General information on internships

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 wetlab (here termed experimental) , can be computational (here termed theorical) or can consist of a combination of those two.  All of them qualify as ‘research internships’ in virtually all of the study programmes.

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 page 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

Currently available

Project titleType of researchSupervisor(s)
How does soluble adenylyl cyclase regulate the Warburg effect in liver cancer cells?Modelling (Master)Jurgen Haanstra
j.r.haanstra[at] and Bas Teusink
It is generally established that most cancer cells have a different metabolic programming than primary tissue cells. In general, tumor cells perform “aerobic glycolysis” which means that they metabolize glucose by a high rate of glycolysis and subsequent fermentation into lactate, but with very limited further oxidative metabolism in the citric acid cycle. This phenomenon is called the Warburg effect. Although much research has been dedicated to the Warburg effect and several targets have been proposed, the overall mechanism remains poorly understood.
Colleagues at the AMC/Tijtgat institute have recently found that soluble adenylyl cyclase (sAC) plays an important regulatory role in the choice for aerobic glycolysis. Although sAC is the evolutionary most ancient adenylyl cyclase in mammals, it has been studied much less intensely than the transmembrane adenyl cyclases which are regulated by G-proteins. In contrast to these, sAC was found to be activated by HCO3- and also by its substrate ATP, which makes sAC an exquisite metabolic sensor.
Inhibition (or knockdown) of sAC in in several cancer cell lines enhances lactate formation and decreases oxidative glucose metabolism, thereby aggravating the Warburg effect. The exact target(s) of sAC in energy metabolism remain to be identified, but its activity affects glycolysis, the pentose phosphate pathway and glycogen homeostasis.
The goal of this project is to identify possible metabolic targets of sAC action in the human hepatoma cell line HepG2 cells. You will do this by using (and expanding) existing kinetic models of HepG2/ hepatocyte metabolism as well as core models of metabolism. With these models you will investigate whether and how metabolic regulation can explain the measured changes in metabolite concentrations after sAC inhibition (or knockdown).
Can CRISPR evolution be used to trace bacterial pathogens in food?
Theoretical (Master)Douwe Molenaar
d.molenaar[at] and Indra Bergval (RIVM)
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) genes are present in the genomes of many bacteria (~ 40%) and Archaea (~ 90%) and together form an adaptive microbial immune system. A typical CRISPR cassette consists of two or more direct repeat-spacer units, where the spacer sequences are mainly active against foreign DNA, such as bacteriophages and plasmids. If the cell is infected with foreign DNA and it is recognized by the CRISPR-Cas immune system, the foreign DNA is selectively degraded so that it can not be incorporated into the genome. Every time the cell is threatened with new foreign DNA, a new, unique direct repeat-spacer unit is built into the CRISPR cassette. The sequences of a CRISPR array in a bacterial genome are therefore subject to rapid evolution, which offers the possibility to use this system for typing purposes. For some bacteria, such as Mycobacterium tuberculosis, this happens routinely.
Typing pathogenic bacteria is important because it contributes to the determination of the origin/source in the event of an outbreak, to identification of transmission patterns in long-term epidemics and to the identification of certain characteristics such as virulence or resistance to specific genotypes of a certain pathogen.
In the case of food-related infections, it is particularly important to determine the source of the contamination as quickly as possible, so that targeted and effective measures can be taken or policy can be made if structural infections are involved. The increasing accessibility of whole genome sequencing technologies has made a significant contribution to the possibility of source attribution. To further investigate this information and to see whether we can specify the source attribution, we want to analyse the diversity and evolution of CRISPR arrays in specific food-related bacterial pathogens (Salmonella, Listeria, Shiga-toxin producing E. coli) throughout the food production chain ("farm to fork").

Activities within this project:

  • Development of a bioinformatics pipeline (in Python, for example) to automate the quantification and identification of CRISPR arrays from the main bacterial pathogens

  • Validation of the methodology to be developed

  • Analysis, using the developed scripts, of selected data collections (NGS data) that represent specific aspects of the food production chain.

