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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)|
|Biopurification of plant proteins||Experimental or computational (combinations are possible) (Bachelor or Master)||Avis Nugroho (firstname.lastname@example.org)|
|The substitution of animal proteins with plant-based derivatives is hindered by off-flavours and anti-nutritional factors in plant protein concentrates and isolates. Physical processes are available to remove such compounds, but they may not provide sufficient removal or lead to undesired side effects e.g. protein functionality loss. To remove undesired molecules from plant proteins we are working on biopurification solutions. For this we screen Lactic Acid Bacteria and yeasts for their ability to degrade unwanted molecules when incubated with different substrates. We are particularly interested in the modulation of the metabolism of non-growing organisms to improve biopurification strategies. Furthermore, we have generated extensive GC-MS (volatile compounds) data sets which we would like to integrate with existing genome data. This project is performed in collaboration with NIZO Food Research within a consortium of 6 industrial and 3 academic partners.
Techniques: Wetlab: cell culture (LAB/yeasts), metabolite measurements (e.g. GC-MS, HPLC), various microtiter plate assays, flow cytometer. Dry lab: GC-MS analysis software, various bioinformatics and chemoinformatics tools.
|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).
|Understanding optimal resource allocation strategies in yeasts||Computational|
Bachelor (with strong interest in computational methods)/Master
|Optimal allocation of limited resources, such as nutrients, energy, or physical volume of the cell enables to sustain cell maintenance and growth of cells, and is critical for unicellular microorganisms to strive. Moreover, the optimal allocation pattern can be context-specific, heavily depending on the environment the microorganisms live in. Therefore, computational techniques are of great help in order to capture and analyse resource allocation strategies/patterns, preferably at genome-scale. Thus in this topic, we blend existing knowledge of biochemistry and microbial physiology together with different types of computational modelling to advance the understanding of the organization of metabolism of two major eukaryal model organisms: budding yeast Saccharomyces cerevisiae and fission yeast Schizosaccharomyces pombe.
Techniques: genome-scale metabolic modelling (both conventional and proteome-constrained) (PySCes CBMpy, COBRA etc.), kinetic modelling (COPASI), programming with Python and/or R for data analysis and visualization
|Metabolism in health and disease||: Experimental or computational (combinations are possible)|
|The work in this topic aims to understanding control and regulation of metabolism to reveal selective drug targets in pathogens and other disease-causing cells. In addition, we also want to understand these aspects for healthy cells to make sure that interventions against the disease will not harm them. We work with the parasite Trypanosoma brucei and with liver cancer cells in the wetlab, but also do research on the parasite Schistosoma mansoni, on head- and neck cancer and blood cell precursors in the dry-lab (always in collaboration with experimental labs
Techniques: Wetlab: cell culture, metabolite measurements, enzyme assays. Dry lab: kinetic modelling (COPASI, PySCes (python-based), genome-scale modelling
|Pichia Kluyveri and the production of alcohol-free beer||Experimental or computational|
|Pichia kluyveri is used for the production of alcohol free beer. Little is known about this yeast, although it is already applied as a starter culture by our industrial partner Chr. Hansen. We study the physiology of the yeast by combining wet- and dry-lab efforts. We both look for answers to fundamental questions within the field of yeast physiology and for innovations with the fermented beverages industry.
Techniques: Within the wet-lab part of the project we investigate yeast physiology by growth studies and molecular biology. By combining the wet lab approaches with the genome of the yeast, we aim to construct genotype- phenotype relationships. Within the dry-lab part of the project genome scale metabolic models, so-called proteome-constrained models and coarse grained models are used to decipher metabolism of Pichia kluyveri.
|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)
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 (firstname.lastname@example.org) 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.