Journal of Theoretical Biology最新文献_第7页

Adaptive dynamic resource allocation can cause tragedy of the commons in plants with nutrient competition 在养分竞争中，植物的适应性动态资源配置会引起公地悲剧。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-19 DOI: 10.1016/j.jtbi.2025.112279

Bo-Moon Kim, Atsushi Yamauchi

Plants exhibit plastic responses to the absence or presence of competitors. When competing for soil nutrients, plants often show root overproliferation compared to when they grow without competitors. This excessive investment in roots to acquire more nutrients can reduce reproductive yield (e.g., seed mass), a phenomenon known as the tragedy of the commons (TOC). The mechanisms of this phenomenon have been investigated theoretically, focusing on resource allocation strategies between the aboveground (shoot) and the belowground (roots) parts. The previous studies have primarily considered these strategies in terms of sizes of those parts or static allocation rates to those over the season, overlooking dynamic change of allocation within the season. In this study, we introduced a concept of dynamic resource allocation into the plant competition game and investigate the optimal resource allocation strategy using Pontryagin’s maximum principle. Based on the solutions of schedules, we explored the mechanism causing TOC in nutrient competition. Our findings reveal that plants adopt the singular control (i.e., simultaneous allocation to shoot and root), where the control trajectory is identical regardless of the presence or absence of competitors, although the period of simultaneous allocation become longer in the presence of competitors. This trend associates with increasing the root size and decreasing the shoot size at the end of season in the competitive case. Our analysis demonstrates that TOC in plant nutrient competition arises from differences in the allocation period to roots in the competitive scenario.

植物对竞争对手的缺席或存在表现出可塑性反应。在争夺土壤养分时，与没有竞争对手生长时相比，植物往往表现出根系过度增生。这种为获取更多营养而对根系进行的过度投资会降低生殖产量（如种子质量），这种现象被称为公地悲剧（TOC）。这一现象的机制已经从理论上进行了研究，重点是地上部分（茎）和地下部分（根）之间的资源分配策略。以往的研究主要是考虑这些策略在这些部分的大小或静态分配率的季节，忽略了动态变化的分配在季节内。本文将动态资源分配的概念引入植物竞争博弈中，利用庞特里亚金最大值原理研究植物竞争博弈的最优资源分配策略。在此基础上，探讨了养分竞争中TOC产生的机理。研究结果表明，植物采用单一控制（即同时分配给茎和根），无论竞争对手是否存在，控制轨迹都是相同的，尽管竞争对手存在时同时分配的时间更长。这一趋势与竞争条件下季末根粗增加、茎粗减少有关。分析表明，植物养分竞争中的TOC是由竞争情景下分配给根系的时间差异引起的。

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引用次数: 0

Effective decoupling of mutations and the resulting loss of biodiversity caused by environmental change 环境变化导致的突变和生物多样性损失的有效解耦。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-19 DOI: 10.1016/j.jtbi.2025.112277

Ruixi Huang , David Waxman

Many biological populations exhibit diversity in their strategy for survival and reproduction in a given environment, and microbes are an example. We explore the fate of different strategies under sustained environmental change by considering a mathematical model for a large population of asexual organisms. Fitness is a bimodal function of a quantitative trait, with two local optima, separated by a local minimum, i.e., a mixture of stabilising and disruptive selection. The optima represent two locally ‘best’ trait values. We consider regimes where, when the environment is unchanging, the equilibrium distribution of the trait is bimodal. A bimodal trait distribution generally requires, for its existence, mutational coupling between the two peaks, and it indicates two coexisting clones with distinct survival and reproduction strategies. When subject to persistent environmental change, the population adapts by utilising mutations that allow it to track the changing environment. The faster the rate of change of the environment, the larger the effect of the mutations that are utilised. Under persistent environmental change, the distribution of trait values takes two different forms. At low rates of change, the distribution remains bimodal. At higher rates, the distribution becomes unimodal. This loss of a clone/biodiversity is driven by a novel mechanism where environmental change decouples a class of mutations.