  • Analysis of the spacer sequences: i.e. can they be used to determine which bacteriophage populations the bacterial strain has been in contact with, and thus what the source of a contamination is?

You will work closely together with Angela van Hoek, Michael Visser and Indra Bergval at the Centrum voor Infectieziektenbestrijding at RIVM, the dutch authority for control of infectious diseases.
The importance of efficiency and redundancy in metabolismTheoretical (Master)Daan de Groot
Julia Lischke
In static environments, the fastest growing microorganisms will be selected by evolution. Since resources are always limited, microbes should make offspring from these resources in the fastest and most efficient manner. Two papers from our group proved that this pressure will lead to the upregulation of only a small number of pathways through the metabolic network. Growth rate maximization will thus lead to a highly ordered metabolism: redundancy and complexity are minimized.

In contrast, in a series of papers, the theoretical ecologist prof. Robert Ulanowicz argued that a minimally complex (ecological) network is maximally unreliable. The professor argues that biology is inherently noisy, and that some redundancy is necessary to cope with unforeseen changes. Maximization of reliability will thus lead to a different metabolism: redundancy and complexity are maximized.

Using information theoretical concepts, Ulanowicz, quantifies the ‘order’ and the ‘redundancy’ in an ecological network, and argues that the two counteracting forces will balance natural networks in an intermediately ordered state.

Goal of the project
We would like to translate the ecological theory of Ulanowicz into a theory about metabolism. Given a metabolic network, and distribution of reaction rates through this network, we want to be able to calculate the order and redundancy. Using different theoretical approaches, we would then like to find out to what level of order the evolutionary pressure pushes cells.

What we expect
This project is very theoretical and somewhat abstract, and will therefore only fit a student with a strong theoretical background, and a seemingly unquenchable curiosity. Skills in Python or related programming languages are a plus, since code should be written to calculate the information theoretical quantities for arbitrary networks.
Do you want to study microorganisms involved in yogurt production in Copenhagen, Denmark?Theoretical (Master)Sebastian Mendoza
Bas Teusink
The yogurt you eat at home is usually produced from milk using batch cultures of two microorganisms: Streptococcus thermophilus and Lactobacillus bulgaricus. These microorganisms grow together in a symbiotic way, they consume the nutrients of milk and produce the compounds that make yogurt a food full of texture and flavors. The use of different strains for each of the species results in yogurts with different organoleptic properties. For example, the use of some strains produce yogurt with higher acidity

Goal of the project
The student will work in a joint project between VU Amsterdam and Chr. Hansen, an important company for the dairy industry. The project will develop at Chr Hansen which is located in Copenhagen, Denmark. In particular, the student will use the genomes of different strains as well as the organoleptic properties of the yogurt they produce to cluster the microorganisms in groups of biological and industrial relevance. The student will explore different bioinformatics techniques to cluster the strains and try to get insights about the mechanisms that govern the relationships between metabolism and organoleptic properties. It is expected that each of the clusters contains strains with similar metabolic properties. In this way, the student will be able to shed light into relevant metabolic features for yogurt production.

