学会新期刊《CSIAM Transactions on Life Sciences》2026年第一期正式上线发行，欢迎查阅
2026年3月，中国工业与应用数学学会新期刊《CSIAM Transactions on Life Sciences》（CSIAM-LS）上线发行2026年第一期。
CSIAM-LS是由中国工业与应用数学学会（CSIAM）继旗舰期刊《CSIAM Transactions on Applied Mathematics》后推出的一本新期刊，是学会CSIAM Transactions系列的第一个子刊。由中国工业与应用数学学会和香港GLOBAL SCIENCE PRESS出版社合作出版，为英文季刊，每年的3月、6月、9月、12月出版。
CSIAM-LS是一本创新性的跨学科期刊，聚焦数学与生命科学的交叉领域，覆盖生物学和医学的数学理论、模型和算法，包括计算系统生物学、生物信息学、生物医学工程、群体动力学、计算神经科学等。旨在推动传统数学生命科学的发展，开拓新兴方向，促进学科交叉与融合。
该期刊由中国科学院院士、武汉大学校长张平文担任主编，上海交通大学数学科学学院讲席教授楼元担任总编辑，拥有一支顶尖学者组成的编辑委员会，包含美国国家科学院院士、英国皇家学会会士等63位数学生命科学领域的国内外知名学者。
期刊2026年第一期共7篇文章，每年第一期均为开放获取（Open Access），诚邀海内外专家学者下载研读。本期论文目录、摘要及作者信息如下：
Xiu Li, Meili Li, Junling Ma
摘要：Under the assumption of the autopilot model, after antigen stimulation exceeds a threshold, the proliferation and effector function of CD4+ T cells are self-sustained and do not need further antigen stimulation. However, CD4+ T cell proliferation is driven by their production of IL-2, which then binds to cells and triggers proliferation. Without regulation, this autocrine process forms a positive feedback loop that causes uncontrolled proliferation. This study mathematically modeled the regulatory mechanisms of the CD4+ T cell response after infection, focusing on the role of IL-2 self-regulation and Treg in this mechanism. We performed a phase-space analysis to study the long-term behavior of the proliferation process. Our results show that IL-2 self-regulation alone is not sufficient to fully inhibit CD4+ T cell response, and that the involvement of Treg cells is essential to regulate the immune response effectively. In particular, when the rate of CD4+ T cell proliferation is controlled by the rate of IL-2-mediated CD4+ T cell removal, Treg cells control CD4+ T cell proliferation by releasing immunosuppressive cytokines such as IL-10 and TGF-β, thus inhibiting the unregulated immune response.
Tangjuan Li, Yanni Xiao
摘要：During infectious disease outbreaks, the dissemination of information and the dynamic adjustment of intervention strategies trigger psychological and behavioral changes among individuals, which significantly influence disease transmission. Mathematical models have played a crucial role in analyzing the interplay between behavioral changes and disease spread. In this review, we revisit research studies that model behavioral changes during epidemics and classify the literature based on different modeling approaches. Specifically, we categorize these models into three main types: (1) modifying the incidence function to incorporate behavior-driven changes, including a novel approach that utilizes neural networks to describe the incidence rate; (2) introducing additional compartments to represent subpopulations with different behaviors; and (3) employing game-theoretic modeling to study the interactions between infectious disease dynamics and behavioral changes. In the game-theoretic framework, we also examine how key epidemiological metrics – such as the peak size and peak time of the first wave, as well as the final epidemic size – are affected when behavioral changes are incorporated into the classic SIR model. For each category, we introduce the classical modeling frameworks and their extensions, analyzing their advantages and limitations. Finally, we summarize the key findings and outline several promising directions for future research.
Cuihong Yang, Xin’an Zhang, Linchao Hu, Jianshe Yu, Jia Li
摘要：In the interactive dynamical models, we include two different competing wild mosquito species and sterile mosquitoes which are the same type as one of the competing wild mosquitoes. We study the dynamics of the interspecific competition models in different circumstances. We explore how the interspecific competition affects the wild mosquito control with releases of sterile mosquitoes and establish a new release threshold based on the effect of the competition. Numerical examples are provided in each case to illustrate the impact on the mosquito control.
