Human Comprehensible Active Learning of Genome-Scale Metabolic Networks

TL;DR

This paper presents ILP-iML1515, a logical reasoning framework for genome-scale metabolic models that actively guides experiments, reducing costs and improving interpretability in synthetic biology applications.

Contribution

It introduces a novel ILP-based machine learning approach that enables logical reasoning and active learning for metabolic network modeling, enhancing efficiency and comprehensibility.

Findings

ILP-iML1515 allows high-throughput simulation of metabolic models.

The framework actively selects experiments that lower validation costs.

It updates models by learning new logical structures from mutant trials.

Abstract

An important application of Synthetic Biology is the engineering of the host cell system to yield useful products. However, an increase in the scale of the host system leads to huge design space and requires a large number of validation trials with high experimental costs. A comprehensible machine learning approach that efficiently explores the hypothesis space and guides experimental design is urgently needed for the Design-Build-Test-Learn (DBTL) cycle of the host cell system. We introduce a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning and actively learns from training examples. In contrast to numerical models, ILP-iML1515 is built on comprehensible logical representations of a genome-scale metabolic model and can update the model by learning new logical structures from auxotrophic mutant trials. The ILP-iML1515 framework 1) allows high-throughput simulations and 2) actively selects experiments that reduce the experimental cost of learning gene functions in comparison to randomly selected experiments.