Optimal Inference of Asynchronous Boolean Network Models

TL;DR

This paper introduces an optimal algorithmic complexity-based method for inferring asynchronous Boolean network models from experimental data, addressing challenges like noise, data diversity, and computational efficiency in biological network inference.

Contribution

The paper presents a novel optimal algorithm that incorporates asynchronicity and pseudo-time inference for Boolean network modeling from noisy biological data.

Findings

Effective inference of Boolean networks from real and simulated data

Handles asynchronous network dynamics and noise robustly

Integrates pseudo-time inference with network modeling

Abstract

The network inference problem arises in biological research when one needs to quantitatively choose the best protein-interaction model for explaining a phenotype. The diverse nature of the data and nonlinear dynamics pose significant challenges in the search for the best methodology. In addition to balancing fit and model size, computational efficiency must be considered. Importantly, underlying the measurements, which are affected by experimental noise, there is a complex computational mechanism that is inherently hard to identify. To address these difficulties, we present a novel approach that uses algorithmic complexity to infer a Boolean network model from experimental data. We present an algorithm that is optimal within this framework and allows for asynchronicity network dynamics. Furthermore, we show that using our methodology a solution to the pseudo-time inference problem, which is pertinent to the analysis of single-cell data, can be intertwined with network inference. Results are described for real and simulated datasets.