Li-Feng Hou, Jun Zhang, Gui-Quan Sun, Zhen Jin
摘要：Vegetation patterns are a hallmark of ecosystem self-organization, emerging from the intrinsic dynamics of nonlinear feedback mechanisms and spatiotemporal interactions. This review systematically explores and examines the structural characteristics of these patterns, the phenomena of multistability, and their implications for ecosystem stability through the lens of mathematical modeling and dynamical systems theory. In particular, reaction-diffusion models serve as a key analytical tool, revealing how local positive feedback and non-local negative feedback drive self-organized spatial structures via Turing bifurcation. Bifurcation theory and potential landscape analysis further elucidate ecosystem multistability, quantifying critical transitions among uniform vegetation, patterned states, and bare soil under environmental conditions. Advances in spatial metrics, including traditional statistical measures (e.g. variance, autocorrelation) and emerging complexity-based indicators (e.g. hyper-uniformity, spatial permutation entropy) provide robust methods for detecting ecological functional shifts and early-warning signs of regime shifts. Additionally, restoration strategies grounded in structural optimization, such as optimal control theory, offer a theoretical framework for vegetation pattern reconstruction and stability regulation, particularly in arid and semi-arid regions. Future research should integrate multiscale modeling and interdisciplinary approaches to deepen our understanding of vegetation structure-function relationships. Such efforts will yield both theoretical insights and practical solutions for mitigating global ecological degradation and climate change.
Baojun Song
摘要：Global warming and deteriorating environmental conditions have raised concerns about the persistence of green sea turtle populations, whose reproduction is governed by temperature-dependent sex determination. This study employs mathematical modeling to investigate these ecological challenges. Building on our previous models of green sea turtle population dynamics, we develop a sex-structured and stage-structured life history model that integrates temperature-dependent sex determination and ecological viability, offering a mechanistic framework for understanding green sea turtle population dynamics under climate and environmental stress. Our findings reveal that population dynamics are governed by an Allee-adjusted reproductive number, which accounts for both thermal and environmental influences. Additionally, we conduct a global stability analysis of the collapsed equilibrium using the singular perturbation approach, offering insights into long-term population viability. While additional parameter validation is necessary for definitive conclusions, our results illustrate how climate change and deteriorating environmental conditions shape the long-term viability of green sea turtle populations.
Haiyan Xu, Zhigui Lin, Michael Pedersen
摘要：In this paper, a juvenile-adult population model incorporating seasonal succession and pulsed harvesting is developed. The seasonal succession captures the cyclical change between favorable and unfavorable environmental conditions, while the pulsed harvesting represents a periodic human intervention, targeting the adult population exclusively during favorable seasons. The principal eigenvalue for the corresponding linearized system is defined and its dependence on both the intensity of the harvesting pulses and the duration of the unfavorable season is analyzed. Explicit expressions and analysis of the principal eigenvalue for a logistic model extended with seasonal succession and pulsed harvesting are provided specifically. Based on the principal eigenvalue, we establish sufficient conditions for population persistence and extinction. Numerical simulations are conducted to validate these analytical results. Our findings demonstrate that higher harvesting intensity during the favorable season is detrimental to species survival. Furthermore, extending the duration of the unfavorable season can trigger a critical transition from population persistence to extinction.
Qingbin Zhou, Tao Ren, Fan Yuan, Jiating Yu, Jiacheng Leng, Jiahao Song, Duanchen Sun, Ling-Yun Wu
摘要：Understanding phenotypic differences at the cell level is critical for comprehending the underlying pathogenesis of related complex diseases. However, the biological variations are obscured by batch effects, posing a challenge for integrating multi-batch and multi-condition single-cell datasets. Here, we present scFLASH, a deep learning-based model specially designed to explore single-cell biological variations while correcting undesired batch effects. scFLASH employs a conditional variational autoencoder with adversarial training to separate biological variations from technical noise and introduces a penalized condition classifier to preserve condition-specific biological signals. Through comprehensive benchmarking evaluations, scFLASH shows superior integration performances compared to other state-of-the-art methods. Applied to datasets such as Alzheimer’s disease, COVID-19, and diabetes, we demonstrate that scFLASH is applicable to various scenarios, effectively integrating datasets with two or more conditions and different batch sources. scFLASH can enhance the gene expression profiles and identify the condition-related cell subpopulations, facilitating downstream analyses and offering biological insights into the cellular mechanisms of disease pathology.
期刊官网：https://global-sci.com/index.php/csiam-ls/index
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