许多生物种群在特定环境中表现出生存和繁殖策略的多样性，微生物就是一个例子。我们通过考虑大量无性生物种群的数学模型，探讨了在持续环境变化下不同策略的命运。适应度是数量性状的双峰函数，有两个局部最优，由一个局部最小值隔开，即稳定选择和破坏性选择的混合。最优值代表两个局部“最佳”性状值。我们考虑在环境不变的情况下，性状的平衡分布是双峰的。性状的这种分布通常需要两个峰之间的突变耦合才能存在。双峰性状分布表明两个共存的无性系具有不同的生存和繁殖策略。当受到持续环境变化的影响时，种群通过利用突变来适应环境的变化。环境变化的速度越快，所利用的突变的影响就越大。在持续的环境变化下，性状值的分布呈现出两种不同的形式。在低变化率下，分布仍然是双峰的。在较高的速率下，分布变成单峰。这种克隆/生物多样性的丧失是由一种新机制驱动的，即环境变化使一类突变解耦。

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引用次数: 0

Gaussian process modelling of infectious diseases using the Greta software package and GPUs 使用Greta软件包和gpu的传染病高斯过程建模。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-18 DOI: 10.1016/j.jtbi.2025.112278

Eva Gunn , Nikhil Sengupta , Ben Swallow

Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta software for Bayesian inference to apply Gaussian process regression to spatio-temporal data of infectious disease outbreaks and predict future outbreaks. Greta builds on Tensorflow, making it comparatively easy to take advantage of the significant gain in speed offered by GPUs. In these complex spatio-temporal models, we show a reduction of up to 70% in computational time relative to fitting the same models on CPUs. We show how the choice of covariance kernel impacts the ability to infer spread and extrapolate to unobserved spatial and temporal units. The inference pipeline is applied to weekly incidence data on tuberculosis in the East and West Midlands regions of England over a period of two years.

高斯过程是一种广泛使用的统计工具，用于在应用科学中进行非参数推理，有许多计算包可用于拟合数据和预测未来的观测。我们研究了使用Greta软件进行贝叶斯推理，将高斯过程回归应用于传染病暴发的时空数据并预测未来的暴发。Greta建立在Tensorflow的基础上，使得它相对容易地利用gpu提供的显著速度增益。在这些复杂的时空模型中，我们发现相对于在cpu上拟合相同的模型，计算时间减少了高达70%。我们展示了协方差核的选择如何影响推断扩散和外推到未观察到的空间和时间单位的能力。推理管道应用于英格兰东部和西部米德兰兹地区两年期间的结核病每周发病率数据。

引用次数: 0

Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study 用于癌症治疗的载药纳米颗粒：高通量多细胞药物模型研究。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-18 DOI: 10.1016/j.jtbi.2025.112266

Yafei Wang , John Metzcar , Elmar Bucher , Heber L. Rocha , Vikram Jadhao , Randy Heiland , Hermann B. Frieboes , Paul Macklin

Interactions between biological systems and engineered nanomaterials have become an important area of study due to their application in medicine. In particular, the opportunity to apply nanomaterials for cancer diagnosis and treatment presents a challenge due to the complex biology of this disease, which spans multiple time and spatial scales. A systems-level analysis from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated system parameters and patient response would be of benefit. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, inheritance of NPs by daughter cells at cell division, cell pharmacodynamic response to intracellular drug levels, and overall drug effect on tumor growth. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on tumor growth. In particular, through the exploration of NP inheritance at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dose of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by heritable NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

