Probabilistic model checking of complex biological pathways

Heath, John K.; Kwiatkowska, Marta; Norman, Gethin; Parker, David; Tymchyshyn, Oksana

doi:10.1016/j.tcs.2007.11.013

Cited by 133 publications

(16 citation statements)

References 39 publications

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“…For brevity, we have to exclude certain works: First, we leave out Boolean GRN simulators, such as Atalia [9], BooleanNet [16], and BoolNet [17]. Second, we omit research based on structures other than Kripke structures; examples are: a work utilizing the LTL (Linear-time Temporal Logic) model checker of the Maude system [34], works using reactive modules with the Mocha model checker [42,43], and those employing probabilistic model checking with PRISM [35,37,38]. We start with systems based on Thomas' formalism and proceed with systems using continuous approaches.…”

Section: Discussionmentioning

confidence: 99%

"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

et al. 2011

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BackgroundIn Thomas' formalism for modeling gene regulatory networks (GRNs), branching time, where a state can have more than one possible future, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a) asynchrony, (b) incompletely specified behavior, and (c) interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulators, because infinitely many paths may appear, limiting ordinary simulators to statistical conclusions. Model checkers for branching time, by contrast, are able to prove properties in the presence of infinitely many paths.ResultsWe have developed Antelope ("Analysis of Networks through TEmporal-LOgic sPEcifications", http://turing.iimas.unam.mx:8080/AntelopeWEB/), a model checker for analyzing and constructing Boolean GRNs. Currently, software systems for Boolean GRNs use branching time almost exclusively for asynchrony. Antelope, by contrast, also uses branching time for incompletely specified behavior and environment interaction. We show the usefulness of modeling these two phenomena in the development of a Boolean GRN of the Arabidopsis thaliana root stem cell niche.There are two obstacles to a direct approach when applying model checking to Boolean GRN analysis. First, ordinary model checkers normally only verify whether or not a given set of model states has a given property. In comparison, a model checker for Boolean GRNs is preferable if it reports the set of states having a desired property. Second, for efficiency, the expressiveness of many model checkers is limited, resulting in the inability to express some interesting properties of Boolean GRNs.Antelope tries to overcome these two drawbacks: Apart from reporting the set of all states having a given property, our model checker can express, at the expense of efficiency, some properties that ordinary model checkers (e.g., NuSMV) cannot. This additional expressiveness is achieved by employing a logic extending the standard Computation-Tree Logic (CTL) with hybrid-logic operators.ConclusionsWe illustrate the advantages of Antelope when (a) modeling incomplete networks and environment interaction, (b) exhibiting the set of all states having a given property, and (c) representing Boolean GRN properties with hybrid CTL.

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Section: Discussionmentioning

confidence: 99%

"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

et al. 2011

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“…Furthermore, it is worth noting that formally proving correctness of the biochemical implementation itself would be extremely challenging due to the inherent massive parallelism. So far, formal verification has only been attempted for biochemical systems with relatively small numbers of each molecular species [73,74], owing to the combinatorial explosion in the size of the state space as the numbers of copies increase. However, recent work on modular verification of DNA strand displacement devices [75] may offer a solution to this problem.…”

Section: Correctness Of the Learning Algorithm And Its Biochemical Immentioning

confidence: 99%

Design of a biochemical circuit motif for learning linear functions

Lakin

Minnich

Lane

et al. 2014

J. R. Soc. Interface.

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Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

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“…17 Recently, it has been also applied to analysis of various biological systems, e.g. ERK/MAPK pathway, 4 FGF signalling pathway, 18 cell cycle in eukaryotes, 19 EGFR pathway, 20 T-cell receptor signaling pathway, 21 cell cycle control, 22 diabetes-cancer signaling network, 23 and genetic Boolean gates. 24,25 Two types of verification approaches are utilised in this paper, a qualitative analysis and a quantitative one.…”

Section: Model Checking Tools Allow Analysing Various Temporal Logic mentioning

confidence: 99%

Qualitative and Quantitative Analysis of Systems and Synthetic Biology Constructs using P Systems

et al. 2014

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Qualitative and quantitative analysis of systems and synthetic biology constructs using P systems. ACS Synthetic Biology, 4 (1): 83-92. AbstractComputational models are perceived as an attractive alternative to mathematical models, e.g. ordinary differential equations. These models incorporate a set of methods for specifying, modelling, testing and simulating biological systems. In addition, they can be analysed using algorithmic techniques, e.g. formal verification. This paper shows how formal verification is utilised in systems and synthetic biology through qualitative vs quantitative analysis. Here, we choose two well known case studies: quorum sensing in P. aeruginosas and pulse generator. The paper reports verification analysis of two systems carried out using some model checking tools, integrated to the

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Probabilistic model checking of complex biological pathways

Cited by 133 publications

References 39 publications

"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

Design of a biochemical circuit motif for learning linear functions

Qualitative and Quantitative Analysis of Systems and Synthetic Biology Constructs using P Systems

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