Unknown

Dataset Information

0

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory.


ABSTRACT: Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We use a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we demonstrate that the optimal design varies as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible design-space analysis we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

SUBMITTER: Jakobson CM 

PROVIDER: S-EPMC5974357 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

altmetric image

Publications

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory.

Jakobson Christopher M CM   Tullman-Ercek Danielle D   Mangan Niall M NM  

Scientific reports 20180529 1


Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We use a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example bio  ...[more]

Similar Datasets

| S-EPMC6886558 | biostudies-literature
| S-EPMC7155931 | biostudies-literature
| S-EPMC6130400 | biostudies-literature
| S-EPMC5368401 | biostudies-literature
| S-EPMC5937540 | biostudies-other
| S-EPMC5250584 | biostudies-literature
| S-EPMC6962706 | biostudies-literature
| S-EPMC7453195 | biostudies-literature
| S-EPMC3874208 | biostudies-literature
| S-EPMC7593805 | biostudies-literature