UPDATE ON THE 2021 Systems Biology lab INTERNSHIPS (Bachelor and Master) – January 2021
To comply with VU Corona regulations, the Systems Biology group established a maximum number of experimental internship students that can be safely hosted in the lab.
For the period from February 2021 to July 2021 all experimental positions are filled. However, theoretical internships are still available.
Summer experimental and theoretical internship positions are available.
Students can do various types of projects in our lab. We supervise bachelor and master internship projects but you can also find supervisors for your literature thesis. Practical internships can be in the wet lab (here termed experimental) , can be computational (here termed theoretical) or can consist of a combination of those two. All of them qualify as ‘research internships’ in virtually all of the study programs.
Below is a list of projects that we thought of. If you are interested in one, just contact the person that is associated to that project. But there are options beyond this list. Check the Team and the Projects pages and click on the different team members to see their lines of research. If you are interested in a specific topic that we work on, but do not see an internship within that project listed below, just contact the team member and check for the possibilities. This is especially relevant if you are looking for a literature thesis project – these are usually tailor-made based on your interests.
If you have general questions about internships in our department see the contact information at the end of the page
|Project title||Type of research||Supervisor(s)|
|The role of GAPDH under nutrient transitions: mRNA quantification in single cell yeast||Theoretical (Bachelor or Master)||Laura (firstname.lastname@example.org)|
|Yeast cells often encounter environmental changes that challenge them to adapt in a specific direction. This response often relies on several layers of regulation, including gene expression. In this work, we use single molecule mRNA imaging technologies to quantify the expression of mRNA encoding the glycolytic enzyme GAPDH in budding yeast S. cerevisiae exposed to various environmental changes. GAPDH is the first enzyme of the lower glycolysis and has been identified as a key player in yeast response to carbon source transitions. To understand how this system works we tagged the 3 isoforms of GAPDH with a new RNA tagging system (MS2) that allows us to follow single mRNA both in space and time.
Aim: in this project we perform a comprehensive study on the role of GAPDH isoforms under different growth conditions.
Activities: analysis of the expression of GAPDH isoforms under different growth conditions.
Type of internship: fully computational. We promote a close collaboration between the student and the supervisor that will perform the experiments and provide the data.
Candidate: the ideal candidate is methodical, organized and willing to learn new skills. Programing skills are a plus but not mandatory. Bachelor internship.
Starting date: June/July2021.
|Automated quantification and interpretation of GC-MS-based metabolomic data ||Experimental(Master)|
between June 2021 and June 2022
|Avis Nugroho (email@example.com) & Herwig Bachmann|
|Volatile molecules are responsible for the sensory perception of a food product. In fermented foods, such volatiles can originate from the raw substrate itself, or from microbial activities. Over the course of fermentation, diverse enzymatic and chemical reactions occur, and the corresponding metabolites have various flavour characteristics and odour-activity thresholds. The analysis of these volatiles is done by GC-MS (gas chromatography - mass spectroscopy) which results in complex datasets. However, such analysis is mainly performed semi-automatically, requires laborious human curation, and consequently reports a small fraction of the detected compounds. Within this project, you will analyse a set of GC-MS data of fermented plant protein ingredients and focus on the untargeted analysis of the data with the aim of identifying the entire remaining compounds that are currently not reported. The project requires affinity with complex data analysis and independent learning to work with different software packages. Furthermore a basic understanding of analytical chemistry - in particular GC-MS - would be beneficial.
Within this project, you will explore and compare various approaches to automate characterization and quantification of GC-MS peaks, with and without a dedicated software. For the dedicated software, you will receive a training from an expert at the beginning of your project. This will ideally result in a nearly fully automated data analysis pipeline starting from the raw GC/MS data and ending in a comprehensive overview of detected compounds. If time allows, we will explore the integration of the metabolomics (volatolomics) data with their corresponding genome data. Next to our supervision, you will be working in close collaboration with GC-MS experts from NIZO Food Research.
