Lunch Pitch with Jakob Hultgren, Barbora Parizkova and Aftab Nadeem

 

As a theorist, one of the aspects of science that I find most fascinating is the versatility of its tools. I will talk about a problem from astronomical imaging. A collaborator and I wanted to automatically detect star clusters in images of the sky from the Hubble telescope. Doing this provides important information about the processes governing the formation of stars. However, in this pitch I want you to lean back, squint your eyes and try to replace star cluster detection with your favorite scientific problem, to see if the tools we use fit. Essentially, the topic is the very basic problem of reconstructing reality from a set of imperfect measurements. In my field there are many fancy conceptual frameworks for this, for example compressed sensing and super resolution. The tool we use is relevant to most of these frameworks. It is called optimal transport, and it provides a way to preserve spatial or geometric information when performing signal processing. 

 

What I am looking for: I’m interested in cross-disciplinary collaboration. Stripping the problem above of its astronomical context, it becomes a general problem in image processing which may have application in medicine, biology and many other fields. I believe that the most effective solutions to real world problems build on sophisticated theory and that the best theoretical frameworks grow out of frequent interplay with applications.

 

 

Abstract:

Nitrogen (N) is an essential macronutrient and a growth-limiting factor for plants. As sessile organisms, plants adapt their root system to acquire sufficient N for optimal growth and development. Diverse concentrations of available N, as well as different forms of N, have distinct impacts on the processes involved in generating new lateral roots, leading to variations in root system architecture as a response to diverse N conditions or sources. Our research has demonstrated that organic N in the form of glutamine (GLN) significantly enhances lateral root formation compared to inorganic N treatment. We have shown that this GLN effect is highly dependent on auxin – a plant hormone that is a major regulator of root development – however, the molecular mechanisms behind the GLN root-promoting effects are still poorly understood. This study aims to identify the genes responsible for GLN-induced lateral root growth through the implementation of forward and reverse genetics screens using mutant lines of Arabidopsis thaliana. In addition, we have developed a set of novel marker lines for genes potentially involved in the early stages of lateral root development. Confocal imaging will be employed to investigate the expression of these genes in response to GLN.

Interested in: finding collaborations to help us develop a system of machine-learning-based high-throughput confocal image analyses of various stages of lateral root development to better understand how this process is regulated by different N forms. But the overall ‘out of the box’ feedback of colleagues experienced in plant physiology out of the sterile lab conditions, the complexity of plant nutrition and facing the new challenges of changing climate will be highly appreciated.