Catálogo de publicaciones - libros

Recent high throughput techniques in molecular biology have brought about the possibility of directly identifying the architecture of regulatory networks on a genome-wide scale. However, the computational task of estimating fine-grained models on a genome-wide scale is daunting. Therefore, it is of great importance to be able to reliably identify submodules of the network that can be effectively modelled as independent subunits. In this paper we present a procedure to obtain submodules of a cellular network by using information from gene-expression measurements. We integrate network architecture data with genome-wide gene expression measurements in order to determine which regulatory relations are actually confirmed by the expression data. We then use this information to obtain non-trivial submodules of the regulatory network using two distinct algorithms, a naive exhaustive algorithm and a spectral algorithm based on the eigendecomposition of an affinity matrix. We test our method on two yeast biological data sets, using regulatory information obtained from chromatin immunoprecipitation.

Based on the logical description of gene regulatory networks developed by R. Thomas, we introduce an enhanced modelling approach that uses timed automata. It yields a refined qualitative description of the dynamics of the system incorporating information not only on ratios of kinetic constants related to synthesis and decay, but also on the time delays occurring in the operations of the system. We demonstrate the potential of our approach by analysing an illustrative gene regulatory network of bacteriophage .

Many eukaryotic cell types share the ability to migrate directionally in response to external chemoattractant gradients. This ability is central in the development of complex organisms, and is the result of billion years of evolution. Cells exposed to shallow gradients in chemoattractant concentration respond with strongly asymmetric accumulation of several signaling factors, such as phosphoinositides and enzymes. This early symmetry-breaking stage is believed to trigger effector pathways leading to cell movement. Although many factors implied in directional sensing have been recently discovered, the physical mechanism of signal amplification is not yet well understood. We have proposed that directional sensing is the consequence of a phase ordering process mediated by phosphoinositide diffusion and driven by the distribution of chemotactic signal. By studying a realistic computational model that describes enzymatic activity, recruitment to the plasmamembrane, and diffusion of phosphoinositide products we have shown that the effective enzyme-enzyme interaction induced by catalysis and diffusion introduces an instability of the system towards phase separation for realistic values of physical parameters. In this framework, large reversible amplification of shallow chemotactic gradients, selective localization of chemical factors, macroscopic response timescales, and spontaneous polarization arise.

We have modelled the within-patient evolutionary process during HIV infection. We have studied viral evolution at population level (competition on the same receptor) and at species level (competitions on different receptors). During the HIV infection, several mutants of the virus arise, which are able to use different chemokine receptors, in particular the CCR5 and CXCR4 coreceptors (termed R5 and X4 phenotypes, respectively). Phylogenetic inference of chemokine receptors suggests that virus mutational pathways may generate R5 variants able to interact with a wide range of chemokine receptors different from CXCR4. Using the chemokine tree topology as conceptual framework for HIV viral speciation, we present a model of viral phenotypic mutations from R5 to X4 strains which reflect HIV late infection dynamics. Our model investigates the action of Tumor Necrosis Factor in AIDS progression and makes suggestions on better design of HAART therapy.

Genetic regulatory networks have been modeled as discrete transition systems by many approaches, benefiting from a large number of formal verification algorithms available for the analysis of discrete transition systems. However, most of these approaches do not scale up well. In this article, we explore the use of compositionality for the analysis of genetic regulatory networks. We present a framework for modeling genetic regulatory networks in a modular yet faithful manner based on the mathematically well-founded formalism of differential inclusions. We then propose a compositional algorithm to efficiently analyze reachability properties of the model. A case study shows the potential of this approach.

Having in mind the large-scale analysis of gene regulatory networks, we review a graph decimation algorithm, called “leaf-removal”, which can be used to evaluate the feedback in a random graph ensemble. In doing this, we consider the possibility of analyzing networks where the diagonal of the adjacency matrix is structured, that is, has a fixed number of nonzero entries. We test these ideas on a network model with fixed degree, using both numerical and analytical calculations. Our results are the following. First, the leaf-removal behavior for large system size enables to distinguish between different regimes of feedback. We show their relations and the connection with the onset of complexity in the graph. Second, the influence of the diagonal structure on this behavior can be relevant.

The buccal ganglia of contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified.

We present a game-theoretic foundation for gene regulatory analysis based on the recent formalism of . Rewriting game theory is discrete and comes with a graph-based framework for understanding compromises and interactions between players and for computing Nash equilibria. The formalism explicitly represents the dynamics of its Nash equilibria and, therefore, is a suitable foundation for the study of steady states in discrete modelling. We apply the formalism to the discrete analysis of gene regulatory networks introduced by R. Thomas and S. Kauffman. Specifically, we show that their models are specific instances of a deduced from the parameter.

Regulatory networks are usually presented as graph structures showing the (combinatorial) regulatory effect of transcription factors (TF’s) on modules of similarly expressed or otherwise related genes. However, from these networks it is not clear when and how TF’s are activated. The actual conditions or perturbations that trigger a change in the activity of TF’s should be a crucial part of the generated regulatory network.

Here, we demonstrate the power to uncover TF activity by focusing on a small, homogeneous, yet well defined set of chemostat cultivation experiments, where the transcriptional response of yeast grown under four different nutrient limitations, both aerobically as well as anaerobically was measured. We define a condition transition as an instant change in yeast’s extracellular environment by comparing two cultivation conditions, where either the limited nutrient or the oxygen availability is different. Differential gene expression as a consequence of such a condition transition is represented in a tertiary matrix, where zero indicates no change in expression; 1 and -1 respectively indicate an increase and decrease in expression as a consequence of a condition transition. We uncover TF activity by assessing significant TF binding in the promotor region of genes that behave accordingly at a condition transition. The interrelatedness of the conditions in the combinatorial setup is exploited by performing specific hypergeometric tests that allow for the discovery of both individual and combined effects of the cultivation parameters on TF activity. Additionally, we create a weight-matrix indicating the involvement of each TF in each of the condition transitions by posing our problem as an orthogonal Procrustes problem. We show that the Procrustes analysis strengthens and broadens the uncovered relationships.

The resulting regulatory network reveals nutrient-limitation-specific effects of oxygen presence on expression behavior and TF activity. Our analysis identifies many TF’s that seem to play a very specific regulatory role at the nutrient and oxygen availability transitions.

In vitro model systems are used to study epithelial cell growth, morphogenesis, differentiation, and transition to cancer-like forms. MDCK cell lines (from immortalized kidney epithelial cells) are widely used examples. Prominent in vitro phenotypic attributes include stable cyst formation in embedded culture, inverted cyst formation in suspension culture, and lumen formation in overlay culture. We present a low-resolution system analogue in which space, events, and time are discretized; object interaction uses a two-dimensional grid similar to a cellular automaton. The framework enables “cell” agents to act independent using an embedded logic based on axioms. In silico growth and morphology can mimic in vitro observations in four different simulated environments. Matched behaviors include stable “cyst” formation. The in silico system is designed to facilitate experimental exploration of outcomes from changing components and features, including the embedded logic (the in silico analogue of a mutation or epigenetic change). Some simulated behaviors are sensitive to changes in logic. In two cases, the change caused cancer-like growth patterns to emerge.