Projects

Main supervisor: Leonie Ringrose

Second supervisor: Davide Mazza

Objectives

  1. To establish single molecule tracking of tagged Polycomb and Trithorax (PcG and TrxG) group proteins in living Drosophila.
  2. To measure residence times of tagged PcG and TrxG proteins and variants thereof at loci of defined transcriptional status in living Drosophila (e.g., RNA pol II domains, tagged reporter gene).
  3. To derive stochastic mathematical models describing PcG and TrxG binding at loci of defined status.

We have previously quantified absolute molecule numbers, average binding rates and residence times for members of the Polycomb and Trithorax regulatory system in living Drosophila. ESR1 will now employ single molecule tracking (SMT) of individual molecules and their binding at defined transgenic loci, whose identity and transcriptional status are known and manipulable. In combination with stochastic modelling, this study will provide a coherent theoretical and experimental framework to understand the Polycomb/Trithorax system with unprecedented accuracy.

Required background and skills

Master’s degree or equivalent in biological sciences, with practical experience in molecular biology and/or biochemistry. Programming skills and experience of mathematical modelling are desirable.

Main supervisor: Edda Schulz

Second supervisor: Martin Howard

Objectives

  1. Analyze the quantitative input-output relationship of X-dosage dependent Xist expression to assess threshold behavior and bistability.
  2. Develop alternative models on how feedback loops in epigenetic regulation can generate threshold and potentially associated epigenetic memory.
  3. Distinguish alternative models based on quantitative perturbation data in mouse embryonic stem cell.
  4. Use single-cell RNA-sequencing combined with CRISPR perturbations to infer the underlying network structure.

Gene-regulatory networks process quantitative information and elicit the appropriate cellular response. To understand the principles that govern quantitative information processing in biological networks, we study how X-chromosomal dosage ensures the female-specificity of X-chromosome inactivation. X dosage transmits information on the cellular sex and controls a bistable epigenetic switch to lock in the initial molecular decision. The project will combine quantitative measurements, functional genomics and mathematical modeling to dissect the underlying gene-regulatory network (als see also Mutzel et el, Nature SMB, 2019, https://rdcu.be/bvn6z).

Required background and skills

Master’s degree or equivalent qualification in Life Sciences or physics. Either experience in molecular biology, computational biology and/or mathematical modeling.

Main supervisor: Martin Howard

Second supervisor: Jane Mellor

Objectives

  1. To precisely segment single cells in the Arabidopsis root and extract the corresponding FLC mRNA and protein levels.
  2. To develop an unbiased ordinary differential equation model of FLC transcriptional dynamics with analogue and digital components.
  3. To link the observations and model to antisense mediated gene regulation.

Preliminary experimental evidence indicates that constitutive downregulation of Arabidopsis FLC expression by the Autonomous pathway involves two methods: state switching (i.e. dynamic digital switching from ON to OFF expression states) and analogue down-regulation (i.e. smoothly decreasing transcription in the ON state). ESR3 will dissect how this regulation is achieved through the development of unbiased ODE models quantifying the importance of the two mechanisms. These models will also incorporate the dynamics of the long non-coding RNA COOLAIR, providing mechanistic insight into antisense-mediated gene regulation.

Required background and skills

Master’s degree or equivalent qualification in Life, Natural or Mathematical Sciences. Prior experience in biological physics or mathematical/computational biology is desirable but not essential.

Main supervisor: Kim Sneppen

Second supervisor: Leonie Ringrose

Objectives

  1. To aggregate existing literature into a map of possible states and transitions in a system of nucleosomes that are governed by the Polycomb-Trithorax groups of proteins.
  2. To build a dynamic stochastic model for the above system, formulated in terms of known read-write enzymes and nucleosome modifications.
  3. To investigate whether the above model can exhibit bi-stability, and investigate the role of bivalent states.

We expect to formulate a new quantitative dynamic model for Polycomb/Trithorax system and implement this in-silico. The model should reproduce epigenetic features of known experimental systems and allow us to explore bivalent states (co occurrence of H3K4me and H3K27me).

