Multiscale Mathematical Modeling in Systems Biology: A Framework to Boost Plant Synthetic Biology.

Area of Science:

• Agricultural Science
• Systems Biology
• Biotechnology

Background:

• Global food insecurity and environmental degradation necessitate sustainable agricultural solutions.
• Plant synthetic biology presents opportunities for crop improvement but also poses environmental risks.
• Current applications of multiscale mathematical modeling in plants are underutilized.

Purpose of the Study:

• To review the complexities and risks of plant synthetic biology.
• To present multiscale mathematical modeling as a tool for risk assessment and mitigation.
• To advocate for integrating data analysis and multiscale modeling for crop understanding.

Main Methods:

• Review of common multiscale mathematical modeling approaches and methodologies applicable to plants.
• Discussion of techniques like parameter estimation, bifurcation analysis, and sensitivity analysis.
• Highlighting ongoing efforts to model maize (Zea mays L.) for enhanced resilience.

Main Results:

• Plant synthetic biology holds potential for enhanced crop traits but requires careful risk assessment.
• Multiscale mathematical modeling can identify mutational targets and anticipate pleiotropic effects.
• Integrated models can improve the safety of genetically engineered plant species.

Conclusions:

• Multiscale mathematical modeling is crucial for assessing and mitigating risks in plant synthetic biology.
• Integrating advanced data analysis with multiscale modeling will deepen crop understanding across biological scales.
• This approach can lead to safer and more resilient genetically engineered crops, addressing challenges like drought and pest resistance.