Abstract
Plants have adapted to most of the planet's ecosystems, offering an amazing range of phenotypes and behaviours. Unlocking the underlying mechanisms like the circadian clock provides novel insights into the molecular hardwiring of plants and how they interact with their environment, furnishing new opportunities to develop better and more sustainable crops. The circadian rhythm is a roughly 24-hour cycle in the biochemical, physiological, or behavioural processes of living entities, including both plants and animals. It plays a crucial role in homeostasis and adaptation; hence, in agriculture. The difficulty in elucidating the circadian clock is rooted in the notion that two independent factors contribute to controllability: the system’s architecture (whose components interact with each other) and the dynamic rules that capture the time dependent interactions between components. We set out to test mathematically the hypothesis that global circadian patterns of plant gene expression may be explained by progressive combinations of multiple promoter elements acting together. We proposed that the net effect of transcription factors acting at these elements could be responsible for the full range of phases observed in circadian output genes. We developed novel methods for the identification of circadian genes from short time-course microarray data and for the identification of the individual regulatory motifs which aggregate into coherent motif clusters capable of predicting the phase of a clock gene with high fidelity. We integrated gene expression profiles and protein interaction maps to provide a systematic and global view of combinatorial network modules underlying representative circadian programs. Furthermore, we integrate the newly discovered cis regulatory modules into the circadian regulatory network. Lastly, we developed circadian differentially inferred networks delineating the contrasting interactions between elements among genotypes. This study presents the analytical framework that should allow one to analyse the controllability of a complex system like the circadian clock in plants through the combination of driver nodes with their time dependent control reflecting the systems' dynamic logic. Such a circadian network provides a quantitative and holistic outlook upon a complex modular network of great agronomic importance.
Original language | English |
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Qualification | Ph.D. |
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Award date | 1 Apr 2015 |
Publication status | Unpublished - 2015 |
Keywords
- circadian, plants, gene epression, transcriptome