在细胞中模拟非遗传信息动态,使用储库计算
Dipesh Niraula1, Issam El Naqa1, Jack Adam Tuszynski2,3,4
1Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
iScience
|April 18, 2024
概括
细胞离子梯度对生命至关重要,可能使细胞能够感知并对环境做出反应. 这种动态系统使用通过道和细胞骨传输信号的离子流,影响细胞功能.
科学领域:
- 细胞生物学 细胞生物学
- 生物物理学的生物物理.
- 进化生物学 进化生物学
背景情况:
- 细胞利用能量依赖的离子,为 (Na+), (K+),化物 (Cl-), (Mg++), (Ca++) 等离子建立跨膜梯度.
- 这些广泛的离子梯度的进化优势尚未完全阐明.
研究的目的:
- 为细胞离子梯度的进化益处提出一个新的假设.
- 表明离子梯度促进细胞信息处理和环境反应.
主要方法:
- 基于对离子运输和细胞动力学现有知识的理论建模和假设制定.
- 整合离子流,膜通道,细胞骨和器官相互作用的概念框架.
主要成果:
- 离子梯度是获取,分析和响应环境信息的动态系统.
- 环境信号可以通过通过特定的膜通道通过离子流转化为细胞反应.
- 细胞质离子度的变化可以触发局部或全球细胞反应,涉及细胞骨,细胞内网,线粒体和细胞核.
结论:
- 细胞离子梯度被认为是多功能生物信息处理系统的基础.
- 该模型为了解细胞如何通过离子流信号与环境动态相互作用提供了一个框架.
- 需要进一步的研究,以实验验证拟议的离子梯度介导细胞信号传输机制.
更多相关视频
11:23A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
Published on: October 6, 2019
10.2K
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
2.1K
相关概念视频
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
53
Mechanistic Models: Compartment Models in Individual and Population Analysis
39
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
39
