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Enlightening the blind spot of the Michaelis–Menten rate law: The role of relaxation dynamics in molecular complex formation 揭示迈克尔-门顿速率定律的盲点:弛豫动力学在分子复合物形成中的作用。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-16 DOI: 10.1016/j.jtbi.2024.111989
Junghun Chae , Roktaek Lim , Thomas L.P. Martin , Cheol-Min Ghim , Pan-Jun Kim
The century-long Michaelis–Menten rate law and its modifications in the modeling of biochemical rate processes stand on the assumption that the concentration of the complex of interacting molecules, at each moment, rapidly approaches an equilibrium (quasi-steady state) compared to the pace of molecular concentration changes. Yet, in the case of actively time-varying molecular concentrations with transient or oscillatory dynamics, the deviation of the complex profile from the quasi-steady state becomes relevant. A recent theoretical approach, known as the effective time-delay scheme (ETS), suggests that the delay from the relaxation time of molecular complex formation contributes to the substantial breakdown of the quasi-steady state assumption. Here, we systematically expand this ETS and inquire into the comprehensive roles of relaxation dynamics in complex formation. Through the modeling of rhythmic protein–protein and protein–DNA interactions and the mammalian circadian clock, our analysis reveals the effect of the relaxation dynamics beyond the time delay, which extends to the dampening of changes in the complex concentration with a reduction in the oscillation amplitude compared to the quasi-steady state. Interestingly, the combined effect of the time delay and amplitude reduction shapes both qualitative and quantitative oscillatory patterns such as the emergence and variability of the mammalian circadian rhythms. These findings highlight the downside of the routine assumption of quasi-steady states and enhance the mechanistic understanding of rich time-varying biomolecular processes.
迈克尔-门顿(Michaelis-Menten)速率定律及其在生化速率过程建模中的修改已沿用了一个世纪,其假设条件是:与分子浓度变化的速度相比,相互作用分子的复合体浓度在每一时刻都迅速接近平衡(准稳态)。然而,对于具有瞬态或振荡动态的时变分子浓度,复合物曲线偏离准稳态的情况就变得非常重要。最近一种被称为有效时间延迟方案(ETS)的理论方法表明,分子复合物形成弛豫时间的延迟会导致准稳态假设的实质性破坏。在这里,我们系统地扩展了这一 ETS,并探究了松弛动力学在复合物形成过程中的综合作用。通过对有节奏的蛋白质-蛋白质和蛋白质-DNA 相互作用以及哺乳动物昼夜节律时钟的建模,我们的分析揭示了弛豫动力学在时间延迟之外的影响,它延伸到了对复合物浓度变化的抑制,与准稳态相比,振荡幅度减小了。有趣的是,时间延迟和振幅减小的综合效应形成了定性和定量振荡模式,如哺乳动物昼夜节律的出现和变化。这些发现凸显了常规准稳态假设的弊端,并加深了对丰富的时变生物分子活动的机理理解。
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引用次数: 0
Vaccination strategies in a pair formation model for human papillomavirus infection: An optimal control approach 人类乳头瘤病毒感染配对形成模型中的疫苗接种策略:最佳控制方法。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-16 DOI: 10.1016/j.jtbi.2024.111994
Fernando Saldaña
Human papillomavirus (HPV) infection is a widespread sexually transmitted infection responsible for several cancers including anal, oropharyngeal, penile, vaginal, and cervical cancer. Despite HPV vaccines have been available for almost 20 years and are incredibly effective in preventing infection, the scale-up of vaccination has been slow in many low and middle-income countries. This analysis uses a pair model that explicitly accounts for sexual partnership formation to investigate HPV immunization programs. The optimality of vaccine interventions is analyzed using optimal control theory. We give formal proof of the existence of optimal control solutions and obtain first-order optimality conditions via Pontryagin’s Maximum Principle. Extensive numerical simulations are used to investigate plausible what-if scenarios to understand under which conditions the inclusion of males should be recommended in addition to female vaccination. The results suggest that a gender-neutral vaccination program should be recommended in regions where vaccination uptake in women is still low whereas for an already existing female-only program with high uptake, it is more effective to keep increasing coverage in females.
