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Gene regulatory networks: from correlative models to causal explanations

Nature Reviews Genetics (2026)Cite this article

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Abstract

Gene regulatory networks (GRNs) explain how the genome controls cellular behaviour and tissue morphogenesis, serving to connect molecular mechanism to functional output. Single-cell technologies now provide descriptions of these networks with unprecedented detail, but this advance has also revealed gene regulatory systems that are too complex for our existing conceptual frameworks. GRNs, which should provide mechanistic explanations, are increasingly reduced to statistical correlations — ‘hairballs’ that fail to capture molecular causation. Here, we explore why this dilemma exists and propose a path forward. We argue that methods in ‘representation learning’ can be used to model GRNs, without needing to capture every molecular detail. For this framework, we advocate three linked principles: models must be inherently mechanistic, with structures grounded in cellular and evolutionary biology; molecular principles and constraints must be used to reduce the solution space for learning GRN models; and more sophisticated forms of experimental perturbation and synthetic biological engineering are needed to train models and test predictions. By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding.

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Fig. 1: Classical versus data-driven GRN construction.
Fig. 2: Challenges of GRN modelling.
Fig. 3: Representational descriptions of GRNs.
Fig. 4: Towards mechanistic abstract representations.
Fig. 5: Establishing molecular principles governing GRNs.

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Acknowledgements

The authors are grateful to T. Brown, J. DiFrisco, D. Erwin, F. Fröhlich, L. Parts, P. Badia-i-Mompel and the members of the Briscoe Lab for their constructive comments. This work was supported by the Wellcome Trust (220379/D/20/Z) and the Francis Crick Institute, which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust (all under FC001051).

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Authors and Affiliations

  1. The Francis Crick Institute, London, UK

    Rory J. Maizels & James Briscoe

  2. EMBL-EBI, Hinxton, UK

    Rory J. Maizels

  3. Wellcome Sanger Institute, Hinxton, UK

    Rory J. Maizels

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  1. Rory J. Maizels
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R.J.M. and J.B. researched the literature, R.J.M. wrote the article, and R.J.M. and J.B. contributed substantially to the discussion of the content, and reviewed and edited the manuscript before submission.

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Correspondence to James Briscoe.

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R.J.M. is a consultant for Omnipotent Biotechnologies.

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Glossary

Bayesian modelling

A probabilistic modelling approach that combines prior beliefs (prior distributions) with observed data (likelihoods) to estimate an updated belief (posterior distributions), allowing inference and prediction with robust uncertainty quantification.

Coarse-graining

A method commonly used in statistical mechanics and chemical simulation methods; details at a lower scale of resolution (for example, atomic level) are removed or averaged such that only features that are essential to preserve macroscopic behaviour at a higher scale (for example, bio-molecular) are retained.

Dimension reduction

A class of methods used to reduce the number of variables in a dataset while retaining the major sources of variation in the data. Methods such as principal component analysis, uniform manifold approximation and projection, and t-distributed stochastic neighbor embedding are commonly used to reduce the dimension of sequencing data.

Dynamical systems

Models of systems whose state evolves through time, often expressed in the form of differential equations; often used to describe biological systems such as population dynamics, biochemical reactions and gene regulatory networks.

Ground truths

Real-world models or outcome of a system, serving as a benchmark against which trained models can be compared to assess performance.

Marr’s levels of analysis

A framework introduced by David Marr to describe cognitive and computational systems that breaks these systems into three layers: the computational level (what the system does and why), the algorithmic or representational level (organization of the system required to fulfill the computational purpose), and the implementational level (the physical material and substrates used to construct the algorithmic organization).

Multi-perspectival

The idea that the representation of a phenomenon may depend on the perspective of the viewer or the question of the researcher; the most useful representation of a gene regulatory system may differ, for example, for questions concerning global cell-type control versus questions concerning regulation of specific genes.

Multi-scale

Models or analysis that represent phenomena across different scales of resolution in time, space or organization, for example, the atomic, molecular, bio-molecular, genetic, cellular, organismal and population scales of biology.

Representation learning

Related to dimension reduction, a field of machine learning focused on learning meaningful, compact representations of data rather than using only the variables observed in the original data; an example of representation learning is the variational autoencoder, a type of deep generative model that encodes data to a latent representation.

Sloppy models

Systems biology models that show extreme sensitivity to a small number of parameters, with the majority of parameters having no effect on model performance.

Structural non-identifiability

When wide variation in the parameters of a model produce small changes in model output such that there is not a unique solution for fitting a model to a dataset.

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Maizels, R.J., Briscoe, J. Gene regulatory networks: from correlative models to causal explanations. Nat Rev Genet (2026). https://doi.org/10.1038/s41576-026-00939-1

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