scholarly journals Kalman Filtering for Genetic Regulatory Networks with Missing Values

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qiongbin Lin ◽  
Qiuhua Liu ◽  
Tianyue Lai ◽  
Wu Wang
Keyword(s):  
Kalman Filtering ◽  
Time Delays ◽  
Regulatory Networks ◽  
Missing Values ◽  
Genetic Regulation ◽  
Genetic Regulatory Networks ◽  
Measurement Noise ◽  
Process Noise ◽  
Missing Value ◽  
Measurement Equations

The filter problem with missing value for genetic regulation networks (GRNs) is addressed, in which the noises exist in both the state dynamics and measurement equations; furthermore, the correlation between process noise and measurement noise is also taken into consideration. In order to deal with the filter problem, a class of discrete-time GRNs with missing value, noise correlation, and time delays is established. Then a new observation model is proposed to decrease the adverse effect caused by the missing value and to decouple the correlation between process noise and measurement noise in theory. Finally, a Kalman filtering is used to estimate the states of GRNs. Meanwhile, a typical example is provided to verify the effectiveness of the proposed method, and it turns out to be the case that the concentrations of mRNA and protein could be estimated accurately.

scholarly journals Dissipativity and Error Feedback Controller Design of Time-Delay Genetic Regulatory Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yonggang Ma ◽  
Junmei Liu ◽  
Jiao Ai
Keyword(s):  
Time Delay ◽  
Regulatory Networks ◽  
Controller Design ◽  
Estimation Error ◽  
Rapid Development ◽  
Genetic Regulation ◽  
Genetic Regulatory Networks ◽  
Error Feedback ◽  
Attraction Set ◽  
Parameter Dependent

Genetic regulatory networks (GRNs) play an important role in the development and evolution of the biological system. With the rapid development of DNA technology, further research on GRNs becomes possible. In this paper, we discuss a class of time-delay genetic regulatory networks with external inputs. Firstly, under some reasonable assumptions, using matrix measures, matrix norm inequalities, and Halanay inequalities, we give the global dissipative properties of the solution of the time-delay genetic regulation networks and estimate the parameter-dependent global attraction set. Secondly, an error feedback control system is designed for the time-delay genetic control networks. Furthermore, we prove that the estimation error of the model is asymptotically stable. Finally, two examples are used to illustrate the validity of the theoretical results.

Set-membership filtering for genetic regulatory networks with missing values

2016 ◽  
Vol 175 ◽  
pp. 466-472 ◽  
Author(s):  
Wu Wang ◽  
Xiaocheng Liu ◽  
Yurong Li ◽  
Yurong Liu
Keyword(s):  
Regulatory Networks ◽  
Missing Values ◽  
Genetic Regulatory Networks ◽  
Set Membership

Discrete Networks as a Suitable Approach for the Analysis of Genetic Regulation

2009 ◽  
pp. 530-540
Author(s):  
Elizabeth Santiago-Cortés
Keyword(s):  
Transcription Factors ◽  
Dynamical Systems ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Genetic Regulation ◽  
Genetic Regulatory Networks ◽  
Theoretical Studies ◽  
Biological Interpretation ◽  
Complex Dynamical Behavior ◽  
Discrete Networks

Biological systems are composed of multiple interacting elements; in particular, genetic regulatory networks are formed by genes and their interactions mediated by transcription factors. The establishment of such networks is critical to guarantee the reliability of transcriptional performance in any organism. The study of genetic regulatory networks as dynamical systems is a helpful methodology to understand the transcriptional behavior of the genome. From a number of theoretical studies, it is known that networks present a complex dynamical behavior that includes stability, redundancy, homeostasis, and multistationarity. In this chapter we present some particular biological processes modeled as discrete networks to show that the theoretical properties of networks have a clear biological interpretation.

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