ArticleMathematical biosciences and engineering : MBE2022
Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space.
Article in Mathematical biosciences and engineering : MBE, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Mathematical analysis of G1/S sub-networks in hematopoietic proliferation: Sequential and lineage-specific activation.iScience · 2026Article
- Single-cell mutational burden distributions in birth-death processes.PLoS computational biology · 2025Article
- Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.NPJ systems biology and applications · 2025Article
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Abstract
Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two cell state geometries for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; (i) by using partial differential equations on a graph representing intermediate cell states between known cell types, and (ii) by using the equations on a multi-dimensional continuous cell state-space. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell states during the pathogenesis of acute myeloid leukemia. We particularly focus on comparing the strength and weakness of the graph model and multi-dimensional model.
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