揭示了细菌中基于基因调节的神经网络计算.
Samitha S Somathilaka1,2, Sasitharan Balasubramaniam2, Daniel P Martins1
1VistaMilk Research Centre, Walton Institute for Information and Communication Systems Science, South East Technological University, Waterford, Ireland.
Biophysical reports
|August 31, 2023
概括
细菌拥有基因调节网络 (GRNs),其功能类似于人工神经网络. 这项研究将这些网络建模为基因调节神经网络 (GRNNs),以了解细菌计算.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 细菌通过复杂的信号转导网络感知环境信号,包括基因调节网络 (GRNs).
- GRN与人工神经网络具有结构和功能上的相似性,这表明它具有固有的计算特性.
- 细菌基因表达动态为它们的非神经元计算能力提供了独特的视角.
研究的目的:
- 开发一种模型来量化GRNs中的基因对基因相互作用动态,称为基因调节神经网络 (GRNNs).
- 使用转录和实验数据,提取和验证一个GRNN用于*Pseudomonas aeruginosa*中的皮亚氨酸生产.
- 分析遗传和环境因素对GRNN计算行为和可靠性的影响.
主要方法:
- 开发了一种重量提取技术,根据转录组数据将GRN转换为GRNN.
- 使用湿实验室实验数据验证的GRNN计算精度,用于*Pseudomonas aeruginosa*中的皮奥素生产.
- 建模了整个生态系统的细胞-细胞通信,并通过突变发生分析了GRNN的结构变化.
主要成果:
- 成功提取并验证了一种GRNN用于皮奥素生产,证明了其计算精度.
- 突变性研究揭示了GRNN结构变化如何影响细菌的计算行为.
- 生态系统建模表明,细胞-细胞通信影响GRNN的计算可靠性,GRNN聚合成类似感知子的单元.
结论:
- 细菌GRN可以被建模为GRNN,揭示它们的自然计算能力.
- 这个框架提供了细菌适应,毒性和生态系统动态的见解.
- 这项研究为分子机器学习和活人工智能系统奠定了基础.
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