合成生物学在生物制造中的潜力与挑战 -生物科学专业

摘要

合成生物学作为一门新兴交叉学科，通过设计与构建标准化生物元件和系统，为生物制造领域提供了革命性技术手段。本研究旨在探讨合成生物学在生物制造中的潜力与挑战，重点分析其在代谢工程、基因组编辑及细胞工厂构建中的应用价值。合成生物学显著提升了目标产物的产量和生产效率，同时降低了成本和环境负担。然而，该领域仍面临若干关键挑战，包括复杂代谢网络的精确调控、异源途径的适配性优化以及大规模工业化生产的稳定性问题。本研究创新性地提出了基于机器学习的代谢模型预测方法，可有效提高路径设计的精准度和成功率，为解决上述瓶颈提供了新思路。总体而言，合成生物学在生物制造领域的潜力巨大，未来需进一步加强跨学科协作，以推动技术转化和产业应用。

关键词：合成生物学；生物制造；代谢工程；机器学习；工业化挑战

The Potential and Challenges of Synthetic Biology in Biomanufacturing

英文人名

Directive teacher:×××

Abstract

As a new interdisciplinary discipline, synthetic biology provides revolutionary technological means for the field of biological manufacturing through the design and construction of standardized biological components and systems. This study aims to explore the potential and challenges of synthetic biology in biofuturing, with a focus on its application in me tabolic engineering, genome editing, and cell factory construction. Synthetic biology significantly increases the yield and production efficiency of target products, while reducing costs and environmental burdens. However, the field still faces several key challenges, including the precise regulation of complex me tabolic networks, the adaptive optimization of allogenic pathways, and the stability of large-scale industrial production. This study innovatively proposes a me tabolic model prediction method based on machine learning, which can effectively improve the accuracy and success rate of path design, and provide a new way to solve the above bottleneck. Overall, the potential of synthetic biology in the field of biological manufacturing is huge, and interdisciplinary collaboration needs to be further strengthened in the future to promote technology transformation and industrial applications.

Keywords: Synthetic Biology；Biomanufacturing；me tabolic Engineering；Machine Learning；Industrialization Challenges

引言 1

一、合成生物学的基础与应用潜力 1

(一)合成生物学的核心概念 1

(二)生物制造的技术需求 2

(三)潜力分析：从实验室到产业化 2

二、合成生物学在生物制造中的关键突破 3

(一)基因编辑技术的进展 3

(二)代谢工程的优化策略 3

(三)工程微生物的设计与构建 4

三、生物制造中的挑战与限制因素 5

(一)技术瓶颈：效率与稳定性 5