Title: Bacterial Gene Regulatory Neural Network as a Biocomputing Library of Mathematical Solvers Show Chinese title

Title: 细菌基因调控神经网络作为数学求解器的生物计算库

Abstract: Current biocomputing approaches predominantly rely on engineered circuits with fixed logic, offering limited stability and reliability under diverse environmental conditions. Here, we use the GRNN framework introduced in our previous work to transform bacterial gene expression dynamics into a biocomputing library of mathematical solvers. We introduce a sub-GRNN search algorithm that identifies functional subnetworks tailored to specific mathematical calculation and classification tasks by evaluating gene expression patterns across chemically encoded input conditions. Tasks include identifying Fibonacci numbers, prime numbers, multiplication, and Collatz step counts. The identified problem-specific sub-GRNNs are then assessed using gene-wise and collective perturbation, as well as Lyapunov-based stability analysis, to evaluate robustness and reliability. Our results demonstrate that native transcriptional machinery can be harnessed to perform diverse mathematical calculation and classification tasks, while maintaining computing stability and reliability. Show Chinese abstract

Abstract: 当前的生物计算方法主要依赖于具有固定逻辑的工程电路，在不同的环境条件下稳定性与可靠性有限。 在这里，我们使用我们在之前工作中引入的GRNN框架，将细菌基因表达动态转化为数学求解器的生物计算库。 我们引入了一种子-GRNN搜索算法，通过评估化学编码输入条件下的基因表达模式，来识别针对特定数学计算和分类任务的功能子网络。 任务包括识别斐波那契数、质数、乘法和考拉兹步骤计数。 然后，使用基因级和集体扰动以及基于李雅普诺夫的稳定性分析来评估所识别的问题特定子-GRNN的鲁棒性和可靠性。 我们的结果表明，天然转录机制可以被利用来执行多种数学计算和分类任务，同时保持计算的稳定性和可靠性。