人类乳头瘤病毒(HPV)感染是一种普遍的性传播感染,可导致多种癌症,包括肛门癌、口咽癌、阴茎癌、阴道癌和宫颈癌。尽管 HPV 疫苗已问世近 20 年,而且在预防感染方面效果显著,但在许多中低收入国家,疫苗接种的推广却十分缓慢。本分析采用明确考虑性伴侣关系形成的配对模型来研究 HPV 免疫接种计划。我们利用最优控制理论分析了疫苗干预的最优性。我们给出了最优控制解存在性的正式证明,并通过庞特里亚金最大原则获得了一阶最优性条件。我们使用大量的数值模拟来研究各种似是而非的情况,以了解在哪些条件下除了建议女性接种疫苗外,还应该建议男性接种疫苗。结果表明,在女性接种率仍然较低的地区,应建议采用不分性别的疫苗接种计划,而对于接种率较高的现有纯女性计划,继续提高女性的覆盖率则更为有效。
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引用次数: 0
Peripheral straightness leads to shape diversification during formations of entire leaves 外围平直度导致整个叶片形成过程中的形状多样化。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-15 DOI: 10.1016/j.jtbi.2024.111990
Akiko M. Nakamasu
The ways to read out positional information are essential to determine final shapes in developmental processes. Relative shaping to different sizes of positional information enables robust morphogenesis; however, the same difference sometimes causes diversity. Different responses to a positional information will enable such switching of identical/diverse shapes, though detail mechanisms remain unknown.
In this paper, we describe growing forms by constructing the contour of a two-dimensional object using propagating points and segments connecting them. In plant morphogenesis that lacks almost cell movements, tissue growth accompanied by cell divisions is central. We focused on peripheral cell composition in leaf formation as a frame. The growth with or without cell division on the periphery was analyzed with simple algorithms. We calculated the shapes of entire leaves with different ovality using combined growth algorithms as a model. Responces of the respective algorithms to simple positional information were explored to seek the origin of the shape diversification.
The algorithm for “growth with cell divisions” maintained identical shapes; however, diverse shapes were generated by the algorithm “growth without cell division” with gradients. The simplified model allowed us to interpret the oval shape diversity due to slants on edges. We concluded that peripheral straightness can generate shape diversity, at least in leaf morphogenesis.
在发育过程中,读出位置信息的方法对于确定最终形状至关重要。对不同大小的位置信息进行相对塑形,可实现稳健的形态发生;然而,相同的差异有时也会导致多样性。对位置信息的不同反应将使相同/不同形状的切换成为可能,但具体机制仍不清楚。在本文中,我们通过使用传播点和连接点的线段构建二维物体的轮廓来描述生长形态。在几乎没有细胞运动的植物形态发生中,伴随细胞分裂的组织生长是核心。我们以叶片形成过程中的外围细胞组成为框架进行研究。我们用简单的算法分析了外围细胞分裂与否的生长情况。我们以组合生长算法为模型,计算了不同椭圆度的整个叶片的形状。我们探讨了各算法对简单位置信息的响应,以寻找形状多样化的起源。有细胞分裂的生长 "算法保持了相同的形状,而 "无细胞分裂的生长 "算法则产生了不同的梯度形状。通过简化模型,我们可以解释椭圆形的形状多样性是由于边缘的斜度造成的。我们的结论是,至少在叶片形态发生过程中,边缘的平直度可以产生形状多样性。
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引用次数: 0
A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis 结核病预测模型综合研究:将机器学习融入流行病学分析。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-13 DOI: 10.1016/j.jtbi.2024.111988
Hamna Mariyam K.B. , Sayooj Aby Jose , Anuwat Jirawattanapanit , Karuna Mathew
Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million individuals in 2022, after COVID-19, surpassing the toll of HIV and AIDS. With an estimated 10.6 million new TB cases worldwide in 2022, the gravity of the disease persists, necessitating urgent attention. Tuberculosis remains a critical public health crisis, and efforts to combat this infectious disease demand intensified global commitment and resources. This study utilizes predictive modeling techniques to forecast the incidence of Tuberculosis (TB), employing a range of machine learning models. Additionally, the research incorporates impactful visualizations for comprehensive data exploration, analysis and comparison. Various machine learning models are developed to anticipate TB incidence, with the optimal performing model to customize a user-defined function. This research provides valuable insights into the potential determinants influencing TB incidence, contributing to the identification of strategies for preventing the spread of Tuberculosis.
