Abstract
To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part. We propose a truncation-based approximation that employs a state-space lumping scheme, aggregating states in a grid structure. The resulting approximate stationary distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection tailored to the stationary behavior. We demonstrate the method’s applicability to a wide range of non-linear problems with complex stationary behaviors.
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Notes
- 1.
Note that in addition mild regularity assumptions are necessary for the existence of a unique CTMC X, such as non-explosiveness [4]. These assumptions are typically valid for realistic reaction networks.
- 2.
In the sequel, we assume an enumeration of all states in \(\mathcal {S}\). We simply write \(x_i\) for the state with index i and drop this notation for entries of a state x.
- 3.
- 4.
We note, that \(\sum _{i=0}^n i / (i + k_7)\) can be solved analytically. However, the approximation presented above is much simpler to compute.
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This work is supported by the DFG project “MULTIMODE”.
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Appendices
A Detailed Results
B Lyapunov Analysis of the p53 Oscillator
We now derive Lyapunov-sets for the p53 oscillator case study (Model 4). Let the Lyapunov function
Then the drift
Clearly, \(c = \sup _{x\in {S}} d(x) = 10800\). In particular, the supremum c is at the origin since all non-constant terms are negative. The slowest rate of decrease for (17) is \(x_{\mathrm {p53}}\) with \(x_{\mathrm {Mdm2}} = x_{\mathrm {pMdm2}} = 0\). We are content with a superset of a Lyapunov set (9) for some threshold \(\epsilon _{\ell }\). Therefore taking (9), we can solve the inequality
for \(x_{\mathrm {p53}}\) and
Therefore
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Backenköhler, M., Bortolussi, L., Großmann, G., Wolf, V. (2021). Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models. In: Abate, A., Marin, A. (eds) Quantitative Evaluation of Systems. QEST 2021. Lecture Notes in Computer Science(), vol 12846. Springer, Cham. https://doi.org/10.1007/978-3-030-85172-9_19
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