Random walk approximation for stochastic processes on graphs
-
Polizzi, Stefano
Department of Physics and Astronomy A. Righi, University of Bologna, Italy
-
Marzi, Tommaso
ORCID
Department of Physics and Astronomy A. Righi, University of Bologna, Italy - Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
-
Matteuzzi, Tommaso
Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
-
Castellani, Gastone
Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy
-
Bazzani, Armando
Department of Physics and Astronomy A. Righi, University of Bologna, Italy
Show more…
Published in:
- Entropy. - 2023, vol. 25, no. 3, p. 394
English
We introduce the Random Walk Approximation (RWA), a new method to approximate the stationary solution of master equations describing stochastic processes taking place on graphs. Our approximation can be used for all processes governed by non-linear master equations without long-range interactions and with a conserved number of entities, which are typical in biological systems, such as gene regulatory or chemical reaction networks, where no exact solution exists. For linear systems, the RWA becomes the exact result obtained from the maximum entropy principle. The RWA allows having a simple analytical, even though approximated, form of the solution, which is global and easier to deal with than the standard System Size Expansion (SSE). Here, we give some theoretically sufficient conditions for the validity of the RWA and estimate the order of error calculated by the approximation with respect to the number of particles. We compare RWA with SSE for two examples, a toy model and the more realistic dual phosphorylation cycle, governed by the same underlying process. Both approximations are compared with the exact integration of the master equation, showing for the RWA good performances of the same order or better than the SSE, even in regions where sufficient conditions are not met.
-
Collections
-
-
Language
-
-
Classification
-
Computer science and technology
-
License
-
CC BY
-
Open access status
-
gold
-
Identifiers
-
-
Persistent URL
-
https://n2t.net/ark:/12658/srd1332016
Statistics
Document views: 2
File downloads:
- Marzi_2023_MDPI_entropy_Random Walk Approximation.pdf: 5