Joint trajectory and network inference via reference fitting

TL;DR

This paper introduces a novel method that combines dynamical trajectories and perturbational data to improve the inference of directed, signed networks in systems biology, addressing the challenge of causal inference from steady-state observations.

Contribution

It presents a new approach that jointly infers cellular trajectories and network interactions using single cell data, leveraging min-entropy estimation for stochastic dynamics.

Findings

Successfully infers directed and signed networks from time-stamped data

Enhances causal inference in systems with limited temporal information

Integrates trajectory and perturbation data for improved network reconstruction

Abstract

Network inference, the task of reconstructing interactions in a complex system from experimental observables, is a central yet extremely challenging problem in systems biology. While much progress has been made in the last two decades, network inference remains an open problem. For systems observed at steady state, limited insights are available since temporal information is unavailable and thus causal information is lost. Two common avenues for gaining causal insights into system behaviour are to leverage temporal dynamics in the form of trajectories, and to apply interventions such as knock-out perturbations. We propose an approach for leveraging both dynamical and perturbational single cell data to jointly learn cellular trajectories and power network inference. Our approach is motivated by min-entropy estimation for stochastic dynamics and can infer directed and signed networks from time-stamped single cell snapshots.