Kirsten ten Tusscher
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Research projects

If you are enthusiastic about the combination of biology and modeling and interested in doing a computational biology research project on one of the below subjects or some idea of your own you are most welcome to contact me (k.h.w.j.tentusscher@uu.nl). Projects are often based on published data, and/or in close collaboration with experimental biologists at UU our outside of UU. While some affinity with mathematics and programming is required, you do not need to be a math or programming wizzard to have fun and succeed with these projects.

• Evo-devo of plant root cell types : To execute their complex functions organs of multi-cellular organisms consist of different cell types. In the case of plant roots, these cell types are organized in concentric rings of cell files of different types. As an example vascular tissue transporting water up and carbon down typically are in the center, while the epidermis protecting the root is positioned on the outside. In between vasculature and epidermis lies the so-called ground tissue consisting of endodermis and cortex. Depending on the species, roots may have a different number of cortical cell layers and this has consequences for their radius (uptake area), strength (soil penetration) and costs (required carbon investments).
  In this project we will investigate how the plant species Cardamine hirsuta has 2 cortex layers, while the plant species studied in most detail, Arabidopsis thaliana, has only 1 cortex layer. Cell files are formed close to the roots stem cell niche, where divisions parallel to root growth axis lead to the formation of extra cell files. In contrast, divisions perpendicular to the length axis cause a growth of the cell file, simply extending the root length. Thus, whether or not a plant species forms an extra cell layer depends on the number of these parallel divisions performed near the tip of the root. So how do plant cells decide in which direction to divide?
  In this project we will make use of in house, C++ based models for the Arabidopsis root, in which we will incorporate a previously published model for the formation of the single cortex layer in Arabidopsis (Cruz-Ramirez et al., Cell, 2012). We will next extend this model to the Cardamine hirsuta anatomy, and investigate the potential of additional molecular pathways for controlling the formation of an extra cortical layer. For the latter we will describe these pathways using ordinary differential equations.
• Remembering who you are in plant roots: The plant root systems of modern plants expand through the repeated branching of of lateral roots. The process of where along the main root new lateral roots are formed starts very close to the root tip. In the model plant Arabidopsis periodic signals of the plant hormone auxin tell subsets of cells (those cells passing through while the signal is high) that they will become competent of future lateral root formation. However, since this signal is only temporary, these cells have to somehow remember what they have been told. In our group we have made models for the early stage of periodic auxin signals (Van den Berg et al., 2021) and proposed a mechanism for cells to stably memorize this signal (Santos-Teixeira et al., 2022). However, surprisingly recent data suggest that the story is less simple and that depending on the time of day and the amount of light the plant receives this memory may either fade out or be enhanced (Ren et al., 2024). But how can cells remember, forget and remember again? Perhaps there are two layers to this memory, with a superficial layer that can either fade or enhance and a deeper layer that is stably maintained? Can a forgotten signal be remembered again after a long time or is there a limit to this? Can a long remembered signal be still forgotten after all? And of course, how do time of day and light impact on these processes?
  In this project we will make use of in house C++ based models for Arabidopsis root development that incorporate the previously described auxin signal and memory processes. We will extend these models to investigate different potential pathways that enable time of day and light to affect the memorization process and investigate which properties the memorization must have to allow both fading and enhancement. We will investigate whether different combinations of properties differ in whether fading and enhancement can occur indefinitely or have a finite temporal window. This will allow us to provide experimentalists with testable predictions. Incorporation of novel processes involves formulating these in terms of differential equations.
• Directional growth of plant roots: Plants can adapt the growth direction of their stems and roots in response to environmental stimuli. As an example plant roots grow towards gravity, but away from a patch of saline soil. To grow towards or away from a signal, the root needs to bend, meaning it has to grow asymmetrically. This growth asymmetry is typically but not always caused by an asymmetry in the growth hormone auxin. Using a model of the root tip describing the anatomy of the plant root tip as well as how plant cells synthesize, degrade and transport auxin, in this project you can investigate how plants sense differences in salt concentrations across their roots and use this to generate such an auxin asymmetry. Additionally, more recent data suggest roles for other hormones in this process. Open questions are whether these othese other hormones mostly work to antagonize auxin, helping maintain the auxin asymmetry by generating an opposite asymmetry, or rather by impacting auxin transport. Depending on your interest alternative focusses may be the details of auxin patterning, the interplay of auxin with other hormones, or the mechanics of root bending. Still in all cases the central question is how does the plant root decide in which direction to grow? The growing away of roots from salt -called halotropism- is investigated in collaboration both with experimentalists in the Testerink lab at Wageningen University and Research that have resulted in nice shared publications (Van den Berg et al., 2016; Korver et al., 2020) as well as with experimentalists in the ECPD group here at UU. The project thus offers opportunities to closely collaborate with experimentalists.
  The project will involve working with differential equations and C++ model code to adapt the existing model, and analysing and plotting model outcomes.
• Adaptation of plant root architecture to nutrient patterns: Plants can not only adapt the direction of growth of individual roots (see above) to environmental conditions. Additionally they can adjust the growth rate of the main root, and the numbers, density and angles of lateral roots. As an example, if nitrate or phosphate is present in certain parts of the soil but not others, root growth and branching is enhanced in the nutrient rich and suppressed in the nutrient poor soil patches. Depending on your preferences in this model we can either look at models of individual roots (as above) to investigate in detail how their growth depends on local nutrient conditions or look at models of the entire root system, describing its growth and branching, to see how its overall patterning emerges from the interplay of different local nutrient levels and plant level nutrient status. In the individual root case, the focus will ly on cell-based root models, and investigating the impact of nutrient presence on the levels and patterns of key hormones and developmental genes known to control root development. In the case of modeling an entire root system, the focus will ly on more course grained models (the root system is subdivided in segments rather than individual cells) and revolve around the signals being exchanged from root to shoot back to root and their integration with local nutrient signals. In either case your research focuses on how plants make wise investment (growth costs carbon) decisions based on limited information (on nutrients)?.
  When you do this project you will be part of a team of 4 people, 2 modelers and 2 experimentalists working on this subject. You will thus have the chance to be part of an interdisciplinary team and work with locally generated experimental data.
  The project will involve working with differential equations, and either C++ (cell-based single root models) or python (segment based whole root system models) model code to adapt the existing model as well as analysing and plotting model outcomes