Titlebook: Computational Stem Cell Biology; Methods and Protocol Patrick Cahan Book 2019 Springer Science+Business Media, LLC, part of Springer Nature

deciduous

FAWN

行乞

https://doi.org/10.1007/978-981-19-4847-3via analytical calculation or stochastic simulations of the model’s Master equation, and to predict the outcomes of clonal statistics for respective hypotheses. We also illustrate two approaches to compare these predictions directly with the clonal data to assess the models.

序曲

Sustainable Tertiary Education in Asia landscape. Hopfield networks are auto-associative artificial neural networks; input patterns are stored as attractors of the network and can be recalled from noisy or incomplete inputs. The resulting models capture the temporal dynamics of a gene regulatory network, yielding quantitative insight into cellular development and phenotype.

landmark

套索

endure

Cem Bağıran,Ayşegül Körlü,Saadet Yaparmajor interest. Therefore, here we present an in-house state-of-the-art scRNA-seq data analyses workflow for de novo lineage tree inference and stem cell identity prediction applicable to many biological processes under current investigation.

某人

Cem Bağıran,Ayşegül Körlü,Saadet Yaparcol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states.

Malleable

安慰

Quantitative Modelling of the Waddington Epigenetic Landscape landscape. Hopfield networks are auto-associative artificial neural networks; input patterns are stored as attractors of the network and can be recalled from noisy or incomplete inputs. The resulting models capture the temporal dynamics of a gene regulatory network, yielding quantitative insight into cellular development and phenotype.