通过生物约束微分方程系统模拟老年痴呆症动物模型中的合并单胺能耗竭。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2024-08-23 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1386841
Samuele Carli, Luigi Brugnano, Daniele Caligiore
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引用次数: 0

摘要

导言:帕金森病(Parkinson's Disease,PD)的研究历来侧重于黑质紧密团多巴胺分泌细胞的功能障碍,该细胞与基底节的运动调节有关。治疗方法主要以恢复多巴胺(DA)水平为目标,虽然有效,但疗效和副作用各不相同。最近的证据表明,帕金森病的复杂性牵涉到DA、去甲肾上腺素(NA)和5-羟色胺(5-HT)系统的紊乱,这可能是治疗效果变化的原因:我们提出了一个系统级生物约束计算模型,该模型全面研究了这些神经递质系统之间的动态相互作用。该模型旨在复制实验数据,证明在帕金森病动物模型中NA和5-羟色胺耗竭的影响,为了解基底神经节区域和神经调节剂释放区域之间的因果关系提供见解:结果:该模型成功地复制了实验数据,并预测了尚未探索的大脑区域的变化,为进一步研究提供了途径。尽管这些初步研究结果还需要进一步验证,但它凸显了针对神经丘脑和背侧剑突核的替代疗法的潜在疗效。敏感性分析确定了关键的模型参数,为了解影响脑区活动的关键因素提供了见解。稳定性分析强调了我们数学表述的稳健性,增强了模型的有效性:我们的整体方法强调了帕金森病是一种多因素疾病,并为利用单胺类药物系统之间错综复杂的相互作用开发早期诊断工具开辟了前景广阔的途径。在研究DA系统的同时研究NA和5-HT系统可能会产生更有效的亚型特异性疗法。对帕金森病多系统失调的探索将彻底改变我们对这种复杂神经退行性疾病的理解和管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulating combined monoaminergic depletions in a PD animal model through a bio-constrained differential equations system.

Introduction: Historically, Parkinson's Disease (PD) research has focused on the dysfunction of dopamine-producing cells in the substantia nigra pars compacta, which is linked to motor regulation in the basal ganglia. Therapies have mainly aimed at restoring dopamine (DA) levels, showing effectiveness but variable outcomes and side effects. Recent evidence indicates that PD complexity implicates disruptions in DA, noradrenaline (NA), and serotonin (5-HT) systems, which may underlie the variations in therapy effects.

Methods: We present a system-level bio-constrained computational model that comprehensively investigates the dynamic interactions between these neurotransmitter systems. The model was designed to replicate experimental data demonstrating the impact of NA and 5-HT depletion in a PD animal model, providing insights into the causal relationships between basal ganglia regions and neuromodulator release areas.

Results: The model successfully replicates experimental data and generates predictions regarding changes in unexplored brain regions, suggesting avenues for further investigation. It highlights the potential efficacy of alternative treatments targeting the locus coeruleus and dorsal raphe nucleus, though these preliminary findings require further validation. Sensitivity analysis identifies critical model parameters, offering insights into key factors influencing brain area activity. A stability analysis underscores the robustness of our mathematical formulation, bolstering the model validity.

Discussion: Our holistic approach emphasizes that PD is a multifactorial disorder and opens promising avenues for early diagnostic tools that harness the intricate interactions among monoaminergic systems. Investigating NA and 5-HT systems alongside the DA system may yield more effective, subtype-specific therapies. The exploration of multisystem dysregulation in PD is poised to revolutionize our understanding and management of this complex neurodegenerative disorder.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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