at least two months
Your CV
Ideally, you will have a theoretical background and programming experience (preferably Python, R or MATLAB).
Start Date
March 2019 or later
Can you predict yoghurt fermentation?
Modelling (Master)Julia Lischke
Yoghurt is an important part of nutrition and its fermentation is carried out since centuries. Though, many aspects of the microbial processes are not understood. Mainly because the fermentation needs the action of two or more microorganisms. Computational approaches to predict the influence of two microorganisms on each other are rare and yet to be developed. We use Yogurt as model process to investigate good strategies and validate our new developed computational approaches.
Yogurt fermentation is no continuous process. Once the milk is inoculated the involved Lactic Acid Bacteria can do their job undisturbed. We observed an unsynchronized growth pattern, when the two main organisms ferment the milk to Yoghurt. The aim of this master project is to use the widely accepted dynamic Flux balance analysis and adapt it to reflect and predict the behavior of yogurt producing Lactic Acid Bacteria. Challenges will be, to identify a suitable optimization function and implement a therefore suited algorithm in python to calculate the objective value.
During this project you will learn how to sophistically use linear programming, used not only in biology but also finance and sociology. You will also learn to generalize biological concepts, rephrase it in mathematical terms and implement it. Finally this should lead to an increased fundamental understanding of the cooperativity between yoghurt building microorganisms.
Back in time with Lactococcus lactisTheoretical/Computational (Master)Chrats Melkonian
Herwig Bachmann & Douwe Molenaar
Lactococcus lactis is one of the most important microorganisms used for the fermentation of cheese and buttermilk. Although L. lactis strains can be found both in dairy products and at plant material, the literature suggests that strains of dairy origin have evolved from plant isolates (van Hylckama Vlieg et al., 2006). In this project we want to find out whether humans played a role in the evolution of dairy strains.
The main metabolic activity of L. lactis is the conversion of lactose (milk sugar) to lactate and it is also involved in the formation of flavor volatiles and texture of dairy products. Such chemical changes are initiated by side products of L. Lactis metabolism and include: synthesis of volatile metabolites and exopolysaccharides, as well as proteolytic activity.
In a recent study of 43 genomes of L. lactis strains a long standing disparity about the two subspecies of Lactococus lactis was solved. (Wels, Michiel et al., 2019). The genomes also showed distinct differences between plant and dairy isolates. While suggestions that humans played a role in the evolution of dairy strains has been postulated a while ago the evidence for this hypothesis is not conclusive and needs re-evaluation.

On this project you will try to answer:
i) Did humans play a role in the evolution of L. lactis? Are the phenotypes of L. lactis dairy isolates shaped by human intervention or did dairy isolates evolve in nature and humans merely selected them for their phenotypic properties?
To achieve this, you will perform targeted phylogenetic analysis on genes of interest from a big collection of L. lactis strains. Concepts of the molecular clock will be applied and you will make comparisons with mammalian pathogens and genes specific to the relationship between mammals and microbes. Such comparisons should enable rough estimates of the time when certain strains evolved and what the possible role of humans in the evolution of L. lactis could have been.

From four months to six months
Your CV
Ideally, you have a bioinformatics background with programming experience (preferably R, Python). Experience in phylogenetics is recommended.
Start Date
September 2019 (date is flexible)

1) Johan ET van Hylckama Vlieg, et al.,Natural diversity and adaptive responses of Lactococcus lactis, Current Opinion in Biotechnology,Volume 17, Issue 2,2006,Pages 183-190,ISSN 0958-1669,
2) Wels, Michiel et al. “Comparative Genome Analysis of Lactococcus lactis Indicates Niche Adaptation and Resolves Genotype/Phenotype Disparity.” Frontiers in microbiology vol. 10 4. 31 Jan. 2019, doi:10.3389/fmicb.2019.00004
A step towards modern winemaking with Lachancea thermotoleransExperimental (Master or enthusiastic Bachelor)Chrats Melkonian
Sebastian Mendoza & Douwe Molenaar. Lab: Marijke Wagner
Archaeological evidence places the first production of wine in the Caucasus area, around 8000 years ago. From that point on, wine accompanied mankind throughout the course of history, during the rise and fall of empires and even associated with ancient gods and religions.

Although the “art” of winemaking evolved and became diverse across time, region and by what nowadays winemakers call ‘terroir’, the role of microbes on the task only recently started to unravel.
To convert grape must into wine, firstly alcoholic fermentation (AF) should be performed, a process that converts glucose and fructose mainly into ethanol and carbon dioxide. For the fulfilment of AF, Saccharomyces cerevisiae is the main candidate. Secondly, in most of the red grape varieties a malolactic fermentation is performed after the AF, where lactic acid bacteria (LAB), especially Oenococcus oeni convert malic acid into lactic acid which improves stability and flavor. Therefore, in industrial winemaking specialized strains of these microbes are used to guarantee consistency and quality of the final wines.