生物系统与工程纳米材料之间的相互作用由于其在医学上的应用而成为一个重要的研究领域。特别是，由于这种疾病的复杂生物学跨越多个时间和空间尺度，将纳米材料应用于癌症诊断和治疗的机会提出了挑战。通过数学建模和计算模拟的系统级分析来探索抗癌药物负载纳米颗粒（NPs）、细胞和组织之间的相互作用，以及相关的系统参数和患者反应将是有益的。尽管过去有许多模型探索了这些相互作用，但很少有模型专注于模拟单个细胞- np相互作用。本研究建立了一种基于多细胞药物的癌症纳米治疗模型，模拟了NP内化、细胞质内药物释放、子细胞分裂时NP的遗传、细胞对细胞内药物水平的药效学反应以及药物对肿瘤生长的总体影响。采用大规模并行计算框架研究药代动力学设计参数（NP内化率、NP衰减率、抗癌药物释放率）和治疗策略（NP剂量和注射频率）对肿瘤生长的影响。特别是，通过对细胞分裂时NP遗传的探索，结果表明，当细胞分裂时遗传NPs用于细胞毒性化疗时，可能会改善癌症治疗。此外，在细胞分裂未被完全抑制的情况下，小剂量的细胞抑制化疗也可提高对肿瘤生长的抑制作用。这项工作表明，遗传NPs的缓慢递送可以推动纳米治疗设计的新维度，以获得更持久的治疗反应。

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引用次数: 0

Stably encoding phylogenetic trees with folios of leaf addresses 稳定地编码具有叶位对开的系统发育树。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-16 DOI: 10.1016/j.jtbi.2025.112265

Mark M. Tanaka , Ruiting Lan , Andrew R. Francis

As genome sequencing data continue to expand, a persistent research challenge is to accommodate the growth of a phylogeny. This situation arises in molecular epidemiology, for example, where new taxonomic groups can appear in real time as pathogen isolates are sequenced. Efficient computational methods have been developed to place new leaves in existing trees, which removes the need to reconstruct trees from scratch. But for these tree extensions to be fully integrated with classification schemes requires a stable encoding of trees that keeps existing tree structures intact as new branches appear. Here, we propose a tree encoding, which we call a folio, that records the path from a reference vertex to each leaf, giving each leaf an address. We present a simple set of rules to assign new addresses to added leaves. The encoding is stable in the sense that it does not change as further leaf addresses are added to the folio. The tree can be uniquely recovered from a folio of addresses. We illustrate the methods using Salmonella genome data. Due to the properties of our encoding framework, we anticipate that it can be used for a range of different phylogenetic analyses.

随着基因组测序数据的不断扩大，一个持续的研究挑战是适应系统发育的增长。这种情况出现在分子流行病学中，例如，随着病原体分离物的测序，新的分类群可以实时出现。有效的计算方法已经被开发出来，可以在现有的树木上放置新的叶子，从而消除了从头开始重建树木的需要。但是，为了使这些树的扩展与分类方案完全集成，需要对树进行稳定的编码，使现有的树结构在新分支出现时保持完整。在这里，我们提出了一种树编码，我们称之为folio，它记录了从参考顶点到每个叶子的路径，给每个叶子一个地址。我们给出了一组简单的规则来为添加的叶子分配新地址。编码是稳定的，因为它不会随着进一步的叶地址添加到组合中而改变。树可以唯一地从一组地址中恢复。我们用沙门氏菌基因组数据来说明方法。由于我们的编码框架的特性，我们预计它可以用于一系列不同的系统发育分析。

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引用次数: 0

Modelling phylogeny in 16S rRNA gene sequencing datasets using string-based kernels 基于字符串核的16S rRNA基因测序数据集系统发育建模。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-12 DOI: 10.1016/j.jtbi.2025.112249

Jonathan Ish-Horowicz , Sarah Filippi

The bacterial microbiome is increasingly being recognised as a key factor in human health, driven in large part by datasets collected using 16S rRNA (ribosomal ribonucleic acid) gene sequencing, which enable cost-effective quantification of the composition of an individual’s bacterial community. One of the defining characteristics of 16S rRNA datasets is the evolutionary relationships that exist between taxa (phylogeny). Here, we demonstrate the utility of modelling these phylogenetic relationships in two statistical tasks (the two sample test and host trait prediction) and propose a novel family of kernels for analysing microbiome datasets by leveraging string kernels from the natural language processing literature. We show via simulation studies that a kernel two-sample test using the proposed kernel is sensitive to the phylogenetic scale of the difference between the two populations. In a second set of simulations we also show how Gaussian process modelling with string kernels can infer the distribution of bacterial-host effects across the phylogenetic tree and apply this approach to a real host-trait prediction task. The results in the paper can be reproduced by running the code at https://github.com/jonathanishhorowicz/modelling_phylogeny_in_16srrna_using_string_kernels.

