New Optimal Transport Framework Reconstructs Gene Networks and Cell Fates From Single-Cell Snapshots

Every cell in the human body carries the same genome, yet a neuron, a liver cell, and an immune cell behave in radically different ways because different sets of genes are switched on and off at different times. The wiring diagram that determines which genes control which other genes is known as a gene regulatory network, or GRN, and reconstructing it from experimental data remains one of the central challenges of computational biology. The difficulty is compounded by a fundamental measurement problem: at the single-cell level, scientists can readily profile messenger RNA, the intermediate molecules that carry genetic instructions, but the proteins that actually execute regulatory decisions are largely invisible to standard sequencing technologies. Without observing protein dynamics directly, establishing a causal link between the activity of a regulator and the response of its targets becomes an exercise in inference rather than observation, and the field has long struggled to distinguish genuine regulatory relationships from statistical coincidences.

A research team led by Yann Maugé and Elias Ventre has now introduced a computational framework designed to tackle this problem head-on. Writing in PLOS Computational Biology, the authors present CardamomOT, a method that builds on their earlier algorithm CARDAMOM and combines a mechanistic model of gene expression with the mathematical machinery of optimal transport. The work, published on June 10, 2026, addresses several long-standing limitations of previous approaches and, according to the authors, represents one of the first methods to explicitly integrate gene regulatory network inference, trajectory reconstruction, and generative modeling of single-cell data within a single unified framework. The implications reach across developmental biology, stem cell research, and drug development, wherever researchers seek to understand how cells change identity over time.

The core idea behind CardamomOT is elegantly simple to state, even if its implementation is mathematically demanding. Single-cell RNA sequencing experiments typically capture cells at a series of time points, producing static snapshots of gene expression across thousands of individual cells. What these snapshots lack is continuity: they show which cell states exist at each moment, but not how individual cells move from one state to the next. CardamomOT bridges these gaps by positing an underlying mechanistic model, a system of differential equations in which a gene regulatory network drives the dynamics of both messenger RNA and the unobserved proteins. The method then uses optimal transport, a mathematical framework for finding the most efficient way to move mass from one distribution to another, to connect the observed snapshots and infer the hidden trajectories that carry cells between them.

Optimal transport has become an increasingly popular tool in single-cell genomics precisely because it provides a principled way to compare distributions of cells across time. Rather than matching individual cells one-to-one, which is impossible when cells are destroyed during measurement, optimal transport computes a coupling between the cell populations at consecutive time points, effectively estimating the flow of cellular mass through state space. What distinguishes CardamomOT from purely statistical applications of this idea is its mechanistic backbone. The couplings are not free-floating mathematical objects; they are constrained to be consistent with a gene regulatory network whose parameters are being calibrated simultaneously. This joint estimation means that the reconstructed trajectories and the inferred network architecture reinforce one another, each constraining the space of plausible solutions for the other.

The new framework directly confronts three specific weaknesses of its predecessor. First, the original CARDAMOM algorithm could only exploit the relative ordering of time points, treating the intervals between measurements as unknown, which discarded valuable temporal information. CardamomOT incorporates the exact time labels of the experimental snapshots, anchoring the inferred dynamics to real physical time. Second, the earlier method relied on restrictive quasi-stationary assumptions about protein dynamics, effectively assuming that protein levels track their messenger RNA targets quickly enough that the transient behavior could be ignored. The new method relaxes this constraint, allowing protein trajectories to be reconstructed as genuine dynamic variables. Third, the original algorithm depended on multiple hyperparameters whose tuning was something of an art; CardamomOT substantially reduces this number, making the method more robust and easier to apply in practice.

A particularly notable feature of the new approach is its use of prior knowledge from the literature. Protein kinetic rates, which describe how quickly proteins are produced and degraded, have been measured in many experimental systems over decades of molecular biology research. CardamomOT allows researchers to incorporate these priors directly into the calibration process, providing biologically grounded constraints on otherwise unobservable parameters. This is a meaningful departure from purely data-driven approaches, which must estimate everything from the data at hand and are therefore prone to identifiability problems. By anchoring the model to known biochemistry, the framework narrows the space of possible solutions and improves the reliability of the inferred network, a strategy that mirrors how mechanistic modeling has long been practiced in systems biology more broadly.

The authors validated their framework on both simulated and experimental datasets, a two-pronged strategy that is standard practice in computational method development but executed here with particular thoroughness. On in silico data, where the ground-truth regulatory network is known because the data were generated from a model, CardamomOT demonstrated consistently improved performance over state-of-the-art methods in both network inference and trajectory reconstruction, while remaining computationally scalable to realistic dataset sizes. On experimental data, where the truth is only partially known, the method reconstructed cellular trajectories, velocity fields, and latent protein levels that were mutually consistent with one another, and the inferred gene regulatory network structures matched known biology from independent experimental studies. This internal coherence across multiple inferred quantities is an important sanity check, since methods that produce trajectories inconsistent with their own inferred networks are unlikely to be capturing real biology.

Perhaps the most forward-looking contribution of the work lies in its generative capabilities. Once the mechanistic model has been calibrated against experimental data, it becomes a predictive engine: researchers can perturb the model in silico, for example by simulating the knockout of a regulatory gene or the application of a differentiation-inducing drug, and observe how the modeled cell population responds. The authors show that the accuracy improvements achieved by CardamomOT make the calibrated model suitable for generating testable predictions of cellular responses to perturbations that have not yet been performed experimentally. This transforms the method from a descriptive tool into a hypothesis-generating one, potentially allowing researchers to prioritize experiments, screen perturbations computationally before committing laboratory resources, and explore reprogramming strategies that push cells toward desired fates.

The significance of this work must be understood against the backdrop of a broader shift in single-cell biology. The field has generated enormous datasets cataloging cell types and states across tissues, development, and disease, but converting these atlases into causal, predictive models of cellular behavior has proven far harder than cataloging itself. Most existing methods for trajectory inference or network inference operate in isolation, each addressing one piece of the puzzle with statistical tools that make no commitment to underlying biological mechanism. CardamomOT’s explicit integration of mechanism, dynamics, and generative modeling points toward a different vision, one in which the ultimate product of a single-cell time-series experiment is not a static map but a calibrated dynamical system that can be interrogated, perturbed, and refined. If approaches of this kind mature as their developers hope, the invisible protein layer that has long constrained the field may finally come into computational view, bringing the dream of predictive, engineering-grade models of cell behavior closer to reality.

Subject of Research: Computational inference of gene regulatory networks and cellular trajectories from single-cell RNA sequencing time-series data using mechanistic optimal transport

Article Title: CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling

Article References: Maugé, Y., & Ventre, E. (2026). CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling. PLOS Computational Biology, 22(10), e1014838. https://doi.org/10.1371/journal.pcbi.1014838

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014838

Keywords: gene regulatory networks, optimal transport, single-cell RNA sequencing, trajectory inference, computational biology, generative modeling, protein dynamics, systems biology, cell differentiation, PLOS Computational Biology, mechanistic modeling, perturbation prediction