Because of climate-change, the sugar content in grapes could in the future reach higher levels, which may lead to undesirable high level of alcohol and lower acidity in wine. Already from the 90s, wine research explores the usage of alternative yeast strains to perform AF with Lachancea thermotolerans which has become popular among winemakers because of higher lactate and lower alcohol production rates (Mora et al., 1990). As a result of lactate production, co-inoculation cultures with S. cerevisiae produce wines with higher acidity and a higher volatile profile (Fleet, 2008; García et al., 2017; Morales et al., 2019).

On this project you will try to answer:
-What are the differences in phenotype between L. thermotolerans strains and S. cerevisiae relevant to wine making?
-How do the two species potentially interact?
-Could we use L. thermotolerans in winemaking to produce low alcohol wines and what is the best method to do so?

From four to six months
Your CV
Ideally, a master student in microbiology or food science, previous lab experience with quantification of growth dynamics and HPLC are desirable
Start Date
October 2019 (date is flexible)

Mora, J., Barbas, J. I., & Mulet, A. (1990). Growth of Yeast Species During the Fermentation of Musts Inoculated with Kluyveromyces thermotolerans and Saccharomyces cerevisiae. Am. J. Enol. Vitic., 41(2), 156–159. Retrieved from
Fleet, G. H. (2008). Wine yeasts for the future. FEMS Yeast Research, 8(7), 979–995.

García, M., Esteve-Zarzoso, B., Crespo, J., Cabellos, J. M., & Arroyo, T. (2017). Yeast Monitoring of Wine Mixed or Sequential Fermentations Made by Native Strains from D.O. "Vinos de Madrid" Using Real-Time Quantitative PCR. Frontiers in Microbiology, 8, 2520.

Morales, M. L., Fierro-Risco, J., Ríos-Reina, R., Ubeda, C., & Paneque, P. (2019). Influence of Saccharomyces cerevisiae and Lachancea thermotolerans co-inoculation on volatile profile in fermentations of a must with a high sugar content. Food Chemistry, 276, 427–435.
Translational toolboxTheoretical (Master)Berdien van Olst
Mine bacterial genomes for obtaining various protein-coding sequence related parameters. These parameters are involved in translation efficiency. The ambition of the project is to create an automated translational toolbox to model the relationship between (publically available or generated within the project) transcriptome and proteome data for several bacterial species.

Proteins are key elements in a cell. In order to produce proteins, mRNA needs to be translated. The translation process can be viewed as a semi-mechanical process in which each mRNA is processed by the same machinery. However, the correlation between mRNA and protein levels is relatively poor. Our results indicate that protein-coding sequence related parameters in combination with mRNA concentrations enable a much more accurate, multivariate model-driven prediction of the cellular proteome in Lactococcus lactis. This project aims to expand this conclusion to other bacterial species.

The project
You will create an automated genome sequence extraction toolbox. This toolbox will be linked to a translation-modelling environment that employs transcriptome (and proteome) data, which preferably will be extracted from public repository databases. However, you will also generate such combined datasets within this project. This involves growing several bacterial species, extracting RNA and proteome samples for analysis by RNA-seq and LC-MSMS pipelines, respectively. This project is a collaboration within Wageningen University between Host-Microbe Interactomics and Laboratory of Biochemistry, but also involves the Systems Bioinformatics group of Vrije Universiteit Amsterdam.

