Discovering stochastic dynamical equations from biological time series data

TL;DR

This paper introduces a novel method for inferring stochastic differential equations from biological time series data, enabling understanding of ecosystem dynamics and biological processes without prior knowledge of governing equations.

Contribution

The authors develop a new equation discovery approach combining stochastic calculus and data-driven techniques to infer stochastic differential equations from real biological datasets.

Findings

Successfully recovered underlying stochastic models from simulated data.

Accurately inferred equations governing fish schooling and cell migration.

Identified limitations and diagnostic measures for the method.

Abstract

Theoretical studies have shown that stochasticity can affect the dynamics of ecosystems in counter-intuitive ways. However, without knowing the equations governing the dynamics of populations or ecosystems, it is difficult to ascertain the role of stochasticity in real datasets. Therefore, the inverse problem of inferring the governing stochastic equations from datasets is important. Here, we present an equation discovery methodology that takes time series data of state variables as input and outputs a stochastic differential equation. We achieve this by combining traditional approaches from stochastic calculus with the equation-discovery techniques. We demonstrate the generality of the method via several applications. First, we deliberately choose various stochastic models with fundamentally different governing equations; yet they produce nearly identical steady-state distributions. We show that we can recover the correct underlying equations, and thus infer the structure of their stability, accurately from the analysis of time series data alone. We demonstrate our method on two real-world datasets -- fish schooling and single-cell migration -- which have vastly different spatiotemporal scales and dynamics. We illustrate various limitations and potential pitfalls of the method and how to overcome them via diagnostic measures. Finally, we provide our open-source codes via a package named PyDaDDy (Python library for Data Driven Dynamics).