细菌微生物组越来越被认为是人类健康的一个关键因素，这在很大程度上是由使用16S rRNA（核糖体核糖核酸）基因测序收集的数据集推动的，这种测序能够经济有效地量化个人细菌群落的组成。16S rRNA数据集的定义特征之一是存在于分类群（系统发育）之间的进化关系。在这里，我们展示了在两个统计任务（两个样本测试和宿主性状预测）中建模这些系统发育关系的效用，并提出了一个新的核家族，通过利用自然语言处理文献中的字符串核来分析微生物组数据集。我们通过模拟研究表明，使用所提出的核的核双样本测试对两个种群之间差异的系统发育规模敏感。在第二组模拟中，我们还展示了使用串核的高斯过程建模如何推断细菌-宿主效应在系统发育树中的分布，并将这种方法应用于真正的宿主性状预测任务。通过运行https://github.com/jonathanishhorowicz/modelling_phylogeny_in_16srrna_using_string_kernels上的代码可以复制本文中的结果。

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引用次数: 0

Intracellular ISG-virus interactions determine viral infection severity and persistence 细胞内isg -病毒相互作用决定病毒感染的严重程度和持久性。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-12 DOI: 10.1016/j.jtbi.2025.112251

Anass Bouchnita , Vitaly Volpert

In innate immune response, type I interferons (IFNs) activate interferon-stimulated genes (ISGs), which suppress viral replication and secretion at the intracellular level. Yet, how these ISG-virus interactions shape infection progression and severity remains poorly understood. Here, we introduce a new viral infection model that explicitly incorporates intracellular ISG-virus dynamics. It structures, for the first time, infected cells based on viral load and ISG expression which offers a computationally efficient and adaptable approach to integrating ISG-virus intracellular dynamics into viral kinetics frameworks. We validate this new approach using patient data for pre-alpha COVID-19 strain and an HIV, then we use it to study the impact of ISG-virus kinetics on viral infection severity and persistence. Our simulations reveal that increased ISG induction prolongs infection by suppressing type I IFN production in infected cells and preventing tissue cell depletion. We further show that effective ISG-mediated viral suppression is critical for controlling infection severity. Finally, the model predicts that moderate viral secretion optimizes viral load production. Overall, the developed framework offers a flexible and computationally efficient tool for exploring the impact of intracellular type I interferon signaling on viral infections. It can be easily adapted to specific diseases and extended with pharmacokinetics-pharmacodynamics models to identify key therapeutic targets for drug development.

在先天免疫应答中，I型干扰素（ifn）激活干扰素刺激基因（ISGs），从而在细胞内水平抑制病毒的复制和分泌。然而，这些isg病毒相互作用如何影响感染进展和严重程度仍然知之甚少。在这里，我们引入了一个新的病毒感染模型，明确纳入细胞内isg病毒动力学。它首次基于病毒载量和ISG表达构建受感染细胞，这为将ISG病毒细胞内动力学整合到病毒动力学框架中提供了一种计算效率高且适应性强的方法。我们使用前α - COVID-19毒株和HIV的患者数据验证了这种新方法，然后我们用它来研究isg病毒动力学对病毒感染严重程度和持久性的影响。我们的模拟显示，ISG诱导的增加通过抑制感染细胞中I型IFN的产生和防止健康细胞耗竭来延长感染。我们进一步表明有效的isg介导的病毒抑制对控制感染严重程度至关重要。最后，该模型预测适度的病毒分泌可以优化病毒载量的产生。总的来说，开发的框架为探索细胞内I型干扰素信号传导对病毒感染的影响提供了一个灵活且计算效率高的工具。它可以很容易地适应于特定疾病，并与药代动力学-药效学模型扩展，以确定药物开发的关键治疗靶点。