• Background in bioinformatics
• Preferably experience in molecular microbiology
Studying the metabolic state and phenotype of immune cells using single-cell RNA-seq dataTheoretical (Master)Douwe Molenaar

Jan van den Bossche
Macrophages are innate immune cell that are functionally very multifaceted and are involved in almost all aspects of life; from immunity and host defense, to homeostasis, tissue repair and development. To fulfil these distinct actions, macrophages adopt a plethora of polarization states. Understanding their regulation and phenotypic heterogeneity is crucial because macrophages are critical in many diseases and have emerged as attractive targets for therapy of cancer, atherosclerosis, asthma and many more “Western” killers.
We recently demonstrated the crucial role of metabolic reprogramming in distinct macrophage activation states. A such, our in vitro assays with well-defined pro-inflammatory (M1) and anti-inflammatory (M2) macrophages clearly show that the way a macrophage metabolizes its nutrients not only provides energy, but directly dictates its function.
To advance our findings to future therapeutic applications, our current knowledge now needs to be translated to the complex in vivo environment, where macrophages are exposed to a complex mixture of microenvironmental stimuli and don’t classify as M1 or M2. In other words, the metabolic roadmaps of non-M1/M2 macrophage subsets need to be defined in health and disease in both humans and animal models. 

By analyzing available single-cell RNA-sequencing (scRNA-seq) transcriptomic data sets, this project will define how the metabolic profile of macrophage and other immune cell subsets relate with their phenotype in function.


-Using scRNA-seq data from cancer patients, metabolic profiles of immune cells that associate with clinical outcome after therapy can be defined.
-We hypothesize that distinct microenvironments will results in altered metabolic profiles and associated immune cell phenotypes. Information obtained from scRNA-seq analysis will allow to validate this experimentally by immune staining and confocal microscopy. Interestingly, the sequencing data itself might already provide indications about the immune cell’s (pseudo)location. Indeed, scRNA-seq data can be used to define pseudo-time cell trajectories and similar principle might be applicable to map microenvironmental (pseudo)location.
-Overall, this project will reveal potential new targets to (re)shape macrophage metabolism, function and disease outcome.
-Depending on the duration of the project, observations and hypotheses generated based on scRNA-seq analysis could be validated experimentally in the laboratory.
Modelling the adaptation of Trypanosoma brucei to sublethal drug doses Theoretical (Master)Julia Lischke
Daan de Groot
Jurgen Haanstra
Trypanosoma brucei is a eukaryotic parasite that causes the deadly disease Sleeping Sickness. It is transmitted to humans by the tsetse fly. To survive in two distinctly different hosts, it remodels its metabolism to the situation in the current host.

We have an extensively validated kinetic model of glycolysis of the blood-stream form of T. brucei, and (non-curated) genome-scale models. Using the kinetic model, we found that glucose transport is the best drug target: it is the enzyme with the highest control over glycolysis and human erythrocytes are, due to quantitative differences in glycolysis, not vulnerable to glucose transport inhibition. However, we then found that prolonged exposure to sublethal doses of glucose transport inhibitors provokes an adaptive response from T. brucei. This adaptation could reduce the effect of glucose transport inhibition.

In this project, the you will try to obtain a better understanding of the adaptive responses of T. brucei. Two important questions are:

Research Questions and Approaches
1. Is the change in metabolic network between T. brucei in its blood-stream form and its insect-stage an optimal adaptation to its changed host-environment?
Approach : Use optimization theory and the genome-scale reconstructions and the kinetic model in the hosts to find the optimal metabolic subnetwork in the two life stages of T. brucei. Compare these optima with the experimentally found metabolic network usages.

2. Can the adaptive response of T. brucei to glucose transport inhibition be understood as an optimal adaptive response? Can we combine kinetic modelling and genome-scale stoichiometric modelling to understand how bloodstream-form T. brucei would adapt to glucose transport inhibition?
Approach : Combine kinetic and genome-scale modelling (using an approach set up by a former master student), to see if T. brucei can overcome the effect of the drug by adapting its enzyme expression. The results can be compared to the mRNA expression data in.

