Fermi Calculations Enable Quick Downselection of Target Genes and Process Optimization in Photosynthetic Systems

Authors

• Ratul Chowdhury, Iowa State UniversityFollow
• Wheaton Schroeder, Washington State University
• Debolina Sarkar, international flavors and fragrances inc.
• Niaz Bahar Chowdhury, University of Nebraska-Lincoln
• Supantha Dey, Iowa State University
• Rajib Saha, University of Nebraska-Lincoln

ORCID IDs

Chowdhury https://orcid.org/0000-0003-4522-6911

Schroeder https://orcid.org/0000-0003-2302-1237

Saha https://orcid.org/0000-0002-2974-0243

Date of this Version

2025

Document Type

Article

Citation

Plant Physiology (2025) 198: kiaf103

doi: 10.1093/plphys/kiaf103

Comments

Open access

License: CC BY 4.0

Abstract

Understanding how photosynthetic organisms including plants and microbes respond to their environment is crucial for optimizing agricultural practices and ensuring food and energy security, particularly in the context of climactic change and sustainability. This perspective embeds back-of-the-envelope calculations across a photosynthetic organism design and scale up workflow. Starting from the whole system level, we provide a recipe to pinpoint key genetic targets, examine the logistics of detailed computational modeling, and explore environmentally driven phenotypes and feasibility as an industrial biofuel production chassis. While complex computer models or high-throughput in vivo studies often dominate scientific inquiry, this perspective highlights the power of simple calculations as a valuable tool for initial exploration and evaluating study feasibility. Fermi calculations are defined as quick, approximate estimations made using back-of-the-envelope calculations and straightforward reasoning to achieve order-of-magnitude accuracy, named after the physicist Enrico Fermi. We show how Fermi calculations, based on fundamental principles and readily available data, can offer a first-pass understanding of metabolic shifts in plants and microbes in response to environmental and genetic changes. We also discuss how Fermi checks can be embedded in data-driven advanced computing workflows to enable bio-aware machine learning. Lastly, an understanding of state of the art is necessary to guide study feasibility and identifying key levers to maximize cost to return ratios. Combining biology- and resource-aware Fermi calculations, this proposed approach enables researchers to prioritize resource allocation, identify gaps in predictions and experiments, and develop intuition about how observed responses of plants differ between controlled laboratory environments and industrial conditions.