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Updated: Jun 16, 2026
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In Vivo Monitoring of Transcriptional Activity During Metabolic Transition Using a Bioluminescent Reporter in Yeast
Published on: February 21, 2025
From static definitions to dynamic landscapes: physiological states in yeast
Paola Nathali Hernández-Valenciano1, Alexis García-Rubio1, Dania Sandoval-Nuñez2
1Biodigital Innovation Lab, Department of Translational Bioengineering, Exact Sciences and Engineering University Center, Universidad de Guadalajara, Guadalajara, Mexico.
Frontiers in Microbiology
|June 15, 2026
Summary
This study proposes a dynamic landscape framework to understand yeast physiological states, moving beyond static definitions. This approach better captures cellular adaptation, viability, and vitality for biotechnological applications.
Area of Science:
- Biotechnology
- Systems Biology
- Microbial Physiology
Background:
- Traditional definitions of physiological states rely on discrete, static measurements, failing to capture the dynamic nature of cellular behavior.
- Understanding dynamic cellular states is crucial for optimizing industrial bioprocesses and predicting cell fate.
Purpose of the Study:
- To shift from static definitions to a dynamic landscape framework for describing yeast physiological states.
- To investigate adaptation, viability, and vitality as key industrial physiological states.
- To provide a theoretical basis for integrating multi-omics data and controlling cellular behavior.
Main Methods:
- Integrated transcriptomic analyses of *Saccharomyces cerevisiae* and *Kluyveromyces marxianus*.
- Conceptualized physiological states as attractors within a high-dimensional dynamic landscape.
- Applied Waddington's epigenetic landscape metaphor to model cell dynamics.
Main Results:
- Yeast physiological states, including adaptation, viability, and vitality, represent gradual transitions rather than isolated entities.
- The dynamic landscape framework conceptualizes these states as attractors in a multi-dimensional space.
- Cellular dynamics are modeled as trajectories between these attraction basins.
Conclusions:
- A dynamic landscape framework offers a more accurate representation of yeast physiological states compared to static definitions.
- This perspective facilitates the integration of multi-omics data for a comprehensive understanding of cellular behavior.
- The framework holds potential for advancing the prediction and control of cellular dynamics in biotechnological systems.
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