Noise at tissue level: intercellular communication improves ICM differentiation robustness by 10–20% compared to purely-binomial baseline scenario.

Noise at tissue level: intercellular communication improves ICM differentiation robustness by 10–20% compared to purely-binomial baseline scenario.

<p><b>[A]</b> Comparison between inferred-theoretical wild-type (ITWT ∼ CV<sub>1</sub>) and purely-binomial (PB ∼ CV<sub>0</sub>) systems. The main plot shows the coefficient of variation (CV) as a function of the system size for each cell fate (colors as in...

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Bibliographic Details
Main Author:	Michael Alexander Ramirez Sierra (20209354) (author)
Other Authors:	Thomas R. Sokolowski (20209357) (author)
Published:	2024
Subjects:	Cell Biology Genetics Developmental Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> understanding specific time window providing quantitative predictions identify parameter sets enabling lineage proportions early mammalian development biophysically realistic fashion intracellular initial conditions driven simulation techniques reproducible icm patterning pre lineage proportions level differentiation arises intracellular gene expression inner cell mass ensures correct epi type icm systems stochastic simulation framework stochastic systems necessary conditions high gene expression heterogeneity stochastic description powered simulation icm specification icm exhibits work offers surprising role seemingly cell robust unfolding remains robust reducing fate purely cell proportioning errors primitive endoderm mouse embryogenesis intercellular signaling inherent stochasticity future exploration fgf4 signaling fate proportioning fate decisions exogenous fgf4 efficiently simulating developmental biology combined action central challenge blastocyst stage based inference autonomous system autonomous mechanisms also provides advancing event adjusted based achieve robust
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Summary:	<p><b>[A]</b> Comparison between inferred-theoretical wild-type (ITWT ∼ CV<sub>1</sub>) and purely-binomial (PB ∼ CV<sub>0</sub>) systems. The main plot shows the coefficient of variation (CV) as a function of the system size for each cell fate (colors as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012473#pcbi.1012473.g004" target="_blank">Fig 4</a>). The inset shows the ratio between CV<sub>0</sub> and CV<sub>1</sub>, highlighting systematically lower fate specification error in the ITWT compared to the PB system. <b>[C]</b> Comparison between reinferred-theoretical mutant (RTM ∼ CV<sub>1</sub>) and purely-binomial (PB ∼ CV<sub>0</sub>) systems. Colors and symbols as in [A]. The inset plot shows that fate specification errors in the spatially uncoupled RTM system are of the same magnitude as in the PB scenario. Axes use logarithmic (base 10) scale. For each system size, the data point is computed from a batch of 1000 simulations. <b>[B, D]</b> Typical time evolution of normalized cell fate counts for three example system sizes (<i>η</i> ∈ [25, 100, 400] cells). Solid lines represent means, dashed lines represent standard deviations around means.</p>

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