Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiome

TL;DR

This paper introduces SPARC, a method that uses dimensionality reduction and bifurcation analysis to control gut microbiome states by adjusting interaction parameters in a generalized Lotka-Volterra model.

Contribution

It develops a novel in silico control approach combining steady-state reduction and bifurcation analysis to steer microbiome outcomes in high-dimensional models.

Findings

Successfully guides the system from diseased to healthy states

Provides a computational framework for microbiome intervention strategies

Enables indirect manipulation of microbial communities

Abstract

The generalized Lotka-Volterra (gLV) equations are a mathematical proxy for ecological dynamics. We focus on a gLV model of the gut microbiome, in which the evolution of the gut microbial state is determined in part by pairwise inter-species interaction parameters that encode environmentally-mediated resource competition between microbes. We develop an in silico method that controls the steady-state outcome of the system by adjusting these interaction parameters. This approach is confined to a bistable region of the gLV model. The two steady states of interest are idealized as either a "healthy" or "diseased" steady state of the gut microbiome. In this method, a dimensionality reduction technique called steady-state reduction (SSR) is first used to generate a two-dimensional (2D) gLV model that approximates the high-dimensional dynamics on the 2D subspace spanned by the two steady states. Then a bifurcation analysis of the 2D model analytically determines parameter modifications that drive a disease-prone initial condition to the healthy steady state. This parameter modification of the reduced 2D model guides parameter modifications of the original high-dimensional model, resulting in a change of steady-state outcome in the high-dimensional model. This control method, called SPARC (SSR-guided parameter change), bypasses the computational challenge of directly determining parameter modifications in the original high-dimensional system. SPARC could guide the development of indirect bacteriotherapies, which seek to change microbial compositions by deliberately modifying gut environmental variables such as gut acidity or macronutrient availability.