Edge usage, motifs and regulatory logic for cell cycling genetic networks

The cell cycle is a tightly controlled process, yet its underlying genetic network shows marked differences across species. Which of the associated structural features follow solely from the ability to impose the appropriate gene expression patterns? We tackle this question in silico by examining the ensemble of all regulatory networks which satisfy the constraint of producing a given sequence of gene expressions. We focus on three cell cycle profiles coming from baker's yeast, fission yeast and mammals. First, we show that the networks in each of the ensembles use just a few interactions that are repeatedly reused as building blocks. Second, we find an enrichment in network motifs that is similar in the two yeast cell cycle systems investigated. These motifs do not have autonomous functions, but nevertheless they reveal a regulatory logic for cell cycling based on a feed-forward cascade of activating interactions.Comment: 9 pages, 9 figures, to be published in Phys. Rev.

Network of inherent structures in spin glasses: scaling and scale-free distributions

The local minima (inherent structures) of a system and their associated transition links give rise to a network. Here we consider the topological and distance properties of such a network in the context of spin glasses. We use steepest descent dynamics, determining for each disorder sample the transition links appearing within a given barrier height. We find that differences between linked inherent structures are typically associated with local clusters of spins; we interpret this within a framework based on droplets in which the characteristic ``length scale'' grows with the barrier height. We also consider the network connectivity and the degrees of its nodes. Interestingly, for spin glasses based on random graphs, the degree distribution of the network of inherent structures exhibits a non-trivial scale-free tail.Comment: minor changes and references adde

Network Transitivity and Matrix Models

This paper is a step towards a systematic theory of the transitivity (clustering) phenomenon in random networks. A static framework is used, with adjacency matrix playing the role of the dynamical variable. Hence, our model is a matrix model, where matrices are random, but their elements take values 0 and 1 only. Confusion present in some papers where earlier attempts to incorporate transitivity in a similar framework have been made is hopefully dissipated. Inspired by more conventional matrix models, new analytic techniques to develop a static model with non-trivial clustering are introduced. Computer simulations complete the analytic discussion.Comment: 11 pages, 7 eps figures, 2-column revtex format, print bug correcte

Grand-Canonical Ensemble of Random Surfaces with Four Species of Ising Spins

The grand-canonical ensemble of dynamically triangulated surfaces coupled to four species of Ising spins (c=2) is simulated on a computer. The effective string susceptibility exponent for lattices with up to 1000 vertices is found to be $\gamma = - 0.195(58)$. A specific scenario for $c > 1$ models is conjectured.Comment: LaTeX, 11 pages + 1 postscript figure appended, preprint LPTHE-Orsay 94/1

Generation-free Agent-based Evolutionary Computing

AbstractMetaheuristics resulting from the hybridization of multi-agent systems with evolutionary computing are efficient in many optimization problems. Evolutionary multi-agent systems (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. Evolutionary operations are performed continuously and no artificial generations need to be distinguished. Our results show that such asynchronous evolutionary operators and the resulting absence of explicit generations lead to significantly better results. An efficient implementation of this algorithm was possible through the use of Erlang technology, which natively supports lightweight processes and asynchronous communication

Energy consumption and capacity utilization of galvanizing furnaces

An explicit equation leading to a method for improving furnace efficiency is presented. This equation is dimensionless and can be applied to furnaces of any size and fuel type for the purposes of comparison. The implications for current furnace design are discussed. Currently the technique most commonly used to reduce energy consumption in galvanizing furnaces is to increase burner turndown. This is shown by the analysis presented here actually to worsen the thermal efficiency of the furnace, particularly at low levels of capacity utilization. Galvanizing furnaces are different to many furnaces used within industry, as a quantity of material (in this case zinc) is kept molten within the furnace at all times, even outside production periods. The dimensionless analysis can, however, be applied to furnaces with the same operational function as a galvanizing furnace, such as some furnaces utilized within the glass industry. © IMechE 2004

Statistical ensemble of scale-free random graphs

A thorough discussion of the statistical ensemble of scale-free connected random tree graphs is presented. Methods borrowed from field theory are used to define the ensemble and to study analytically its properties. The ensemble is characterized by two global parameters, the fractal and the spectral dimensions, which are explicitly calculated. It is discussed in detail how the geometry of the graphs varies when the weights of the nodes are modified. The stability of the scale-free regime is also considered: when it breaks down, either a scale is spontaneously generated or else, a "singular" node appears and the graphs become crumpled. A new computer algorithm to generate these random graphs is proposed. Possible generalizations are also discussed. In particular, more general ensembles are defined along the same lines and the computer algorithm is extended to arbitrary (degenerate) scale-free random graphs.Comment: 10 pages, 6 eps figures, 2-column revtex format, minor correction