面向生物人工智能的基于基因调控神经网络的非线性分类器稳定性分析
Stability Analysis of Non-Linear Classifiers using Gene Regulatory Neural Network for Biological AI
AI总结：
该研究构建基因感知器数学模型将基因调控网络转化为GRNN，分析其稳定性并验证非线性分类应用，发现可调反应参数实现可编程GRNN，为生物人工智能发展提供支撑。
摘要：
生物细胞的基因调控网络（Gene Regulatory Network, GRN）调控着诸多关键功能，使细胞能够在不同环境条件下适应并存活。对GRN的深入观察表明，其结构和运行原理与人工神经网络（Artificial Neural Network, ANN）相似，这可为生物人工智能的发展奠定基础。具体而言，基因的转录和翻译过程呈现出基于转录因子输入的类sigmoid特性。\n本文中，我们利用双层转录-翻译化学反应模型构建了基因感知器的数学模型，从而能够将GRN转化为基因调控神经网络（Gene Regulatory Neural Network, GRNN）。我们对全连接GRNN子网络中的每个基因感知器进行了稳定性分析，以确定能带来可靠计算性能的时间动态及稳态浓度输出。我们聚焦于GRNN的非线性分类器应用，分析了通用多层GRNN以及由跨组学实验数据推导得到的大肠杆菌（E.Coli）GRNN。我们的分析发现，改变化学反应的参数可以调整分类区域的边界，为构建适配不同应用需求的可编程GRNN搭建了平台。
英文摘要：
The Gene Regulatory Network (GRN) of biological cells governs a number of key functionalities that enables them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resembles an Artificial Neural Network (ANN), which can pave the way for the development of Biological Artificial Intelligence. In particular, a gene's transcription and translation process resembles a sigmoidal-like property based on transcription factor inputs. In this paper, we develop a mathematical model of gene-perceptron using a dual-layered transcription-translation chemical reaction model, enabling us to transform a GRN into a Gene Regulatory Neural Network (GRNN). We perform stability analysis for each gene-perceptron within the fully-connected GRNN sub network to determine temporal as well as stable concentration outputs that will result in reliable computing performance. We focus on a non-linear classifier application for the GRNN, where we analyzed generic multi-layer GRNNs as well as E.Coli GRNN that is derived from trans-omic experimental data. Our analysis found that varying the parameters of the chemical reactions can allow us shift the boundaries of the classification region, laying the platform for programmable GRNNs that suit diverse application requirements.
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