Prediction of fitness in bacteria with causal jump dynamic mode decomposition

TL;DR

This paper introduces a novel causal jump dynamic mode decomposition framework for predicting bacterial growth dynamics based on media conditions, achieving high accuracy in growth curve predictions.

Contribution

It develops a new hybrid operator-theoretic modeling approach combining Hankel DMD and Koopman operators for bacterial growth prediction.

Findings

Achieves 96.6% accuracy on training data

Predicts test data with 91% accuracy

Demonstrates effectiveness on Pseudomonas putida growth curves

Abstract

In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step,2-step,...,{\tau}-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy.