Project 1: Bacteria cell wall systems biology
Bacteria are surrounded by a protective peptidoglycan cell wall. Hence, the enzymes responsible for making this wall are the main targets of our most successful antibiotics. However, emerging resistant bacteria are eroding the efficacy of our antibiotic arsenal. Identifying new genetic determinants of the bacterial cell wall as antibiotic targets is of highest international priority but requires an integrative, multidisciplinary approach.
We have recently pioneered unique systems-level analytical approaches of the bacterial cell wall with dedicated bioinformatic tools. The unprecedented high throughput of this technology makes it possible to profile the cell wall of bacterial mutant libraries under diverse culture conditions. The postdoc will get the opportunity to use a suite of systems biology and machine learning techniques to predict functional networks connecting cell wall chemical profiles with genetics and phenotypes for further mechanistic and physiological studies. Our ultimate goal is to identify the repertoire of proteins associated to cell wall synthesis and regulation in a number of important bacterial pathogens, determine the mechanisms that maintain cell wall homeostasis and those that promote plasticity, and ultimately find new and more effective antimicrobial therapies.
This postdoc will be based in IceLab, hosted by the Department of Molecular Biology, and supervised by a multidisciplinary team with complementing expertise in molecular infection biology, systems biology, and machine learning.
Project 2: Arctic soil pore architecture
The fate of deep soil organic matter in arctic soils represents one main uncertainty in models predicting feedbacks between terrestrial ecosystems and our planet’s climate. One existing theory postulate that the arrangement of soil pores, being the hotspot of biological activities, may regulate microbial activities, and thus, determine if this deep soil reservoir of organic matter becomes mineralized into greenhouse gases. This project aims to put this theory to the test and increase our understanding about what role soil-pore architectures play in regulating turnover of soil organic matter in the Arctic. Could alterations of soil pore systems cause an increase in greenhouse gas emissions from Arctic soils?
In this project, we value candidates who have experience modeling and analyzing complex networks using packages like NetworkX or self-written code. We search for a creative candidate interested in understanding differences in functionality between complex 3D structures formed by abiotic and biotic processes. The project will utilize expertise available at the Umeå Institute of Design to create 3D replicas of natural soil pore systems that will allow us to empirically validate theoretical models. Traveling to arctic regions will be optional.
This postdoc will be based in IceLab, hosted by the Department of Ecology and Environmental Science and supervised by a multidisciplinary team with complementing expertise in soil science, design and network modelling.
Project 3: Image Classification and Machine Learning to analyze wood formation using 3D tomography images
The demand for renewable wood-based products is constantly increasing as we move away from fossil resources-based products. Wood is the most important renewable resource for construction in the World, yet we still know very little about how it forms on the cellular level. A deeper understanding of the mechanisms of wood formation on the cellular level could lead to better trees with improved growth and mechanical properties for more sustainable biomaterial production.
To improve our understanding of how wood forms, this project aims to reveal the dynamics of cell rearrangement during wood formation. We will generate high-resolution time-lapse X-ray images of live aspen wood formation in 3D. Images containing thousands of cells in the wood formation zone, small cells with dense cytoplasm, that divides, grow and rearrange will, after that, be segmented from the images to assess cell descriptors. We will analyze cell shape, cell growth rate, anisotropy and cell-cell displacement to decipher how this network of dividing and expanding cells evolves to form the complex structure of wood from the segmented images. Thus, to solve the research question related to how wood forms on the cellular level we propose the following objectives:
- To develop new methods to assess accurate 3D segmentation of the cells from 3D X-ray images
- To analyze the morphological changes and the rearrangements of the network of cells that are taking place during wood formation
This will require expertise in image-processing but also data analysis to decipher the complex process of wood formation at the cellular level.
This postdoc will be based in IceLab, hosted by the Department of Forest Genetics and Plant Physiology (SLU), and the Department of Physics (UmU), supervised by an interdisciplinary team of researchers with expertise in developmental biology and computational physics.