Simulated evolution of mass conserving reaction networks

With the rise of systems biology, the systematic analysis and construction of behavioral mechanisms in both natural and artificial biochemical networks has become a vital part of understanding and predicting the inner workings of intracellular signaling networks. As a modeling platform, artificial chemistries are commonly adopted to study and construct artificial reaction network motifs that exhibit complex computational behaviors. Here, we present a genetic algorithm to evolve networks that can compute elementary mathematical functions by transforming initial input molecules into the steady state concentrations of output molecules. Morespecifically, the proposed algorithm implicitly guarantees mass conservation through an atom based description of the molecules and reaction networks. We discuss the adopted approach for the artificial evolution of these chemical networks, evolve networks to compute the square root function. Finally,we provide an extensive deterministic and stochastic analysis of a core square root network motif present in these resulting networks, confirming that the motif is indeed capable of computing the square root function

Wind-Driven Gas Networks and Star Formation in Galaxies: Reaction-Advection Hydrodynamic Simulations

The effects of wind-driven star formation feedback on the spatio-temporal organization of stars and gas in galaxies is studied using two-dimensional intermediate-representational quasi-hydrodynamical simulations. The model retains only a reduced subset of the physics, including mass and momentum conservation, fully nonlinear fluid advection, inelastic macroscopic interactions, threshold star formation, and momentum forcing by winds from young star clusters on the surrounding gas. Expanding shells of swept-up gas evolve through the action of fluid advection to form a ``turbulent'' network of interacting shell fragments whose overall appearance is a web of filaments (in two dimensions). A new star cluster is formed whenever the column density through a filament exceeds a critical threshold based on the gravitational instability criterion for an expanding shell, which then generates a new expanding shell after some time delay. A filament- finding algorithm is developed to locate the potential sites of new star formation. The major result is the dominance of multiple interactions between advectively-distorted shells in controlling the gas and star morphology, gas velocity distribution and mass spectrum of high mass density peaks, and the global star formation history. The gas morphology observations of gas in the LMC and in local molecular clouds. The frequency distribution of present-to-past average global star formation rate, the distribution of gas velocities in filaments (found to be exponential), and the cloud mass spectra (estimated using a structure tree method), are discussed in detail.Comment: 40 pp, 15 eps figs, mnras style, accepted for publication in MNRAS, abstract abridged, revisions in response to referee's comment

Temporal evolution of mesoscopic structure of some non-Euclidean systems using a Monte Carlo model

A Monte Carlo based computer model is presented to comprehend the contrasting observations of Mazumder et al. [Phys. Rev. Lett. 93, 255704 (2004) and Phys. Rev. B 72, 224208 (2005)], based on neutron-scattering measurements, on temporal evolution of effective fractal dimension and characteristic length for hydration of cement with light and heavy water. In this context, a theoretical model is also proposed to elucidate the same.Comment: 31 Pages, 13 Figure

Automatic kinetic model generation and selection based on concentration versus time curves

The goal of the paper is to automatize the construction and parameterization of kinetic reaction mechanisms that can describe a set of experimentally measured concentration versus time curves. Using the framework and theorems of formal reaction kinetics, first, we build a set of possible mechanisms with a given number of measured and unmeasured (real or fictitious) species and reaction steps that fulfill some chemically reasonable requirements. Then we fit all the corresponding mass‐action kinetic models and offer the best one to the chemist to help explain the underlying chemical phenomenon or to use it for predictions. We demonstrate the use of the method via two simple examples: on an artificial, simulated set of data and on a small real‐life data set. The method can also be used to do a kind of lumping to generate a model that can reproduce the simulation results of a detailed mechanism with less species and thereby can largely accelerate spatially inhomogeneous simulations

A guided tour of asynchronous cellular automata

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We survey the recent research that has been carried out on this topic and present a wide state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat

Overcoming the data crisis in biodiversity conservation

How can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gap leads to uncertainty about the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species’ status with unparalleled quantitative rigor