A robust scheme for free surface and pressurized flows in channels with arbitrary cross-sections

Flows in closed channels, such as rain storm sewers, often contain transitions from free surface flows to pressurized flows, or viceversa. These phenomena usually require two different sets of equations to model the two different flow regimes. Actually, a few specifications for the geometry of the channel and for the discretization choices can be sufficient to model closed channel flows using only the open channel flow equations. Transitions can also occur in open channels, like those from super- to subcritical flow, or vice versa. These particular flows are usually difficult to reproduce numerically and strong restrictions are imposed on the numerical scheme to simulate them. In this paper, an implicit finite-difference conservative algorithm is proposed to deal properly with these problems. In addition, a special flux limiter is described and implemented to allow accurate flow simulations near hydraulic structures such as weirs. A few computational examples are given to illustrate the properties of the scheme and the numerical solutions are compared with experimental data, when possible

Signaling cascades as cellular devices for spatial computations

Signaling networks usually include protein-modification cycles. Cascades of such cycles are the backbones of multiple signaling pathways. Protein gradients emerge from the spatial separation of opposing enzymes, such as kinases and phosphatases, or guanine nucleotide exchange factors (GEFs) and GTPase activating proteins (GAPs) for GTPase cycles. We show that different diffusivities of an active protein form and an inactive form leads to spatial gradients of protein abundance in the cytoplasm. For a cascade of cycles, using a discrete approximation of the space, we derive an analytical expression for the spatial gradients and show that it converges to an exact solution with decreasing the size of the quantization. Our results facilitate quantitative analysis of the dependence of spatial gradients on the network topology and reaction kinetics. We demonstrate how different cascade designs filter and process the input information to generate precise, complex spatial guidance for multiple GTPase effector processes. Thus, protein-modification cascades may serve as devices to compute complex spatial distributions of target proteins within intracellular spac

Computational design of synthetic gene circuits with composable parts

Motivation: In principle, novel genetic circuits can be engineered using standard parts with well-understood functionalities. However, no model based on the simple composition of these parts has become a standard, mainly because it is difficult to define signal exchanges between biological units as unambiguously as in electrical engineering. Corresponding concepts and computational tools for easy circuit design in biology are missing. Results: Taking inspiration from (and slightly modifying) ideas in the ‘MIT Registry of Standard Biological Parts', we developed a method for the design of genetic circuits with composable parts. Gene expression requires four kinds of signal carriers: RNA polymerases, ribosomes, transcription factors and environmental ‘messages' (inducers or corepressors). The flux of each of these types of molecules is a quantifiable biological signal exchanged between parts. Here, each part is modeled independently by the ordinary differential equations (ODE) formalism and integrated into the software ProMoT (Process Modeling Tool). In this way, we realized a ‘drag and drop' tool, where genetic circuits are built just by placing biological parts on a canvas and by connecting them through ‘wires' that enable flow of signal carriers, as it happens in electrical engineering. Our simulations of well-known synthetic circuits agree well with published computational and experimental results. Availability: The code is available on request from the authors. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Large-scale computation of elementary flux modes with bit pattern trees

Motivation: Elementary flux modes (EFMs)—non-decomposable minimal pathways—are commonly accepted tools for metabolic network analysis under steady state conditions. Valid states of the network are linear superpositions of elementary modes shaping a polyhedral cone (the flux cone), which is a well-studied convex set in computational geometry. Computing EFMs is thus basically equivalent to extreme ray enumeration of polyhedral cones. This is a combinatorial problem with poorly scaling algorithms, preventing the large-scale analysis of metabolic networks so far. Results: Here, we introduce new algorithmic concepts that enable large-scale computation of EFMs. Distinguishing extreme rays from normal (composite) vectors is one critical aspect of the algorithm. We present a new recursive enumeration strategy with bit pattern trees for adjacent rays—the ancestors of extreme rays—that is roughly one order of magnitude faster than previous methods. Additionally, we introduce a rank updating method that is particularly well suited for parallel computation and a residue arithmetic method for matrix rank computations, which circumvents potential numerical instability problems. Multi-core architectures of modern CPUs can be exploited for further performance improvements. The methods are applied to a central metabolism network of Escherichia coli, resulting in ≈26 Mio. EFMs. Within the top 2% modes considering biomass production, most of the gain in flux variability is achieved. In addition, we compute ≈5 Mio. EFMs for the production of non-essential amino acids for a genome-scale metabolic network of Helicobacter pylori. Only large-scale EFM analysis reveals the >85% of modes that generate several amino acids simultaneously. Availability: An implementation in Java, with integration into MATLAB and support of various input formats, including SBML, is available at http://www.csb.ethz.ch in the tools section; sources are available from the authors upon request. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Scaling theory of transport in complex networks