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引用次数: 0

A kinetic study of multi-substrate uniporters 多底物单转运体的动力学研究。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-09 DOI: 10.1016/j.jtbi.2025.112267

Ana S. de Pereda, Jihyun Park, Lily S. Cheung

Transporters play key roles in regulating the movement of molecules into and out of cells. Uniporters, the simplest class of transporters, use facilitated diffusion to translocate molecules across membranes down their concentration gradient. This process can be affected by the presence of additional substrates in the intra- and extracellular environment, which can either increase the net transport rate of a molecule via trans acceleration or decrease it via competitive inhibition. In this study, we derived mathematical models to describe the net transport rate of uniporters in the presence of multiple extracellular substrates or inhibitors. Analyses of these models identified four possible states for the system when two substrates are present, with two states leading to trans acceleration and the other two states resulting in inhibition. Finally, we found that the relation between kinetic constants that controls the fraction of transporters in the inward-facing open state is responsible for these behaviors. Our theoretical results provide a mathematical framework for understanding the dynamic response of uniporters in the presence of multiple substrates and inhibitors, which could have implications for various processes, from nutrient utilization to metabolic engineering.

转运蛋白在调节分子进出细胞的运动中起着关键作用。单转运蛋白是最简单的一类转运蛋白，它利用便利的扩散使分子沿着浓度梯度跨膜转移。这一过程可能受到细胞内和细胞外环境中存在的其他底物的影响，这些底物可以通过反加速增加分子的净运输速率，也可以通过竞争性抑制降低分子的净运输速率。在这项研究中，我们推导了数学模型来描述存在多种细胞外底物或抑制剂时单转运蛋白的净转运率。对这些模型的分析确定了两种底物存在时系统的四种可能状态，其中两种状态导致反加速，另外两种状态导致抑制。最后，我们发现控制向内开放态转运体比例的动力学常数之间的关系是这些行为的原因。我们的理论结果为理解单转运蛋白在多种底物和抑制剂存在下的动态响应提供了一个数学框架，这可能对从营养利用到代谢工程的各种过程产生影响。

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引用次数: 0

Phenomenological modeling of gene transcription by approximating cooperativity of transcription factors improves prediction and reduces complexity in gene regulatory network models 通过近似转录因子的协同性来建立基因转录的现象学模型，可以提高预测能力，降低基因调控网络模型的复杂性。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-07 DOI: 10.1016/j.jtbi.2025.112264

Thiruvickraman Jothiprakasam, Siddharth Jhunjhunwala

Several computational models are available for representing the gene expression process, with each having their advantages and disadvantages. Phenomenological models are widely used as they make appropriate simplifications that aim to find a middle ground between accuracy and complexity. The existing phenomenological models compete in terms of how the transcription initiation process is approximated, to achieve high accuracy while having the lowest complexity possible. However, most current models still suffer from high parameter complexity in the case of complex promoters. Herein, we formally derive a phenomenological approach to model RNA polymerase recruitment, stating approximations on cooperativity between transcription factors that are applicable to promoters requiring multifactorial input, which reduces parameter complexity. We then apply this method to biologically relevant networks of varying complexities to show that the approximations improved predictive ability compared to existing models. In summary, our reduced parameter model (RPM) had lower complexity while maintaining high accuracy, which leads to better scalability for complex networks.

