7,545 research outputs found
Quantitative evaluation of Pandora Temporal Fault Trees via Petri Nets
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Using classical combinatorial fault trees, analysts are able to assess the effects of combinations of failures on system behaviour but are unable to capture sequence dependent dynamic behaviour. Pandora introduces temporal gates and temporal laws to fault trees to allow sequence-dependent dynamic analysis of events. Pandora can be easily integrated in model-based design and analysis techniques; however, the combinatorial quantification techniques used to solve classical fault trees cannot be applied to temporal fault trees. Temporal fault trees capture state and therefore require a state space solution for quantification of probability. In this paper, we identify Petri Nets as a possible framework for quantifying temporal trees. We describe how Pandora fault trees can be mapped to Petri Nets for dynamic dependability analysis and demonstrate the process on a fault tolerant fuel distribution system model
Construction and analysis of causally dynamic hybrid bond graphs
Engineering systems are frequently abstracted to models with discontinuous behaviour (such as a switch or contact),
and a hybrid model is one which contains continuous and discontinuous behaviours. Bond graphs are an established
physical modelling method, but there are several methods for constructing switched or ‘hybrid’ bond graphs, developed
for either qualitative ‘structural’ analysis or efficient numerical simulation of engineering systems. This article proposes a
general hybrid bond graph suitable for both. The controlled junction is adopted as an intuitive way of modelling a discontinuity in the model structure. This element gives rise to ‘dynamic causality’ that is facilitated by a new bond graph notation. From this model, the junction structure and state equations are derived and compared to those obtained by
existing methods. The proposed model includes all possible modes of operation and can be represented by a single set
of equations. The controlled junctions manifest as Boolean variables in the matrices of coefficients. The method is more
compact and intuitive than existing methods and dispenses with the need to derive various modes of operation from a
given reference representation. Hence, a method has been developed, which can reach common usage and form a platform for further study
Mathematical modelling plant signalling networks
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting
A temporal logic approach to modular design of synthetic biological circuits
We present a new approach for the design of a synthetic biological circuit
whose behaviour is specified in terms of signal temporal logic (STL) formulae.
We first show how to characterise with STL formulae the input/output behaviour
of biological modules miming the classical logical gates (AND, NOT, OR). Hence,
we provide the regions of the parameter space for which these specifications
are satisfied. Given a STL specification of the target circuit to be designed
and the networks of its constituent components, we propose a methodology to
constrain the behaviour of each module, then identifying the subset of the
parameter space in which those constraints are satisfied, providing also a
measure of the robustness for the target circuit design. This approach, which
leverages recent results on the quantitative semantics of Signal Temporal
Logic, is illustrated by synthesising a biological implementation of an
half-adder
Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms
Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability
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
Computational core and fixed-point organisation in Boolean networks
In this paper, we analyse large random Boolean networks in terms of a
constraint satisfaction problem. We first develop an algorithmic scheme which
allows to prune simple logical cascades and under-determined variables,
returning thereby the computational core of the network. Second we apply the
cavity method to analyse number and organisation of fixed points. We find in
particular a phase transition between an easy and a complex regulatory phase,
the latter one being characterised by the existence of an exponential number of
macroscopically separated fixed-point clusters. The different techniques
developed are reinterpreted as algorithms for the analysis of single Boolean
networks, and they are applied to analysis and in silico experiments on the
gene-regulatory networks of baker's yeast (saccaromices cerevisiae) and the
segment-polarity genes of the fruit-fly drosophila melanogaster.Comment: 29 pages, 18 figures, version accepted for publication in JSTA
Computers from plants we never made. Speculations
We discuss possible designs and prototypes of computing systems that could be
based on morphological development of roots, interaction of roots, and analog
electrical computation with plants, and plant-derived electronic components. In
morphological plant processors data are represented by initial configuration of
roots and configurations of sources of attractants and repellents; results of
computation are represented by topology of the roots' network. Computation is
implemented by the roots following gradients of attractants and repellents, as
well as interacting with each other. Problems solvable by plant roots, in
principle, include shortest-path, minimum spanning tree, Voronoi diagram,
-shapes, convex subdivision of concave polygons. Electrical properties
of plants can be modified by loading the plants with functional nanoparticles
or coating parts of plants of conductive polymers. Thus, we are in position to
make living variable resistors, capacitors, operational amplifiers,
multipliers, potentiometers and fixed-function generators. The electrically
modified plants can implement summation, integration with respect to time,
inversion, multiplication, exponentiation, logarithm, division. Mathematical
and engineering problems to be solved can be represented in plant root networks
of resistive or reaction elements. Developments in plant-based computing
architectures will trigger emergence of a unique community of biologists,
electronic engineering and computer scientists working together to produce
living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing
inspired by physics, chemistry and biology. Essays presented to Julian Miller
on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew
Adamatzky (Springer, 2017
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