Lamoline François, Haasler Isabel, Karlsson Johan, Gonçalves Jorge, Aalto Atte
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg.
Department of Information Technology, Uppsala University, Uppsala, 751 05, Sweden.
Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf394.
Modelling gene expression is a central problem in systems biology. Single-cell technologies have revolutionized the field by enabling sequencing at the resolution of individual cells. This results in a much richer data compared to what is obtained by bulk technologies, offering new possibilities and challenges for gene regulatory network inference.
In this work, we introduce GRIT (gene regulation inference by transport)-a method to fit a differential equation model and to infer gene regulatory networks from single-cell data using the theory of optimal transport. The idea consists in tracking the evolution of the cell distribution over time and finding the system whose temporal marginals minimize the transport cost with the observations. GRIT is finally used to identify genes and pathways affected by two Parkinson's disease associated mutations.
Matlab implementation of the method and code for data generation are at gitlab.com/uniluxembourg/lcsb/systems-control/grit together with a user guide. A snapshot of the code used for the results of this article is at doi: 10.5281/zenodo.15582432.
基因表达建模是系统生物学中的核心问题。单细胞技术通过实现单个细胞分辨率的测序，给该领域带来了变革。与批量技术所获得的数据相比，这产生了更为丰富的数据，为基因调控网络推断带来了新的可能性和挑战。
在这项工作中，我们引入了GRIT（通过传输进行基因调控推断）——一种使用最优传输理论来拟合微分方程模型并从单细胞数据推断基因调控网络的方法。其思路是追踪细胞分布随时间的演变，并找到其时间边际与观测值的传输成本最小的系统。GRIT最终被用于识别受两种帕金森病相关突变影响的基因和通路。
该方法的Matlab实现以及数据生成代码可在gitlab.com/uniluxembourg/lcsb/systems-control/grit上获取，同时还有用户指南。用于本文结果的代码快照可在doi: 10.5281/zenodo.15582432获取。
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