2026年9月，中国工业与应用数学学会新期刊《CSIAM Transactions on Life Sciences》（CSIAM-LS）正式上线发行2026年第三期。

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。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。

本刊2026年第三期共7篇文章，论文目录、摘要及作者信息如下：

01

Ling Yan, Pei Zhang, Yanli Wang, Zhennan Zhou

Weak Formulation and Spectral Approximation of a Fokker-Planck Equation for Neural Ensembles. CSIAM Transactions on Life Sciences, 2(3), 385-419. https://doi.org/10.4208/csiam-ls.SO-2025-0013.

摘要：In this paper, we focus on efficiently and flexibly simulating the Fokker-Planck equation associated with the nonlinear noisy leaky integrate-and-fire model, which reflects the dynamic behavior of neuron networks. We apply the Galerkin spectral method to discretize the spatial domain by constructing a variational formulation that satisfies complex boundary conditions. Moreover, the boundary conditions in the variational formulation include only zeroth-order terms, with first-order conditions being naturally incorporated. This allows the numerical scheme to be further extended to an excitatory-inhibitory population model with synaptic delays and refractory states. Additionally, we establish the consistency of the numerical scheme. Experimental results, including accuracy tests, blow-up events, and periodic oscillations, validate the properties of our proposed method.

02

Wensi Hu, Mingji Huang, H. P. Zhang, Feng Zhang, Wim Vyverman, Quan-Xing Liu

Circular Run-and-Reversal Motion Optimizes Foraging Behavior in Benthic Diatoms. CSIAM Transactions on Life Sciences, 2(3), 420-438. https://doi.org/10.4208/csiam-ls.SO-2025-0026.

摘要：Adaptive locomotion of living organisms contributes to enhancing their competitive abilities and helps to maintain their fitness in diverse environments. To date, our understanding of searching behavior and its ultimate cause remains limited, partly because integrative experimental–theoretical studies are still scarce in ecology and evolutionary biology. Here, we investigate how motion patterns of biofilm-inhabiting marine raphid diatom (Navicula arenaria var. rostellata) optimize nutrient search efficiency in two-dimensional space. We report that individual Navicula cells display a universal “circular run-and-reversal” motion behavior at different concentrations of dissolved silicic acid. We then show that the gliding motions of diatom cells can be elucidated accurately by behavioral optimization of the rotational diffusivity, reversal rate, and angular velocity coupled to a Langevin model. We hypothesize that the optimal search efficiency is achieved near a critical region of specific behavioral parameters. Using the maximized diffusivity as the search-efficiency metric, our theoretical results demonstrate that these optimal parameters coincide with the experimental observations on diatom cells undergoing natural selection.

03

Rui Yue, Chenghang Li, Jinzhi Lei

Modeling Tumor Cell Heterogeneity and Plasticity in Adaptive Therapy. CSIAM Transactions on Life Sciences, 2(3), 439-468. https://doi.org/10.4208/csiam-ls.SO-2026-0001.

摘要：Adaptive therapy (AT) is designed to postpone the emergence of drug resistance by exploiting evolutionary competition among tumor subclones. Most mathematical models of AT assume a binary population structure of drug-sensitive and drug-resistant cells, which neglects the continuous nature of phenotypic plasticity. In this study, we propose a mathematical model that integrates a continuous drug susceptibility index with a probabilistic inheritance function to describe clonal dynamics under therapy. The resulting integro-differential system generalizes traditional two-type competition models and captures both heterogeneity and plasticity of tumor cells. Analytical and numerical studies show that (i) continuous therapy drives rapid expansion of resistant clones, (ii) adaptive therapy maintains long-term tumor control by dynamically regulating sensitive populations, and (iii) high phenotypic plasticity accelerates phenotype switching, leading to earlier tumor relapse following continuous therapy. These results identify critical parameter regimes where adaptive therapy outperforms fixed regimens and highlight the essential role of plasticity in shaping treatment outcomes. The proposed framework provides a more realistic mathematical foundation for the design of clinically relevant adaptive therapy strategies.

04

Luhong Ye, Hao Wang

Traveling Waves in a Diffusive Single-Species Model with a Weak Spatiotemporal Memory Kernel. CSIAM Transactions on Life Sciences, 2(3), 469-494. https://doi.org/10.4208/csiam-ls.so-2026-0402.

