工业生物制造的跨尺度协同设计：路径选择、放大验证与数据反馈

于杰淼; 杨良嵘; 党懿茜; 李金倩; 刘会洲

doi:10.12211/2096-8280.2026-063

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工业生物制造的跨尺度协同设计：路径选择、放大验证与数据反馈

特约评述 | 更新时间：2026-10-27

- 工业生物制造的跨尺度协同设计：路径选择、放大验证与数据反馈
- Cross-scale co-design for industrial biomanufacturing： pathway selection， scale-up validation and data feedback
- 合成生物学 2026年页码：1-22
- 作者机构：
  
  中国科学院过程工程研究所，绿色过程与工程重点实验室，北京 100190
- 作者简介：
  
  于杰淼（1982-），男，副研究员，博士，硕士生导师。研究方向为新型绿色分离工艺与新型分离材料。
  杨良嵘（1983-），女，研究员，博士，博士生导师。研究方向为新型绿色分离工艺与新型分离材料，外场强化反应分离技术等。
- 基金信息：
  
  京津冀环境综合治理国家科技重大专项资助(2025ZD1203400);国家自然科学基金(22138012;22421003;22378398;92475204;22408372;22408371;22508392;22278407)
- DOI：10.12211/2096-8280.2026-063
  中图分类号： Q819;TQ02
- 收稿：2026-07-20，
  
  修回：2026-10-26，
  
  网络首发：2026-10-27，
- 稿件说明：
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于杰淼，杨良嵘，党懿茜，李金倩，刘会洲. 工业生物制造的跨尺度协同设计：路径选择、放大验证与数据反馈［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-063

YU Jiemiao， YANG Liangrong， DANG Yiqian， LI Jinqian， LIU Huizhou. Cross-scale co-design for industrial biomanufacturing： pathway selection， scale-up validation and data feedback［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-063
于杰淼，杨良嵘，党懿茜，李金倩，刘会洲. 工业生物制造的跨尺度协同设计：路径选择、放大验证与数据反馈［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-063 DOI：

YU Jiemiao， YANG Liangrong， DANG Yiqian， LI Jinqian， LIU Huizhou. Cross-scale co-design for industrial biomanufacturing： pathway selection， scale-up validation and data feedback［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-063 DOI：

摘要

工业生物制造的关键问题，是将分子尺度获得的菌株性能转化为放大后仍稳定、可控制且具全流程竞争力的生产表现。本文依照“设计候选形成-过程约束检验-反馈再设计”的逻辑，梳理合成生物学与过程工程的协同。首先，从标准化组装和宿主资源约束出发，阐明设计-构建-测试-学习（Design-Build-Test-Learn，DBTL）DBTL循环如何整合多位点编辑与基因组尺度代谢模型，将遗传设计转化为氧需求、辅因子平衡、副产物形成及遗传稳定性等可检验的工程变量。继而，以反应、混合、传质和分离之间的接口承接这些变量，分析工业放大造成的溶氧、底物和pH时空波动如何改变代谢通量与群体感应调控，并影响补料、供氧、产物回收及全流程经济和环境表现。设计端由此提出候选路径及其约束，过程端通过动态暴露试验和全流程评价检验候选，再将性能失配反馈至路径与底盘设计。在这一脉络上，本文以“路径-尺度-数据”框架连接逆生物合成、计算流体力学与缩尺度试验、过程分析技术及模型辅助决策，并以1，3-丙二醇和β-法呢烯说明两类细胞—过程耦合策略。最后，提出结合生产指标、放大性能保持率、供氧裕量、批次变异、模型误差及边界一致的经济与环境指标评价工程成熟度。

Abstract

Synthetic biology has advanced genetic circuit design and metabolic pathway engineering

yet industrial translation remains limited by scale-dependent heterogeneity and process variability. A central challenge in industrial biomanufacturing is translating strain performance achieved at the molecular scale into production that remains stable

controllable

and competitive after scale-up. This review follows a sequence of generating design candidates

testing them against process constraints

and feeding the results back into redesign. First

starting from standardized assembly and host resource constraints

we examine how the design-build-test-learn (DBTL) cycle integrates multiplex genome editing and genome-scale metabolic models to translate genetic designs into testable engineering variables

including oxygen demand

cofactor balance

by-product formation

and genetic stability. We then connect these variables to the interfaces among reaction

mixing

mass transfer

and separation. Spatial and temporal fluctuations in dissolved oxygen

substrate

and pH during scale-up can alter metabolic fluxes and quorum-sensing regulation

with consequences for feeding

oxygen supply

product recovery

and process-wide economic and environmental performance. Accordingly

pathway and chassis designs provide candidates and their operating constraints; dynamic-exposure experiments and whole-process evaluation test those candidates and return performance gaps to the next design cycle. Building on this logic

a pathway–scale–data framework connects bioretrosynthesis

computational fluid dynamics and scale-down experiments

process analytical technology

and model-assisted decision-making. The cases of 1

3-propanediol and β-farnesene illustrate two strategies for coupling cellular design with process engineering. Finally

engineering readiness is evaluated using production metrics

scale-up performance retention

oxygen-supply margin

batch variability

model error

and economic and environmental indicators assessed within consistent system boundaries.

关键词

Keywords

references

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