结核病(TB)是全球第二大传染病杀手,2022 年将夺走 130 万人的生命,仅次于 COVID-19,超过艾滋病毒和艾滋病的致死人数。据估计,2022 年全球将新增 1060 万肺结核病例,该疾病的严重性依然存在,亟需引起重视。结核病仍然是一个严重的公共卫生危机,全球必须加大力度、投入更多资源来抗击这一传染病。本研究采用一系列机器学习模型,利用预测建模技术预测结核病(TB)的发病率。此外,该研究还采用了极具影响力的可视化方法来进行全面的数据探索、分析和比较。研究人员开发了各种机器学习模型来预测结核病的发病率,并根据用户定义的函数定制了性能最佳的模型。这项研究为了解影响结核病发病率的潜在决定因素提供了宝贵的见解,有助于确定预防结核病传播的策略。
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引用次数: 0
On the misuse of evolutionary theory to bolster the ‘scientific’ case for intelligent design: A cautionary note 关于滥用进化论为智能设计 "科学 "辩护:警言。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-09 DOI: 10.1016/j.jtbi.2024.111985
Arne Traulsen , Mikkel Nif Rasmussen , Joachim Krug , Andreas Beyer
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引用次数: 0
Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission 基于随机差分模型的空气污染及相关疾病传播的统计推理和神经网络训练。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-08 DOI: 10.1016/j.jtbi.2024.111987
Sha He, Mengqi He, Sanyi Tang
A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the mechanism of air pollution and its effect on the spread of diseases. The key is to characterize the intrinsic correlation between the disease infection and the change in air pollutant concentration. In this paper, we establish a coupled discrete susceptible–exposed–infectious–susceptible (SEIS) model with demography to characterize the transmission of disease, and the change in the concentration of air pollutants is described in the form of the Beverton–Holt (BH) model with a time-varying inflow rate of air pollutants. Considering the periodic variation characteristics of data, time-varying parameters are defined as specific functional forms. We estimate the change point at which the parameters switch and the parameter values within the switching interval based on Bayesian statistical theory. The data fitting of the model can reflect the seasonal peaks and annual growth trends of values of air quality index (AQI) and the number of influenza-like illnesses (ILI) cases. However, the bias in data fitting indicates a more complex correlation pattern between disease and pollutant concentration changes. To explore unknown mechanisms, we propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining deep learning with difference equations and obtain the curves of the transmission rate and inflow rate functions over time. The results show that neural network models can help us determine time-varying parameters in the model, thereby better reflecting the trend of data changes.
污染的空气环境有可能引发各种呼吸道疾病的感染。建立数学模型可以研究空气污染的机理及其对疾病传播的影响。关键是要确定疾病感染与空气污染物浓度变化之间的内在相关性。本文建立了一个具有人口统计学特征的离散易感-暴露-传染-易感(SEIS)耦合模型来表征疾病的传播,并以空气污染物流入率时变的贝弗顿-霍尔特(BH)模型的形式来描述空气污染物浓度的变化。考虑到数据的周期性变化特征,时变参数被定义为特定的函数形式。我们根据贝叶斯统计理论估算了参数切换的变化点以及切换区间内的参数值。模型的数据拟合能够反映空气质量指数(AQI)值和流感样病例数(ILI)的季节性峰值和年度增长趋势。然而,数据拟合的偏差表明疾病与污染物浓度变化之间存在更复杂的相关模式。为了探索未知的机制,我们通过将深度学习与差分方程相结合,提出了扩展的传播动力学信息神经网络(TDINN)算法,并得到了传播率和流入率函数随时间变化的曲线。结果表明,神经网络模型可以帮助我们确定模型中的时变参数,从而更好地反映数据的变化趋势。
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引用次数: 0
Optimal seasonal schedule for producing biogenic volatile organic compounds for tree defense 生产用于树木防御的生物挥发性有机化合物的最佳季节安排。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-08 DOI: 10.1016/j.jtbi.2024.111986
Yoh Iwasa, Rena Hayashi, Akiko Satake
The leaves of many trees emit biogenic volatile organic compounds (BVOCs) that protect them from various threats, including herbivory, pathogens, and heat stress. In a previous study, we analyzed the optimal seasonal schedule for producing isoprene, a highly volatile BVOC, in leaves to mitigate heat damage and maximize net carbon gain. In this paper, we investigate the seasonal production schedule of BVOCs stored in leaves, such as monoterpenes and sesquiterpenes, which decay slowly. When the leaves are bitten, these chemicals are emitted and help to prevent further herbivory. The optimal seasonal schedule, analyzed using Pontryagin’s maximum principle, includes a period of singular control. Producing BVOCs for defense is advantageous if their decay rate is slow and the photosynthetic rate is fast. The amount of BVOCs produced increases with slower decay rate and faster photosynthetic rate. But it does not increase monotonically with the magnitude of the threat. BVOCs are produced earlier than the peak period of the threat for which the chemicals are intended. Based on the results of the model, we discuss the reported variations in BVOC production among different chemical species and tree species, as well as the seasonal patterns of gene expression in different pathways for BVOC production.
