Networking development by Boolean logic
TL;DR: Eric Davidson's laboratory has become a leading force in constructing gene regulatory networks (GRNs) operating in sea urchin development and drilled down into other genes and gene families and the factors that regulate their coordinated regulation, leading them into the GRN era.
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Abstract: Eric Davidson at Caltech has spent several decades investigating the molecular basis of animal development using the sea urchin embryo as an experimental system1,2 although his scholarship extends to all of embryology as embodied in several editions of his landmark book.3 In recent years his laboratory has become a leading force in constructing gene regulatory networks (GRNs) operating in sea urchin development.4 This axis of his work has its roots in this laboratory’s cDNA cloning of an actin mRNA from the sea urchin embryo (for the timeline, see ref. 1)—one of the first eukaryotic mRNAs to be cloned as it turned out. From that point of departure, the Davidson lab has drilled down into other genes and gene families and the factors that regulate their coordinated regulation, leading them into the GRN era (a field they helped to define) and the development of the computational tools needed to consolidate and advance the GRN field.
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References
Gene Regulation for Higher Cells: A Theory
Roy J. Britten,Eric H. Davidson +1 more
TL;DR: Direct support for the idea that regulation of gene activity underlies cell differentiation comes from evidence that much of the genome in higher cell types is inactive and that different ribonucleic acids are synthesized in different cell types.
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Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis
Andrea Pauli,Eivind Valen,Michael F. Lin,Manuel Garber,Nadine L. Vastenhouw,Joshua Z. Levin,Lin Fan,Albin Sandelin,John L. Rinn,Aviv Regev,Alexander F. Schier +10 more
TL;DR: This study provides the first systematic identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic, and evolutionary studies.
•Journal Article
Introduction to Causal Inference
TL;DR: This introduction to the Special Topic on Causality provides a brief introduction to graphical causal modeling, places the articles in a broader context, and describes the differences between causal inference and ordinary machine learning classification and prediction problems.