3 years ago

Probabilistic Model Checking for Systems Biology - PRISM

Probabilistic Model Checking for Systems Biology - PRISM

DRAFT2 Marta

DRAFT2 Marta Kwiatkowska, Gethin Norman, and David Parkerthe temporal logic CSL and the probabilistic model checking tool PRISM. In Sections3, 4 and 5, we consider three examples to demonstrate both how to modelbiological pathways in the PRISM language and how probabilistic model checkingcan be used to analyse such pathways. In Section 3 we start with a simpleset of reactions. Then, in Sections 4 and 5, we extend this simple example, firstby increasing the number of components of each molecular species in the systemand then by extending the possible reactions of the system. Finally, Section 6outlines related topics of interest.2 Probabilistic Model Checking and PRISMProbabilistic model checking is a technique for the modelling and analysis of systemswhich exhibit stochastic behaviour. This technique is a variant of modelchecking, a well-established and widely used formal method for ascertaining thecorrectness of real-life systems. Model checking requires two inputs: a descriptionof the system in some high-level modelling formalism (such as a Petri netor process algebra), and specification of one or more desired properties of thatsystem, usually in temporal logic (e.g. CTL or LTL). From the former, a modelof the system is constructed, typically a labelled state-transition system in whicheach state represents a possible system configuration and the transitions representthe evolution of the system from one configuration to another over time. Itis then possible to automatically verify whether or not each property is satisfied,based on a systematic and exhaustive exploration of the model.In probabilistic model checking, the models are augmented with quantitativeinformation regarding the likelihood that transitions occur and the timesat which they do so. In practice, these models are typically Markov chainsor Markov decision processes. In this chapter, we use continuous-time Markovchains (CTMCs), in which transitions between states are assigned (positive,real-valued) rates, which are interpreted as the rates of negative exponentialdistributions. Properties, while still expressed in temporal logic, are now quantitativein nature. For example, rather than verifying that ‘the protein alwayseventually degrades’, we may ask ‘what is the probability that the protein eventuallydegrades?’ or ‘what is the probability that the protein degrades within thours?’. The most common temporal logic for this purpose is is CSL (ContinuousStochastic Logic). Furthermore, by adding rewards to a CTMC, we canalso specify properties such as ‘what is the expected energy dissipation throughprotein binding within the first t time units?’ and ‘what is the expected numberof binding reactions before relocation occurs?’.In the remainder of this section we present an introduction to CTMCs, CSL,and the software tool PRISM, which provides support for probabilistic modelchecking of CTMCs using CSL. For further details, see e.g. [13].2.1 Continuous-Time Markov ChainsContinuous-time Markov chains (CTMCs), frequently used in performance analysis,model both continuous real time and probabilistic choice. This is done by

DRAFTProbabilistic Model Checking for Systems Biology 3specifying the rate at which a transition between two states occurs. Formally,we have the following definition.Definition 1. A CTMC is a tuple C = (S, R, L) where: S is a finite set ofstates; R : S × S → R ≥0 is a transition rate matrix; L : S → 2 AP is a labellingfunction.The transition rate matrix R assigns rates to each pair of states, which are usedas the parameter of an exponential distribution. A transition can only occurbetween states s and s ′ if R(s, s ′ )>0 and, in this case, the probability of thistransition being triggered within t time-units equals 1 − e −R(s,s′ )·t . Typically, ina state s, there is more than one state s ′ for which R(s, s ′ )>0, this is known as arace condition and the first transition to be triggered determines the next state.The choice of successor state s ′ from state s is thus probabilistic. The probabilityof moving to s ′ is R(s,s′ )E(s) , where E(s) = ∑ s ′ ∈S R(s, s′ ) is the exit rate of state s.The total time spent in s before any transition occurs is exponentially distributedwith rate E(s). The labelling function L assigns atomic propositions from a setAP to each state of the CTMC. These are used to label states with propertiesof interest.A CTMC can be augmented with reward structures which are used to annotateits states and/or transitions with additional quantitative information.Formally, a reward structure for a CTMC is a pair (ρ, ι) where:– ρ : S → R ≥0 is the state reward function;– ι : S × S → R ≥0 is the transition reward function.A path of a CTMC C = (S, R, L) is a non-empty sequence s 0 t 0 s 1 t 1 s 2 . . . wheres i ∈ S, t i ∈ R >0 and R(s i , s i+1 )>0 for all i≥0. The value t i represents theamount of time spent in the state s i and we denote by ω@t the state occupiedat time t, i.e. s j where j is the smallest index for which ∑ ji=0 t i ≥ t. We denoteby Path(s) the set of all (infinite and finite) paths of the CTMC C starting instate s. A probability measure Pr s over Path(s) can then be derived [2].Two traditional properties of CTMCs are transient behaviour, which relatesto the state of the model at a particular time instant; and steady-statebehaviour, which describes the state of the CTMC in the long-run. For a CTMCC = (S, R, L), the transient probability π s,t (s ′ ) is defined as the probability,having started in state s, of being in state s ′ at time instant t. The steady-stateprobability π s (s ′ ) is the probability of, having started in state s, being in state s ′in the long-run. The steady-state probability distribution, i.e. the values π s (s ′ )for all s ′ ∈ S, can be used to infer the percentage of time, in the long-run, thatthe CTMC spends in each state.2.2 Continuous Stochastic LogicThe temporal logic CSL originally introduced by Aziz et al. [1] and since extendedby Baier et al. [2] is based on the temporal logics CTL [5] and PCTL[7] and provides a powerful means to specify both path-based and traditional

A leisurely look at probabilistic modelling in systems biology
Counterexamples in Probabilistic LTL Model Checking ... - Researcher
Probabilistic Models for Automatic Diagnosis of System ... - RAD Lab
Counterexamples in Probabilistic LTL Model Checking ... - Researcher
Probabilistic Models Of Sensory-motor Systems
Probabilistic And Stochastic UML Statecharts - Software Modeling ...
Probabilistic model of realistic pesticide concentrations in surface ...
Systems biology in constructing a dynamic model of the androgen ...
Constraint-based Modeling: Part II - Systems Biology Research Group
PRISM: Probabilistic Model Checking for Performance and ...
PRISM: Probabilistic Model Checking for Performance and ...
Probabilistic Model Checking of Complex Biological ... - PRISM
Probabilistic Model Checking of the CSMA/CD protocol ... - PRISM
PRISM 2.0: A Tool for Probabilistic Model Checking
Symbolic Model Checking for Probabilistic Timed Automata ⋆ - PRISM
Probabilistic Model Checking of Randomised Distributed ... - PRISM
Model checking the probabilistic π-calculus - PRISM
Symbolic Model Checking of Concurrent Probabilistic ... - PRISM
Probabilistic Model Checking - References - PRISM
Probabilistic Symbolic Model Checking with PRISM - Quantitative ...
Model Checking Hierarchical Probabilistic Systems* - Singapore ...
PRISM: Probabilistic Symbolic Model Checker* - Quantitative ...
Automated Verification Techniques for Probabilistic Systems - PRISM
PRISM: A Tool for Automatic Verification of Probabilistic Systems
Analyzing Multi-agent Systems with Probabilistic Model Checking ...
Using Probabilistic Model Checking for Dynamic Power Management
Verification of partial-information probabilistic systems ... - PRISM