有几种计算模型可用于表示基因表达过程，每种模型都有其优点和缺点。现象学模型被广泛使用，因为它们进行了适当的简化，旨在找到准确性和复杂性之间的中间地带。现有的现象学模型在如何近似转录起始过程方面存在竞争，以达到高精度，同时具有尽可能低的复杂性。然而，目前大多数模型在复杂启动子的情况下仍然存在较高的参数复杂度。在此，我们正式推导了一种现象学方法来模拟RNA聚合酶募集，说明了适用于需要多因子输入的启动子的转录因子之间的协同性的近似，从而降低了参数的复杂性。然后，我们将这种方法应用于不同复杂性的生物相关网络，以表明与现有模型相比，近似提高了预测能力。总之，我们的降参数模型（RPM）在保持高精度的同时具有较低的复杂性，这使得复杂网络具有更好的可扩展性。

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引用次数: 0

Mathematical model suggests current CAR-macrophage dosage is efficient to low pre-infusion tumour burden but refractory to high tumour burden 数学模型表明，目前的car -巨噬细胞剂量对低输注前肿瘤负荷有效，但对高肿瘤负荷难以耐受。

IF 2 4区数学 Q2 BIOLOGY

Journal of Theoretical Biology

Pub Date : 2025-09-04 DOI: 10.1016/j.jtbi.2025.112263

Shilian Xu , Maoxuan Liu

Chimeric antigen receptor (CAR)-macrophage therapy is a promising approach for tumour treatment due to antigen-specific phagocytosis and tumour clearance. However, the precise impact of tumour burden, dose and dosing regimens on therapeutic outcomes remains poorly understood. We developed ordinary differential equation (ODE) mathematical modelling and utilised parameter inference to analyse in vitro FACS-based phagocytosis assay data testing CD19-positive Raji tumour cell against CAR-macrophage, and revealed that phagocytosing efficiency of CAR-macrophage increases but saturates as both Raji cell and CAR-macrophage concentrations increase. This interaction resulted in bistable Raji cell kinetics; specifically, within a particular range of CAR-macrophage concentration, low tumour burdens are effectively inhibited, while high tumour burdens remain refractory. Furthermore, our model predicted that CAR-macrophage dosages typically suggested by current clinical trials yield favourable therapeutic outcomes only when tumour burden is low. For split CAR-macrophage infusion with fixed total dosage, the first infusion with high CAR-macrophage dose delivers superior treatment outcomes. Finally, we identified alternative infusion regimens: five billion cells administered monthly for three months, or seven billion cells every two months for six months, can efficiently suppress Raji cell replication irrespective of tumour burden. Our findings highlight CAR-macrophage therapeutic outcomes are strongly influenced by both tumour burden and different dosing regimens. This work underscores that reducing tumour burden, increasing CAR-macrophage dose in the first infusion and prolonging CAR-macrophage persistence are key strategies for achieving durable responses.

嵌合抗原受体(CAR)-巨噬细胞疗法是一种很有前途的治疗肿瘤的方法，由于抗原特异性吞噬和肿瘤清除。然而，肿瘤负荷、剂量和给药方案对治疗结果的确切影响仍然知之甚少。我们建立了常微分方程（ODE）数学模型，并利用参数推理分析了体外基于facs的吞噬实验数据，测试cd19阳性Raji肿瘤细胞对car -巨噬细胞的吞噬能力，发现car -巨噬细胞的吞噬效率随着Raji细胞和car -巨噬细胞浓度的增加而增加，但饱和。这种相互作用导致双稳态Raji细胞动力学；具体来说，在特定的car -巨噬细胞浓度范围内，低肿瘤负荷被有效抑制，而高肿瘤负荷仍然难以治愈。此外，我们的模型预测，当前临床试验通常建议的car -巨噬细胞剂量只有在肿瘤负荷较低时才能产生良好的治疗效果。对于总剂量固定的car -巨噬细胞分裂输注，首次高剂量car -巨噬细胞输注具有较好的治疗效果。最后，我们确定了替代输注方案：每月给药50亿个细胞，持续3个月，或每两个月给药70亿个细胞，持续6个月，可以有效抑制Raji细胞的复制，而不考虑肿瘤负荷。我们的研究结果强调了car -巨噬细胞治疗结果受到肿瘤负荷和不同给药方案的强烈影响。这项工作强调，减少肿瘤负担、增加首次输注car -巨噬细胞剂量和延长car -巨噬细胞持久性是实现持久反应的关键策略。

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引用次数: 0