One of the greatest challenges in animal biology is to learn how genomic sequence information is read by transcription factors to produce patterns of gene expression within the context of regulatory networks in developing embryos. This project is part of a broader Program Project that will integrate computational modeling and wet laboratory methods to address this challenge in the belief that only quantitative, predictive mathematical models that have been validated experimentally can provide the rigorous understanding required for modeling transcriptional networks of animals. This Project's contribution to the overall program will be to provide absolutely critical experimentally tests of the predictions of computational models. Two classes of predictions will be tested. In the first Specific Aim, we will generate approximately 100 transgenic lines to test models from Project 4 that predict which genomic regions bound in vivo by transcription factors are functional CRMs, seeking to refine current evidence that genomic regions that are only bound at low levels by transcription factors are not functional CRMs. In the second Specific Aim, we will seek to distinguish between different models for CRM regulatory grammar. We will do this by making defined mutations in transcription factor recognition sites that alter the predicted affinity of interactions within a set of 7 known CRMs and by constructing de novo CRMs. Around 400 transcription factor site mutated CRMs will be constructed and around 30 de novo CRMs. The three dimensional expression patterns driven by wild type, mutant and de novo CRMs will be recorded in collaboration with the Expression and Database Core and the levels of transcription factor DNA binding will be determined in collaboration with Project 1. The non wildtype CRMs will be designed by the modeling groups (Projects 3 and 4). These will be used for comparing the measured effect of quantitative perturbations in DNA occupancy to those predicted by models for transcription factor DNA binding and transcriptional activity. This will allow an iterative series of validation and model refinement experiments to be performed by the overall Program Project. By helping to establishing how to read transcriptional information in animal genomes, this Project will aid both the development of therapeutics for human genetic diseases and the understanding of animal development.
| Li, Jingyi Jessica; Chew, Guo-Liang; Biggin, Mark D (2017) Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them. Nucleic Acids Res 45:11821-11836 |
| Li, Jingyi Jessica; Bickel, Peter J; Biggin, Mark D (2014) System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2:e270 |
| Knowles, David W; Biggin, Mark D (2013) Building quantitative, three-dimensional atlases of gene expression and morphology at cellular resolution. Wiley Interdiscip Rev Dev Biol 2:767-79 |
| Fisher, William W; Li, Jingyi Jessica; Hammonds, Ann S et al. (2012) DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila. Proc Natl Acad Sci U S A 109:21330-5 |