If you are interested or would like to discuss about this project, do not hesitate to contact Avis Nugroho (firstname.lastname@example.org).
|A single cell perspective on the diauxic shift in Saccharomyces cerevisiae||Practical||Philipp Savakis (p.e.savakis[at]vu.nl])|
|The favourite carbon source of the budding yeast Saccharomyces cerevisiae is glucose, which at high levels is metabolised to ethanol, a compound of industrial importance. Preferential metabolism of glucose is ensured by a regulatory program (glucose repression) that implements sensory input mainly on the transcriptional level.|
After glucose has been consumed, yeast utilises the previously excreted ethanol as carbon source. This requires a complete turnaround of metabolism, which is mediated by several transcription factors. The switch to ethanol metabolism can be decomposed into different commited steps; for instance the highly regulated phosphoenolpyruvate carboxykinase, which allows gluconeogenesis by tapping the TCA cycle.
While the transcriptional regulation and proteome changes have been mapped out in fair detail on the level of the entire population, a zoomed-in perspective of the regulation in single cells is still missing, and the influence of noise on this aspect of cellular decision making is still poorly understood, in part due to restrictions in experimental design.
In this project, we will investigate the coordination of cellular sensing and the commitment to different metabolic states in vivo in dynamic environments and in living cells. This project provides an opportunity to learn molecular cloning, fluorescence microscopy, microfluidics, and flow cytometry.
|FILLED until July 2021|
|Measuring glucose metabolism in single yeast cells||Experimental (Bachelor or Master)||Dennis Botman
|Glucose is the major carbon source for Saccharomyces cerevisiae to grow on. Therefore, a robust glucose biosensor is desired to measure glucose levels inside the cell which will help elucidate kinetics of glucose transport and its metabolism at a single-cell level. In this internship, you will develop and characterize a fluorescent glucose biosensor.|
|Production of cultured red blood cells for transfusion purposes: analysis of metabolomics data to achieve high cell density erythroblast cultures||Theoretical (Master)||Jurgen Haanstra (in collaboration with Sanquin)|
|Transfusion of donor-derived red blood cells (RBC) to alleviate anemia is the most common form of cellular therapy. In addition, red blood cells hold great promise as delivery agents of e.g. specific drugs or enzymes. However, the availability of transfusion units depends on volunteers and carries a potential risk of alloimmunization and blood borne diseases. More than 30 bloodgroup systems encode >300 bloodgroup antigens and bloodgroup matching becomes increasingly challenging in a multiethnic society. Particularly the chronically transfused patients are at risk for alloimmunisation. In vitro cultured, customizable red blood cells (cRBC) would negate these concerns and introduce precision medicine both in transfusion medicine as well as in drug delivery applications. We aim to produce human cRBC at large-scale and cost effective, for which we need to optimize culture conditions and reduce cost-drivers.
Transfusion-ready erythrocytes can be cultured from hematopoietic progenitors but at market-incompatible high costs. A limitation in maximum cell density, 2 million cells/mL, has been observed in in vitro erythroblast expansion. Understanding the origin of this cell density limitation may provide strategies, both at media composition and feeding regime levels, to facilitate economically feasible upscaling.
Analysis of cell-conditioned media indicated that small molecules (<3kDa) are responsible for growth limitation. A metabolic by-product may be the culprit. Alternatively, or in addition, depletion of nutrients may also contribute to the growth stop. Therefore we aim to analyse the metabolic activity of erythroblasts with the aim to adapt the media such that cells can be cultured at much higher densities.
We have produced transcriptome and proteome data from which we can deduce the metabolic pathways that are active in our erythroblast cultures. We also determined metabolic profiles of erythroblasts seeded in defined medium, and the corresponding profiles of the medium, and sampled at timepoints 0, 12, 24 and 36 hours.
In this project you are going to use the genome-scale reconstruction of human metabolism. The transcriptome and proteome data will be used to restrict the enzymes in this model to what is actually expressed in erythroblasts. The measured metabolic profiles will be either used as input to understand internal flux distributions and elucidate potential unwanted byproducts - or to compare them to predictions of flux distributions that would give optimal growth. Furthermore, investigating theoretical flux profiles that would give high growth rates (i.e. biomass production) will reveal options to adjust the culture media.
