References of "Crespo, Isaac 40021213"
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See detailAnalysis of the dynamic co-expression network of heart regeneration in the zebrafish
Rodius, Sophie; Androsova, Ganna UL; Götz, Lou et al

in Scientific Reports (2016), 6

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact ... [more ▼]

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration. [less ▲]

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See detailDiscrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET
Rodriguez, Ana; Crespo, Isaac UL; Androsova, Ganna UL et al

in PLoS ONE (2015), 10(6), 0127216

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular ... [more ▼]

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states. [less ▲]

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See detailIntegrating Pathways of Parkinson's Disease in a Molecular Interaction Map
Fujita, Kazuhiro A.; Ostaszewski, Marek UL; Matsuoka, Yukiko et al

in Molecular Neurobiology (2014)

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is ... [more ▼]

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map . [less ▲]

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See detailNetwork analysis to model diseases and cellular reprogramming for therapeutic intervention
Crespo, Isaac UL

Doctoral thesis (2013)

Applications of network analysis to the study of disease can be divided into two main categories: disease description, including characterisation, diagnosis and prognosis; and disease treatment, including ... [more ▼]

Applications of network analysis to the study of disease can be divided into two main categories: disease description, including characterisation, diagnosis and prognosis; and disease treatment, including drug target discovery and cellular reprogramming, together with its applications to regenerative medicine. In this dissertation, I will critically discuss some research projects on which I have been working during my PhD program. In correspondence with the two aforementioned categories, these projects can be broken down into two different blocks of content, with the common goal of acquiring insights into the study of disease. In the first block of contents, corresponding with Chapter 2, I will explain and discuss novel strategies for network-based analysis and modelling which have been applied for disease description and characterisation in different case-studies, namely the metabolic syndrome, prion disease and the epithelial to mesenchymal transition in breast cancer. Indeed, these projects exploited the evolutionary conservation of motifs of regulatory interactions and consistency between computed and experimentally validated expression so as to reconstruct dynamical models and create a network-based characterisation of the corresponding systems. With regards the second block of content, corresponding with Chapter 3, I explain and discuss novel computational methods which have been developed during my PhD program to address the task of the artificial induction of cellular reprogramming; something with a wealth of potential applications when it comes to the creation of disease models and in the field of regenerative medicine. Within the general conclusion discussion focuses on the fact that, although the methodology explained in this work was developed in the context of disease study, one may find the application of some of these ideas and strategies fitting for other problems. Indeed, the same principles applied to detect driver genes capable of changing the cell phenotype when perturbed can also be applied to control biological living systems for basic research or industrial purposes. These principles could also be potentially extended to higher level systems than the cellular level (tissue or cell population level). [less ▲]

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See detailThe Parkinson's Disease Map: A Framework for Integration, Curation and Exploration of Disease-related Pathways
Ostaszewski, Marek UL; Fujita, Kazuhiro; Matsuoka, Yukiko et al

Poster (2013, March 09)

Objectives: The pathogenesis of Parkinson's Disease (PD) is multi-factorial and age-related, implicating various genetic and environmental factors. It becomes increasingly important to develop new ... [more ▼]

Objectives: The pathogenesis of Parkinson's Disease (PD) is multi-factorial and age-related, implicating various genetic and environmental factors. It becomes increasingly important to develop new approaches to organize and explore the exploding knowledge of this field. Methods: The published knowledge on pathways implicated in PD, such as synaptic and mitochondrial dysfunction, alpha-synuclein pathobiology, failure of protein degradation systems and neuroinflammation has been organized and represented using CellDesigner. This repository has been linked to a framework of bioinformatics tools including text mining, database annotation, large-scale data integration and network analysis. The interface for online curation of the repository has been established using Payao tool. Results: We present the PD map, a computer-based knowledge repository, which includes molecular mechanisms of PD in a visually structured and standardized way. A bioinformatics framework that facilitates in-depth knowledge exploration, extraction and curation supports the map. We discuss the insights gained from PD map-driven text mining of a corpus of over 50 thousands full text PD-related papers, integration and visualization of gene expression in post mortem brain tissue of PD patients with the map, as well as results of network analysis. Conclusions: The knowledge repository of disease-related mechanisms provides a global insight into relationships between different pathways and allows considering a given pathology in a broad context. Enrichment with available text and bioinformatics databases as well as integration of experimental data supports better understanding of complex mechanisms of PD and formulation of novel research hypotheses. [less ▲]

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See detailA general strategy for cellular reprogramming: the importance of transcription factor cross-repression
Crespo, Isaac UL; del Sol Mesa, Antonio UL

in Stem Cells (2013)

Transcription factor cross-repression is an important concept in cellular differentiation. A bistable toggle switch constitutes a molecular mechanism that determines cellular commitment and provides ... [more ▼]

Transcription factor cross-repression is an important concept in cellular differentiation. A bistable toggle switch constitutes a molecular mechanism that determines cellular commitment and provides stability to transcriptional programs of binary cell fate choices. Experiments support that perturbations of these toggle switches can interconvert these binary cell fate choices, suggesting potential reprogramming strategies. However, more complex types of cellular transitions could involve perturbations of combinations of different types of multistable motifs. Here we introduce a method that generalizes the concept of transcription factor cross-repression to systematically predict sets of genes, whose perturbations induce cellular transitions between any given pair of cell types. Furthermore, to our knowledge, this is the first method that systematically makes these predictions without prior knowledge of potential candidate genes and pathways involved, providing guidance on systems where little is known. Given the increasing interest of cellular reprogramming in medicine and basic research, our method represents a useful computational methodology to assist researchers in the field in designing experimental strategies. [less ▲]