摘要：We study traveling wave solutions in a nonlinear reaction-diffusion model incorporating a weak spatiotemporal distributed memory kernel. The model describes a single-species population whose movement is influenced by both random diffusion and memory-based dispersal, with the latter expressed as a convolution term involving a temporal weighting function and a spatial Green’s function. This framework captures the gradual decay of spatial memory and its effect on dispersal dynamics. Using perturbation expansions, operator theory, and the Banach fixed-point theorem, we establish the existence of traveling wavefronts connecting equilibrium states in two parameter regimes: (i) a small memory-based diffusion coefficient and (ii) a large wave speed. The analysis addresses significant challenges arising from the nonlocal, nonlinear memory term by employing integral equation representations and precise estimates. Numerical simulations illustrate how the memory diffusion coefficient and mean delay influence wave speed, front shape, and population distribution. The results provide a rigorous characterization of wave propagation in systems with weak distributed memory, offering a unified approach applicable to models in population dynamics, biological invasion, and other spatiotemporal processes with memory effects.

05

Kun Wang, Rui Meng, Zheng Hu, Da Zhou

Towards a Quantitative Understanding of Cellular Dynamics via Lineage Tracing Inference. CSIAM Transactions on Life Sciences, 2(3), 495-521. https://doi.org/10.4208/csiam-ls.SO-2026-0426.

摘要：Cell lineage tracing has evolved into a rigorous quantitative discipline, enabling the inference of complex cellular dynamics from static snapshots of genealogical trees. This Review examines representative mathematical and statistical frameworks that underpin this field, including selected recent contributions from our own work. We categorize the current inference landscape into three hierarchical scales: (1) Population Dynamics, where we discuss the use of Markov branching processes and coalescent theory—including our work on quantifying division and death rates—to decode progenitor pool behaviors; (2) Cell Fate Dynamics, focusing on the integration of transcriptomic information with lineage topology, highlighting our development of velocity-based models for reconstructing continuous state transitions; and (3) Gene Regulatory Dynamics, exploring how lineage structures serve as indispensable priors for inferring directed regulatory networks. By addressing the fundamental challenge of identifiability, this review synthesizes how these multi-scale frameworks allow researchers to move beyond descriptive mapping toward a quantitative understanding of tissue morphogenesis and tumor evolution.

06

Xian Chen, Tianci Zhang, Jinchao Lv, Chunhe Li

RESIDE: Reconstructing Network Interactions and Stochastic Dynamics from Single-Cell Snapshot Expression Data. CSIAM Transactions on Life Sciences, 2(3), 522-550. https://doi.org/10.4208/csiam-ls.so-2026-0518.

摘要：Deciphering stochastic gene regulatory dynamics and their governing network architectures remains a central challenge in systems biology. While single-cell sequencing advancements have significantly enhanced our understanding of large-scale gene regulatory networks, these snapshot measurements inherently lack temporal resolution, thereby limiting our ability to capture underlying dynamical processes. Current dynamical reconstruction approaches face complementary limitations: data-driven methods usually lack interpretation of molecular mechanisms, whereas model-driven strategies usually lack integration of quantitative experimental data. Here, we introduce RESIDE, a unified single-cell framework that integrates model- and data-driven methods for stochastic dynamics reconstruction, coupled with concurrent network structure inference and quantification of steady-state distributions, vector fields, and underlying landscapes. Across the tested in silico systems, RESIDE showed favorable performance relative to the compared methods. Applied to single-cell data from mouse preimplantation development, it recovered differentiation features consistent with the observed cell-state organization and predicted directionally asymmetric transdifferentiation paths. Overall, RESIDE provides a mechanistically interpretable framework for jointly inferring network interactions and effective stochastic dynamics from single-cell snapshot data, with potential value for hypothesis generation and experimental investigation.

 07

Canrong Tian, Mengxin Chen, Lan Zou

Chemotaxis-Induced Turing Instability and Steady-State Bifurcation in a Ross–Lotka Epidemic Model on Compact Metric Graphs. CSIAM Transactions on Life Sciences, 2(3), 551-578. https://doi.org/10.4208/csiam-ls.so-2026-0517.

摘要：We study a chemotactic Ross–Lotka malaria model on a compact metric graph, where spatial movement is described by graph diffusion together with mosquito chemotaxis toward infectious humans. Using the Crandall-Rabinowitz theorem together with a higher-order expansion, we derive a second-order Lyapunov-type coefficient, which determines the local stability of the bifurcating branch. Near the bifurcation point, the stable nonconstant steady state is locally unique and dominated by the critical Laplacian eigenmode. Numerical simulations are presented on a diamond-shaped graph, which illustrate that the chemotaxis can induce the occurrence of Turing patterns, and the emerging pattern is selected by the first nonconstant Laplacian mode.

期刊官网：https://www.global-sci.com/csiam-ls

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