1,721,229 research outputs found
Development of integrated models of hepatocyte cells
No full textDissertação de mestrado em BioinformáticaMetabolism acts a machinery by maintaining the functionality of the cell in response
to several perturbations, keeping a balance in the levels of crucial metabolites and cell
components and producing energy by breaking down certain compounds. A better understanding
of these mechanisms cannot be restricted to the knowledge of the function of
specific tissues or cell types, it also requires knowledge about their interactions.
The human liver has a high number of physiological functions related to the metabolism,
such as the production of the bile, hormones and vitamins. The hepatocytes have a major
impact in human metabolism, being the most metabolically active cell types in humans.
Malfunction on the metabolism of this type of cells is related to several diseases, like
hepatitis, cirrhosis or non-alcoholic fatty liver disease (NAFLD), where the last one is
considered a manifestation of obesity.
A particular pathway has been associated not only with obesity, but also with cancer
and type 2 diabetes, the mechanistic TOR (mTOR) pathway. Signalling of this pathway
has an effect on most of cellular functions and regulates growth and proliferation. It has
been shown that alterations in this pathway can lead to fat accumulation in the liver of
obese people. A better understanding of this complex pathway may help researchers to
unveil more information on how this pathway works and how it can help in the treatment
of several diseases.
The increase of high-throughput data, due to the advances in sequencing and other
experimental techniques, allowed us to better understand the molecular characteristics of the cell. A useful tool to process all this information are Genome-scale metabolic
models (GSMMs). A GSMM is a list of mass-balanced reactions, which can be related to
cellular compartments, like the cytoplasm. Given high-throughput data, GSMMs can be
utilized for the simulation of the metabolism of a certain cell type through a constraintbased
modelling framework. There are several algorithms/ tools to create tissue-specific
metabolic models (based on a generic human model, such as Recon2) including tINIT,
MBA or mCADRE.
Although all these methods still face a number of issues, the generated models can
simulate human tissues and can be a good starting point for a better understanding of
complex diseases. An important limitation of these models is the fact that they only
represent the metabolic layer of the cells, while for models to be able to support accurate
simulations, a number of other important sub-systems (e.g. regulation, signalling) should
also be taken into account. This models (Integrative models) combine the information
and material flow of the three previous mentioned sub-systems, delivering a more robust
tool with more predictive strength.O metabolismo actua como uma máquina que mantém a funcionalidade da célula como
resposta a várias perturbações, mantendo os níveis de metabolitos cruciais e componentes
celulares e produzindo energia através da quebra de determinados compostos. Uma melhor
compreensão destes mecanismos não se pode restringir ao conhecimento das funções
de tecidos ou tipos celulares, também requer um conhecimento sobre as suas interacções.
O fígado humano tem um grande número de funções fisiológicas relacionadas com o
metabolismo, como a produção de bile, hormonas e vitaminas. Os hepatócitos têm um
grande impacto no metabolismo humano, sendo as suas células as metabolicamente mais
activas. Um mau funcionamento do metabolismo destas células está associado com algumas
doenças, como hepatite, cirrose ou doença hepática gordurosa não alcoólica, onde
esta última se encontra associada a obesidade.
Uma via metabólica particular tem sido associada não só com a obesidade, mas também
com cancro e diabetes tipo 2, a via metabólica mecânica TOR (mTOR). A sinalização
desta via tem efeito na maior parte das funções celulares e regula o crescimento e proliferação.
Foi demonstrado que alterações nesta via pode levar a acumulação de gordura em pessoas
obesas. Uma melhor compreensão desta via complexa pode ajudar os investigadores para
revelar mais informação sobre como esta via funciona e como pode ajudar no tratamento
de diversas doenças.
O aumento de dados provenientes de alto débito, devido aos avanços na sequenciação
e outras técnicas experimentais, permitiram-nos ter um melhor conhecimento sobre as características moleculares da célula. Uma ferramenta útil para processar toda esta informação
são os Modelos Metabólicos à Escala Genómica (MMEG). Um MMEG é uma lista de
reacções balanceadas pela massa, que pode ser relacionada com compartimentos celulares,
como o citoplasma. Dados os dados de alto rendimento, MMEG podem ser utilizados
para a simulação do metabolismo de um certo tipo celular através de modelação
baseados em restrições. Existem vários algoritmos/ferramentas para criar modelos metabólicos
específicos para um tecido (baseado em modelos metabólicos humanos, como o Recon2)
incluindo o tINIT, MBA ou mCADRE.
