27,780 research outputs found
Flux Analysis in Process Models via Causality
We present an approach for flux analysis in process algebra models of
biological systems. We perceive flux as the flow of resources in stochastic
simulations. We resort to an established correspondence between event
structures, a broadly recognised model of concurrency, and state transitions of
process models, seen as Petri nets. We show that we can this way extract the
causal resource dependencies in simulations between individual state
transitions as partial orders of events. We propose transformations on the
partial orders that provide means for further analysis, and introduce a
software tool, which implements these ideas. By means of an example of a
published model of the Rho GTP-binding proteins, we argue that this approach
can provide the substitute for flux analysis techniques on ordinary
differential equation models within the stochastic setting of process algebras
Quantifying the implicit process flow abstraction in SBGN-PD diagrams with Bio-PEPA
For a long time biologists have used visual representations of biochemical
networks to gain a quick overview of important structural properties. Recently
SBGN, the Systems Biology Graphical Notation, has been developed to standardise
the way in which such graphical maps are drawn in order to facilitate the
exchange of information. Its qualitative Process Diagrams (SBGN-PD) are based
on an implicit Process Flow Abstraction (PFA) that can also be used to
construct quantitative representations, which can be used for automated
analyses of the system. Here we explicitly describe the PFA that underpins
SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to
capture the quantitative details of a biochemical reaction network. We
implemented SBGNtext2BioPEPA, a tool that demonstrates how such quantitative
details can be used to automatically generate working Bio-PEPA code from a
textual representation of SBGN-PD that we developed. Bio-PEPA is a process
algebra that was designed for implementing quantitative models of concurrent
biochemical reaction systems. We use this approach to compute the expected
delay between input and output using deterministic and stochastic simulations
of the MAPK signal transduction cascade. The scheme developed here is general
and can be easily adapted to other output formalisms
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
Probabilistic and fuzzy reasoning in simple learning classifier systems
This paper is concerned with the general stimulus-response problem as addressed by a variety of simple learning c1assifier systems (CSs). We suggest a theoretical model from which the assessment of uncertainty emerges as primary concern. A number of representation schemes borrowing from fuzzy logic theory are reviewed, and sorne connections with a well-known neural architecture revisited. In pursuit of the uncertainty measuring goal, usage of explicit probability distributions in the action part of c1assifiers is advocated. Sorne ideas supporting the design of a hybrid system incorpo'rating bayesian learning on top of the CS basic algorithm are sketched
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