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Inference of Regulatory System for TAG Biosynthesis in Lipomyces starkeyi
Sachiyo Aburatani1, Koji Ishiya2, Toshikazu Itoh3
1Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 305-8568, Japan.
Bioengineering (Basel, Switzerland)
|November 24, 2020
Summary
This study infers the regulatory network for triacylglycerol (TAG) biosynthesis in *Lipomyces starkeyi*. Network modeling identified key regulatory factors and unknown feedback controls, advancing bioengineering strategies.
Area of Science:
- Biotechnology and Synthetic Biology
- Molecular Biology
- Metabolic Engineering
Background:
- Efficient host microorganisms are crucial for bioproduction.
- Understanding cellular biosynthesis regulatory systems is key to controlling metabolic pathways.
Purpose of the Study:
- To infer the regulatory system for triacylglycerol (TAG) biosynthesis in *Lipomyces starkeyi* using network modeling.
- To construct regulatory network models for oil productivity and overall TAG biosynthesis.
Main Methods:
- Applied network modeling, factor analysis, and structural equation modeling.
- Classified 89 TAG biosynthesis-related genes into nine regulatory sub-systems.
- Developed distinct regulatory models for oil productivity and TAG biosynthesis.
Main Results:
- Identified well-characterized genes DGA1 and ACL1 as regulatory factors in oil productivity.
- Discovered previously unknown feedback controls within oil productivity regulation.
- Identified genes unrelated to TAG biosynthesis regulation within the broader network.
Conclusions:
- Inferred regulatory models provide insights into controlling TAG biosynthesis.
- Careful selection of regulatory factor induction targets is suggested.
- Network modeling aids the advancement of bioengineering for improved bioproduction.
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