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Published October 9, 2012 | Published + Supplemental Material
Journal Article Open

Predictive computation of genomic logic processing functions in embryonic development


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.

Additional Information

© 2012 by the National Academy of Sciences. Edited by Eric N. Olson, University of Texas Southwestern Medical Center, Dallas, TX, and approved July 27, 2012 (received for review May 10, 2012). Published online before print August 27, 2012. This Feature Article is part of a series identified by the Editorial Board as reporting findings of exceptional significance. This work was supported by National Institutes of Health Grants HD 037105 (to E.H.D.), GM 061005 (to E.H.D.), and HD 0656016A (to R. A. Cameron). Author contributions: I.S.P. and E.H.D. designed research; I.S.P., E.F., and E.H.D. performed research; E.F. contributed new reagents/analytic tools; I.S.P., E.F., and E.H.D. analyzed data; and I.S.P. and E.H.D. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission.

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