Duration and start of project : 5-6 months and starting as soon as possible

Your CV : Ideally you will have a theoretical background and programming experience (preferably Python or Matlab).
Selection for interactions between bacteria using water-in-oil emulsions
Experimental (Master)Rinke van Tatenhove
In most controlled microbiological experiments the behavior of one single organism is studied. However, in the human body and the environment micro-organisms are almost never alone, but they live together with many different species and they interact with each other. These microbial consortia consist amongst others of cooperating species, which share metabolites with each other, and so-called “cheaters”, which do benefit from metabolites secreted by their neighbors but do not contribute to the cooperation. To get a better understanding of these microbial consortia and how different environments affect the interactions, we would like to study the population dynamics of a synthetic consortium. We will try to select the interacting cells out of a population with cooperators and cheaters. We will grow cells in suspension (no spatial structure) and in water-in-oil emulsions (spatial structure) and we will use amongst others flow cytometry to analyze the population dynamics during propagation of the consortium.
Fishing for mRNA spatial patterns in Fission YeastExperimental (Master)Evelina Tutucci (evelina.tutucci[at]
and Johan van Heerden (j.van.heerden[at]
Gene expression is a highly regulated process in all organisms, from bacteria to man. While we know many of the details of gene expression regulation, a largely unexplored phenomenon is the role that mRNA localization plays. There are indications that the regulation of mRNA localization can function as an additional layer to control protein activity. For example, many proteins function in specific sub-cellular locations, and translating such proteins in close proximity to their final destination would promote their activity. In contrast, translation at distant cellular locations would serve to delay activity. In this view, spatial regulation of mRNA provides a means to temporally control the activity of proteins.

In eukaryotic cells, the cell cycle is a complex process involving many proteins that are expressed in a specific order. Here the timing of expression of each protein is particularly important to ensure progression from one cell cycle stage to next. Whether mRNA localization could function an as additional regulatory layer to control the timing of cell cycle progression, is an open question. As a first step towards answering this question, in this project we will investigate whether the transcripts of genes involved in cell cycle regulation exhibits spatial organization in the fission yeast, Schizosaccharomyces pombe.

In this project you will use single-molecule FISH (smFISH) to study and quantify the spatial distribution of mRNA's of genes involved in cell cycle-regulation, during the cell cycle of fission yeast.

You will apply basic micro- and molecular biological techniques, in addition to more advanced microscopy and image-processing procedures.

mRNA localization patterns will be used to identify candidate genes for further investigation into the role of spatial organization in gene expression regulation.

Your CV
Practical experience working in a laboratory is a must. In addition, as this project will involve a lot of computation and data processing, ideally you have some affinity with programming (R, Python, Java or Matlab).

Number of positions available

Duration of project:
5 months

Start date
~Feb 2020

Automating lineage reconstruction of growing microbes from time-lapse microscopy imagesTheoretical (Master)Philipp Savakis (p.e.savakis[at]])
When working with time-lapse microscopy datasets of microorganisms, a major challenge is to keep track of individual cells. This is especially difficult in image sequences with densely growing microbes. If the microorganisms divide in the course of the experiment, their lineages can be reconstructed in a graph structure (a lineage tree).

Manually reconstructing lineages is time-consuming and prone to error, and methods to automatically track identities and ancestral relationships are therefore important when dealing with large datasets.

In this project, we will build synthetic datasets that allow us to design and test algorithms which automatically reconstruct lineages.
Studying cell-cycle gene expression using single mRNA imaging technologies in S. cerevisiaeExperimental (Master)Evelina Tutucci (evelina.tutucci[at]

The dynamic control of gene expression lies at the basis of cellular adaptation. mRNAs are transcribed from genes, transported in the cytoplasm and translated into proteins. The number of mRNAs available for translation is determined by the balance between their synthesis and degradation rates. Cells tune both processes to rapidly change the mRNA levels.

