Johannes Jaeger
Johannes Jaeger worked on reverse-engineering the gap gene network of the Drosophila embryo with dynamical gene circuit models. Fitting the earlier Jaeger et al. models took about two CPU years1. Later papers with Jaeger as author simplified the method34 and tested post-transcriptional regulation5. Other authors cite this line of work as a successful case of reverse engineering8.
- In the library
- 3 works, named in 8 passages elsewhere
- Facts from
- ORCID
Costly early model fitting
Perkins, Jaeger, Reinitz and Glass (2006) describe the cost of the earlier Jaeger et al. gap gene models. Fitting them by parallel simulated annealing needed an estimated 20,000 CPU hours1. The authors report that alternative regulatory explanations were therefore not explicitly explored. Their own three-stage strategy ended in a local search that they call much simpler than the approach used by Jaeger et al.2. Durant and colleagues (2016) later list the reverse engineering of early Drosophila patterning, with Jaeger et al. 2004 among the citations, as one of the most successful uses of such heuristic methods8.
A simpler protocol
Crombach, Wotton, Cicin-Sain, Ashyraliyev and Jaeger (2012) present a simplified reverse-engineering protocol. They show that mRNA data from a colorimetric in situ hybridisation protocol can be used to infer the structure and dynamics of the gap gene network3. They compare the results with earlier protein-based studies and report equivalent regulatory mechanisms. They also reduce the data set further and define minimal requirements for correct inference. They suggest the method can be applied to other developmental systems with reasonable effort4.
Post-transcriptional regulation
Becker and colleagues (2013), with Jaeger as last author, combined global optimisation with identifiability analysis to ask how much post-transcriptional regulation matters. They report that it is not required for correct timing and positioning of gap protein domain boundaries5. It does appear to be needed for accurate protein levels. Their models predict an early boost in translational efficiency and a later stabilisation of gap proteins6.
Fitting all the models for the recent Jaeger et al. study [6,7], for example, which was done by parallel simulated annealing [5,10,11], required an estimated 20,000 CPU hours, or approximately two CPU years.
Perkins TJ, Jaeger J, Reinitz J, Glass L, 2006 · Reverse engineering the gap gene network of Drosophila melanogaster · open at passage 4The optimization approach we use in the third stage is much simpler than the approach used by Jaeger et al. [5–7,10,11].
Perkins TJ, Jaeger J, Reinitz J, Glass L, 2006 · Reverse engineering the gap gene network of Drosophila melanogaster · open at passage 16We demonstrate how mRNA expression data derived from a colorimetric (enzymatic) protocol for in situ hybridisation can be used to infer the regulatory structure and dynamics of the gap gene network.
Crombach A, Wotton KR, Cicin-Sain D, Ashyraliyev M, Jaeger J, 2012 · Efficient reverse-engineering of a developmental gene regulatory network · open at passage 12In this way, we define a set of minimal requirements for the successful inference of gap gene regulatory network structure and dynamics.
Crombach A, Wotton KR, Cicin-Sain D, Ashyraliyev M, Jaeger J, 2012 · Efficient reverse-engineering of a developmental gene regulatory network · open at passage 12Our fits reveal that post-transcriptional regulation is not required for the correct timing and positioning of gap protein domain boundaries.
Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens…, 2013 · Reverse-engineering post-transcriptional regulation of gap genes in Drosophila… · open at passage 12Specifically, our models predict an early boost in translational efficiency, plus a general stabilisation of gap protein products towards the end of the blastoderm stage.
Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens…, 2013 · Reverse-engineering post-transcriptional regulation of gap genes in Drosophila… · open at passage 12we conclude that our model parameters are globally structurally identifiable, i. e. our optimisation problem has a specific and unique solution.
Becker K, Balsa-Canto E, Cicin-Sain D, Hoermann A, Janssens…, 2013 · Reverse-engineering post-transcriptional regulation of gap genes in Drosophila… · open at passage 20The reverse engineering of the regulatory network controlling the early patterning of the Drosophila embryo from one‐dimensional gene expression data represents one of the most successful applications of these heuristic methods
Durant F, Lobo D, Hammelman J, Levin M, 2016 · Physiological controls of large-scale patterning in planarian regeneration: a… · open at passage 42
2006
2006 · read
2012
2012 · read
2013
2013 · read