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Study of the molecular control of organ growth requires establishment of the causal relationship between gene expression and cell behaviors. We seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live-cell imaging techniques allow us to observe real-time organ primordia growth and gene expression dynamics at cellular resolution. In this paper, we propose a framework for the measurement of growth features at the 3D reconstructed surface of organ primordia, as well as algorithms for robust time alignment of primordia. We computed areas and deformation values from reconstructed 3D surfaces of individual primordia from live-cell imaging data. Based on these growth measurements, we applied a multiple feature landscape matching (LAM-M) algorithm to ensure a reliable temporal alignment of multiple primordia. Although the original landscape matching (LAM) algorithm motivated our alignment approach, it sometimes fails to properly align growth curves in the presence of high noise/distortion. To overcome this shortcoming, we modified the cost function to consider the landscape of the corresponding growth features. We also present an alternate parameter-free growth alignment algorithm which performs as well as LAM-M for high-quality data, but is more robust to the presence of outliers or noise. Results on primordia and guppy evolutionary growth data show that the proposed alignment framework performs at least as well as the LAM algorithm in the general case, and significantly better in the case of increased noise.

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No abstract

The study of the molecular control of organ growth requires the establishment of the causal relationship between gene expression and cell behaviors. Specifically, we seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live cell imaging techniques give us the ability to observe organ primordial growth and gene expression dynamics at cellular resolution in real time.In this paper, we propose a framework for measurement of growth features at the 3D reconstructed surface of organ primordia, as well as an algorithm for robust time alignment of primordia. Given a time series of live imaging data, we computed surface areas and deformation values from reconstructed 3D surfaces of individual primordia. Based on these growth measurements, we applied a modified landscape matching algorithm (which we refer to as LAM-M for presentation purposes), to ensure a reliable temporal alignment of multiple primordia. Although the original landscape matching algorithm (LAM) motivated our alignment approach, it fails to properly align growth curves in the presence of high noise/distortion. To overcome this shortcoming, we modified the cost function to consider the landscape of the corresponding deformation time series. Results on both synthetic and real data show that the proposed framework performs at least as well as the LAM algorithm, and better in the case of increased noise.