Currently available

Segmentation of microscopy images using deep learningTheoretical (Bachelor or Master)Philipp Savakis
Under dynamic conditions (changes in nutrient availability, ion concentrations, toxic compounds etc.), individual cells within a population show different responses. In order to quantify these responses, we aim to use a combination of microfluidics, phase contrast, and fluorescence microscopy. Images taken with the microscope need to be pre-processed for further analysis: the analysis algorithm needs to know in which part of the image the cells are located and what their shape is. So, in the first part of the data analysis, we need to solve a pixel-wise classification problem. The project focuses on implementing deep learning techniques (convolutional neural networks in caffe) to generate segmented images, which are then fed into a downstream processing pipeline. Proficiency in python and a general interest in deep learning techniques and pattern recognition are required, knowledge of Matlab/octave and caffe would be very helpful.
How does Lactococcus lactis respond to new conditions?
Experimental (Bachelor)Sieze Douwenga
When a new environment occurs for an organism like L. lactis, different proteins, or metabolic states may be required to continue growth. This sometimes results in a lag-phase –observed for example when L. lactis is exposed to a sudden environmental transition from -80°C glycerol medium to fresh medium at 30°C. The relation between L. lactis response to sudden environmental transitions and various pre-culture conditions remains incomplete.
To uncover this relation we will study the response of L. lactis to around 100 environmental transitions, consisting mainly of transitions between various carbon sources and stress conditions. We will test transitions in parallel using 96 well or 384 well microplates and a plate-reader, which yields optical density growth curves. To extract the duration of the lag phase for each transition we will analyse the data with readily available scripts in python or R. This will shed light on the relation between various environmental transitions and the lag-phase exhibited by L. lactis.
An EFM-based rationale behind FBA solutionsTheoretical (Bachelor or Master)Daan de Groot ( Eunice van Pelt (
In systems biology, Flux Balance Analysis (FBA) is commonly used to determine which reaction steps carry flux in a metabolic network. This provides insight in the biological activity of a cell in a specific condition. FBA is a linear programming problem and is therefore very computationally efficient, but it does not give information about the reason why some reactions are selected and others are not. To resolve this issue, we found a new way to interpret FBA in terms of Elementary Flux Modes (EFM) and the constraints that were hit by the network. An EFM is a minimal functional unit in which a metabolic network can be decomposed, i.e. EFMs are the minimal pathways that can produce biomass in steady-state. Each flux distribution is either a combination of EFMs or a single EFM. In this project we would like to investigate which and why certain EFMs are selected by FBA.

We expect that the optimal EFMs can be found by calculating the sensitivity of the optimal solutions to a small change in the input constraints (cap on uptake reactions). This could be the first step in a pipeline that needs to be developed (in Python) to visualize EFMs in a vector plot based on the FBA distribution. This will be tested and developed using a small toy model. Afterwards we will apply the pipeline on larger (genome-scale) metabolic models to study the choice of EFMs for specific conditions (e.g. growth on glucose, lactose, etc.). This would provide more insight in the number and quality of alternatives that a micro-organism has when regulating its metabolism.
How I wish I could measure phosphate in single cellsExperimental (Bachelor)Laura Guilherme Luzia
Description: Pi is an important molecule in metabolism, signal transduction, enzyme regulation and a structural component of nucleic acids and cell membranes. In order to better understand how yeast central metabolism adapt and evolve in response to environmental changes, we want to quantify Pi levels in single cells. To do that, we will explore a FRET (Fluorescence Resonance Energy Transfer) sensor to measure the intracellular concentration of Pi under different carbon sources conditions in time-scale. Here, we want to characterize this ratiometric Pi sensor using kinetics assays and fluorescence spectrometry/microscopy techniques. In the end, we should be able to monitor Pi fluctuations in Saccharomyces cerevisiae in a range of conditions and to understand how free energy flows inside cells and is affected by external changes.
Biodegradation of novel pharmaceutical products.
Experimental (Master)Baptiste Poursat
Organic pollutants, such as pharmaceutical and industrial products, are frequently detected in the environment, especially in aquatic habitats and in wastewater treatment plant (WWTP) discharges. They are considered as “emerging contaminants” since most of them are not currently covered by existing water-quality regulations, and their effects on the environment are still poorly understood. Knowledge on environmental persistency is essential to make an efficient environmental risk assessment of new manufactured chemicals. Indeed, a key component of both hazard determination and environmental risk assessment is the accurate estimation of the degradation of a chemical in the environment. Ready biodegradation tests (RBT) from the OECD guideline are required by the European REACH regulation to evaluate the biodegradability of chemical substances produced by industry. However, RBT suffer from several drawbacks related to the heterogeneity of their inoculum, due to their different origin and history. Indeed, the source, concentration and pretreatment of the inoculum are one of the most important factors that influence the RBT results and the extent of biodegradation of chemicals in the environment. The aim of this master thesis project is to compare the biodegradation rates of several pharmaceutical and industrial products (using LC-MS/MS) by different inocula from municipal, hospital and industrial WWTPs. Microbial communities will be characterized by Illumina sequencing and metagenomic analyses. Laboratory work for this project will mostly be performed in the laboratory of the University of Amsterdam (Science Park). For more information, please contact Baptiste Poursat, Dr John Parsons (UvA-ESS) or Dr Rob Van Spanning (VU Amsterdam).
Linking microbial communities to crude oil weathering in mangrove environmentsExperimental (Master)Paul Iturbe Espinoza
Shell uses land farming to clean up sites impacted by oil spills. Land farming reduces the concentration of petroleum constituents present in soils through processes associated with bioremediation. In sensitive ecosystems, such as mangroves, traditional remediation methodologies such as land farming can be particularly difficult due the soft ground conditions and sensitive nature of vegetation. In those types of environments, removal of surficial oil and gentle flushing of sediments to remove free oil can be effective in reducing oil concentrations. Any residual oil is further reduced by biodegradation to a level where mangrove vegetation can recover. The rate at which the biodegradation occurs is an important factor in the restoration of the mangrove ecosystem but relatively few data are available on this. Also, while it is widely acknowledged that microorganisms play an important role in the breakdown, linking rates to microbial community composition and other abiotic factors is very hard.

