Sentence examples for stochastic differentiation from inspiring English sources

The impasse was gradually resolved by first using an empirical approach that relies on the stochastic differentiation of mouse embryos or ESCs to establish lineage-restricted cell lines with endothelial-like potential.

A possible solution to this problem was proposed in [12] and is briefly summarized in the next section, where it is also shown that by a proper interpretation it can describe in an elegant way the fact that there exist different degrees of differentiation, and that it provides a natural way to simulate stochastic differentiation (i.e. properties 1 and 2).

In this case, by definition, if the threshold is increased one observes differentiation towards different TESs: therefore according to the model one can observe stochastic differentiation (albeit of a different type) even when chemical signals are present; a statement which can be subject to a experimental verification.

While the above hypothesis explains in a straightforward way the fact that there are different degrees of differentiation (i.e. property 1), related to different threshold values, it should be noted that also stochastic differentiation [34] [37] (property 2) is described by the model.

A population of replicons with these characteristics cannot explore the whole neutral space of an adaptive landscape and, consequently, the stochastic differentiation of the molecules is possible.

The model does not consider other sources of heterogeneity, like stochastic differentiation at other times (i.e. in between replication events) or heterogeneity in the environment.

The first approach is based on a generalized version of the Itô differentiation formula for stochastic differential equations with Poisson white noise.

We implement our model both with ordinary differential equations (ODEs), to explore the role of bifurcations in producing the one-way character of differentiation, and with stochastic differential equations (SDEs), to demonstrate the effect of noise on the system.

We found that environmental factors dictate the distribution of the most abundant taxa in this system, but stochastic niche differentiation processes, such as mutation and dispersal, also contribute to observed diversity patterns.

Similarly, few studies explicitly addressed both deterministic and stochastic population differentiation at the same time.

In contrast, other genetic concepts (epigenetics, ploidy, mutation, stochastic population differentiation) were among the most poorly connected in the network.