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See detailNetwork analysis for systems biology
Chaiboonchoe, A.; Jurkowski, Wiktor UL; Pellet, J. et al

in Prokop, Aleš; Csukás (Eds.) Springer book in Systems Biology, Vol.1: Systems Biology:, Integrative Biology and Simulation Tools (2013)

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways, which involve DNA, RNA proteins and metabolites as key elements, coordinate most aspects of cellular functioning. Cellular processes, which are dependent on the structure and dynamics of gene regulatory networks, can be studied by employing a network representation of molecular interactions. In this chapter we describe several types of networks and how combination of different analytic approaches can be used to study diseases. We provide a list of selected tools for visualization and network analysis. We introduce protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks. We then define concepts underlying network representation of cellular processes and molecular interactions. We finally discuss how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular network type. [less ▲]

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See detailOn different aspects of network analysis in systems biology
Chaiboonchoe, Amphun; Jurkowski, Wiktor UL; Pellet, Johann et al

in Systems Biology (2013), 1

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type. © Springer Science+Business Media Dordrecht 2013. All rights are reserved. [less ▲]

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See detailPredicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states
Crespo, Isaac UL; Krishna, Abhimanyu UL; Le Béchec, Antony UL et al

in Nucleic Acids Research (2013), 41(1), 8

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are ... [more ▼]

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions. [less ▲]

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See detailGene regulatory network analysis supports inflammation as a key neurodegeneration process in prion disease.
Crespo, Isaac UL; Rump, Kirsten UL; Jurkowski, Wiktor UL et al

in BMC Systems Biology (2012), 6(132),

The activation of immune cells in the brain is believed to be one of the earliest events in prion disease development, where misfolded PrionSc protein deposits are thought to act as irritants leading to a ... [more ▼]

The activation of immune cells in the brain is believed to be one of the earliest events in prion disease development, where misfolded PrionSc protein deposits are thought to act as irritants leading to a series of events that culminate in neuronal cell dysfunction and death. The role of these events in prion disease though is still a matter of debate. To elucidate the mechanisms leading from abnormal protein deposition to neuronal injury, we have performed a detailed network analysis of genes differentially expressed in several mouse prion models [less ▲]

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See detailA Novel Network Integrating a miRNA-203/SNAI1 Feedback Loop which Regulates Epithelial to Mesenchymal Transition.
Moes, Michèle UL; Le Béchec, Antony UL; Crespo, Isaac UL et al

in PLoS ONE (2012), 7(4), 35440

Background: The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as ... [more ▼]

Background: The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as epithelial to mesenchymal transition (EMT), cells change their genetic and trancriptomic program leading to phenotypic and functional alterations. The challenge of understanding this dynamic process resides in unraveling regulatory networks involving master transcription factors (e.g. SNAI1/2, ZEB1/2 and TWIST1) and microRNAs. Here we investigated microRNAs regulated by SNAI1 and their potential role in the regulatory networks underlying epithelial plasticity. Results: By a large-scale analysis on epithelial plasticity, we highlighted miR-203 and its molecular link with SNAI1 and the miR-200 family, key regulators of epithelial homeostasis. During SNAI1-induced EMT in MCF7 breast cancer cells, miR-203 and miR-200 family members were repressed in a timely correlated manner. Importantly, miR-203 repressed endogenous SNAI1, forming a double negative miR203/SNAI1 feedback loop. We integrated this novel miR203/SNAI1 with the known miR200/ZEB feedback loops to construct an a priori EMT core network. Dynamic simulations revealed stable epithelial and mesenchymal states, and underscored the crucial role of the miR203/SNAI1 feedback loop in state transitions underlying epithelial plasticity. Conclusion: By combining computational biology and experimental approaches, we propose a novel EMT core network integrating two fundamental negative feedback loops, miR203/SNAI1 and miR200/ZEB. Altogether our analysis implies that this novel EMT core network could function as a switch controlling epithelial cell plasticity during differentiation and cancer progression. [less ▲]

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See detailPPARγ population shift produces disease-related changes in molecular networks associated with metabolic syndrome
Jurkowski, Wiktor UL; Roomp, Kirsten UL; Crespo, Isaac UL et al

in Cell Death & Disease (2011), 2(8), 192

Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding ... [more ▼]

Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding domain at serine 273 has been shown to change the expression of a large number of genes implicated in obesity. The difference in gene expression seen when comparing wild-type phosphorylated with mutant non-phosphorylated PPARγ may have important consequences for the cellular molecular network, the state of which can be shifted from the healthy to a stable diseased state. We found that a group of differentially expressed genes are involved in bi-stable switches and form a core network, the state of which changes with disease progression. These findings support the idea that bi-stable switches may be a mechanism for locking the core gene network into a diseased state and for efficiently propagating perturbations to more distant regions of the network. A structural analysis of the PPARγ-RXRα dimer complex supports the hypothesis of a major structural change between the two states, and this may represent an important mechanism leading to the differential expression observed in the core network. [less ▲]

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