A numerical analysis of the normalized time-fractional Keller–Segel equations

Abstract We propose and investigate a normalized time-fractional Keller–Segel (KS) model that incorporates logarithmic chemotactic sensitivity and a non-diffusive chemical response. The normalized time-fractional derivatives maintain a constant total memory effect across different fractional orders. This formulation enables clear interpretation of the effect of the fractional order and facilitates fair comparisons across varying fractional orders. An implicit-explicit finite difference scheme is applied to the density equation, and an analytical solution combined with the frozen coefficient technique is used to solve the chemoattractant equation. The numerical scheme achieves second-order accuracy in space and first-order accuracy in time, as demonstrated by convergence tests. Numerical experiments demonstrate that the fractional order significantly influences the blow-up behavior of the solution; specifically, smaller fractional orders result in stronger memory effects and lead to faster blow-up. Furthermore, the chemotactic sensitivity exponent and the fractional order play critical roles in determining the dynamics and the blow-up time of the system. The study confirms the validity and efficiency of the proposed algorithm and provides insights into the influence of the fractional process on chemotactic aggregation. The proposed method offers a promising direction for further investigation of biological systems influenced by nonlocal and memory-driven processes.

Authors

Publication Details

Journal
Advances in Continuous and Discrete Models
Published
2026-06-17
DOI
https://doi.org/10.1186/s13662-026-04116-w
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A numerical analysis of the normalized time-fractional Keller–Segel equations

Yibao Li, Darae Jeong, Junseok Kim, Chaeyoung Lee et al.
Advances in Continuous and Discrete Models
Mathematical Biology Tumor Growth
article

A numerical analysis of the normalized time-fractional Keller–Segel equations

Yibao Li, Darae Jeong, Junseok Kim, Chaeyoung Lee, Yunjae Nam, Juho Ma, Ana Yun
article en

Abstract

Abstract We propose and investigate a normalized time-fractional Keller–Segel (KS) model that incorporates logarithmic chemotactic sensitivity and a non-diffusive chemical response. The normalized time-fractional derivatives maintain a constant total memory effect across different fractional orders. This formulation enables clear interpretation of the effect of the fractional order and facilitates fair comparisons across varying fractional orders. An implicit-explicit finite difference scheme is applied to the density equation, and an analytical solution combined with the frozen coefficient technique is used to solve the chemoattractant equation. The numerical scheme achieves second-order accuracy in space and first-order accuracy in time, as demonstrated by convergence tests. Numerical experiments demonstrate that the fractional order significantly influences the blow-up behavior of the solution; specifically, smaller fractional orders result in stronger memory effects and lead to faster blow-up. Furthermore, the chemotactic sensitivity exponent and the fractional order play critical roles in determining the dynamics and the blow-up time of the system. The study confirms the validity and efficiency of the proposed algorithm and provides insights into the influence of the fractional process on chemotactic aggregation. The proposed method offers a promising direction for further investigation of biological systems influenced by nonlocal and memory-driven processes.

Advances in Continuous and Discrete Models
National Research Foundation of Korea
Openalex Percentile: Top 8%
Mathematical Biology Tumor Growth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.