The objective of this MSc research project is to increase the understanding biodegradation processes in mangroves, both in terms of kinetics, involved micro-organisms and contributing abiotic factors. Shell can provide soil samples which will span a range of severity and type of oil contamination (fresh versus weathered) which can be used in experiments. It is envisaged that the student will develop a work plan based on a comprehensive literature study on this topic. Laboratory experiments can be used to establish laboratory estimates of the rate of biodegradation which can be combined with microbial community profiling as determined by Illumina amplicon sequencing.

The project is in collaboration with the Shell Technology Centre Amsterdam.
Impact of remedial agents on microbial communitiesExperimental (Master)Paul Iturbe Espinoza
The IUCN-Niger Delta Panel (IUCN-NDP) was established at the request of Shell Petroleum Development Company of Nigeria Limited (SPDC), to provide science-based recommendations for the remediation and rehabilitation of biodiversity and habitats of oil spill sites in the Niger Delta. Land farming is widely used by SPDC to clear residual oil from spillages to soil in the Niger Delta. This approach reduces the concentration of petroleum constituents present in soils through processes associated with bioremediation. It is proposed by the IUCN that the use of bio surfactants, enzymes and sorbents can improve the effectiveness of this treatment. SPDC is planning to perform comparative pilot scale testing in Nigeria to determine the relative efficacy of the IUCN-NDP recommended remediation agents. It is anticipated that the tests will be performed in fully contained bio cells. The success of soil remediation by land farming is generally dependent on availability of nutrients and oxygen within the system plus the ability to overcome some of the mass transfer issues associated with hydrocarbon biodegradation. Consequently, the effects of oxygen, nutrients mass transfer will also be incorporated into the test plan in order to gain a better understanding of what influences efficient biodegradation in Nigeria soils. The experiment will test the effectiveness of the following factors on remediation success:

Nutrients (Agricultural Fertilizer), (Bio)surfactants, Enzyme products, Natural Sorbents, Tilling (oxygen and mass transfer)

The IUCN have commented that the success of remediation maybe dependent on the composition of the indigenous microbial community within the soil as determined by Illumina amplicon sequencing. Therefore, we would like to understand if and how microbial communities differ during treatment with various forms of remediation agent. The size and scope and specific objectives of the study can be discussed in more detail if you are interested developing a project. The project is in collaboration with the Shell Technology Centre Amsterdam.
The role of metabolic interactions in antibiotic toleranceTheoretical (Master)Bas Teusink
Bacteria in polymicrobial infections interact with each other. These interactions can change the environment to such an extent that some bacteria can become more or less susceptible to antibiotics. Metabolic interactions likely underlie this antibiotic tolerance effect. By means of metabolic modeling you will investigate some of these potential antibiotic-tolerance interactions in polymicrobial infections.

