Modelling Biological Compartments in Bio-PEPA

Ciocchetta, Federica; Guerriero, Maria Luisa

doi:10.1016/j.entcs.2008.12.105

Cited by 29 publications

(22 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have studied it using a selection of the analysis techniques supported by the language and implemented in the Bio-PEPA Workbench [43]. With our model we were able to describe in detail important features of the system, such as compartments and the activation of NF-κB by an external stimulus, using a recent extension of the Bio-PEPA language which incorporates explicit representation of locations [32] and time-dependent events [31]. A main feature of Bio-PEPA is that it is a intermediate formal representation of biochemical systems, on which various kinds of analysis can be performed.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling and Analysis of the NF-κB Pathway in Bio-PEPA

Ciocchetta

Degasperi

Heath

et al. 2010

Transactions on Computational Systems Biology XII

Self Cite

View full text Add to dashboard Cite

Abstract. In this work we present a Bio-PEPA model describing the Nuclear Factor κB (NF-κB) signalling pathway. In particular our model focuses on the dynamic response of NF-κB to an external stimulus. Each biochemical species in the pathway is represented by a specific Bio-PEPA component and the external stimulus is abstracted by time-dependent Bio-PEPA events describing the start and the end of the signal. The Bio-PEPA model is a formal intermediate representation of the pathway on which various kinds of analysis can be performed. Both stochastic and deterministic simulations are carried out to validate our model against the experimental data and in-silico experiments in the literature and to verify some properties, such as, the impact of the duration of the external stimulus and of the total NF-κB initial amount on the behaviour of some species of interest. Furthermore we use stochastic simulation to compare the behaviour of the single cell against the average behaviour of a population of cells. Finally, sensitivity analysis is considered to investigate the most influential parameters of the model. Importantly, the approach taken suggests that the sensitivity of some parameters alters with the time evolution of the pathway.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Recently Bio-PEPA has been extended to incorporate events [31] and to support biological locations [32], two features which will be useful in developing our model of the NF -κB pathway.…”

Section: Bio-pepamentioning

confidence: 99%

Modelling and Analysis of the NF-κB Pathway in Bio-PEPA

Ciocchetta

Degasperi

Heath

et al. 2010

Transactions on Computational Systems Biology XII

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this section we briefly describe Bio-PEPA [7,8,6], a language that has recently been developed for the modelling and analysis of biochemical systems. The main components of a Bio-PEPA system are the "species" components, describing the behaviour of individual entities, and the model component, describing the interactions between the various species.…”

Section: Bio-pepa and Fluid Flow Analysismentioning

confidence: 99%

Modelling Non-linear Crowd Dynamics in Bio-PEPA

Massink

Latella

Bracciali

et al. 2011

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

Abstract. Emergent phenomena occur due to the pattern of non-linear and distributed local interactions between the elements of a system over time. Surprisingly, agent based crowd models, in which the movement of each individual follows a limited set of simple rules, often re-produce quite closely the emergent behaviour of crowds that can be observed in reality. An example of such phenomena is the spontaneous self-organisation of drinking parties in the squares of cities in Spain, also known as "El Botellón" [20]. We revisit this case study providing an elegant stochastic process algebraic model in Bio-PEPA amenable to several forms of analyses, among which simulation and fluid flow analysis. We show that a fluid flow approximation, i.e. a deterministic reading of the average behaviour of the system, can provide an alternative and efficient way to study the same emergent behaviour as that explored in [20] where simulation was used instead. Besides empirical evidence, also an analytical justification is provided for the good correspondence found between simulation results and the fluid flow approximation.

show abstract

“…As defined by [24,21] Where, S: describe the species (different types of individuals); P: the model describing the system and interaction between species. The term (α, k) op S, express that the action α is described by k rate and performed by the species S, "op" define the role of S. Op={ ↓: reactant, ↑: product, ⊕ : activator, ⊖ : inhibitor, ⊙ : generic modifier}.…”

Section: Bio-pepa Syntaxmentioning

confidence: 99%

From Simulated Model By Bio-Pepa to Narrative Language Through SBML

Hamami¹,

Atmani²

2014

IJCTCM

View full text Add to dashboard Cite

Many theoretical works and tools on epidemiological field reflect the emphasis on decision-making tools by both public health and the scientific community, which continues to increase.Indeed, in the epidemiological field, modeling tools are proving a very important way in helping to make decision. However, the variety, the large volume of data and the nature of epidemics lead us to seek solutions to alleviate the heavy burden imposed on both experts and developers. Among the important steps of modeling and simulation: model validation. It refers to the process of determining how well a model corresponds to the system that it intended to represent. So the question is: what happens if the model is invalid? Do we need to reproduce another one, or just optimize the existing one?In this paper, we present a new approach consisting on the passage of an epidemic model realized in Bio-PEPA to a narrative language using the basics of SBML language. Our goal is to allow on one hand, epidemiologists to verify and validate the model, and the other hand, developers to optimize the model in order to achieve a better model of decision making. We also present some preliminary results and some suggestions to improve the simulated model.

show abstract

Modelling Biological Compartments in Bio-PEPA

Cited by 29 publications

References 5 publications

Modelling and Analysis of the NF-κB Pathway in Bio-PEPA

Modelling and Analysis of the NF-κB Pathway in Bio-PEPA

Modelling Non-linear Crowd Dynamics in Bio-PEPA

From Simulated Model By Bio-Pepa to Narrative Language Through SBML

Contact Info

Product

Resources

About