Scientific sessions, CRG Group Leader Seminars
Systems Biology Programme, CRG
One of the most distinguishing features of biological systems is their incredible complexity. For this reason, it remains very challenging to gain any kind of mechanistic understanding of developmental processes, a central problem in modern biology. Tackling this challenge requires a quantitative systems-level understanding of the underlying gene regulatory networks. Such understanding must span multiple levels, from the molecular to the organismic. It is difficult to achieve due to the large number of factors to be considered. For this reason, we depend on data-driven computational modeling for this task.
Biography of the author:
Yogi Jaeger started his career as a Drosophila developmental geneticist, but soon grew dissatisfied with the prevailing reductionist explanations for the causes of development and evolution.
During his time as a graduate student, he acquired the mathematical and computational skills required to combine experimental work with modeling. In particular, during his PhD project in the laboratory of John Reinitz (at Stony Brook University, NY) Yogi helped develop a reverse-engineering approach which allowed him to reconstitute the gap gene network of Drosophila melanogaster in silico based on quantitative gene expression data. Yogi then spent his postdoc at the University Museum of Zoology in Cambridge to adapt this approach to non-drosophilid species of flies, work which he continued after moving to the CRG as a group leader in 2008. The main aim of his laboratory is to perform a quantitative comparative study of the gap gene network across different species of dipterans. No such systems-level analysis of an evolving developmental gene regulatory networks has been achieved to date.
In his group leader seminar, Yogi will focus on the reverse-engineering approach used by researchers in his lab. In particular, he will present results from two projects. The first used cutting-edge algorithms to fit and analyze a model of translational regulation for Drosophila gap genes. The advantage of this model is that it is relatively simple compared to the full model of gap gene transcriptional regulation. This allowed us to perform very rigorous analyses which show that our model fits are specific, recovering plausible and biologically interpretable parameters, which yield solid and reproducible biological predictions. In biological terms, the model showed that translational regulation of gap genes is not required for pattern formation, but does play a role in the fine-tuning of gap gene expression levels during development. The second project focused on the question how much and what kind of quantitative expression data is required for successful reverse-engineering. It shows that much less, and much less precisely measured, data is required for successful network reconstruction than previously expected. It also identifies the timing and positioning of expression boundaries as the crucial features of gene expression.
These results imply that reverse-engineering can be applied in various biological contexts with reasonable amounts of investment. Only the wide application of quantitative methods to reconstruct realistic regulatory networks will allow us to uncover wether there are common principles and mechanisms underlying biological pattern formation.