This internship is a cooperation with Dr Marjon de Vos from Wageningen University
Building a genome scale metabolic model for the fission yeast S. pombe Theoretical (Master)Johan van Heerden
Eunice van Pelt-Kleinjan
The two distantly related yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe, have been instrumental in our current understanding of the eukaryotic cell and its processes. Much of what we know about the cell cycle and its regulation was borne from pioneering studies with S. pombe, while S. cerevisiae has been a favourite workhorse in studies on eukaryotic metabolism (especially central carbon metabolism) and textbooks on this subject are therefore filled with insights derived from this organism.

While the cell-cycle field has embraced S. cerevisiae as a complementary eukaryotic model, researchers with a focus on metabolism have yet to harvest the potential insights to be gained from systematic (and comparative) studies of S. pombe metabolism. Today, the majority of research on S. pombe is still dedicated to unraveling cell cycle regulation and other cellular functions such as DNA repair, maintenance and aging, with very little fundamental research on the central metabolism of this yeast.

While there are many similarities between these two species, there are also several important differences including: cell morphology, the mode of cell division, the position of cell-size checkpoints during cell-cycle progression, the role of glucose sensors, the ability to respire ethanol, the presence of a glyoxylate cycle and differences in mitochondrial functions.

As many of the differences between these two species pertain to metabolic functions, a better understanding of the metabolic profile of S. pombe should serve to greatly expand our compendium of knowledge on eukaryotic metabolism, beyond that of S. cerevisiae and cancer cell lines.

In this project you will build a new genome-scale metabolic model (GSMM) using software developed in our group, MetaDraft. This will enable the construction of a draft genome-scale model based only on a sequenced genome and a curated database of template models.

You will use this GSMM model to predict the ability of S. pombe to produce biomass (i.e. to grow) on various carbon substrates. These predicitons can then be compared to existing and new experimental data, to improve and validate the model.

It is expected that this work will lead to a functional GSMM that will provide a valuable asset to the burgeoning field of S. pombe metabolic studies.

Duration of project: 5 months

Your CV: Ideally you will have a theoretical background and programming experience (preferably Python or Matlab).

Start date: May 2017 or later.
Genetic manipulations in S. Cerevisiae using CrispR/Cas9
Experimental (Bachelor)Rick Nijhout
Genetic manipulations of the Fructose 2-6 Bisphosphate synthesis and degradation pathway will be performed in the W303 yeast strain. Subsequently you will perform phenotypical characterization of the mutants using the ratiometric pH sensor "Phluorin" in Flow Cytometry experiments.
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.
What are the costs of protein turnover?Theoretical (Master)Eunice van Pelt-Kleinjan
Douwe Molenaar
External supervisor Berdien van Olst
A cell needs proteins to catalyze reactions. These proteins do not have an indefinite lifetime and are degraded over time. In steady state conditions, the protein level should be constant, so the cell needs to synthesize new proteins to replace the degraded ones. This constant protein production, degradation and renewal is called protein turnover. In addition, the proteins are diluted upon growth, so in steady state, the cell also needs to produce protein during growth. There are many different factors involved in protein production, like transcription and translation. Therefore, turnover rate is expected to be protein specific. We want to find out whether there is a relation between turnover rate and these different factors. Ultimately, we want to know how the costs of protein turnover determine optimal metabolic and growth strategies of bacteria.

We have turnover data of hundreds of proteins of a bacterial strain grown in a chemostat. The data consists of time-series of concentrations of heavy lysine wash-out and light lysine incorporation in proteins. Based on this data we want to calculate the turnover rate of each protein. For this we need to develop a statistical modeling approach. When we have the turnover rates we will analyze them (e.g. are there certain groups of proteins that cluster together because of a similar turnover rate, what does this mean in relation to e.g. their function?). This will improve our understanding of the protein economy in the cell.
Defining macrophage immunometabolism through omics data analysis
Theoretical (Master)Jan vd Bossche,
Douwe Molenaar d.molenaar[at]
Macrophages are innate immune cells that phagocytose and kill microbes. Although these are key features,
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 cues. LPS+IFNg-induced pro-inflammatory “M1” macrophages undergo metabolic rewiring, illustrated by heightened glycolysis. In contrast, IL-4-induced anti-inflammatory “M2” macrophages show and require high mitochondrial oxidative phosphorylation (OXPHOS). Thus, the way a macrophage digests 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 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.

In this project you will define the metabolic characteristics of different macrophage subsets using transcriptomics, proteomics and metabolomics data that are available online and with unique data that we generate(d) ourselves.

Revealing the metabolic characteristics and needs of macrophages will reveal new (metabolic) targets to manipulate macrophage function and to improve disease outcome.

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.

Please 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.