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Dr. Joel Smith is a developmental biologist focused on the evolution of gene regulatory networks. A major goal of his research is to understand how the animal body plan evolved, specifically its organization along orthogonal axes. The several animals he studies reflect this goal. First among these is the sea anemone, Nematostella vectensis, which as a Cnidarian (along with sea jellies and corals) is a member of the sister group to Bilaterians. However, despite its apparent simplicity and distant relationship, Nematostella exhibits differential gene expression along orthogonal embryonic axes similar to Bilaterians. Other subjects of study are likewise chosen as representatives of evolutionarily important taxa: the spiral cleaving slipper snail, Crepidula fornicata, and the biradially symmetric comb jelly, Mnemiopsis ledyii. A major challenge working with non-model organisms of this sort is building up genomic resources and integrating data sets. The Smith lab has developed methods a de novo transcriptome assembly from short-read RNA-seq data for non-model organisms. His group uses absolute quantitative RNA-seq data from high-resolution time series expression, a method called RNA-seq HD, to infer network relationships. The use of dynamic gene expression data in this way represents a distinct technique from more common statistical methods. The Smith lab has also developed techniques to integrate large-scale data sets for the purpose of identifying the network interactions, or switches, that control fate decisions. Dr. Smith pioneered “cis-reengineering” methods to test network dynamics controlling differential fates. These studies suggested general principles of biological regulation based on the recurring topologies associated with network switches. Though the Smith group asks questions about evolutionary biology, this broader interest in biological information processing is reflected in collaborations with researchers from diverse fields as control theory and regenerative biology.
For more information please visit the Smith Lab website
Gene Regulatory Networks
The process of development of any organism is in essence a process of information transformation: information stored in the genome of the fertilized egg, along with certain maternally-derived determinants, are progressively transformed into spatially-restricted patterns of gene expression in the embryo, eventually giving rise to the differentiated cells, tissues, organs and systems making up the adult organism.
Gene Regulatory Networks (GRNs) help us see the information processing functions of development by mapping out the regulatory relationships between different genes in the network. Distinct regulatory subnetworks, sometimes called network motifs, perform distinct information processing functions. By understanding the network topology, we therefore gain deep insight into the molecular mechanisms of development.
GRN research combines systems biology approaches – the broad strokes – with detailed developmental biology and gene expression studies – the finer touches. It is this combination which allows for the generation and analysis of large data sets within a specific developmental context. As if by pulling a thread to unwind a densely interwoven tapestry, the unfolding events of specification are revealed in mechanistic detail.
- How the static gene regulatory code drives dynamic gene expression in a moving torus
Smith, J., Theodoris, C., and Davidson, E. A gene regulatory network subcircuit drives a dynamic pattern of gene expression. Science 318, 794-7, 2007.
- How the gene regulatory code drives expression of a cohort of genes in a moving torus pattern
Smith, J., Kraemer, E., Liu, H.-D., Theodoris, C., and Davidson, E. A spatially dynamic cohort of regulatory genes in the endomesodermal gene network of the sea urchin embryo. Dev Biol. 313, 863-875, 2008.
While the program of development is written in the genomic regulatory code – the primary heritable element – the evolution of development is the process where this code is continuously rewritten. And just as we gain mechanistic insight into developmental biology through an understanding of network architecture, we can make strong inferences into evolutionary mechanisms through a comparison of regulatory network topologies between species.
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