许多树木的叶子会释放生物挥发性有机化合物(BVOCs),保护它们免受各种威胁,包括草食性动物、病原体和热应力。在之前的一项研究中,我们分析了叶片产生异戊二烯(一种高挥发性生物挥发性有机化合物)的最佳季节安排,以减轻热损伤并最大限度地增加净碳增量。在本文中,我们研究了储存在叶片中的 BVOC(如单萜烯和倍半萜烯)的季节生产计划,这些 BVOC 腐烂速度较慢。当树叶被咬时,这些化学物质就会释放出来,有助于防止进一步的食草动物侵害。根据庞特里亚金的最大原则分析,最佳季节安排包括一个单一控制期。如果 BVOCs 的衰减速度慢而光合作用速度快,则产生 BVOCs 进行防御是有利的。产生的 BVOC 量会随着衰变速度的减慢和光合作用速度的加快而增加。但它并不随威胁程度的增加而单调增加。BVOC 的产生早于化学品所针对的威胁的高峰期。根据该模型的结果,我们讨论了不同化学品种类和树种之间 BVOC 生成量的变化,以及 BVOC 生成不同途径中基因表达的季节性模式。
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引用次数: 0
Implementation of actin polymerization and depolymerization in a two-dimensional cell migration model and its implications on mammalian cell morphology and velocity 二维细胞迁移模型中肌动蛋白聚合和解聚的实现及其对哺乳动物细胞形态和速度的影响。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-05 DOI: 10.1016/j.jtbi.2024.111977
Lingxing Yao , Yizeng Li
Cell migration, a pivotal process in wound healing, immune response, and even cancer metastasis, manifests through intricate interplay between morphology, speed, and cytoskeletal dynamics. Mathematical modeling emerges as a powerful tool to dissect these complex interactions. This work presents a two-dimensional immersed boundary model for mammalian cell migration, incorporating both filamentous actin (F-actin) and monomeric actin (G-actin) to explicitly capture polymerization and depolymerization. This model builds upon our previous one-dimensional efforts, now enabling us to explore the impact of G-actin on not just cell velocity but also morphology. We compare predictions from both models, revealing that while the one-dimensional model captures core dynamics along the cell’s axis, the two-dimensional model excels in portraying cell shape evolution and transverse variations in actin concentration and velocity. Our findings highlight the crucial role of including G-actin in shaping cell morphology. Actin velocity aligned with migration direction elongates the cell, while velocity normal to the membrane promotes spreading. Importantly, the model establishes a link between these microscopic aspects and macroscopic observables like cell shape, offering a deeper understanding of cell migration dynamics. This work not only provides a more comprehensive picture of cell migration but also paves the way for future studies exploring the interplay of actin dynamics, cell morphology, and biophysical parameters in diverse biological contexts.
