Modeling and Microbial Communities
We publish internships via the Biosb-interns mailing list server. If you subscribe to this mailing server you will obtain emails with internship projects in the fields of Bioinformatics and Systems Biology. You can also browse the archive of earlier mails.
You can also contact the team leader Douwe Molenaar directly with questions about opportunities for internships: d.molenaar@vu.nl
For additional internships, see the table below or contact Matti Gralka.
| Project title | Type of research | Supervisor(s) |
|---|---|---|
| Discovering and predicting rules for bacterial interactions | Experimental (Master) | Matti Gralka (m.gralka[at]vu.nl) |
| Background The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. A fundamental problem in this regard is that the available wealth of ‘omics data remains difficult to integrate into an understanding of microbial physiology and interactions. Your project You will tackle this problem in the context of trophic interactions between marine bacteria. Trophic interactions are interactions where one bacteria’s excreted metabolites serve as food for another bacteria. Recently, our lab has developed methods to measure the metabolic strategies of bacteria in terms of their relative preference for sugar or organic and amino acids, and predict those preferences from genomes. Ecological intuition would predict that two species with complementary preferences would be more likely to coexist – in this project, you will put this hypothesis to the test. To do this, you will combine high-throughput physiological characterization and coculture of pairs of bacteria with targeted and untargeted metabolomics to i) elucidate interaction mechanisms, ii) discover statistical patterns of their interactions, and iii) develop mathematical models of their interactions. The outcome will be both mechanistic insight into and the metabolic principles structuring bacterial trophic interactions. A prior familiarity with programming languages is useful but not required. Methods and techniques: quantitative microbial physiology (growth rates, excreted and catabolized metabolites with HPLC and LC/MS), high throughput coculture (plating, qPCR), data analysis, mathematical modeling In addition to regular meetings with your supervisor, you will participate and present in our Systems biology group meeting and Journal club. Number of positions available 2 Duration of project: 5-10 months Start date anytime |
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| How do alternative stable states in ecological communities arise? | Theory/computational (Master) | Matti Gralka (m.gralka[at]vu.nl) |
| Background The concept of alternative stable states has long been a dominant framework for studying the influence of historical contingency in the assembly of ecological community. This project will focus on alternative stable states in communities of microbes, which are not only very important for humans and the environment, but also provide ideal model systems to explicitly test mathematical theories of community assembly in experiments. Common example of alternative stable states exhibited by complex microbial systems are i) clear-water states and turbid, algae- or cyanobacteria-dominated states in freshwater lakes, ii) healthy and dysbiotic gut microbiomes in apparently similar patients, and iii) rumen microbiomes in cows, sheep, or goats with high or low production of methane (an important greenhouse gas). Recent experiments in microbial microcosms have clearly shown that alternative stable states can stochastically emerge from a common starting point. Similarly, novel theory has shown the boundaries of a single stable community state in some models. However, we still don’t know what determines the number of alternative stable states, what facilitates transitions between them, and which mathematical models are appropriate to model these processes. A better understanding of alternative stable states and how to manipulate them may improve our ability to steer microbiomes into a healthier or more productive state. Your project In this project, you will use two established mathematical models of microbial communities as a starting point to understand alternative stable states. These models make fundamentally different assumptions: one class assumes direct pairwise or higher-order interactions between species (generalized Lotka-Volterra models), the other class models interactions explicitly through the exchange of chemicals, such as the uptake of glucose or the excretion of acetate or antibiotics (consumer-resource models). You will compare these models regarding their ability to produce alternative stable states and their predictions for the number of alternative stable states. As we start to understand these models better, we will tune and extend them to discover principles governing alternative stable states in these models, such as to what degree (and what kind of) nonlinearities are required to produce alternative stable states. A prior familiarity with programming languages (e.g., R, Python, Mathematica) and differential equations is highly recommended. Methods and techniques: development of mathematical models, numerical solution of differential equations, analytical approaches (as appropriate). In addition to regular meetings with your supervisor, you will participate and present in our Systems biology group meeting and journal club. Number of positions available 1 Duration of project: 5-10 months Start date anytime |
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| The role of multi-copy genes in central metabolism | Theory/computational (Master) | Matti Gralka (m.gralka[at]vu.nl) |
| Background Many bacterial genomes harbor multiple copies of genes from the same family (orthologs) that are predicted to have the same function. Gene copies in a given genome can be quite divergent and more closely related to orthologs in distantly related species than to the other copies in the same genome. By comparing with phenotypic data, we have recently found that having multiple copies of genes in certain central metabolic pathways is correlated with preferential and efficient use of that pathway, suggesting that having multiple copies of the same genes can make species more competitive in the ecological niche where that pathway is active. Why do bacteria harbor these multiple copies of the same gene, and how did they obtain them? How are they organized on the chromosome? Do the resulting enzymes serve different purposes? Is each enzyme better adapted to certain environmental conditions? Are the different copies expressed at the same time, or are their relative expression levels regulated? Your project In this project, you will address these questions in two parts. Firstly, you will analyze and compare large numbers of genomes, visualizing the positions of genes copies on different genomes to attempt to find patterns in their organization. You will compare the sequences of the copies between organisms for different gene families and derive statistical insight into the evolution of these gene copy numbers. This will be performed on a smaller set of 200 genomes from marine bacteria, for which phenotypic data is available, as well as a large (50,000) set of genomes, for which you will develop automated data analysis pipelines. Secondly, you will reanalyze existing transcriptomics and proteomics data of different model species, e.g., E. coli or B. subtilis, and analyze the expression patterns of genes present in multiple copies across different conditions. This will give you insight into the functional role of these gene copies, whether they are always coexpressed or more tightly regulated. Ultimately, this will allow you to speculate on the role of gene copy numbers in the evolution of metabolic pathways in bacteria. A prior familiarity with basic bioinformatics tools and a programming language is required for this project. In addition to regular meetings with your supervisor, you will participate and present in our Systems biology group meeting and Journal club. Number of positions available 1 Duration of project: 5-10 months Start date anytime |
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