Apesar de todos estes métodos ainda apresentarem algumas limitações, os modelos
gerados pode simular tecidos humanos e ser um bom ponto de partida para uma melhor
compreensão de doenças complexas. Uma limitação importante destes modelos é o facto
de apenas representarem a camada metabólica da célula, enquanto para os modelos serem
capazes de suportar simulações precisas, outros sub-sistemas (ex: regulação, sinalização)
devem ser também tidos em consideração. Estes modelos (modelos integrados) combinam
a informação e fluxo de material dos três sistemas previamente descritos, fornecendo
assim uma ferramenta robusta com maior poder preditivo
Modelling and analysis of large-scale models of signalling networks
No full textCells rely on a system of signal processing and transmission networks to perform and coordinate their basic activities. The transmission process is governed through chemical signals and reactions which can be mediated by proteins or other smaller molecules. This process is collectively named cell signalling. Errors in cell signalling can lead to the development of severe diseases such as cancer. As such, an understanding at a system level of cell signalling can help us identify aberrant processes which can be therapeutically targeted. Considering the huge variety of proteins and their modifications, these kinds of systems can be quite large, complex and dynamic. This makes the modelling of cell signalling systems particularly challenging. The aim of the PhD project consists of developing new and existing methods used for the modelling cell signalling networks for the understanding of processes involved in it and the mechanisms of cell function. This was achieved through the development of various modelling tools (CARNIVAL, PHONEMeS, CellNOptR and Dynamic-Feeder). At the core of these tools stands the integration of optimisation techniques with simulation analysis in order to provide information about the behaviour of the biological system representing our signalling networks. Such an approach allows the prediction of perturbation outcomes (i.e. treating cells with drugs) and can give help in the planning of prospective treatment strategies. Model-based design of cell signalling systems, especially optimisation-based, is a major focus of this work, and here I will be mostly focusing on understanding, exploiting and designing methods of integer linear and non-linear dynamic optimisation techniques for structural and parametric identification of large signalling networks based on experimental data. The approaches followed, allow for a more efficient analysis of large-scale models of signalling networks and ever-increasing data
Abordagens integrativas de análise de vias para a investigação e desenvolvimento de fármacos para cancro
No full textTese de doutoramento em Biomedical EngineeringO cancro é um grupo altamente heterogéneo de doenças que constitui uma das principais causas
de morte no mundo moderno. A complexidade envolvida nos mecanismos moleculares que induzem
neoplasias suscita a necessidade de desenvolver métodos de análise de dados para os identificar e compreender.
O número cada vez maior de dados ómicos e ferramentas computacionais para a sua análise
expandiu o conhecimento sobre vários tipos de cancro. Os modelos metabólicos baseados em restrições
são particularmente interessantes, apresentando-se como uma estrutura flexível para integração de dados
ómicos, com várias aplicações comprovadas no estudo do cancro. No entanto, estes modelos estão
restritos à representação do metabolismo, descartando processos de expressão, regulação e sinalização.
Além disso, os métodos atuais de integração de dados ómicos carecem de um processo padronizado e
unificado para o seu uso. Neste trabalho, serão apresentados dois métodos de contextualização de dados
ómicos. Foi desenvolvido um processo para a reconstrução de modelos metabólicos contextualizados,
integrando transcriptómica para extrair, de forma genérica, modelos para tecidos humanos. Seguidamente,
foi concebida uma nova abordagem de representação de modelos e um método de previsão de
fenótipos (ipFBA), estabelecendo uma plataforma capaz de representar vários tipos de redes biológicas
no mesmo método. As duas abordagens fazem parte de plataformas de software modulares e abertas
para uso e contribuições por parte da comunidade. A validação dos métodos foi realizada usando dados
ómicos detalhados para a linha celular de cancro da mama MCF7, revelando o impacto da parametrização
nas abordagens acima mencionadas, e estabelecendo uma base sólida para um caso de estudo
mais alargado em que os métodos desenvolvidos foram usados para identificar aspectos importantes do
metabolismo do cancro consistentes com a literatura. Os modelos contextualizados revelaram melhores
previsões de genes essenciais quando comparados com trabalhos anteriores, enquanto que o método
ipFBA melhorou significativamente as previsões de atividade de fluxo. Este método também foi usado
para caracterizar diferenças entre tecido saudável e cancro renal com uma representação detalhada da
interação entre fluxos, genes metabólicos e os seus reguladores.Cancer is a highly heterogeneous group of diseases that constitutes one of the leading causes of
death in the modern world. The complexity involved in the molecular mechanisms that induce neoplasms
elicits the need for data-driven approaches to identify and understand it. The increasingly large number of
multi-omics datasets and in silico tools for their analysis, contributed positively with new insights on many
cancer types. Constraint-based models of metabolism are particularly interesting as a flexible scaffold for
omics data integration with several proven applications in cancer. However, the scope of constraint-based
models is usually restricted to metabolism, discarding gene expression, regulation and signalling pathways.
