Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions

Differential-geometric approach to the integrability of hydrodynamic chains: the Haantjes tensor

The integrability of an m-component system of hydrodynamic type, u_t=V(u)u_x, by the generalized hodograph method requires the diagonalizability of the mxm matrix V(u). This condition is known to be equivalent to the vanishing of the corresponding Haantjes tensor. We generalize this approach to hydrodynamic chains -- infinite-component systems of hydrodynamic type for which the infinite matrix V(u) is `sufficiently sparse'. For such systems the Haantjes tensor is well-defined, and the calculation of its components involves finite summations only. We illustrate our approach by classifying broad classes of conservative and Hamiltonian hydrodynamic chains with the zero Haantjes tensor. We prove that the vanishing of the Haantjes tensor is a necessary condition for a hydrodynamic chain to possess an infinity of semi-Hamiltonian hydrodynamic reductions, thus providing an easy-to-verify necessary condition for the integrability.Comment: 36 pages, the classification results and proofs are refined. A section on generating functions is adde

Structural Analysis and Stochastic Modelling Suggest a Mechanism for Calmodulin Trapping by CaMKII

Activation of CaMKII by calmodulin and the subsequent maintenance of constitutive activity through autophosphorylation at threonine residue 286 (Thr286) are thought to play a major role in synaptic plasticity. One of the effects of autophosphorylation at Thr286 is to increase the apparent affinity of CaMKII for calmodulin, a phenomenon known as “calmodulin trapping”. It has previously been suggested that two binding sites for calmodulin exist on CaMKII, with high and low affinities, respectively. We built structural models of calmodulin bound to both of these sites. Molecular dynamics simulation showed that while binding of calmodulin to the supposed low-affinity binding site on CaMKII is compatible with closing (and hence, inactivation) of the kinase, and could even favour it, binding to the high-affinity site is not. Stochastic simulations of a biochemical model showed that the existence of two such binding sites, one of them accessible only in the active, open conformation, would be sufficient to explain calmodulin trapping by CaMKII. We can explain the effect of CaMKII autophosphorylation at Thr286 on calmodulin trapping: It stabilises the active state and therefore makes the high-affinity binding site accessible. Crucially, a model with only one binding site where calmodulin binding and CaMKII inactivation are strictly mutually exclusive cannot reproduce calmodulin trapping. One of the predictions of our study is that calmodulin binding in itself is not sufficient for CaMKII activation, although high-affinity binding of calmodulin is

Gait Analysis of Horses for Lameness Detection with Radar Sensors

This paper presents the preliminary investigation of the use of radar signatures to detect and assess lameness of horses and its severity. Radar sensors in this context can provide attractive contactless sensing capabilities, as a complementary or alternative technology to the current techniques for lameness assessment using video-graphics and inertial sensors attached to the horses' body. The paper presents several examples of experimental data collected at the Weipers Centre Equine Hospital at the University of Glasgow, showing the micro- Doppler signatures of horses and preliminary results of their analysis

A Single Geostationary Satellite for Mobile Terrestrial Transmitter Tracking

This paper will describe the Energetics Satellite Locating Service (ESLS) which is a unique, patented, proprietary satellite based geolocation system. This system called SAT/TRAC for Satellite Tracking, Ranging and Communications may be used to quickly determine the present location within 50 feet of any person, vehicle or object that is equipped with a ESLS low power transmitter. This technology represents a novel approach to radio tracking. The single point location system uses a single satellite with a 165 foot inflatable antenna

Majorisation with applications to the calculus of variations

This paper explores some connections between rank one convexity, multiplicative quasiconvexity and Schur convexity. Theorem 5.1 gives simple necessary and sufficient conditions for an isotropic objective function to be rank one convex on the set of matrices with positive determinant. Theorem 6.2 describes a class of possible non-polyconvex but multiplicative quasiconvex isotropic functions. This class is not contained in a well known theorem of Ball (6.3 in this paper) which gives sufficient conditions for an isotropic and objective function to be polyconvex. We show here that there is a new way to prove directly the quasiconvexity (in the multiplicative form). Relevance of Schur convexity for the description of rank one convex hulls is explained.Comment: 13 page

Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence

The inference of gene regulatory networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean networks (PBNs) from observed temporal sequences of network states. Since a PBN is composed of a finite number of Boolean networks, a basic observation is that the characteristics of a single Boolean network without perturbation may be determined by its pairwise transitions. Because the network function is fixed and there are no perturbations, a given state will always be followed by a unique state at the succeeding time point. Thus, a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the network also has perturbations, with small perturbation probability, then the transition counting matrix would have some insignificant nonzero entries replacing some (or all) of the zeros. If a data sequence is sufficiently long to adequately populate the matrix, then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean network. We address the PBN inference procedure in several steps: (1) separate the data sequence into "pure" subsequences corresponding to constituent Boolean networks; (2) given a subsequence, infer a Boolean network; and (3) infer the probabilities of perturbation, the probability of there being a switch between constituent Boolean networks, and the selection probabilities governing which network is to be selected given a switch. Capturing the full dynamic behavior of probabilistic Boolean networks, be they binary or multivalued, will require the use of temporal data, and a great deal of it. This should not be surprising given the complexity of the model and the number of parameters, both transitional and static, that must be estimated. In addition to providing an inference algorithm, this paper demonstrates that the data requirement is much smaller if one does not wish to infer the switching, perturbation, and selection probabilities, and that constituent-network connectivity can be discovered with decent accuracy for relatively small time-course sequences