DocumentCode :
47005
Title :
Extended Master Equation Models for Molecular Communication Networks
Author :
Chun Tung Chou
Author_Institution :
Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia
Volume :
12
Issue :
2
fYear :
2013
fDate :
Jun-13
Firstpage :
79
Lastpage :
92
Abstract :
We consider molecular communication networks consisting of transmitters and receivers distributed in a fluidic medium. In such networks, a transmitter sends one or more signaling molecules, which are diffused over the medium, to the receiver to realize the communication. In order to be able to engineer synthetic molecular communication networks, mathematical models for these networks are required. This paper proposes a new stochastic model for molecular communication networks called reaction-diffusion master equation with exogenous input (RDMEX). The key idea behind RDMEX is to model the transmitters as time series of signaling molecule counts, while diffusion in the medium and chemical reactions at the receivers are modeled as Markov processes using master equation. An advantage of RDMEX is that it can readily be used to model molecular communication networks with multiple transmitters and receivers. For the case where the reaction kinetics at the receivers is linear, we show how RDMEX can be used to determine the mean and covariance of the receiver output signals, and derive closed-form expressions for the mean receiver output signal of the RDMEX model. These closed-form expressions reveal that the output signal of a receiver can be affected by the presence of other receivers. Numerical examples are provided to demonstrate the properties of the model.
Keywords :
Markov processes; biochemistry; biodiffusion; covariance analysis; master equation; molecular biophysics; physiological models; reaction kinetics theory; reaction-diffusion systems; time series; transceivers; Markov processes; RDMEX model; chemical reactions; closed-form expressions; covariance receiver output signal; extended master equation models; fluidic medium; mathematical models; mean receiver output signal; medium diffusion; molecular communication network model; multiple receivers; multiple transmitters; reaction kinetics; reaction-diffusion master equation with exogenous input; signaling molecule counts; stochastic model; synthetic molecular communication network engineering; time series; transmitter model; Chemicals; Equations; Mathematical model; Molecular communication; Receivers; Transmitters; Vectors; Master equations; molecular communication networks; nano communication networks; stochastic models; synthetic biology; synthetic molecular communication networks; Algorithms; Computer Simulation; Models, Molecular; Stochastic Processes;
fLanguage :
English
Journal_Title :
NanoBioscience, IEEE Transactions on
Publisher :
ieee
ISSN :
1536-1241
Type :
jour
DOI :
10.1109/TNB.2013.2237785
Filename :
6451289
Link To Document :
بازگشت