While previous studies extensively characterized the regulation of transcription, mRNA decay remains poorly understood. Interestingly, mRNAs coding for proteins with similar functions (e.g. cell-cycle regulators, histones, ribosomes) have similar mRNA and protein half-lives, suggesting that evolution led to correlated mRNA and protein turnover rates to control the temporal order of gene expression. So, it is likely that mRNA degradation plays an important role in the temporal control of gene expression.

The aim of this project is to investigate whether and how cells dynamically regulate mRNA degradation in response to changing environmental conditions. At what stages of the mRNA life cycle is the decay modulated? How is mRNA decay coordinated with the other steps of mRNA metabolism, namely transcription, localization and translation?

To address these questions, we will characterize the degradation of mRNAs encoding proteins controlling cell cycle progression. Using the yeast Saccharomyces cerevisiae as model organism, we will investigate the importance of periodic mRNA degradation for cell cycle progression.


In this project you will use single-molecule FISH (smFISH) to study and quantify the expression of mRNAs of genes involved in yeast cell cycle-regulation.

You will apply molecular biology and yeast genetics techniques, in addition to more advanced microscopy and image-processing procedures.


By generating different cell cycle gene mutants we are going to identify the mRNA characteristics controlling the timing and specificity of mRNA decay.

Your CV
Practical experience working in a laboratory is a required. In addition, as this project will involve a lot of computation and data processing, ideally you have some affinity with programming (R, Python, Java or Matlab).

Number of positions available


Duration of project

5 months

Start date

Jan/Feb 2020

Targeting a pH sensor to the intermembrane space of mitochondria in Saccharomyces cerevisiaeExperimental (Bachelor)Laura Luzia

Cells continuously control and regulate their metabolism in response to environmental fluctuations by adjusting metabolic fluxes. Since transitions in nutrients can give rise to population heterogeneity, we aim to develop tools to study metabolism at the single-cell level. In this context, an important player is free H+, which is involved in essential processes of the central metabolism. In this project we aim to target a pH sensor to the intermembrane space of mitochondria in Saccharomyces cerevisiae. We will use the ratiometric pH sensor pHluorin currently available to measure cytosolic and mitochondrial pH during cell growth. The existing cytosolic pH sensor will be modified so that it can be targeted to the intermembrane space of mitochondria in S. cerevisiae.
Ideally you will have an experimental background, with experience in molecular cloning techniques.
Starting time: January 2020 or later
Duration: 3-4 months
Manual curation and validation of a Trypanosoma brucei genome scale model Theoretical (Bachelor or Master)Julia Lischke
Sebastian Mendoza
Jurgen Haanstra
Trypanosoma brucei is a eukaryotic parasite that causes the deadly disease Sleeping Sickness. It is transmitted to humans by the tsetse fly. To survive in two distinctly different hosts, it remodels its metabolism to the situation in the current host.

We have an extensively validated kinetic model of glycolysis of the blood-stream form of T. brucei, and (non-curated) genome-scale models. Using the kinetic model, we found that glucose transport is the best drug target: it is the enzyme with the highest control over glycolysis and human erythrocytes are, due to quantitative differences in glycolysis, not vulnerable to glucose transport inhibition. However, we then found that prolonged exposure to sublethal doses of glucose transport inhibitors provokes an adaptive response from T. brucei. This adaptation could reduce the effect of glucose transport inhibition.

In this project, the you will try to obtain a better understanding of the potential adaptive responses of T. brucei.

Manual curation and validation of a earlier published model for one of the two potential hosts (in close cooperation with another master student)

Your CV : Ideally you will have a theoretical background and basic programming experience (preferably Python or Matlab).
Determining the adaptability of Lactococcus lactis NCDO712 when grown on a poor nitrogen sourceExperimental (Bachelor)Sieze Douwenga
When a new environment occurs for an organism like Lactococcus lactis, different proteins, or metabolic states may be required to continue growth. L. lactis may prepare for new environments by expressing proteins that are currently not required. This often comes at a growth rate cost however. When growing quickly on the catabolite glucose, L. lactis NCDO712 is known to repress the catabolic genes required for the consumption of other sugars. When it is growing slowly on maltose, L. lactis is known to express redundant genes (not required for maltose growth). For example, L. lactis also expresses the catabolic genes required for growth on lactose and glucose, when only maltose is present.