Transport is an important function in many network systems and understanding its behavior on biological, social, and technological networks is crucial for a wide range of applications. However, it is a property that is not well-understood in these systems and this is probably due to the lack of a general theoretical framework. Here, based on the finding that renormalization can be applied to bio-networks, we develop a scaling theory of transport in self-similar networks. We demonstrate the networks invariance under length scale renormalization and we show that the problem of transport can be characterized in terms of a set of critical exponents. The scaling theory allows us to determine the influence of the modular structure on transport. We also generalize our theory by presenting and verifying scaling arguments for the dependence of transport on microscopic features, such as the degree of the nodes and the distance between them. Using transport concepts such as diffusion and resistance we exploit this invariance and we are able to explain, based on the topology of the network, recent experimental results on the broad flow distribution in metabolic networks.Comment: 8 pages, 6 figure

Quantitative performance metrics for robustness in circadian rhythms

Motivation: Sensitivity analysis provides key measures that aid in unraveling the design principles responsible for the robust performance of biological networks. Such metrics allow researchers to investigate comprehensively model performance, to develop more realistic models, and to design informative experiments. However, sensitivity analysis of oscillatory systems focuses on period and amplitude characteristics, while biologically relevant effects on phase are neglected. Results: Here, we introduce a novel set of phase-based sensitivity metrics for performance: period, phase, corrected phase and relative phase. Both state- and phase-based tools are applied to free-running Drosophila melanogaster and Mus musculus circadian models. Each metric produces unique sensitivity values used to rank parameters from least to most sensitive. Similarities among the resulting rank distributions strongly suggest a conservation of sensitivity with respect to parameter function and type. A consistent result, for instance, is that model performance of biological oscillators is more sensitive to global parameters than local (i.e. circadian specific) parameters. Discrepancies among these distributions highlight the individual metrics' definition of performance as specific parametric sensitivity values depend on the defined metric, or output. Availability: An implementation of the algorithm in MATLAB (Mathworks, Inc.) is available from the authors. Contact: [email protected] Supplementary information: Supplementary Data are available at Bioinformatics onlin

El modelado matemático en el estudio de mecanismos de señalización celular: apoptosis inducida por interferón de tipo I

Comunicaciones a congreso

Eruption and deposition of the fisher tuff (Alaska) : evidence for the evolution of pyroclastic flows.

International audienceRecognition that the Fisher Tuff (Unimak Island, Alaska) was deposited on the leeside of ~500-700 m high mountain range (Tugamak Range) more than 10 km away from its source played a major role in defining pyroclastic flows as momentum-driven currents. We re-examined the Fisher Tuff to evaluate whether deposition from expanded turbulent clouds can better explain its depositional features. We studied the tuff at 89 sites, and sieved bulk samples from 27 of those sites. We find that the tuff consists of a complex sequence of deposits that record the evolution of the eruption from a buoyant plume (22 km) that deposited ~0.2 km3 of dacite magma as a pyroclastic fall layer to erupting ~10-100 km3 of andesitic magma as scoria-rich pyroclastic falls and flows that were mainly deposited to the north and northwest of the caldera, including those in valleys within the Tugamak Range. The distribution of the flow deposits and their welding, internal stratification, and occurrence of lithic breccia, all suggest that the pyroclastic flows were fed from a fountaining column that vented from an inclined conduit, the first time such a conduit has been recognized during a large volume caldera eruption. Pyroclastic flow deposits before and after the mountain range, and thin veneer deposits high in the Range, are best explained by a flow that was stratified into a dense undercurrent and an over-riding dilute turbulent cloud, from which deposition before the range was mainly from the undercurrent. When the flow ran into the mountain range, however, the undercurrent was blocked, but the turbulent cloud continued on. As the flow continued north, it re-stratified, forming another undercurrent. The Fisher Tuff thus records the passing of a flow that was significantly higher (800-1100 m thick) than the mountain range, and thus did not require excessive momentum

Structural Kinetic Modeling of Metabolic Networks

To develop and investigate detailed mathematical models of cellular metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, explicit kinetic modeling based on differential equations is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a detailed and quantitative account of the dynamical capabilities of metabolic systems, without requiring any explicit information about the particular functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible, or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a systematic statistical exploration of the comprehensive parameter space. The method is applied to two paradigmatic examples: The glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.Comment: 14 pages, 8 figures (color