A unified framework for limit results in chemical reaction networks on multiple time-scales

被引:0
|
作者
Enger, Timo [1 ]
Pfaffelhuber, Peter [1 ]
机构
[1] Univ Freiburg, Freiburg, Germany
来源
关键词
Markov jump process; functional central limit thoerem; stochastic averaging; APPROXIMATIONS; ELIMINATION; REDUCTION; THEOREMS;
D O I
10.1214/22-EJP897
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
If (X-N)(N=1,2,...) is a sequence of Markov processes which solve the martingale problems for some operators (G(N))(N=1,2,...), it is a classical task to derive a limit result as N -> infinity, in particular a weak process limit with limiting operator G. For slow-fast systems X-N = (V-N, Z(N)) where V-N is slow and Z(N) is fast, G(N) consists of two (or more) terms, and we are interested in weak convergence of V-N to some Markov process V. In this case, for some f is an element of D(G), the domain of G, depending only on v, the limit Gf can sometimes be derived by using some g(N) -> 0 (depending on v and z), and study convergence of G(N) (f + g(N)) -> Gf. We develop this method further in order to obtain functional Laws of Large Numbers (LLNs) and Central Limit Theorems (CLTs). We then apply our general result to various examples from Chemical Reaction Network theory. We show that we can rederive most limits previously obtained, but also provide new results in the case when the fast-subsystem is first order. In particular, we allow that fast species to be consumed faster than they are produced, and we derive a CLT for Hill dynamics with coefficient 2.
引用
收藏
页数:33
相关论文
共 50 条
  • [1] A unified framework for limit results in chemical reaction networks on multiple time-scales
    Enger, Timo
    Pfaffelhuber, Peter
    [J]. ELECTRONIC JOURNAL OF PROBABILITY, 2023, 28
  • [2] Sensitivity analysis for stochastic chemical reaction networks with multiple time-scales
    Gupta, Ankit
    Khammash, Mustafa
    [J]. ELECTRONIC JOURNAL OF PROBABILITY, 2014, 19 : 1 - 53
  • [3] Locally recurrent networks with multiple time-scales
    Juan, JK
    Harris, JG
    Principe, JC
    [J]. NEURAL NETWORKS FOR SIGNAL PROCESSING VII, 1997, : 645 - 653
  • [4] Multiple Time-Scales in Network-of-Networks
    Chapman, Airlie
    Mesbahi, Mehran
    [J]. 2016 AMERICAN CONTROL CONFERENCE (ACC), 2016, : 5563 - 5568
  • [5] SEPARATION OF TIME-SCALES AND MODEL REDUCTION FOR STOCHASTIC REACTION NETWORKS
    Kang, Hye-Won
    Kurtz, Thomas G.
    [J]. ANNALS OF APPLIED PROBABILITY, 2013, 23 (02): : 529 - 583
  • [6] Multiple dynamical time-scales in networks with hierarchically nested modular organization
    Sinha, Sitabhra
    Poria, Swarup
    [J]. PRAMANA-JOURNAL OF PHYSICS, 2011, 77 (05): : 833 - 842
  • [7] Multiple dynamical time-scales in networks with hierarchically nested modular organization
    SITABHRA SINHA
    SWARUP PORIA
    [J]. Pramana, 2011, 77 : 833 - 842
  • [8] Topology Identification of Time-Scales Complex Networks
    Pei, Yong
    Chen, Churong
    Pi, Dechang
    [J]. MATHEMATICS, 2022, 10 (10)
  • [9] MULTIPLE RELAXATION TIME-SCALES IN STELLAR DYNAMICS
    BOCCALETTI, D
    PUCACCO, G
    RUFFINI, R
    [J]. ASTRONOMY & ASTROPHYSICS, 1991, 244 (01) : 48 - 51
  • [10] WZ Cas - variability on multiple time-scales
    Lebzelter, T
    Griffin, RF
    Hinkle, KH
    [J]. ASTRONOMY & ASTROPHYSICS, 2005, 440 (01) : 295 - 303