We are looking for an enthusiastic Master student with a bioinformatics background and an interest in metabolic networks
This project will be conducted in close cooperation between Sanquin Research, dept Hematopoiesis, and the Amsterdam Institute of Molecular and Life Sciences (AIMMS), Systems Biology Lab.
|Integration of quantitative multi-omics data into genome-scale metabolic models||Theoretical (Master)||Pranas Grigaitis
Eunice van Pelt-KleinJan (email@example.com)
|Computational models of microbial metabolism are useful tools in biotechnology and medical sciences due to their ability to predict and analyze microbial cell behavior in silico. Stoichiometric modeling is an attractive technique to use knowledge-bases to aid analysis of microbial metabolism at genome-scale level. However, these models have limited predictive power in a number of situations due to the assumption of analyzing (1) optimally-functioning networks in (2) a steady state, fully driven by reaction stoichiometry. Recent approaches to improve the predictions of these models usually rely on the detailed descriptions of protein turnover costs (proteome-constrained, pc-Models), and would provide a platform to aid the model by using quantitative -omics data.
In this project, we want to develop a framework of straightforward and standardized integration of multiple types of -omics data, namely, transcriptomics, proteomics and fluxomics.
Planned activities (and methods)
- Automatizing the integration of RNA-seq and mass spectrometry-driven label-free quantitative proteomics experiments into pcModels of Saccharomyces cerevisiae, Lactococcus lactis and/or Schizosaccharomyces pombe (scripting: Python, bash)
- Simulation of cross-condition pcModels on both local machine and compute cluster (Lisa/SURFsara) and biological interpretation of the resulting simulation results (scripting: Python, bash; data analysis: Python, R)
|Pseudohyphal Growth and Biofilm formation in S. cerevisiae resolved by single cell imaging||Theoretical (Bachelor or Master)||Evelina Tutucci (firstname.lastname@example.org)|
|Many fungi such as Saccharomyces cerevisiae or Candida albicans are able to switch between a unicellular (yeast) form to a multicellular filamentous form in response to changes in the environment (e.g. nutrients availability, stress). This morphological transition allows fungi to adopt different survival strategies and in some instances become pathogenic.
During filamentous growth cells acquire an elongated shape and unipolar budding pattern, allowing for greater exploration of the environment. A more advanced strategy is the formation of biofilms, multicellular structures that consist of different cell types (both yeast form and filamentous form) as well as an extracellular matrix, offering both increased structural integrity and resistance to antifungal drugs. While many of the genes required for this differentiation process have been identified through bulk analysis (e.g. RNA seq), their expression in single cells and during differentiation has remained largely unstudied.
In this project, we investigate at the single cell level, the gene expression changes occurring during fungal differentiation. By using a fluorescence-based RNA imaging technique called smFISH (see pictures here: http://teusinkbruggemanlab.nl/evelina-tutucci/) we visualize and quantify individual mRNA molecules in single cells to investigate the spatiotemporal control of gene expression during filamentation. Furthermore, we investigate how the spatial organization of cells in biofilms influences gene expression.
Planned activities (and methods)
This computational project consists of analysing the acquired ‘3D’ smFISH images of pseudohyphal yeast and early biofilm forming colonies. With this analysis, you will investigate the heterogeneity in the gene expression of cells in colonies and you will couple spatial information such as cell volume and cell length to the RNA spot count in filamentous cells. Since the microscopy data is very rich of information it will be possible to further expand the analysis, depending on your computational skills and your curiosity.
With this internship, you will learn how to perform imaging analysis and to perform basic coding using Python, Image J and R . You will participate and present in our Single-cell group meeting and Journal club. Previous experience with coding is a plus.
3 Months (Availble from July 2021)
Internships are in principle open for students from other universities in the Netherlands or outside the Netherlands. We welcome for example ERASMUS students. Please note a few things:
If you are a non-VU student and from abroad you should contact Dr. Rob van Spanning (email@example.com) if you want to do an internship in our group, and indicate your preferred project(s), starting date, length of internship, and how you will support yourself (costs for food, accommodation), as we are unable to support you financially.
VU students and other Dutch students can contact the putative internship supervisor directly.