Deducing Interactions in Partially Unspecified Biological Systems

Baldan, Paolo; Bracciali, Andrea; Brodo, Linda; Bruni, Roberto

doi:10.1007/978-3-540-73433-8_19

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…A somewhat related problem for partially-speci ed formal models is treated in [5]: there, the focus is not on tting uncertain parameters but on using symbolic analysis to reason about the behaviour of the unspeci ed components. As an alternative to parameter estimation, Brim et al [10] consider calculating the satisfaction probability of a formula over an entire parameter space, and propose an approximate way of performing this computation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

ProPPA

Georgoulas

Hillston

Sanguinetti

2018

ACM Trans. Model. Comput. Simul.

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Formal languages like process algebras have been shown to be e ective tools in modelling a wide range of dynamic systems, providing a high-level description that is readily transformed into an executable model. However their application is sometimes hampered because the quantitative details of many real-world systems of interest are not fully known. In contrast, in machine learning there has been work to develop probabilistic programming languages, which provide system descriptions that incorporate uncertainty and leverage advanced statistical techniques to infer unknown parameters from observed data. Unfortunately current probabilistic programming languages are typically too low-level to be suitable for complex modelling. In this paper we present ProPPA, the rst instance of the probabilistic programming paradigm being applied to a high-level, formal language, and its supporting tool suite. We explain the semantics of the language in terms of a quantitative generalisation of Constraint Markov Chains and describe the implementation of the language, discussing in some detail the di erent inference algorithms available, and their domain of applicability. We conclude by illustrating the use of the language on simple but non-trivial case studies: here ProPPA is shown to combine the elegance and simplicity of high-level formal modelling languages with an e ective way of incorporating data, making it a promising tool for modelling studies. CCS Concepts: • Computing methodologies → Modeling methodologies; Uncertainty quanti cation; • Mathematics of computing → Markov processes; Bayesian computation; Markov-chain Monte Carlo methods;

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Section: Related Workmentioning

confidence: 99%

“…Each term p(x i | i−1 , θ ) can be calculated via (5). Since the likelihood can be computed, it is then possible to use a MH scheme to sample from the exact posterior of the parameters.…”

Section: Direct Solutionmentioning

confidence: 99%

ProPPA

Georgoulas

Hillston

Sanguinetti

2018

ACM Trans. Model. Comput. Simul.

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“…-G [3,5] (I > 0): There are still ignorants at all time points between time 3 and 5, i.e. the rumour has not reached everyone in the population.…”

Section: Inference From Specificationmentioning

confidence: 99%

“…The somewhat related problem of reasoning about the behaviour of incompletely specified process algebra models, rather than inferring their parameters, is dealt with in [3]. Brim et al [5] propose a method of approximating quantitative model checking results over an entire parameter space as an alternative to parameter estimation.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Programming Process Algebra

Georgoulas

Hillston

Milios

et al. 2014

Quantitative Evaluation of Systems

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Abstract. Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications. Here we introduce ProPPA, a process algebra which incorporates uncertainty in the model description, allowing the use of Machine Learning techniques to incorporate observational information in the modelling. We define the semantics of the language by introducing a quantitative generalisation of Constraint Markov Chains. We present results from a prototype implementation of the language, demonstrating its usefulness in performing inference in a non-trivial example.

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Process Calculi Abstractions for Biology

Guerriero

Prandi

Priami

et al. 2009

Algorithmic Bioprocesses

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Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntaxdriven rules.A comprehensive picture of the state of the art of the process calculi approach to biological modeling is still missing. This paper goes in the direction of providing such a picture by presenting a comparative survey of the process calculi that have been used and proposed to describe the behavior of living entities.

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Deducing Interactions in Partially Unspecified Biological Systems

Cited by 3 publications

References 30 publications

ProPPA

ProPPA

Probabilistic Programming Process Algebra

Process Calculi Abstractions for Biology

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