Catálogo de publicaciones - libros

In our previous study [3] we described the method for a direct three-dimensional (3D) computer simulation of ferredoxin-dependent cyclic electron transport around the photosystem 1 pigment-protein complex. Simulations showed that the spatial organization of the system plays a significant role in shaping the kinetics of the redox turnover of P700 (the reaction center of a photosystem 1 pigment-protein complex). In this paper we develop the direct 3D model of cyclic electron transport and apply it to study the nature of fast and slow components of the P700+ dark reduction process. We demonstrate that the slow phase of this process is diffusion controlled and is determined by the diffusion of reduced plastoquinone and plastocyanin molecules from the granal to the stromal areas of the thylakoid membrane.

Calcium oscillations play an important role in intracellular signal transduction. As a second messenger, Ca2+ represents a link between several input signals and several target processes in the cell. Whereas the frequency of simple Ca2+ oscillations enables a selective activation of a specific protein and herewith a particular process, the question arises of how at the same time two or more classes of proteins can be specifically regulated. The question is general and concerns the problem of how one second messenger can transmit more than one signal simultaneously (bow-tie structure of signalling). To investigate whether a complex Ca2+ signal like bursting, a succession of low-peak and high-peak oscillatory phases, could selectively activate different proteins, several bursting patterns with simplified square pulses were applied in a theoretical model. The results indicate that bursting Ca2+ oscillations allow a differential regulation of two different calcium-binding proteins, and hence, perform the desired function.

Many eukaryotic cell types share the ability to migrate directionally in response to external chemoattractant gradients. The binding of chemoattractants to specific receptors leads to a wide range of biochemical responses that become highly localized as cells polarize and migrate by chemotaxis. This ability is central in the development of complex organisms, and is the result of a billion years of evolution. Cells exposed to shallow gradients in chemoattractant concentration respond with strongly asymmetric accumulation of several factors, including the phosphoinositides PIP3 and PIP2, the PI 3-kinase PI3K, and phosphatase PTEN. This early symmetry-breaking stage is believed to trigger effector pathways leading to cell movement. Although many signaling factors implied in directional sensing have been recently discovered the physical mechanism of signal amplification is not yet well understood. We propose that directional sensing is the consequence of a phase ordering process mediated by phosphoinositide diffusion and driven by the distribution of the chemotactic signal. By studying a realistic reaction-diffusion lattice model that describes PI3K and PTEN enzymatic activity, recruitment to the plasma membrane, and diffusion of their 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 naturally.

We evaluate different mechanisms for spatial domain formation of guanosine triphosphatases (GTPases) on cellular membranes. A kinetic model of the basic guanine-nucleotide cycle common to all GTPases is developed and coupled along a one-dimensional axis by diffusion of inactive and activated GTPases. We ask whether a parameter set exists such that domain formation is possible by Turing’s mechanism, i.e., purely by reactions and diffusion, and show that the Turing instability does not occur in this model for any parameter combination. But, as revealed by stability and bifurcation analysis, domain formation is reproduced after augmenting the model with combinations of two spatial interaction mechanisms: 1. attraction and 2. adhesion among active GTPases. These interactions can be mediated by effector proteins that bind active GTPases, and the model therefore predicts domains to disintegrate if effector binding is inhibited.

The formation of new blood vessels is a multistep process in which sprouting endothelial cells (ECs) form tubes with lumina, these tubes being additionally organized as capillary networks. models of tubulogenesis have been developed to investigate this highly regulated multifactorial process, with special attention paid to the determinant role of mechanical interactions between ECs and the extracellular matrix (ECM). In agreement with experimental results obtained when culturing endothelial EAhy926 cells on fibrin gels, we defined theoretical thresholds between cellular traction and active cell migration along ECM strain fields above which tubulogenesis is induced.We additionally illustrated how mechanical factors may provide long-ranged positional information signals leading to localized network formation, thus providing an alternative view to the classical approach of morphogenesis based on gradients of diffusible morphogens.

Formal kinetic methods to analyze biocatalytic systems are traditionally based on the law of mass action. This law involves the assumption that each molecular state has an exponentially distributed lifetime. We regard this assumption as unduly restrictive and advocate a more general, service theory based approach (termed mass service kinetics or, briefly, service kinetics). In service-theoretic terms biocatalysts are servers and their ligands are customers. The time intervals between arrivals of ligand molecules at special service loci (active or binding sites) as well as the service periods at these loci need not be exponentially distributed; rather, they may adopt any distribution (e.g., Erlangian, hyperexponential, variomorphic). We exemplify the impact of nonexponential time distributions on a performance measure of wide interest: the steady-state throughput. For its computation we use matrix-analytic methods. Specifically, we show that nonexponential interarrival times convert hyperbolic mass action systems (whose characteristic is a hyperbolic velocity-concentration or dose-response curve) into nonhyperbolic mass service systems and that the type and extent of their nonhyperbolicity are determined by the type and parameters of the interarrival time distribution. Furthermore, we analyze the combined effect of a non-Poissonian arrival process and a waiting site near the catalyst’s active site on the throughput of the system. A major conclusion of our and other studies is that it is a questionable practice to routinely and exclusively use mass action kinetics for the interpretation and performance evaluation of biocatalytic systems.

The chemical master equation in combination with chemical rate equations is used as a tool to study Markovian models of genetic regulatory networks in prokaryotes. States of the master equation represent the binding and unbinding of protein complexes to DNA, resulting in a gene being expressed or not expressed in a cell, while protein and substrate concentrations are represented by continuum variables which evolve via differential equations.

The model is applied to a moderately complex biological system, the switching mechanism of the Bacteriophage driven by competition between production of CI and Cro proteins. Numerical simulations of the model successfully move between lysogenic and lytic states as the host bacterium is stressed by the application of ultraviolet light.

The adaptation of the growth of to the availability of a carbon source is controlled by a complex genetic regulatory network whose functioning is still little understood. Using a qualitative method based on piecewise-linear differential equations, which is able to overcome the current lack of quantitative data on kinetic parameters and molecular concentrations, we model the carbon starvation response network and simulate the response of cells to carbon deprivation. This allows us to identify essential features of the transition between exponential and stationary phase and to make new predictions on the qualitative system behavior, following a carbon upshift.

Gene expression microarrays have become a popular high-throughput technique in functional genomics. By enabling the monitoring of thousands of genes simultaneously, this technique holds enormous potential to extend our understanding of various biological processes. However, the large amount of data poses a challenge when interpreting the results. Moreover, microarray data often contain frequent missing values, which may drastically affect the performance of different data analysis methods. Therefore, it is essential to effectively exploit additional biological information when analyzing and interpreting the data. In the present study, we investigate the relationship between gene expression profile and promoter sequence profile in the context of missing value imputation. In particular, we demonstrate that the selection of predictive genes for expression value estimation can be considerably improved by the incorporation of transcription factor binding information.

The theory of chemical organizations is employed as a novel method to analyze and understand biological network models. The method allows us to decompose a chemical reaction network into sub-networks that are (algebraically) closed and self-maintaining. Such sub-networks are termed . Although only stoichiometry is considered to compute organizations, the analysis allows us to narrow down the potential dynamic behavior of the network: organizations represent potential steady state compositions of the system. When applied to a model of sugar metabolism in including gene expression, signal transduction, and enzymatic activities, some organizations are found to coincide with inducible biochemical pathways.