Seventh Workshop on Quantitative Aspects of Programming Languages (QAPL 2009)March 28-29, 2009York, UK |
Language design | Information systems | Asynchronous HW analysis |
Language extension | Multi-tasking systems | Automated reasoning |
Language expressiveness | Logic | Verification |
Quantum languages | Semantics | Testing |
Time-critical systems | Performance analysis | Safety |
Embedded systems | Program analysis | Risk and hazard analysis |
Coordination models | Protocol analysis | Scheduling theory |
Distributed systems | Model-checking | Security |
Biological systems | Concurrent systems |
The rule-based approach allows for descriptions of protein-protein interaction networks despite their combinatorial complexity. Using such descriptions we propose an abstract interpretation framework to extract reduced differential systems. We show that our approach is sound in that the solutions of the reduced system are projections of solutions of the concrete system. Importantly, the concrete system is never explicitly computed. We also Ūnd that the approach yields a spectacular compression on a realistic example.
Stochastic process algebras have been successfully applied to quantitative evaluation of systems for over a decade. For example, in the context of performance analysis, PEPA has been used to describe both software and hardware systems and has helped to incorporate early performance prediction into the design process.
By modelling systems as collections of individual agents, the process algebra approach allows the modeller to capture the exact form of interactions and constraints between entities. However, there are situations where, although we model the behaviour of individuals, we aim to analyse the behaviour of the populations to which they belong. These systems are characterised by their collective dynamics. Examples range from representing the biochemical signalling that underlies many cellular processes to studying the scalability of software systems under increasing numbers of clients.
In this talk I will discuss recent work on such population-oriented models described in stochastic process algebra. The semantics of individual-oriented stochastic process algebra models generally give rise to discrete state space, continuous time Markov chains. In the context of collective dyanamics, an alternative mathematical framework based on sets of ordinary differential equations can be more appropriate. I will discuss the relationship between these alternative forms of representation and show that the new approach becomes feasible in many cases when the previous discrete approach becomes impossible due to problems of state space explosion.
Submission (abstract): | December 18, 2008 |
Submission (full paper): | December 26, 2008 |
Notification: | January 28, 2009 |
Final version (ETAPS proceedings): | February 2, 2009 |
Final version (ENTCS proceedings): | TBA |
Submission: | January 28, 2009. |
Notification: | January 31, 2009. |