Cell Behavior

• New submissions
• Replacements

See recent articles

Showing new listings for Thursday, 25 September 2025

New submissions (showing 1 of 1 entries )

[1] arXiv:2509.20303 [cn-pdf, pdf, html, other]

Title: An energy-based mathematical model of actin-driven protrusions in eukaryotic chemotaxis Show Chinese title

Title: 一种基于能量的真核趋化作用中肌动蛋白驱动的突出数学模型

Samuel W.S. Johnson, Maddy Parsons, Ruth E. Baker, Philip K. Maini

Comments: 44 pages, 8 figures

Subjects: Cell Behavior (q-bio.CB)

In eukaryotic cell chemotaxis, cells extend and retract transient actin-driven protrusions at their membrane that facilitate both the detection of external chemical gradients and directional movement via the formation of focal adhesions with the extracellular matrix. Although extensive experimental work has detailed how cellular protrusions and morphology vary under different environmental conditions, the mechanistic principles linking protrusive activity to these factors remain poorly understood. Here, we model the extension of actin-based protrusions in chemotaxis as an optimisation problem, wherein cells balance the detection of chemical gradients with the energetic cost of protrusion formation. Our model, built on the assumption of energy minimisation, provides a framework that successfully reproduces experimentally observed patterns of protrusive activity across a range of biological systems and environmental conditions, suggesting that energetic efficiency may underpin the morphology and chemotactic behaviour of motile eukaryotic cells. Additionally, we leverage the model to generate novel predictions regarding cellular responses to other, experimentally untested environmental perturbations, providing testable hypotheses for future experimental work that may be used to validate and refine the model presented here. Show Chinese abstract

在真核细胞趋化过程中，细胞在其膜上延伸和收缩短暂的肌动蛋白驱动的突起，这些突起有助于通过与细胞外基质形成粘附斑来检测外部化学梯度和方向性运动。 尽管已有大量实验工作详细描述了细胞突起和形态在不同环境条件下的变化，但将突起活动与这些因素联系起来的机制原理仍不清楚。 在此，我们将趋化作用中基于肌动蛋白的突起延伸建模为一个优化问题，其中细胞平衡化学梯度的检测与突起形成的能量成本。 我们的模型基于能量最小化的假设，提供了一个能够成功再现多种生物系统和环境条件下实验观察到的突起活动模式的框架，表明能量效率可能构成了运动真核细胞的形态和趋化行为的基础。 此外，我们利用该模型生成了关于细胞对其他实验未测试的环境扰动反应的新预测，为未来实验工作提供了可验证的假设，可用于验证和改进本文提出的模型。

Replacement submissions (showing 1 of 1 entries )

[2] arXiv:2509.19536 (replaced) [cn-pdf, pdf, html, other]

Title: Integrating Mechanistic Modeling and Machine Learning to Study CD4+/CD8+ CAR-T Cell Dynamics with Tumor Antigen Regulation Show Chinese title

Title: 将机制建模与机器学习相结合，研究CD4+/CD8+ CAR-T细胞动力学与肿瘤抗原调控

Saranya Varakunan, Melissa Stadt, Mohammad Kohandel

Comments: 26 pages, 8 figures

Subjects: Quantitative Methods (q-bio.QM) ; Cell Behavior (q-bio.CB)

Chimeric antigen receptor (CAR) T cell therapy has shown remarkable success in hematological malignancies, yet patient responses remain highly variable and the roles of CD4 and CD8 subsets are not fully understood. We present an extended mathematical framework of CAR-T cell dynamics that explicitly models CD4 helper and CD8 cytotoxic lineages and their interactions with tumor antigen burden. Building on the Kirouac et al. (2023) model of antigen-regulated memory, effector, and exhaustion transitions, our system of differential equations incorporates cytokine-mediated modulation of CD8 proliferation, cytotoxicity, and memory regeneration by CD4 T cells. Sensitivity analyses identify effector proliferation burst size, antigen turnover, and CD8 expansion rates as dominant determinants of treatment outcome. Virtual patient simulations reproduce clinical findings that a 1:1 CD4:CD8 ratio enhances CAR-T expansion and tumor clearance relative to CD8-only products. Finally, we integrate a feed-forward neural network trained on noisy virtual patient data to improve predictive robustness, and apply SHAP analysis to interpret the network predictions and compare them with mechanistic sensitivity analyses. This work highlights the synergistic roles of CD4 and CD8 CAR-T cells and provides a quantitative foundation for optimizing treatments and patient stratification. Show Chinese abstract

嵌合抗原受体（CAR）T细胞疗法在血液系统恶性肿瘤中表现出显著的成功，但患者的反应仍然高度可变，CD4和CD8亚群的作用尚未完全了解。 我们提出了一种扩展的CAR-T细胞动力学数学框架，该框架明确模拟了CD4辅助和CD8细胞毒性谱系及其与肿瘤抗原负担的相互作用。 基于Kirouac等人（2023）对抗原调控的记忆、效应和耗竭转换的模型，我们的微分方程系统结合了CD4 T细胞对CD8增殖、细胞毒性及记忆再生的细胞因子介导调节。 敏感性分析确定效应细胞增殖爆发大小、抗原周转率和CD8扩增率是治疗结果的主要决定因素。 虚拟患者模拟再现了临床发现，即1:1的CD4:CD8比例相对于仅CD8产品能增强CAR-T扩增和肿瘤清除。 最后，我们将一个在噪声虚拟患者数据上训练的前馈神经网络进行整合，以提高预测的鲁棒性，并应用SHAP分析来解释网络预测并与机制敏感性分析进行比较。 这项工作突出了CD4和CD8 CAR-T细胞的协同作用，并为优化治疗和患者分层提供了定量基础。