Required background and skills

Master’s degree or equivalent qualification in Life, Natural or Mathematical Sciences. Prior experience in biologicalphysics or mathematical/computational biology is desirable. Ability to implement simple dynamical models on computer is a requirement.

Main supervisor: Luca Giorgetti

Second supervisor: Nacho Molina

Objectives

  1. To quantify the correlation between the amount of nascent transcription from a promoter and the physical distance that separates it from known regulatory sequences, using live imaging.
  2. To build quantitative and predictive models of enhancer-promoter interactions, to interpret the experimental data and predict the outcome of independent experiments.

We have previously shown that in mouse embryonic stem cells, cell-to-cell differences in gene expression correlate to difference in the three-dimensional conformation of the chromatin fibre in the genomic region surrounding the gene and containing its long-range regulatory sequences. ESR5 will now use single-cell fluorescence microscopy to explore the relationship between the three-dimensional conformation of chromatin and transcription at a large number of chromosomal loci, and build multi-state stochastic models of promoter operation in the presence of enhancer looping.

Required background and skills

Master’s degree or equivalent in biological, physical or natural sciences, with practical experience in molecular biology, biochemistry and/or biophysics. Experience with programming, image analysis or mathematical modelling will be considered a plus but are not mandatory.

Main supervisor: Leonie Ringrose

Second supervisor: Marc Rehmsmeier

Objectives

  1. To establish an editable reporter gene in Drosophila at an endogenous Polycomb target locus, and use it to evaluate contribution of specific DNA motifs to Polycomb Response Element (PRE) activity.
  2. To optimise existing high throughput reporter assay in mouse ESCs using NGS and barcoding, and use it to evaluate contribution of specific DNA motifs to PRE activity.
  3. To refine machine learning approaches based on the outcome of these experiments.

We have previously developed a machine learning approach in collaboration with Marc Rehmsmeier, for prediction of Polycomb response elements (PREs) in Drosophila. The same approach is currently being applied to the mouse genome. The model makes several predictions in fly and mouse about which DNA sequence motifs are required for PRE function. ESR6 will test these predictions in fly and in mouse cell culture, both by genome editing and by high throughput reporter assays using ChIP-seq, and barcoding. Mouse ESC differentiation protocols will be used to reveal developmental aspects of mammalian PRE activity. The results will be used to refine the model and reiterate predictions and testing.

Required background and skills

Master’s degree or equivalent in biological sciences, with practical experience in molecular biology and/ or biochemistry. Programming skills and experience of bioinformatics or computational biology are desirable.

Main supervisor: Marc Rehmsmeier

Second supervisor: Ana Pombo

Objectives

  1. To map PRE-target gene interactions genome-wide in the fly and in the mouse using genome architecture mapping (GAM).
  2. To assess whether statistical search-space reduction through GAM can identify statistically weak PREs.
  3. To assess whether a combination of PRE prediction and GAM can distinguish between PRE and non-PRE Polycomb Group binding events in genome-wide profiling data.

We have previously developed a machine learning approach for the identification of Polycomb Response Elements (PREs). Our collaborators in the Pombo lab have developed the genome architecture mapping (GAM) method. Given a set of PREs, ESR7 will identify their target genes by GAM. Given a set of Polycomb target genes, ESR7 will identify by GAM candidate regions that might contain targeting PREs and use PRE prediction to identify those PREs.

Required background and skills

Master’s degree or equivalent qualification in Life or Natural Sciences, Mathematics or Computer Science, with a focus on Bioinformatics or Computational Biology; experience in programming.

Main supervisor: Nacho Molina

Second supervisor: Davide Mazza

Objectives

  1. To develop a mathematical model of chromatin condensation-decondensation based on Hi-C maps around the cell cycle.
  2. To extend our previous stochastic reaction-diffusion model of TF dynamics in the context of a dynamic chromatin structure.
  3. To validate the model using available FRAP and ChIP-seq data around the cell cycle for Sox2, Oct4, Nanog and Esrrb.

We have previously derived a model to describe the nuclear diffusion of TFs taking into account the chromatin structure. ESR8 will now extend the model to incorporate the dynamics of the chromatin structure in the context of the cell cycle.