Furthermore, current methods for omics integration lack a standardised and unified pipeline for their usage.
In this work, two methods for the contextualisation of multi-omics data are presented. Firstly, a pipeline
for the reconstruction of context-specific metabolic models was developed, integrating transcriptomics
data to extract models for human tissues. Using insights from this effort, a novel model representation
approach was devised, complemented with a phenotype prediction method (ipFBA), providing a scaffold
for multi-omics integration and representing various layers of biological networks in the same formalism.
Both methods have been made available through the development of modular software frameworks that
are open for usage and contributions from the community. Validation was performed using detailed MCF7
breast cancer cell line multi-omics measurements, revealing the impact of parametrisation, setting a solid
basis for a larger case study with the aim of identifying critical aspects of cancer metabolism consistent
with those reported in literature. Context-specific models revealed higher predictive accuracy for gene
essentiality predictions than previous works, while ipFBA greatly improved flux activity predictions. The
latter approach was also used to characterise differences in healthy and renal cancer patients, allowing a
detailed visualisation of the interplay between fluxes, metabolic genes and their regulators.I would first like to thank Fundação para a Ciência e Tecnologia for the PhD studentship I was awarded
with (SFRH/BD/118657/2016)
The Logic of EGFR/ErbB Signaling: Theoretical Properties and Analysis of High-Throughput Data
Full text linkThe epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions. Using a recently introduced logical modeling framework, we first examined general topological properties and the qualitative stimulus-response behavior of the network. With species equivalence classes, we introduce a new technique for logical networks that reveals sets of nodes strongly coupled in their behavior. We also analyzed a model variant which explicitly accounts for uncertainties regarding the logical combination of signals in the model. The predictive power of this model is still high, indicating highly redundant sub-structures in the network. Finally, one key advance of this work is the introduction of new techniques for assessing high-throughput data with logical models (and their underlying interaction graph). By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions. Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2. Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway
Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling
Full text linkBackground
The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels.
Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments.
Results
In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one.
Conclusion
The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems.Ministry of Education of Saxony-Anhalt (Research Center "Dynamic Systems")German Federal Ministry of Education and Research (MaCS, Magdeburg Centre for Systems Biology)Helmholtz Alliance on Systems Biology (project CoReNe) (Initiative and Networking Fund of the Helmholtz Association
Discovering biomarkers of drug efficacy in cancer from pharmacogenomic data
No full textCancer is a biologically complex disease with clinically diverse outcomes. Successful therapies are most often hampered by the observed high molecular heterogeneity of this disease. For these reasons, effective cancer treatment is still a challenge. Nowadays, it is clear that a cancer therapy that fits all cases cannot be found, and as a result there is a pressing need of methods to tailor therapeutic strategies on a single patient level, based on the molecular features of their cancers. Pharmacogenomics aims to study the relationship between an individual's genotype and drug response. Scientists use different biological models, ranging from cell lines to mouse models, as proxies for patients for preclinical and translational studies. The rapid development of "-omics" technologies is increasing the amount of features that can be measured in these models, expanding the possibilities of finding predictive biomarkers of drug response. Uncovering these relationships requires diverse computational approaches ranging from machine learning to dynamic modeling. Despite major advances, we are still far from being able to precisely predict drug efficacy in cancer models, let alone directly on patients. Here, I deal with the topic of in vivo drug response prediction and the discovery of predictive biomarkers of drug response approached in two different ways. Firstly, I integrate publicly available gene expression profiles of immortalised human cancer cell lines and primary tumor samples in order to bridge rich pharmacogenomic data derived from the former to the latter, thus predicting drug sensitivity in vivo. I apply this pipeline in the context of breast and colon cancer and validate the predictions across independent datasets. This study provides a conceptual framework to tackle the problem of predicting anti-cancer drug