Analysis of signalling pathways using the prism model checker

We describe a new modelling and analysis approach for signal transduction networks in the presence of incomplete data. We illustrate the approach with an example, the RKIP inhibited ERK pathway [1]. Our models are based on high level descriptions of continuous time Markov chains: reactions are modelled as synchronous processes and concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis of queries such as if a concentration reaches a certain level, will it remain at that level thereafter? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

In silico clinical trials through AI and statistical model checking

A Virtual Patient (VP) is a computational model accounting for individualised (patho-) physiology and Pharmaco-Kinetics/Dynamics of relevant drugs. Availability of VPs is among the enabling technology for In Silico Clinical Trials. Here we shortly outline the state of the art as for VP generation and summarise our recent work on Artificial Intelligence (AI) and Statistical Model Checking based generation of VPs

Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Orton et al.BACKGROUND: The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway. RESULTS: We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors. CONCLUSION: Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.This work was funded by the Department of Trade and Industry (DTI), under their Bioscience Beacon project programme. AG was funded by an industrial PhD studentship from Scottish Enterprise and Cyclacel

A general computational method for robustness analysis with applications to synthetic gene networks

Motivation: Robustness is the capacity of a system to maintain a function in the face of perturbations. It is essential for the correct functioning of natural and engineered biological systems. Robustness is generally defined in an ad hoc, problem-dependent manner, thus hampering the fruitful development of a theory of biological robustness, recently advocated by Kitano

Formal Cellular Machinery

International audienceVarious calculi have been proposed to model diff erent levels of abstraction of cell signaling and molecular interactions. In this paper we propose a framework inspired by some of these calculi that structures interactions and agents from the most basic elements of the cell (protein interaction sites) to higher order ones (compartments and molecular species)

Apprentissage de règles de réactions biochimiques à partir de propriétés en logique temporelle

Avec le développement de langages formels pour modéliser les systèmes d' interactions biomoléculaires, la possibilité d'effectuer des calculs symboliques au delà des simulations numér iques ouvre la voie à la conception de nouveaux outils de raisonnement automatique destinés au biologiste modélisateur. La machine abstraite biochimique BIOCHAM est un environnement logiciel qui offre un langage simple de règles pour modéliser les interactions biomoléculaires et un langage original fondé sur la logique temporelle pour formaliser les propriétés biologiques du système. En s'appuyant sur ces deux langages formels, il devient possible d'utiliser des techniques d'apprentissage automatique pour inférer de nouvelles règles de réaction moléculaire à partir de propriétés temporelles observées. Dans ce contexte, le but est de corriger ou compléter les modèles BIOCHAM semi-automatiquement. Dans cet article, nous décrivons le système d'apprentissage automatique de BIOCHAM, qui permet, d'une part, de trouver de nouvelles règles d'interaction à partir d' un modèle partiel et de contraintes exprimées en logique temporelle, et d'autre part, d'estimer les valeurs de paramètres cinétiques à partir de propriétés formalisées en logique temporelle avec contraintes numériques sur les concentrations ou leurs dérivées