细胞迁移是伤口愈合、免疫反应甚至癌症转移的关键过程,它通过形态、速度和细胞骨架动力学之间错综复杂的相互作用表现出来。数学建模是剖析这些复杂相互作用的有力工具。本研究提出了哺乳动物细胞迁移的二维沉浸边界模型,其中包含丝状肌动蛋白(F-actin)和单体肌动蛋白(G-actin),以明确捕捉聚合和解聚过程。该模型建立在我们之前的一维模型基础之上,使我们现在能够探索 G-actin 不仅对细胞速度而且对形态的影响。我们比较了两种模型的预测结果,发现一维模型能捕捉到沿细胞轴向的核心动态,而二维模型则能出色地描绘细胞形态演变以及肌动蛋白浓度和速度的横向变化。我们的发现凸显了 G-肌动蛋白在塑造细胞形态中的关键作用。与迁移方向一致的肌动蛋白速度会拉长细胞,而与膜正常方向一致的速度则会促进细胞扩散。重要的是,该模型在这些微观方面与细胞形状等宏观观测指标之间建立了联系,从而加深了对细胞迁移动力学的理解。这项工作不仅为细胞迁移提供了一个更全面的图景,还为未来探索肌动蛋白动力学、细胞形态和生物物理参数在不同生物环境中的相互作用的研究铺平了道路。
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引用次数: 0
Event-based biological pest control: An LMI approach 基于事件的生物害虫控制:LMI 方法
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-10-28 DOI: 10.1016/j.jtbi.2024.111975
M. Sathishkumar , Maya Joby , Srimanta Santra , Yong-Ki Ma , S. Marshal Anthoni
This study focuses on the event-triggered control approach for the mathematical model describing the interaction between the sugarcane borer (Diatraea saccharalis) and its egg parasitoid Trichogramma galloi, as well as the combined interaction of Trichogramma galloi and Cotesia flavipes. By employing digital control design, an effective strategy can be devised to minimize the population of natural enemies. Therefore, proposing an event-triggered control mechanism for the sugarcane borer is essential. The primary objective of this study is to develop an event-triggered reliable state feedback controller, ensuring that the states of the sugarcane borer system converge to the desired steady-state equilibrium points. Additionally, this control design significantly reduces control updates and maintains the introduction of natural enemies into the environment. Ultimately, simulations are carried out using sugarcane borer systems to demonstrate the benefits and effectiveness of the proposed event-triggered design technique.
本研究的重点是对描述甘蔗螟(Diatraea saccharalis)与其卵寄生虫Trichogramma galloi以及Trichogramma galloi和Cotesia flavipes之间相互作用的数学模型进行事件触发控制。通过采用数字控制设计,可以设计出有效的策略,最大限度地减少天敌数量。因此,提出一种针对甘蔗螟的事件触发控制机制至关重要。本研究的主要目标是开发一种事件触发的可靠状态反馈控制器,确保甘蔗螟系统的状态收敛到所需的稳态平衡点。此外,这种控制设计还能大大减少控制更新,并保持将天敌引入环境。最后,我们利用甘蔗螟虫系统进行了模拟,以证明所提出的事件触发设计技术的好处和有效性。
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引用次数: 0
A continuous approach of modeling tumorigenesis and axons regulation for the pancreatic cancer 针对胰腺癌的肿瘤发生和轴突调控的连续建模方法。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-10-23 DOI: 10.1016/j.jtbi.2024.111967
Marie-Jose Chaaya , Sophie Chauvet , Florence Hubert , Fanny Mann , Mathieu Mezache , Pierre Pudlo
The pancreatic innervation undergoes dynamic remodeling during the development of pancreatic ductal adenocarcinoma (PDAC). Denervation experiments have shown that different types of axons can exert either pro- or anti-tumor effects, but conflicting results exist in the literature, leaving the overall influence of the nervous system on PDAC incompletely understood. To address this gap, we propose a continuous mathematical model of nerve-tumor interactions that allows in silico simulation of denervation at different phases of tumor development. This model takes into account the pro- or anti-tumor properties of different types of axons (sympathetic or sensory) and their distinct remodeling dynamics during PDAC development. We observe a “shift effect” where an initial pro-tumor effect of sympathetic axon denervation is later outweighed by the anti-tumor effect of sensory axon denervation, leading to a transition from an overall protective to a deleterious role of the nervous system on PDAC tumorigenesis. Our model also highlights the importance of the impact of sympathetic axon remodeling dynamics on tumor progression. These findings may guide strategies targeting the nervous system to improve PDAC treatment.
胰腺神经支配在胰腺导管腺癌(PDAC)的发展过程中经历了动态重塑。去神经支配实验表明,不同类型的轴突可发挥促癌或抗癌作用,但文献中存在相互矛盾的结果,导致人们对神经系统对 PDAC 的整体影响认识不足。为了填补这一空白,我们提出了一种神经-肿瘤相互作用的连续数学模型,可以在肿瘤发展的不同阶段对神经支配进行硅模拟。该模型考虑了不同类型轴突(交感神经或感觉神经)的促瘤或抗瘤特性,以及它们在 PDAC 发展过程中不同的重塑动态。我们观察到了一种 "转变效应",即交感神经轴突去神经化的初始促瘤效应后来被感觉轴突去神经化的抗瘤效应所抵消,从而导致神经系统对PDAC肿瘤发生的作用从整体保护性转变为有害性。我们的模型还强调了交感神经轴突重塑动态对肿瘤进展影响的重要性。这些发现可能会指导针对神经系统的策略,以改善 PDAC 的治疗。
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引用次数: 0
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