response in vivo via public data integration and defines a general strategy applicable to multiple cancer types. Subsequently, I shift from the aforementioned data-driven analysis to a network-based approach, in an attempt to add a more mechanistic insight to the link between a biomarker and the corresponding drug. To that end, I use the tool CARNIVAL which is based on causal reasoning principles. Basal gene expression from cancer cell lines were combined with a prior knowledge network, transcription factor and pathway activities in an attempt to reconstruct pathways linked with drug sensitivity. Despite the challenges, my analysis based on cancer-specific gene expression analysis could yield predictive biomarkers of drug response that could be validated consistently across different independent datasets in both cancer types. I envision that the framework presented here can contribute to generate new biomarker-drug hypotheses in various types of cancer, aid pharmacogenomic discovery and elucidate their clinical implications
Network Inference from Perturbation Data: Robustness, Identifiability and Experimental Design
Full text linkHochdurchsatzverfahren quantifizieren eine Vielzahl zellulärer Komponenten, können aber selten deren Interaktionen beschreiben. Daher wurden in den letzten 20 Jahren verschiedenste Netzwerk-Rekonstruktionsmethoden entwickelt. Insbesondere Perturbationsdaten erlauben dabei Rückschlüsse über funktionelle Mechanismen in der Genregulierung, Signal Transduktion, intra-zellulärer Kommunikation und anderen Prozessen zu ziehen. Dennoch bleibt Netzwerkinferenz ein ungelöstes Problem, weil die meisten Methoden auf ungeeigneten Annahmen basieren und die Identifizierbarkeit von Netzwerkkanten nicht aufklären.
Diesbezüglich beschreibt diese Dissertation eine neue Rekonstruktionsmethode, die auf einfachen Annahmen von Perturbationsausbreitung basiert. Damit ist sie in verschiedensten Zusammenhängen anwendbar und übertrifft andere Methoden in Standard-Benchmarks. Für MAPK und PI3K Signalwege in einer Adenokarzinom-Zellline generiert sie plausible Netzwerkhypothesen, die unterschiedliche Sensitivitäten von PI3K-Mutanten gegenüber verschiedener Inhibitoren überzeugend erklären.
Weiterhin wird gezeigt, dass sich Netzwerk-Identifizierbarkeit durch ein intuitives Max-Flow Problem beschreiben lässt. Dieses analytische Resultat erlaubt effektive, identifizierbare Netzwerke zu ermitteln und das experimentelle Design aufwändiger Perturbationsexperimente zu optimieren. Umfangreiche Tests zeigen, dass der Ansatz im Vergleich zu zufällig generierten Perturbationssequenzen die Anzahl der für volle Identifizierbarkeit notwendigen Perturbationen auf unter ein Drittel senkt.
Schließlich beschreibt die Dissertation eine mathematische Weiterentwicklung der Modular Response Analysis. Es wird gezeigt, dass sich das Problem als analytisch lösbare orthogonale Regression approximieren lässt. Dies erlaubt eine drastische Reduzierung des nummerischen Aufwands, womit sich deutlich größere Netzwerke rekonstruieren und neueste Hochdurchsatz-Perturbationsdaten auswerten lassen.'Omics' technologies provide extensive quantifications of components of biological systems but rarely characterize the interactions between them. To fill this gap, various network reconstruction methods have been developed over the past twenty years. Using perturbation data, these methods can deduce functional mechanisms in gene regulation, signal transduction, intra-cellular communication and many other cellular processes. Nevertheless, this reverse engineering problem remains essentially unsolved because inferred networks are often based on inapt assumptions, lack interpretability as well as a rigorous description of identifiability.
To overcome these shortcoming, this thesis first presents a novel inference method which is based on a simple response logic. The underlying assumptions are so mild that the approach is suitable for a wide range of applications while also outperforming existing methods in standard benchmark data sets. For MAPK and PI3K signalling pathways in an adenocarcinoma cell line, it derived plausible network hypotheses, which explain distinct sensitivities of PI3K mutants to targeted inhibitors.
Second, an intuitive maximum-flow problem is shown to describe identifiability of network interactions. This analytical result allows to devise identifiable effective network models in underdetermined settings and to optimize the design of costly perturbation experiments. Benchmarked on a database of human pathways, full network identifiability is obtained with less than a third of the perturbations that are needed in random experimental designs.
Finally, the thesis presents mathematical advances within Modular Response Analysis (MRA), which is a popular framework to quantify network interaction strengths. It is shown that MRA can be approximated as an analytically solvable total least squares problem. This insight drastically reduces computational complexity, which allows to model much bigger networks and to handle novel large-scale perturbation data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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