A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Mendez, Mario J.; Hoffman, Matthew J.; Cherry, Elizabeth M.; Lemmon, Christopher A.; Weinberg, Seth H.

doi:10.1016/j.bpj.2022.07.014

Cited by 3 publications

(3 citation statements)

References 69 publications

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“…The resulting optimal trajectories for the time-dependent and dose-dependent data with confidence intervals are shown in Figures 5 B and 5F respectively. Although our 45-gene approach exhibited some differences between the predicted and simulated phenotypic fractions as a function of dose reported by previous studies, our overall trends were in general agreement 56 ( Figure 5 F). From the steady state fractions obtained from the dose-dependent data, it is evident that the distribution of fractions for the flow cytometry data at day 18 resembles the equilibrium state fractions closely (roughly 113.6 pM, see supplemental information for dimensionality analysis).…”

Section: Resultssupporting

confidence: 83%

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

2023

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Section: Resultssupporting

confidence: 83%

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

2023

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“…The resulting optimal trajectories for the time-dependent and dose-dependent data with confidence intervals are shown in Figure 5B and 5F respectively. Although our 45-gene approach exhibited some differences between the predicted and simulated phenotypic fractions as a function of dose reported by previous studies, our overall trends were in general agreement 35 (Figure 5F). From the steady state fractions obtained from the dose-dependent data, it is evident that the distribution of fractions for the flow cytometry data at day 18 oesemoles the equilibrium state fractions closely (roughly 113.6 pM, see Supplementary Information for dimensionality analysis).…”

Section: Resultssupporting

confidence: 83%

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

Najafi¹,

Jolly

George

2023

Preprint

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The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understand- ing EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from single-cell RNA sequencing data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

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“…The resulting optimal trajectories for the time-dependent and dose-dependent data with confidence intervals are shown in Figures 5B and 5F respectively. Although our 45-gene approach exhibited some differences between the predicted and simulated phenotypic fractions as a function of dose reported by previous studies, our overall trends were in general agreement 56 (Figure 5F). From the steady state fractions obtained from the dose-dependent data, it is evident that the distribution of fractions for the flow cytometry data at day 18 resembles the equilibrium state fractions closely (roughly 113.6 pM, see supplemental information for dimensionality analysis).…”

Section: Ll Open Accesssupporting

confidence: 83%

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-Tumoral Heterogeneity

Najafi

Jolly

George

2023

Preprint

View full text Add to dashboard Cite

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Cited by 3 publications

References 69 publications

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-Tumoral Heterogeneity

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