identity in BF and plasma, respectively. Diabetes mellitus had profound effects on maternal and embryonic metabolic profiles, with almost half of the metabolites changed. As predicted, we observed an increase in glucose and a decrease in 1,5-anhydroglucitol in diabetic plasma samples. In plasma, fructose, mannose, and sorbitol were elevated in the diabetic group, which may be a way of dealing with excess glucose. In BF, metabolites of the pentose metabolism were especially increased, indicating the need for ribose-based compounds relevant to DNA and RNA metabolism at this very early stage of embryo development. Other changes were more consistent between BF and plasma. Both displayed elevated acylcarnitines, body3-hydroxybutyrate, and multiple compounds within the branched chain amino acid metabolism pathway, suggesting that lipid beta-oxidation is occurring at elevated levels in the diabetic group. This study demonstrates that maternal and embryonic metabolism are closely related. Maternal diabetes mellitus profoundly alters the metabolic profile of the preimplantation embryo with changes in all subclasses of metabolite