Computational fluid dynamics simulation to evaluate aortic coarctation gradient with contrast-enhanced CT

Coarctation of aorta (CoA) is a narrowing of the aorta leading to a pressure gradient (ΔP) across the coarctation, increased afterload and reduced peripheral perfusion pressures. Indication to invasive treatment is based on values of maximal (systolic) trans-coarctation ΔP. A computational fluid dynamic (CFD) approach is herein presented for the non-invasive haemodynamic assessment of ΔP across CoA. Patient-specific CFD simulations were created from contrast-enhanced computed tomography (CT) and appropriate flow boundary conditions. Computed ΔP was validated with invasive intravascular trans-CoA pressure measurements. Haemodynamic indices, including pressure loss coefficient (PLc), time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI), were also quantified. CFD-estimated ΔP values were comparable to the invasive ones. Moreover, the aorta proximal to CoA was exposed to altered TAWSS and OSI suggesting hypertension. PLc was found as a further geometric marker of CoA severity. Finally, CFD-estimated ΔP confirmed a significant reduction after percutaneous balloon dilatation and stenting of the CoA in one patient (e.g. from ΔP～52 mmHg to ΔP～3 mmHg). The validation of the ΔP computations with catheterisation measurements suggests that CFD simulation, based on CT-derived anatomical data, is a useful tool to readily quantify CoA severity.     

Computational approaches to the topology, stability and dynamics of metabolic networks

Cellular metabolism is characterized by an intricate network of interactions between biochemical fluxes, metabolic compounds and regulatory interactions. To investigate and eventually understand the emergent global behavior arising from such networks of interaction is not possible by intuitive reasoning alone. This contribution seeks to describe recent computational approaches that aim to asses the topological and functional properties of metabolic networks. In particular, based on a recently proposed method, it is shown that it is possible to acquire a quantitative picture of the possible dynamics of metabolic systems, without assuming detailed knowledge of the underlying enzyme-kinetic rate equations and parameters. Rather, the method builds upon a statistical exploration of the comprehensive parameter space to evaluate the dynamic capabilities of a metabolic system, thus providing a first step towards the transition from topology to function of metabolic pathways. Utilizing this approach, the role of feedback mechanisms in the maintenance of stability is discussed using minimal models of cellular pathways.     

Reconstruction of noisy patterns by bistable gradient neural-like network

An attractor bistable gradient neural-like network (BGN) described in Chinarov and Menzinger (BioSystems 55 (2000) 137) is applied to restoration of unknown patterns, which have been highly corrupted by multiplicative and additive Gaussian white noise. This becomes possible due to competitive advantage of BGN that derives from it: nice generalization capabilities, existence of the unique attractor with the lowest energy that is worked out when several patterns are stored by the network, and fast guaranteed convergence to this attractor.     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Computational functions in biochemical reaction networks.

In prior work we demonstrated the implementation of logic gates, sequential computers (universal Turing machines), and parallel computers by means of the kinetics of chemical reaction mechanisms. In the present article we develop this subject further by first investigating the computational properties of several enzymatic (single and multiple) reaction mechanisms: we show their steady states are analogous to either Boolean or fuzzy logic gates. Nearly perfect digital function is obtained only in the regime in which the enzymes are saturated with their substrates. With these enzymatic gates, we construct combinational chemical networks that execute a given truth-table. The dynamic range of a network's output is strongly affected by "input/output matching" conditions among the internal gate elements. We find a simple mechanism, similar to the interconversion of fructose-6-phosphate between its two bisphosphate forms (fructose-1,6-bisphosphate and fructose-2,6-bisphosphate), that functions analogously to an AND gate. When the simple model is supplanted with one in which the enzyme rate laws are derived from experimental data, the steady state of the mechanism functions as an asymmetric fuzzy aggregation operator with properties akin to a fuzzy AND gate. The qualitative behavior of the mechanism does not change when situated within a large model of glycolysis/gluconeogenesis and the TCA cycle. The mechanism, in this case, switches the pathway's mode from glycolysis to gluconeogenesis in response to chemical signals of low blood glucose (cAMP) and abundant fuel for the TCA cycle (acetyl coenzyme A).     

