2008
DOI: 10.1042/bse0450001
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Modelling the dynamics of signalling pathways

Abstract: In the present chapter we discuss methodologies for the modelling, calibration and validation of cellular signalling pathway dynamics. The discussion begins with the typical range of techniques for modelling that might be employed to go from the chemical kinetics to a mathematical model of biochemical pathways. In particular, we consider the decision-making processes involved in selecting the right mechanism and level of detail of representation of the biochemical interactions. These include the choice between… Show more

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Cited by 36 publications
(30 citation statements)
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“…But simulation at a gene regulatory network level is a challenge in itself. This is so since at this level a stochastic description of the reactions occurring within the cell must be done, since it is nonsense to speak of concentration at this level, where only few specimens of each component are present [9,92,93]. It is commonly accepted that under some weak hypothesis (well stirred mixture, fixed volume and temperature), the system can be considered as Markovian, and can be consequently modeled by the so-called Chemical Master Equation (CME), [94], which is in fact no more than an ordinary differential equations stating the conservation of the probability density function P in time:…”
Section: Adding Physiological Details: Third To Fifth Generations Of mentioning
confidence: 99%
“…But simulation at a gene regulatory network level is a challenge in itself. This is so since at this level a stochastic description of the reactions occurring within the cell must be done, since it is nonsense to speak of concentration at this level, where only few specimens of each component are present [9,92,93]. It is commonly accepted that under some weak hypothesis (well stirred mixture, fixed volume and temperature), the system can be considered as Markovian, and can be consequently modeled by the so-called Chemical Master Equation (CME), [94], which is in fact no more than an ordinary differential equations stating the conservation of the probability density function P in time:…”
Section: Adding Physiological Details: Third To Fifth Generations Of mentioning
confidence: 99%
“…In this situation, the continuum approach itself is questioned, as justified clearly in the excellent review by Turner et al [26] and references therein. Here, the concept of concentration of the species does not make sense [6,27]. On the contrary, under some weak hypothesis (well-stirred mixture, fixed volume, and temperature), the system can be considered as Markovian and can be consequently modeled by the so-called Chemical Master Equation (CME), [28], which is in fact no more than a set of ordinary differential equations stating the conservation of the probability density function P in time:…”
Section: Beating the Curse Of Dimensionalitymentioning
confidence: 99%
“…Such systems are more appropriately modeled using reaction diffusion equations, reviewed in Slepchenko et al ., 2002. A second assumption of the rate equation is that the concentration of each participating species is sufficiently “large” (Sreenath et al ., 2008). If this is not the case, then random fluctuations can no longer be ignored and the reaction velocity must be modeled by a propensity function, called the chemical master equation (Gillespie, 1992).…”
Section: Overview Of Algorithmmentioning
confidence: 99%