Abstract
In this work we propose an automatic way of generating and verifying formal hybrid models of signaling and transcriptional events, gathered in large-scale regulatory networks.This is done by integrating temporal and stochastic aspects of the expression of some biological components. The hybrid approach lies in the fact that measurements take into account both times of lengthening phases and discrete switches between them. The model proposed is based on a real case study of keratinocytes differentiation, in which gene time-series data was generated upon Calcium stimulation.
To achieve this we rely on the Process Hitting (PH) formalism that was designed to consider large-scale system analysis. We first propose an automatic way of detecting and translating biological motifs from the Pathway Interaction Database to the PH formalism. Then, we propose a way of estimating temporal and stochastic parameters from time-series expression data of action on the PH. Simulations emphasize the interest of synchronizing concurrent events.
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Acknowledgements
This work was supported by a PhD grant from the CNRS and the French region Pays de la Loire and grants from the German Ministry for Research and Education (BMBF) funding program MedSys (grant number FKZ0315401A) and AGENET (FKZ0315898).
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A Algorithm of Patterns Detection
A Algorithm of Patterns Detection
Here are the algorithms that allow to detect and construct a process hitting model from an RSTC network. These algorithms have a polynomial time running that correspond to the running time of the procedure 2.
Proposition 1
Algorithm 2 has a time complexity of \(\mathcal {O}(|V|\log {}(h))\). Where h is the average height of the patterns in the RSTC network. In the worst case \(h = \log _{V}(|V|)\).
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Fitime, L.F., Schuster, C., Angel, P., Roux, O., Guziolowski, C. (2015). Integrating Time-Series Data in Large-Scale Discrete Cell-Based Models. In: Abate, A., Šafránek, D. (eds) Hybrid Systems Biology. HSB 2015. Lecture Notes in Computer Science(), vol 9271. Springer, Cham. https://doi.org/10.1007/978-3-319-26916-0_5
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