Computational approaches for modeling regulatory cellular networks

Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.     

Computational models of signalling networks for non-linear control

Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.     

Prion diseases: dynamics of the infection and properties of the bistable transition

Prion diseases are thought to result from a pathogenic, conformational change in a cellular protein, the prion protein. The pathogenic isoform seems to convert the normal isoform in an autocatalytic process. In contrast to the conditions used for in vitro studies of enzyme kinetics, the concentration of the catalyst is not much lower than that of the substrate in the course of infection. This feature may endow the system with a time-hierarchy allowing the pathogenic isoform to relax very slowly in the course of infection. This may contribute to the long incubation periods observed in prion diseases. The dynamic process of prion propagation, including turnover of the cellular prion protein, displays bistable properties. Sporadic prion diseases may result from a change in one of the parameters associated with metabolism of the prion protein. The bistable transition observed in sporadic disease is reversible, whereas that observed in cases of exogenous contamination is irreversible. This model is consistent with the occurrence of rare, sporadic forms of prion diseases. It may also explain why only some individuals of a cohort develop a prion disease following transient food contamination.     

Population dynamics in migratory networks

Migratory animals are comprised of a complex series of interconnected breeding and nonbreeding populations. Because individuals in any given population can arrive from a variety of sites the previous season, predicting how different populations will respond to environmental change can be challenging. In this study, we develop a population model composed of a network of breeding and wintering sites to show how habitat loss affects patterns of connectivity and species abundance. When the costs of migration are evenly distributed, habitat loss at a single site can increase the degree of connectivity (mixing) within the entire network, which then acts to buffer global populations from declines. However, the degree to which populations are buffered depends on where habitat loss occurs within the network: a site that has the potential to receive individuals from multiple populations in the opposite season will lead to smaller declines than a site that is more isolated. In other cases when there are equal costs of migration to two or more sites in the opposite season, habitat loss can result in some populations becoming segregated (disconnected) from the rest of the network. The geographic structure of the network can have a significant influence on relative population sizes of sites in the same season and can also affect the overall degree of mixing in the network, even when sites are of equal intrinsic quality. When a migratory network is widely spaced and migration costs are high, an equivalent habitat loss will lead to a larger decline in global population size than will occur in a network where the overall costs of migration are low. Our model provides an important foundation to test predictions related to habitat loss in real-world migratory networks and demonstrates that migratory networks will likely produce different dynamics from traditional metapopulations. Our results provide strong evidence that estimating population connectivity is a prerequisite for successfully predicting changes in migratory populations.     

Seasonal plankton dynamics along a cross-shelf gradient

Much interest has recently been devoted to reconstructing the dynamic structure of ecological systems on the basis of time-series data. Using 10 years of monthly data on phyto- and zooplankton abundance from the Bay of Biscay (coastal to shelf-break sites), we demonstrate that the interaction between these two plankton components is approximately linear, whereas the effects of environmental factors (nutrients, temperature, upwelling and photoperiod) on these two plankton population growth rates are nonlinear. With the inclusion of the environmental factors, the main observed seasonal and inter-annual dynamic patterns within the studied plankton assemblage also indicate the prevalence of bottom-up regulatory control.     

Calcium imaging of cortical networks dynamics

Studies relating spontaneous network activities to cognitive processes and/or brain disorders constitute a recently expanding field of investigation. They are mostly based either on cellular recordings--usually performed in pharmacologically induced oscillations in brain slices--or on multi-cellular recordings using tetrodes or multiple electrodes. However, these research strategies cannot link the electrical recordings with morphological characterization of the neurons. The progress made in imaging techniques allows for the first time to have simultaneously a dynamic and global characterization of network activity and to determine the single-cell properties of the unitary microcircuits involved in this activity.