The main result will be to understand and predict mitotic bookmarking by pluripotent TFs. In addition, ESR8 will investigate the power of mitotic bookmarking to maintain a specific gene regulatory landscape through the cell cycle.

Required background and skills

Master’s degree or equivalent qualification in Physics, Mathematics, Computational Biology or Life Sciences. Strong background in mathematical or biophysical modelling and good programming skills are required. Experience analysing large-scale genomic data and/or microscopy images will be a plus.

Main supervisor: Davide Mazza

Second supervisor: Nacho Molina

Objectives

  1. To quantify chromatin reorganisation in response to genotoxic stress by microscopy and/or biochemistry.
  2. To quantify chromatin mobility in response to genotoxic stress by single molecule imaging.
  3. To model and validate the combined effect of connectivity and chromatin mobility on TF search by single molecule imaging.

We have developed methods to quantify the diffusion and binding of TFs to chromatin in living cells by single molecule tracking, and applied it to the tumour-suppressor p53 a key TF activated by genotoxic stress. Here ESR9 will determine how modifications in chromatin structure and mobility induced by DNA damage can affect the efficient targeting of TFs to responsive elements. In combination with mathematical modelling of the TF search mechanism, using Monte-Carlo simulations of TF search mechanism in environments with heterogeneous crowding, we aim to identify how the physical epigenetic state of the cell can direct TFs to subsets of putative responsive elements.

Required background and skills

Master’s degree or equivalent qualification in Physics, Biophysics, Life or Natural Science, with a focus on biophysical analysis of cellular and molecular biology data. Previous experience in advanced fluorescence microscopy will be considered a plus.

Main supervisor: Ana Pombo

Second supervisor: Mario Nicodemi

Objectives

  1. To produce genome architecture mapping (GAM) datasets in early mouse embryos.
  2. To compare GAM datasets from early mouse embryos with embryonic stem cells.
  3. To validate chromatin contacts using single cell imaging approaches.

We have produced a GAM dataset in mouse ESCs, quantified the probability of 3D chromatin interactions genome- wide, and identified specific pairwise contacts between active genes and enhancer regions. To expand our understanding of chromatin contacts in vivo, we will apply GAM at different stages of development in wild type mouse embryos (E3.5 and E4.5), and in Nanog and Gata6 knockout embryos. In combination with mathematical and polymer modelling (NAPOLI), we will produce a coherent framework to understand the dynamics of promoter-enhancer contacts at the single cell level during early development.

Required background and skills

Master’s degree or equivalent qualification in life sciences or computational biology. Previous experience in gene and/or epigenetic regulation, mammalian cell culture, molecular biology and/or genomics approaches are preferred.

Main supervisor: Jane Mellor

Second supervisor: Martin Howard

Objectives

  1. To map 3C interactions during the yeast metabolic cycle.
  2. To demonstrate the consequences of ablating pervasive antisense transcription on local 3D organisation.
  3. To derive stochastic mathematical models describing the effect of pervasive antisense transcription on the 3D architecture and associated sense transcription.

Statistical Mechanics techniques will be used to simulate a bead-spring polymer fibre upon which the structural constraints observed in HiC data from mitotic cells are imposed. We have shown that the µ3C 3D architecture of the yeast genome is related to pervasive antisense transcription but it is not known whether the relationship is causal or if so, whether it is the act of transcription or the non-coding transcripts that play a role in the formation of µ3C interactions. Using an established stochastic model, coupled to simulations of experimental data, developed to describe the relationship between antisense and sense transcription, ESR11 will develop the model to address causal relationships. ESR 11 will exploit the temporal resolution of the yeast metabolic cycle (YMC), quantitative nascent transcript and µ3C events, and the dCas9 CRISPRi system to ablate selected well-characterised antisense transcription events, to derive data to populate the model and describe these relationships.

Required background and skills

Master’s degree or equivalent in biological sciences, with practical experience in molecular biology and/or biochemistry. Programming skills and/or experience of mathematical modelling are an advantage.

Main supervisor: Mario Nicodemi

Second supervisor: Ana Pombo

Objectives

  1. To develop the analysis of genome architecture (GAM) high-throughput chromatin contact data.
  2. To develop polymer models to understand the underlying molecular mechanisms.

