The identifiability of gene regulatory networks: the role of observation data

Huang, Xiao-Na; Shi, Wenjia; Zuo, Zheng; Zhang, Xuejun

doi:10.1007/s10867-021-09595-4

Cited by 1 publication

(1 citation statement)

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“…Nevertheless, the availability of such assumptions is a valuable resource that must not be neglected ( Peng et al 2010 ). At the same time, inference from experimental data is limited by the number of measurements ( Cheng and Zhao 2011 , Cheng et al 2011 ) and their quality ( Huang et al 2022 ). Despite the recent progress in technologies allowing observation of gene expression, it is still not easy nor cheap to obtain the necessary data in sufficient volume and precision.…”

Section: Introductionmentioning

confidence: 99%

Boolean network sketches: a unifying framework for logical model inference

et al. 2023

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Motivation The problem of model inference is of fundamental importance to systems biology. Logical models (e.g., Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only. Results We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g., a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g., the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an initial sketch which is extended by adding restrictions representing experimental data to a data-informed sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data. Availability All software and data are freely available as a reproducible artefact at https://doi.org/10.5281/zenodo.7688740. Supplementary information Supplementary data available online through Bioinformatics.

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