Global Mittag-Leffler boundedness of nabla discrete-time fractional-order fuzzy complex-valued molecular models of mRNA and protein in regulatory mechanisms

Narayanan, G.; Ali, M. Syed; Karthikeyan, Rajagopal; Rajchakit, Grienggrai; Thakur, Ganesh Kumar; Garg, Sudesh Kumar

doi:10.1016/j.cnsns.2023.107669

Communications in Nonlinear Science and Numerical Simulation

2024

DOI: 10.1016/j.cnsns.2023.107669

|View full text |Cite

Global Mittag-Leffler boundedness of nabla discrete-time fractional-order fuzzy complex-valued molecular models of mRNA and protein in regulatory mechanisms

G. Narayanan,

M. Syed Ali,

Rajagopal Karthikeyan

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints

Chen,

Fang

2024

Neural Comput & Applic

View full text Add to dashboard Cite

Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints

Chen,

Fang

2024

Neural Comput & Applic

View full text Add to dashboard Cite

Positivity and Stability of Caputo Fractional Order Gene Regulatory Networks: The System Comparison Method

2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

As well known, the positivity is an essential topic when studying gene regulatory networks since the variables involved, e.g., the concentrations of mRNA and proteins, can never be negative. However, the positivity of Caputo fractional order models has been a longstanding problem due to the nonlocality of Caputo fractional derivatives (CFD). In this paper, we present the system comparison method to prove the positivity of Caputo fractional order gene regulatory networks (CFOGRNs) only under positive initial conditions. Moreover, it is found that the positivity results can make it feasible to give proper comparison systems for CFOGRNs, in which the upper and lower estimations can be used to guarantee the stability of the objective CFOGRNs. Thus, the system comparison method for the stability of CFOGRNs is also provided here. Compared to the existing Lyapunov direct method, the proposed system comparison method affords an alternative method for stability analysis and different insights in stability conditions. Finally, these theoretical derivations are illustrated and validated by an example with numerical simulations.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Global Mittag-Leffler boundedness of nabla discrete-time fractional-order fuzzy complex-valued molecular models of mRNA and protein in regulatory mechanisms

Cited by 2 publications

References 48 publications

Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints

Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints

Positivity and Stability of Caputo Fractional Order Gene Regulatory Networks: The System Comparison Method

Contact Info

Product

Resources

About