By combining Statistical Physics polymer models, computer simulations, and epigenomics high-throughput data analysis, we aim to understand genome-wide chromatin contact data produced by Hi-C based and our novel GAM technology (developed with Ana Pombo, also in this consortium). In particular, we expect to derive an understanding of the general mechanisms of chromosome folding and of the specific molecular factors acting in model loci (e.g, Hox loci) linked to human phenotypes (Sox9, Epha4, etc.) and embryo development.

Required background and skills

Master’s degree (or equivalent) in Physics, Math,Computer Sciencesor Engineering, ideally with a background in Statistical Physics or Computational Biology and programming.

Main supervisor: Céline Sabatel

Second supervisor: Jane Mellor

Objectives

  1. Development and optimization of a method for library preparation of RNA, especially long non- coding RNA (lncRNA).
  2. Application of this methodology to investigate the role of lncRNAs in long-range chromatin contacts.

Diagenode is expert in wet lab and dry lab chromatin immunoprecipitation and methylation analysis, marketing kits and reagents but also offering service to external customers. Recently, the company has expanded its product portfolio to include kits for library preparation on RNA using CATS, a template switch-based method. ESR13 will further developp the CATS method in order to improve the analysis of lncRNAs. In addition, the CATS will be optimized to deal with limited amounts of RNA. ESR13 will then apply the optimized CATS method to investigate how lncRNAs are involved in higher order chromatin structure via secondment to the Mellor lab.

Required background and skills

Master’s degree or equivalent in biological sciences, with experience in molecular biology and/ or biochemistry.

Main supervisor: Céline Sabatel

Second supervisor: Mario Nicodemi

Objectives

  1. To develop a Hi-C bioinformatics pipeline.
  2. To learn wet lab techniques to understand where experimental biases potentially lie.

Diagenode is expert in wet lab and dry lab chromatin immunoprecipitation and methylation analysis, marketing kits and reagents but also offering service to external customers. The company has plans to expand its product portfolio to chromatin conformation analysis. ESR 14 will develop dry-lab Hi-C data analysis, first using datasets from the public sector and the consortium, then from in-house generated data from ESR 13. Specialised sequencing data alignment/peak calling and advanced statistical analysis will form the backbone of the bioinformatics pipeline required for efficient Hi-C data processing.

Required background and skills

Master’s degree or equivalent in life sciences or bioinformatics with experience in command line programming, linux environment and ideally epigenetic software tools.

Main supervisor: Jane Mellor

Second supervisor: Alexandre Akoulitchev (OBD)

Objectives

  1. To use selected glioma (e.g. BT142) and leukemic (e.g. EOL-1) cell lines and matched controls, treated with and without receptor tyrosine kinase inhibitors (RTKI), to define metabolism, gene expression, CTCF binding (DNA methylation), and targeted 3C interactions focusing on RTK oncogenes such as PDGFRA and c-KIT.
  2. To develop a stochastic model for feedback regulation of 3C interactions by the inhibited RTK via control of the chromatin environment.

Tyrosine kinase inhibitors (TKIs) such as the ATP mimetic Imatinib are being developed to treat chronic malignancies by targeting the activated enzyme. Higher order structures in chromatin (3C interactions) change in these diseases and accurately stratify patients suitable for TKI treatment. In addition to reducing RTK activity, our preliminary data suggests that TKIs may also function by resetting the higher order structures in chromatin, explaining why single doses of these TKIs are often successful in treating these conditions. Stochastic modelling and simulations of coding and non-coding transcripts can be used to predict parameters leading to switching of higher order structures. ESR15 will use tyrosine kinase inhibitors to monitor chromosome conformations and the associated changes to gene expression, metabolism and chromatin over time. By integrating time resolved molecular changes and modelling positive and negative feedback loops, this study will provide mechanistic and regulatory insights into the disease process and its treatment.

Required background and skills

Master’s degree or equivalent in biological sciences, with practical experience in molecular biology and/ or biochemistry. Experince in 3C and/or programming skills are an advantage.