A Systems Approach to Tomato Ripening

The regulatory network controlling tomato ripening

Tomato is the most widely consumed fruit, with the global value of the crop estimated at $10billion per annum. It is also a model fleshy fruit species, with excellent genetic and genomic resources – the first assembly of the tomato genome having been recently released.

Transcriptomic and metabolomic datasets across 13 time-points of the fruit ripening process have been generated for wild type and 3 well characterised ripening-related mutants. Differential expression analysis has identified over 10,000 transcripts involved in the ripening process, containing almost 700 predicted transcription factors (TFs). A combination of clustering and more advanced network inference techniques are being employed to connect TFs of as yet unknown function to modules of genes with functions of interest.

Inference algorithms based on Bayesian, artificial neural network and ODE techniques have been implemented. A transient expression system has been established to validate the results of the network inference with promising initial results, and work on stable mutant lines has started.

This project was lead by Charlie Hodgman (network modelling) and Graham Seymour (tomato ripening expertise). The CPIB researcher was [[glyn-bradley]].

This project was funded via the BBSRC’s Exploiting Systems Biology LINK initiative (ESB-LINK) in collaboration with Syngenta