Abstract
The design of genetic or protein networks that satisfy a given set of behavioural specifications is one of the main challenges of synthetic biology. Model-based design is a natural choice in this respect. Here we consider the problem of tuning parameters of a stochastic model to force one or more behavioural goals to hold. In particular, we consider several objectives specified by signal temporal logic formulae, and we look for a parameter set making their satisfaction probability as large as possible. This formalisation results in a multi-objective optimisation problem, which we solve by considering an optimisation scheme combining satisfaction probability and average robustness of STL properties, leveraging state of the art multi-objective optimisation routines.
LB acknowledges partial support from EU-FET project QUANTICOL (nr. 600708) and from FRA-UniTS.
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Notes
- 1.
Rate coefficients are as follows: \(k_{si}=0.5\), \(k_{ir}=0.05\), \(k_{is}=0.1\), \(k_{rs}=0.05\), \(k_{v}\in [0.08,10]\). The vaccination rate is the only free parameter that is optimised.
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Bortolussi, L., Policriti, A., Silvetti, S. (2016). Logic-Based Multi-objective Design of Chemical Reaction Networks. In: Cinquemani, E., Donzé, A. (eds) Hybrid Systems Biology. HSB 2016. Lecture Notes in Computer Science(), vol 9957. Springer, Cham. https://doi.org/10.1007/978-3-319-47151-8_11
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