Energy Landscapes for Early T Cell Development

Abstract

T cell lineage development from an early thymic progenitor involves programmed shutoff of progenitor gene expression, upregulation of T cell specification genes, and proliferation. This biological system has been deeply studied and a gene regulatory network (GRN) that can accurately describe the system has been presented. Such a GRN offers an excellent opportunity to study lineage commitment from a multipotent progenitor. In this work, we investigated the GRN governing early T cell development by considering cell commitment as movements in an energy landscape. Our energy landscape method provided predictions that are consistent with experimental observations, both with and without simulated gene knockdowns. Moreover, we implemented cell development and proliferation within the energy landscape framework. We were able to show the impact of noise levels on cell developmental speed. The results are coherent with what would be expected for realistic biological systems.

Popular Abstract

T cells are one of the most important blood cells in our body that help us defend against outside invaders and diseases. Even though T cells may seem quite different from some other blood cells in terms of both their shapes and functions, they are born from the same kind of stem cells and share the same genetic information, like most blood cells. What differentiates T cells from other blood cells lies in the highly controlled modifications in gene expression. Such differences in gene expression are a result of the path that T cells have taken from stem cell stages during a cellular differentiation process.
Differentiation from a stem cell to a mature cell is a key cellular process in normal tissue development. This process is where a stem cell becomes a more specific type of cell with unique functions. A differentiation process usually involves many genes. Genes work together and interact with each other during this process. The final results of such interactions are that some genes are activated (expressed), but some are not. The information from activated genes are then used to produce functional products such as proteins. The products of activated genes will ultimately affect the properties of the cells.
To help understand the differentiation process better, we now treat an overall state of the genes as a cell state. Following this treatment, if we look at how the cell state develops during a differentiation process, there are patterns to follow. Some cell states develop into other states, but not the other way around. This is like a ball rolling on a road: the ball tends to go downhill but not uphill because the gravitational energy is higher uphill. In a similar vein as this gravitational energy, we can evaluate the energy of different cell states, which leads us to an energy landscape.
In the energy landscape, a state with lower energy would be more favorable. The differentiation process can be viewed as a ball rolling in the energy landscape, and the fate of a cell will depend on the pathway the ball takes and where it settles down. The landscape view of the cellular differentiation process can be a powerful tool for analyzing biological systems. T cell development provides a great example to carry out such studies.
In this work, we tested a landscape method based on a modified binary logic on a gene interaction model for early T cell development. Our landscape method can make predictions that agree with experimental results.

• author: Cheng, Yukun
• supervisor: Victor Olariu
• organization: Computational Biology and Biological Physics - Undergoing reorganization
• course: FYTM03 20221
• year: 2022
• type: H2 - Master's Degree (Two Years)
• subject: Physics and Astronomy
• language: English
• id: 9092298
• date added to LUP: 2022-06-23 10:54:29
• date last changed: 2022-06-29 15:28:35