Modelos com excesso de zeros e modelos de duas partes: a sua utilização no estudo da Schistosomose

Tese de mestrado em Estatística, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2011Para modelar variáveis que apresentam um grande número de zeros existem os modelos com excesso de zeros e os modelos de duas partes, que têm sido amplamente usados numa grande variedade de áreas de estudo. Estes modelos serão aplicados ao estudo, numa população Angolana, da distribuição da Schistosomose, uma parasitose superada em frequência apenas pela malária. Os resultados sugerem que o modelo GLM (Generalized Linear Models) binomial negativa, o modelo binomial negativa com excesso de zeros e o modelo de duas partes com binomial negativa são preferíveis aos modelos com distribuição de Poisson. Os resultados obtidos mostram que não existem diferenças significativas entre os modelos com binomial negativa. Escolheu-se o modelo binomial negativa com excesso de zeros pois existem duas fontes possíveis para os zeros: os indivíduos que não estão infectados (zeros verdadeiros) e os indivíduos que, apesar estarem infectados, não apresentam ovos na urina (zeros falsos). Assim, a estrutura deste modelo é mais concordante com a situação estudada.The zero inflated and the Hurdle models are widely applied in a large variety of fields of study for modeling variables that exhibit a large number of zeros. These models are here used for studying the number of cases of Schistosomiasis observed in some regions of Angola. The Schistosomiasis is the second most frequent human parasitic disease after the malaria. The results suggest that the Generalized Linear Model Negative Binomial, the Zero Inflated Negative Binomial and the Hurdle Negative Binomial models are preferable to the Poisson models. No significant differences were found between the various negative binomial models. The Zero Inflated Negative Binomial model was chosen because the sample contains two possible sources of zeros: either the individuals did not have the infection or they were already sick, although no eggs were found in the urine. Therefore, the structure of this model is the most similar to the case being studied

What’s next for computational systems biology?

Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the next-generation of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis

Mapping the evidence of the effects of environmental factors on the prevalence of antibiotic resistance in the non-built environment: Protocol for a systematic evidence map

Background: Human, animal, and environmental health are increasingly threatened by the emergence and spread of antibiotic resistance. Inappropriate use of antibiotic treatments commonly contributes to this threat, but it is also becoming apparent that multiple, interconnected environmental factors can play a significant role. Thus, a One Health approach is required for a comprehensive understanding of the environmental dimensions of antibiotic resistance and inform science-based decisions and actions. The broad and multidisciplinary nature of the problem poses several open questions drawing upon a wide heterogeneous range of studies. Objective: This study seeks to collect and catalogue the evidence of the potential effects of environmental factors on the abundance or detection of antibiotic resistance determinants in the outdoor environment, i.e., antibiotic resistant bacteria and mobile genetic elements carrying antibiotic resistance genes, and the effect on those caused by local environmental conditions of either natural or anthropogenic origin. Methods: Here, we describe the protocol for a systematic evidence map to address this, which will be performed in adherence to best practice guidelines. We will search the literature from 1990 to present, using the following electronic databases: MEDLINE, Embase, and the Web of Science Core Collection as well as the grey literature. We shall include full-text, scientific articles published in English. Reviewers will work in pairs to screen title, abstract and keywords first and then full-text documents. Data extraction will adhere to a code book purposely designed. Risk of bias assessment will not be conducted as part of this SEM. We will combine tables, graphs, and other suitable visualisation techniques to compile a database i) of studies investigating the factors associated with the prevalence of antibiotic resistance in the environment and ii) map the distribution, network, cross-disciplinarity, impact and trends in the literature.This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. The funder had no role in the development of this protocol.info:eu-repo/semantics/publishedVersio

A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from multiple unrelated cell types (mosaic data) or from purified enzyme data. In this work, we develop a model of the PI cycle informed by experimental and omics data taken from a single cell type, namely platelets. We were able to make a number of predictions regarding the regulation of PI cycle enzymes, the importance of the number of receptors required for successful GPCR signaling and the importance of lipid- and protein-binding proteins in regulating second messenger outputs. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain consistent. However, when informed by mosaic experimental data model predictions greatly vary illustrating the risks of using mosaic datasets from unrelated cell types

