Modeling, Control and Optimization of Biological Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 274

Special Issue Editor


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Guest Editor
Complex Systems Research Center, Shanxi University, Taiyuan 030006, China
Interests: game theory; epidemiology; complex networks

Special Issue Information

Dear Colleagues,

Biological systems, from cellular pathways to ecosystems, exhibit intricate dynamics shaped by nonlinear interactions. Advances in biological systems, computational tools and synthetic biology have created opportunities to model, control and optimize these systems for transformative applications in healthcare, biomanufacturing and environmental sustainability. However, challenges persist in balancing model accuracy, scalability and real-world applicability. This proposal aims to achieve specific goals such as maintaining homeostasis, improving bioprocesses or developing novel therapeutic approaches. By integrating modeling, control and optimization techniques, researchers can gain a deeper understanding of biological systems and potentially develop more effective strategies for their manipulation and application.

Prof. Dr. Junyuan Yang
Guest Editor

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Keywords

  • ecological modeling
  • mutiscale immuno-epidemiologial models
  • parameter identifiaction
  • disease-informed neural networks
  • optimal control theory

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Published Papers (1 paper)

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Research

26 pages, 387 KiB  
Article
Identification and Empirical Likelihood Inference in Nonlinear Regression Model with Nonignorable Nonresponse
by Xianwen Ding and Xiaoxia Li
Mathematics 2025, 13(9), 1388; https://doi.org/10.3390/math13091388 - 24 Apr 2025
Viewed by 137
Abstract
The identification of model parameters is a central challenge in the analysis of nonignorable nonresponse data. In this paper, we propose a novel penalized semiparametric likelihood method to obtain sparse estimators for a parametric nonresponse mechanism model. Based on these sparse estimators, an [...] Read more.
The identification of model parameters is a central challenge in the analysis of nonignorable nonresponse data. In this paper, we propose a novel penalized semiparametric likelihood method to obtain sparse estimators for a parametric nonresponse mechanism model. Based on these sparse estimators, an instrumental variable is introduced, enabling the identification of the observed likelihood. Two classes of estimating equations for the nonlinear regression model are constructed, and the empirical likelihood approach is employed to make inferences about the model parameters. The oracle properties of the sparse estimators in the nonresponse mechanism model are systematically established. Furthermore, the asymptotic normality of the maximum empirical likelihood estimators is derived. It is also shown that the empirical log-likelihood ratio functions are asymptotically weighted chi-squared distributed. Simulation studies are conducted to validate the effectiveness of the proposed estimation procedure. Finally, the practical utility of our approach is demonstrated through the analysis of ACTG 175 data. Full article
(This article belongs to the Special Issue Modeling, Control and Optimization of Biological Systems)
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