The activation of endothelium is important in recruiting neutrophils to sites of inflammation and in modulating their function. We demonstrate that conditioned medium from cultured, activated endothelial cells acts to significantly delay the constitutive apoptosis of neutrophils, resulting in their enhanced survival and increased phagocytic function. The antiapoptotic activity is, in part, attr...

Oil seeds are an important source of protein in many developing countries. However this protein is not readily available because of anti-nutrients, hence information on the content of anti-nutrients is required. The objective of the study was to determine the status of anti-nutritive factors in some new varieties of oil seeds, viz Sunflower ( Helianths annus ) LSF -11, Sunflower ( Helianths ann...

Probabilistic Boolean Networks (PBNs) provide a convenient tool for studying the interactions among different genes while allowing uncertainty. This paper deals with the issue of finite-horizon control with multiple hard-constraints in a PBN. More precisely, under the constraint of the number of times that each control method can be applied, we develop a control strategy by which the state of a...

In this paper we introduce the idea of probability in the definition of a Sequential Dynamical System (SDS), thus obtaining a new concept, that of Probabilistic Sequential System (PSS). Due to its particular dynamic, the Probabilistic Boolean Network (PBN) model has been applied to genetic regulatory networks. The model we introduce combines the sequential aspect of the SDSs and the dynamic of ...

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. Th...

Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dynamical systems. However, identification of large networks and of the underlying discrete Markov Chain which describes their temporal evolution, still remains a challenge. In this paper, we introduce an equivalent representation for the PBN, the Stochastic Conjunctive Normal Form (SCNF), wh...

In this paper we introduce the idea of probability in the definition of a Sequential Dynamical System (SDS), thus obtaining a new concept, that of Probabilistic Sequential System (PSS). Due to its particular dynamic, the Probabilistic Boolean Network (PBN) model has been applied to genetic regulatory networks. The model we introduce combines the sequential aspect of the SDSs and the dynamic of ...

Abstract Observability is a fundamental property of partially observed dynamical system, which means whether one can use an input sequence and the corresponding output to determine initial state. provides bases for many related problems, such as state estimation, identification, disturbance decoupling, controller synthesis, etc. Until now, improvement has been obtained in observability Boolean ...

Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when costly to collect and/or noisy, e.g., in case expression profile data. In this paper, we present a reproducible method inferring real measurements taken...