Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models

The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology

Large Loops of Magnetic Current and Confinement in Four Dimensional U(1)U(1) Lattice Gauge Theory

We calculate the heavy quark potential from the magnetic current due to monopoles in four dimensional $U(1)$ lattice gauge theory. The magnetic current is found from link angle configurations using the DeGrand-Toussaint identification method. The link angle configurations are generated in a cosine action simulation on a $24^4$ lattice. The magnetic current is resolved into large loops which wrap around the lattice and simple loops which do not. Wrapping loops are found only in the confined phase. It is shown that the long range part of the heavy quark potential, in particular the string tension, can be calculated solely from the large, wrapping loops of magnetic current.Comment: 15 pages (Latex file plus 3 postscript files appended), Univeristy of Illinois Preprint ILL-(TH)-93-\#1

Numerical simulation of flow in a high head Francis turbine with prediction of efficiency, rotor stator interaction and vortex structures in the draft tube

The paper presents numerical simulations of flow in a model of a high head Francis turbine and comparison of results to the measurements. Numerical simulations were done by two CFD (Computational Fluid Dynamics) codes, Ansys CFX and OpenFOAM. Steady-state simulations were performed by k-epsilon and SST model, while for transient simulations the SAS SST ZLES model was used. With proper grid refinement in distributor and runner and with taking into account losses in labyrinth seals very accurate prediction of torque on the shaft, head and efficiency was obtained. Calculated axial and circumferential velocity components on two planes in the draft tube matched well with experimental results

A lattice study of the strangeness content of the nucleon

We determine the quark contributions to the nucleon spin Delta s, Delta u and Delta d as well as their contributions to the nucleon mass, the sigma-terms. This is done by computing both, the quark line connected and disconnected contributions to the respective matrix elements, using the non-perturbatively improved Sheikholeslami-Wohlert Wilson Fermionic action. We simulate n_F=2 mass degenerate sea quarks with a pion mass of about 285 MeV and a lattice spacing a = 0.073 fm. The renormalization of the matrix elements involves mixing between contributions from different quark flavours. The pion-nucleon sigma-term is extrapolated to physical quark masses exploiting the sea quark mass dependence of the nucleon mass. We obtain the renormalized value sigma_{piN}=38(12) MeV at the physical point and the strangeness fraction f_{Ts}=sigma_s/m_N=0.012(14)(+10-3) at our larger than physical sea quark mass. For the strangeness contribution to the nucleon spin we obtain in the MSbar scheme at the renormalization scale of 2.71 GeV Delta s = -0.020(10)(2).Comment: 7 pages, 3 figures, Invited Talk at the 33rd Erice School on Nuclear Physics, Erice, 16-24 September 2011, Ital

Identification of a novel a-L-arabinofuranosidase gene associated with mealiness in apple.

In order to investigate the genetic bases of the physiological syndrome mealiness that causes abnormal fruit softening and juice loss in apples, an integrative approach was devised, consisting of sensory, instrumental, biochemical, genetic, and genomic methods. High levels of activity of a-L-arabinofuranosidase (a-AFase), a hydrolase acting on the pectic component of the cell walls, were found in individuals exhibiting the mealiness phenotype in a segregating population. The expression levels of the previously uncharacterized apple AF gene MdAF3 are higher in fruits from plants consistently showing mealiness symptons and high a-AFase activity. The transcription of MdAF3 is differentially regulated in distinct genomic contexts and appears to be independent of ethylene. Thus, it is likely to be controlled by endogenous developmental mechanisms associated with fruit ripening. The use of integrative approaches has allowed the identification of a novel contributor to the mealiness phenotype in apple and it has been possible to overcome the problems posed by the unavailability of near-isogenic lines to dissect the genetic bases of a complex physiological trait in woody perennial species

Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks

Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be learned. However, we show that the widely applied BGe score is susceptible to learning spurious feedback loops, which are a consequence of non-linear regulation and autocorrelation in the data. We propose a non-linear generalisation of the BGe model, based on a mixture model, and demonstrate that this approach successfully represses spurious feedback loops