A proof-of-concept experimental-theoretical model to predict pesticide resistance evolution

Area of Science:

• Agricultural Science
• Evolutionary Biology
• Computational Biology

Background:

• Insecticide resistance is a significant threat to sustainable agriculture, hindering pest management.
• Studying resistance evolution in laboratory settings is challenging due to difficulties in maintaining large pest populations.
• Existing theoretical models lack experimental validation for resistance management strategies.

Purpose of the Study:

• To establish a feasible experimental system for studying insecticide resistance evolution.
• To validate in silico population genetics models using laboratory experiments.
• To assess the generalizability of the system across different insecticide resistance mechanisms.

Main Methods:

• Developed an in silico population genetics model for insecticide resistance.
• Utilized Caenorhabditis elegans (C. elegans) as a model organism for laboratory experiments.
• Compared in silico predictions with in vivo resistance selection dynamics across multigenerational experiments using two distinct compounds.

Main Results:

• In silico predictions showed strong resemblance to experimental in vivo resistance selection outcomes.
• Demonstrated the feasibility of integrating in silico and in vivo modeling approaches for resistance research.
• Validated the system's generalizability across different resistance mechanisms.

Conclusions:

• The integrated in silico and in vivo framework effectively models insecticide resistance evolution.
• This approach bridges the gap between theoretical and empirical research in resistance management.
• Facilitates the development of more informed and effective insecticide resistance management strategies for agriculture.