Quantitative Verification Techniques for Biological Processes

Abstract

Summary. Probabilistic model checking is a formal verification framework for systems which exhibit stochastic behaviour. It has been successfully applied to a wide range of domains, including security and communication protocols, distributed algorithms and power management. In this chapter we demonstrate its applicability to the analysis of biological pathways and show how it can yield a better understanding of the dynamics of these systems. Through a case study of the MAP (Mitogen-Activated Protein) Kinase cascade, we explain how biological pathways can be modelled in the probabilistic model checker PRISM and how this enables the analysis of a rich selection of quantitative properties.

Towards Communication-Based Steering of Complex Distributed Systems

“…it is given as a function σ : Q * → D(Σ). For any such adversary and any temporal property ϕ defining a measurable set ϕ ⊆ Q ω of paths, we get a probability p σ A (ϕ) of the behaviour of A satisfying ϕ in standard fashion [19]. The supremum sup σ p σ A (ϕ) and infimum inf σ p σ A (ϕ) are denoted by p max A (ϕ) and p min A (ϕ), respectively.…”

“…energy usage. Quantitative verification [19] is a technique which combines formal verification with numerical computation, and is able to automatically answer the questions such as "what is the maximum probability of reaching an error state? ", and "what is the expected energy usage in the start up phase?".…”

General-Purpose Autonomic Computing

“…This choice was based on an extensive performance analysis of a range of model checkers [18] that ranked PRISM as the best option for analysing large behavioural models such as the ones encountered in autonomic computing systems. Furthermore, PRISM comes with a command-line interface that made possible its direct integration into the existing version of the policy engine, and the runtime execution of quantitative analysis experiments [23,24] that self-managing systems can use to realise powerful goal and utility-function policies as illustrated in Sect. 7.3-7.4.…”

“…Thus, we describe for the first time how multiple instances of the same general-purpose autonomic architecture can be organised into self-managing systems of systems by means of a new type of autonomic policy termed a resource-definition policy. Also, we present the first-ever integration of quantitative model checking techniques [23,24] into autonomic policy engines, and show how the use of this new capability enables the specification of powerful utility-function policies. Finally, we present a new four-step method for the development of self-managing systems starting from a model of their ICT resources, and we illustrate its application to several case studies that spawn different application domains and employ a wide range of policy types.…”

Advances in Quantitative Verification for Ubiquitous Computing

“…Probability is needed because of inherent unreliability of wireless communication technologies such as Bluetooth and ZigBee, which use randomised back off schemes to minimise collisions; also, embedded devices are frequently powered by battery and components may be prone to failure. Quantitative verification [31] techniques are well suited to this case, where systems are modelled as variants of Markov chains, annotated with real-time and quantitative costs/rewards. The aim is to automatically establish quantitative properties, such as "the probability of a monitoring device failing to issue alarm when a dangerous rise in pollutant level is detected", "the worst-case expected time for a Bluetooth device to discover another device in vicinity", or "the minimum expected power consumption of the smartphone while looking up directions with GPS".…”