Goal of the project
We hypothesize that the growth rate of L. lactis does not influence which catabolic genes it represses. Instead, it is likely only the presence of an excess amount high quality sugar, like glucose, that governs this behaviour. We would like to test this by growing L. lactis on glucose in medium with a poor nitrogen source –resulting in a low growth rate with a high quality sugar. Additionally, we would like to know whether glycolytic flux might be more predictive of which catabolic genes are repressed.

What you will be doing
You will run minimally two batch reactor experiments. From each reactor you will determine the product and substrate fluxes of L. lactis, through the use of HPLC and optical density measurements. Furthermore, you will determine the active catabolic pathways of L. lactis through the use of biolog plates in a platereader. Analysing the data will be fastest if you have experience with R, python or another programming language, although it could also be done in excel.

• Background in biotechnology/biochemistry and previous experience in microbiological culturing
• Some experience with data analysis in R, python or another programming language will be useful
Integration of thermodynamics data into large-scale metabolic networks
Theoretical, Bachelor/MasterPranas Grigaitis
Stoichiometric modelling is a powerful technique to analyze large metabolic networks and has been successfully applied at genome-scale for many microbial species (for a review, see Thiele Nat Prot 2010). Unlike kinetic models, which include detailed descriptions for enzyme kinetics, as well as the thermodynamics of the undergoing processes, stoichiometric models are based on the assumption of (1) optimally-functioning networks in (2) a steady state. Yet, for a more realistic representation of processes at genome-scale, a number of approaches (e.g. Lerman Nat Comms 2012, O’Brien Mol Syst Biol 2014, Lu Nat Comms 2019) has been recently proposed. While these implementations rely mainly on introducing descriptions of kinetics of these enzymatic reactions, the impact of thermodynamics to the behavior of these networks is still to be analyzed in a greater detail.
Aim: In this project, we aim to develop a comprehensive database of thermodynamic information in selected microbial species and use the data collected to augment existing genome-scale metabolic models.
We expect you to have: While a working knowledge in any of these programming languages is an advantage, you will have an opportunity to gain extensive hands-on experience from scratch in Python/MATLAB (required) and SQL (optional). In principle, you should have interest in learning how to apply computational techniques (e.g. programming/database management) for solving biological questions

Joint projects of Molecular Cell Biology and Earth Sciences for master students

For students not studying at the VU University Amsterdam

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:

  • We ask for a minimum stay of 4 months (3 months research, one month report writing) with a starting date outside the holiday season (July, August).
  • You have to be able to support your daily expenses (e.g. food, accommodation) and travelling to/from the Netherlands. Often, the international office at your own university can assist you in obtaining grants (e.g. ERASMUS grants). You may also check Research costs will be covered by the internship project. We can help you with providing letters etc. for applications.
  • Finding accommodation in Amsterdam often takes 3 to 6 months. Erasmus students are helped via our international office in arranging accommodation in the VU hospitium in Amsterdam. Non-Erasmus students are advised to look for accommodation well in advance, in particular if you want to start in September, October or November.
  • Students from outside the EU should take into account that they may require a visa for the Netherlands, and that arranging this may also take a few months. Please check with the Dutch embassy in your country.
  • Non-Erasmus students may have to register at the faculty (this is relatively easy for students from other Dutch universities), or alternatively, require a statement of hospitability, and possibly have to pay a fee.

Contact information

If you are a non-VU student and from abroad you should contact Dr. Rob van Spanning ( 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. In case of general questions they can contact dr. Jurgen Haanstra (j.r.haanstra[at]