A mathematical model of the phosphoinositide pathway in human pulmonary epithelial cells

Tese de doutoramento, Biologia (Biologia de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2018Cystic fibrosis is a condition caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), a chloride and bicarbonate channel. The epithelial sodium channel (ENaC) may also be affected. The defective function of these ion channels is thought to reduce the airway surface liquid (ASL) and lead to the accumulation of mucus in the airways that characterizes the disease and causes the recurrent pulmonary infections and inflammation that will ultimately destroy the lungs of the affected subjects. Phosphoinositides are rare signaling lipids that constitute a complex network regulating many cellular processes. One of phosphoinositides’ many functions is as cell membrane protein regulators, and several studies implicate phosphatidylinositol 4,5-biphosphate (PI(4,5)P2) in ENaC regulation. Diacylglycerol kinase (DGK), an enzyme of the phosphoinositide pathway that catalyses the phosphorylation of diacylglycerol (DAG) into phosphatidic acid (PA). When DGK is inhibited, it will cause the moderation of ENaC function, and this could be exploited as a therapeutic in cystic fibrosis. But the mechanism of ENaC regulation by DGK is not completely understood. The usually accepted hypothesis is that DGK influences PI(4,5)P2 production by halting the phosphoinositide recycling. In Chapter 2 we present a model of the phosphoinositide pathway that simulates one square micrometer of the inner layer of the membrane. The objective of this project was to create a model that could simulate the phosphoinositide pathway and be used to study how perturbations to the pathway impact the levels of pertinent lipids, especially the ones known to affect ENaC. The model replicates the steady-state of the phosphoinositide pathway as recorded in the literature and replicates most known dynamic phenomena. Furthermore, sensitivity analysis demonstrates that the model is robust to moderate perturbations to the parameters. The model suggests that the main source of material to the PI(4,5)P2 pool is a flux representing the direct transformation of phosphatidylinositol (PI) into PI(4,5)P2 that defies the traditional view that the main source is the sequential phosphorylation of phosphoinositol into phosphatidylinositol 4-phosphate (PI(4)P) by the enzyme phosphoinositol 4-kinase (PI4K) followed by the transformation to PI(4,5)P2 by phosphoinositide 4-phosphate 5-kinase I (PIP5KI). The model also suggests that phosphatidylinositol 5-phosphate (PI(5)P) could be a significant source for PI(4,5)P2 production. We compared the model results to data from a siRNA screens, where the expression of several enzymes in the pathway were knocked down and the activity of ENaC was monitored. Our model suggests control strategies where the activity of the enzyme PIP5KI or the PI4 +PIP5K +DVL protein complex are decreased and cause an efficacious reduction in PI(4,5)P2 levels while avoiding undesirable alterations in other phosphoinositide pools. In Chapter 3 we present a model that enables the study of the interplay between ENaC, CFTR, airway surface liquid (ASL), PI(4,5)P2 and the protein SPLUNC1 (short palate, lung, and nasal epithelial clone). It presents a good fit to experimental observations, and the available data can constrain the model’s parameters without ambiguities. The model analysis shows that ASL at the steady state is sensitive to small changes in PI(4,5)P2 abundance, particularly in cystic fibrosis conditions, which suggests that manipulation of phosphoinositide metabolism may promote therapeutic benefits for cystic fibrosis patients. Finally, in Chapter 4, we bring the phophoinositide pathway and ENaC/ASL model together. These models enabled us to study DGK and ENaC and strongly suggest that, contrary to the usually accepted hypothesis, this regulation is effected by the control of PI(4,5)P2 production by the PIP5KI that in turn is controlled by PA, the product of DGK. In this work we also use a model of the phosphoinositide cycle to test the hypothesis that DGK influence PI(4,5)P2 production by halting the phosphoinositide recycling. This model is unable to replicate the available data if the activation of PIP5KI by PA is not implemented, which strengthens our belief that ENaC regulation by phosphoinositides is accomplished through PA and PIP5KI.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52486/2014; Programa doutoral - BioSys em Sistemas Biológicos, Genómica Funcional & Integrativa, (FCT/PD/00065/2012