Yogi Jaeger

@yoginho
active 3 years, 8 months ago
Our #BioPreDyn collaboration paper with Julio Banga is out in PLoS Computational Biology! This is the first time somebody has done proper parameter identifiability in a realistic model of a developmental system: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003281. (And yes, the subject matter is a trifle technical… 😉 View

Base

Full Name

Yogi Jaeger

Current position

Group Leader

Affiliation

Centre for Genomic Regulation (CRG)

Location

Barcelona, Spain

Research interests

My lab investigates the evolutionary and developmental dynamics of the gap gene network in different dipteran species using a combination of quantitative experimental approaches and mathematical modeling.

Webpage

http://www.crg.eu/johannes_jaeger

Research activity

Research

My research is driven by an interest in the complex relationship between genotype and phenotype. To gain a mechanistic and quantitative understanding of the genotype-phenotype map is one of the big challenges in biology today. Tackling this challenge requires a quantitative systems-level understanding of the gene networks underlying development across multiple levels, from the molecular to the organismic. This is difficult due to the large number of factors involved. We depend on computational models for this task. I present a reverse-engineering approach, where gene regulatory interactions are inferred from quantitative expression data, using data-driven mathematical models (called gene circuits). We have established that the gap gene network can be consistently reconstructed in this way using both protein or mRNA expression data. Gap gene circuit models in Drosophila reproduce observed gene expression with high precision and temporal resolution and reveal a dynamic mechanism for the control of positional information through shifts of gap gene expression domains. My group is extending this approach to a comparative study of the gap gene network between different species of dipterans. No such quantitative systems-level analysis of an evolving developmental gene regulatory network has been achieved to date. We have created and analyzed quantitative data sets for gap gene expression in the scuttle fly Megaselia abdita, and the moth midge Clogmia albipunctata, which are now used for model fitting. Our approach yields precise, quantitative predictions of how changes of gene regulatory feedback affect the timing and positioning of expression domains in these species. These predictions are now being tested experimentally using RNA interference.

Representative publications

11894989, 15254541, 15342511, 17041900, 17196796 , 18776142, 19876378, 20023719, 20433825, 20196855, 20927566

Research keywords

evo-devo, systems biology, gene regulation, pattern formation, mathematical modeling, image processing, dynamical systems theory, non-linear optimization

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Latitude and Longitude

41.38531,2.193242