Over the last two weeks, I have been reading a fascinating book that came out recently and would like to strongly recommend it to our readers. This book, written by Caltech professor Eric Davidson and his former student Isabelle Peter, is one of those rare gems that will continue to influence researchers many decades from today.
With the cost of nucleotide sequencing going down, it is now possible to sequence the genomes of many interesting organisms. The first few steps of the genome projects, namely sequencing, assembly and gene annotation, have become fairly straightforward, but how to connect the genome and genes with the biological novelties of the organisms is not at all clear. Davidson and Peter’s book presents a systematic way to make sense of the genomes based on their work on sea urchin over the last fifteen years.
For those who do not know, Eric Davidson used to be the keynote speaker at almost all bioinformatics conferences in early 2000s. The draft human and mouse genomes just got published at that time, and many scientists were looking for ways to connect the genome with complex body plan. Eric Davidson, a developmental biologist, described a way to do so, and many researchers found his ideas thought-provoking. His method on gene regulatory network combined experiments and computation with the computational aspects being developed in close collaboration with Hamid Bolouri, an electrical engineer. Bolouri was at Caltech at that time, but later moved to Institute of Systems Biology, Seattle and then Fred Hutch Cancer Research Center.
The fact that Davidson and his collaborators really understood how to make sense of the genome became clear to me, when I worked closely with them in mid-2000 as part of the sea urchin genome project. At that time, I worked with a number of other genome projects, but the sea urchin one stood out as unusual. The community knew exactly why they needed the genome, and even before the genome was properly assembled, they were ready with annotating all transcription factors and measuring their expression profile during early development. The community manually annotated over 10,000 genes and it was quite a remarkable experience working with such a vibrant group of scientists.
Equally remarkable was what Davidson and his collaborators achieved since then. In 2012, Peter, Faure and Davidson published a PNAS paper titled -
“Predictive computation of genomic logic processing functions in embryonic development”, where they presented a predictive Boolean model to describe each step of the early development process up to gastrulation. Nothing like this has been done in any other organism.
Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations.
I came to further grasp the tremendous significance of Davidson’s work, when I was looking for ideas to make sense of the electric eel genome. The electric organs evolved six times independently in various fish species, but how can one go from the genome or annoated genes to say something biologically meaningful? At first, we were trying all kinds of popular bioinformatics methods (sequence conservation, phylogeny, accelerated evolution), but came to realize that Davidson’s approach was the only way to go. So, we decided to get gene expression profiles (RNAseq) in muscle and electric organ in multiple electric fish independently evolving electric organs and compared data to identify relevant genes. Although our work is far short of what the sea urchin community has done, it is a step in right direction.
After the electric fish experience, I decided to get both of his earlier books (“Genomic Regulatory System” and “The Regulatory Genome”) and read them completely. The latest book presents many of the concepts in simple language, and I recommend it to every bioinformatics researcher so that they have good understanding of the big picture.
We convinced the publisher (Elsevier) to share a chapter with our readers (click here to read, if embed does not work). Also, if you are purchasing at the Elsevier website by going from our blog, please use the code BIOMED315 to get a 30% discount on the price.
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