Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle

TL;DR

This study investigates how the number of time-series samples affects the accuracy of gene regulatory network inference in yeast, revealing a sigmoid relationship and identifying an optimal sampling point for reliable inference.

Contribution

It provides quantitative guidelines for experimental design in gene network inference by analyzing the trade-off between sampling rate and inference quality using a computational model.

Findings

A sigmoid relationship between sample number and inference accuracy.

Identification of an optimal sample size for reliable network inference.

Use of the AUPR metric to evaluate network inference quality.

Abstract

Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve AUPR. By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values on the ridge of the sigmoid.