Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET

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 ▲]

Network analysis to model diseases and cellular reprogramming for therapeutic intervention

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 ▲]

On different aspects of network analysis in systems biology

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 ▲]

A general strategy for cellular reprogramming: the importance of transcription factor cross-repression

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 ▲]

Gene regulatory network analysis supports inflammation as a key neurodegeneration process in prion disease.

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 ▲]

PPARγ population shift produces disease-related changes in molecular networks associated with metabolic syndrome

in Cell Death and 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 ▲]