Distributed Gradient Descent in Bacterial Food Search

TL;DR

This paper introduces a new distributed gradient descent algorithm that models how bacteria coordinate to find food in challenging environments with minimal communication, explaining observed behaviors and enabling new computational applications.

Contribution

It presents a novel gradient descent method tailored for bacterial food search, requiring fewer assumptions than previous models and applicable to restricted communication scenarios.

Findings

The method converges despite dynamic interaction networks.

Simulations match experimental bacterial swarm behaviors.

The approach predicts bacterial responses to environmental changes.

Abstract

Communication and coordination play a major role in the ability of bacterial cells to adapt to ever changing environments and conditions. Recent work has shown that such coordination underlies several aspects of bacterial responses including their ability to develop antibiotic resistance. Here we develop a new distributed gradient descent method that helps explain how bacterial cells collectively search for food in harsh environments using extremely limited communication and computational complexity. This method can also be used for computational tasks when agents are facing similarly restricted conditions. We formalize the communication and computation assumptions required for successful coordination and prove that the method we propose leads to convergence even when using a dynamically changing interaction network. The proposed method improves upon prior models suggested for bacterial foraging despite making fewer assumptions. Simulation studies and analysis of experimental data illustrate the ability of the method to explain and further predict several aspects of bacterial swarm food search.