Estimating 3D movements from 2D observations using a continuous model of helical swimming

Area of Science:

• Microbiology
• Biophysics
• Ecological Dynamics

Background:

• Helical swimming is a common microbial movement impacting ecological processes.
• Current 2D tracking data limits understanding of 3D helical swimming biomechanics and ecology.
• Quantifying the third movement dimension is crucial for microbial ecology.

Purpose of the Study:

• Introduce a novel continuous stochastic model for helical swimming.
• Enable efficient 3D movement parameter estimation from 2D data.
• Apply the model to analyze strains of the toxic alga Heterosigma akashiwo.

Main Methods:

• Developed the Correlated Velocity Helical Movement (CVHM) model.
• Separated instantaneous velocity into advective and rotational components.
• Estimated model parameters from 2D projections of helical movement, requiring minimal numerical fitting.

Main Results:

• The CVHM model efficiently characterizes helical swimming with randomness and autocorrelation.
• Most 3D parameters are derivable from 2D projections.
• Quantified cell-level and strain-level movement differences in Heterosigma akashiwo.

Conclusions:

• The CVHM model provides a computationally efficient method for analyzing 3D microbial swimming from 2D data.
• This approach aids in understanding the ecological roles of microbial movement.
• The model can differentiate between strains of harmful algal bloom-causing algae like Heterosigma akashiwo.