Development of a computational model of synaptome architecture.

突触体结构计算模型的开发。

• 批准号: BB/X009343/1
• 负责人: James Armstrong
• 金额: $ 53.52万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX009343%2F1
• 关键词: Development; computational; model; synaptome; architecture.

The brain is the most complex organ in biology and its complexity comes from its many layers of organisation Regions within the brain are specialised to serve different functions (e.g some regions handle vision while other underpin cognition and learning and others motor control). Each of these regions comprises neurons which connect to one another forming neuronal networks or circuits. At yet another level each of these neurons connects to its partners using highly specialised and complex molecular complexes known as synapses. The components of these synapses are highly dynamic through the lifespan and are region specific. However, our understanding of how brains work at this chemical level and how these age and region specific changes impact on brain function is very poorly understood. The long term impact of this understanding is vitally important to society as a whole. We need to understand these changes so we can develop new methods to treat the brain when it deteriorates with age, with disease or in response to trauma. Our project will develop new ways to bring together the information about these chemical synapses to give us a new understanding of this critical level. The data to start addressing these fundamental questions exists in part but is widely scattered and the methods for integrating these data together are in their infancy. This research will address both issues seeking to release a computational model of how these synapses can be predicted from the data available. We will release a public database for other researchers in academia and in the pharmaceutical industry so impact of what we do can be maximised. We will also validate our work by testing a selection of our predictions in the aging brain to confirm how well our computational models and algorithms perform.While our project is based on understanding the fundamental principles it is also of critical importance in terms of long-term health: These chemical synapses are the site at which drugs act but our understanding of how they vary need to be improved to unlock a new generation of drugs can be targeted to keep the brain healthy.

大脑是生物学中最复杂的器官，它的复杂性来自于它的许多组织层，大脑中的区域专门为不同的功能服务（例如，一些区域处理视觉，而其他区域支持认知和学习，其他区域控制运动）。这些区域中的每一个都由神经元组成，这些神经元相互连接，形成神经元网络或回路。在另一个层面上，这些神经元中的每一个都通过称为突触的高度专业化和复杂的分子复合物与同伴连接。这些突触的组成部分在整个生命周期中都是高度动态的，并且是特定区域的。然而，我们对大脑在这种化学水平上是如何工作的，以及这些年龄和区域的特定变化是如何影响大脑功能的，了解得很少。这种认识的长期影响对整个社会至关重要。我们需要了解这些变化，这样我们才能开发出新的方法来治疗随着年龄增长、疾病或创伤而恶化的大脑。我们的项目将开发新的方法来汇集有关这些化学突触的信息，以使我们对这一关键水平有新的了解。开始解决这些基本问题的数据部分存在，但广泛分散，将这些数据整合在一起的方法还处于起步阶段。这项研究将解决这两个问题，寻求发布一个如何从现有数据预测这些突触的计算模型。我们将为学术界和制药行业的其他研究人员发布一个公共数据库，以便我们所做的工作的影响能够最大化。我们还将通过在老化的大脑中测试我们的预测来验证我们的工作，以确认我们的计算模型和算法的表现如何。虽然我们的项目是基于对基本原理的理解，但就长期健康而言，它也至关重要：这些化学突触是药物起作用的部位，但我们对它们如何变化的理解需要得到改进，以解锁新一代的药物，可以有针对性地保持大脑健康。