Workflow summary: GRN motif, cell signaling model, and inference framework.
<p><b>[A]</b> The developing ICM is represented by a static spatial lattice of biochemical reaction volumes (cells) coupled via FGF4, which mimics a monolayer or 2D cellular culture. Each cell contains a core gene-regulatory network (GRN) featuring mutual repression between the gen...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| الملخص: | <p><b>[A]</b> The developing ICM is represented by a static spatial lattice of biochemical reaction volumes (cells) coupled via FGF4, which mimics a monolayer or 2D cellular culture. Each cell contains a core gene-regulatory network (GRN) featuring mutual repression between the genes <i>Nanog</i> (N) and <i>Gata6</i> (G), and their self-activation. Both N and G regulate the expression of internal FGF4 (FI). External FGF4 (FE) can either diffuse to a neighboring cell (paracrine signaling), bind to the membrane of the origin cell (autocrine signaling), or be exchanged between neighboring cell membranes. FGF receptors transmit the sensed FGF4 signal back to the core GRN by activating ERK (EI ↔ EA). <b>[B]</b> Pipeline of data generation and analysis. Key stages of parameter inference (columns 1 through 5). Initially, all cells display the undifferentiated (UND) fate (row 2 column 3). Rows 1 and 3 show simulations without and with FGF4 signaling activated, respectively. All generated stochastic trajectories are processed by the same steps: (I) resampling data onto a regular time grid and calculating relevant system observables at cell scale (total NANOG, GATA6, and FGF4 levels); (II) determining the lineage for each cell at every time point (EPI, PRE, or UND); (III) summing up the corresponding total cell count for each fate at tissue scale; (IV) constructing the (joint) pattern score time series. The map between simulation parameters and resultant score time series is used for training a deep neural density estimator via the sequential neural posterior estimation (SNPE) algorithm (row 5 column 5), which directly estimates the parameter posterior distribution (row 5 column 3) conditioned on a target observation (row 4 column 5). Multiple posterior estimates are produced with the same training set, selecting the best learned distribution conditional on the target observation by analyzing a “meta score” distribution (row 4 col 3). This summary/meta score is calculated per posterior, and it relies on the maximum a posteriori (MAP) estimate of all the model parameters. In general, the next-round prior does not need to be the current-round posterior: it is plausible to obtain a well-informed next-round mixture distribution (row 5 col 1). Several iterations of the workflow are performed until the meta score surpasses an arbitrarily prescribed level. For additional information about this data-processing pipeline, please see Model parameter inference framework.</p> |
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