Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling

Abstract

Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.

Graphical abstract

Keywords:

• astaxanthin
• mathematical modeling
• machine learning
• kinetic model
• genome-scale metabolic model

Acknowledgments

The authors also gratefully acknowledge financial support from Taiwan’s Ministry of Science and Technology under grants MOST 111-3116-F-006 -004, 111-2621-M-029 -002, 110-2221-E-029 -004 -MY3, and 110-2622-E-110 -016.

Disclosure statement

No potential conflict of interest was reported by the author(s).