网络最佳检索稀疏扰动,用于稳定状态控制
Krithika Krishnan1, Tiange Shi1, Satyam Kumar1
1Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY 14228, USA www.buffalo.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
我们开发了NORSP (Network Optimal Retrieval of Sparse Perturbations),这是一个计算框架,用于找到控制生物系统的最小基因扰动集. 它能够精确地操纵生物网络,尽管有实验限制.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 网络科学 网络科学
背景情况:
- 鉴定生物网络操纵的最佳目标是具有挑战性的,因为实验的限制.
- 系统生物学需要有效的方法来预测和实现所需的系统状态.
研究的目的:
- 介绍NORSP (稀有干扰的网络最佳检索),一个新的计算框架.
- 使生物系统稳定状态的预测和控制能够使用最小的干扰.
- 为系统级实验设计提供可通用的解决方案.
主要方法:
- 整合网络传播与监督子集选择.
- 使用来自网络拓学的灵敏度矩阵来进行控制预测.
- 适用于非定向,定向和签名生物网络.
主要成果:
- NORSP识别了最小扰动集,以将生物系统转移到所需的稳定状态.
- 该框架在验证中显示出强度,可扩展性和实验相关性.
- NORSP可靠地推断出有效的替代目标,即使隐藏了真正的干扰.
结论:
- 在复杂的生物系统中,NORSP为稳定状态控制提供了一种实用和可通用的方法.
- 该框架支持多主题假设生成和系统级实验设计.
- 在现实的约束条件下,NORSP促进了针对性扰动实验.
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