2023 CMP Academic Projects

In the research group of Professor Nitschke, we are working with large molecules in the shapes of polyhedra [1]. Examples of such molecular polyhedra that our group has seen in the lab are tetrahedrons, cubes, octahedrons, cuboctahedrons, icosidodecahedrons etc. These types of molecular polyhedra, or also called metal organic cages, can encapsulate other smaller molecules inside their cavity. Various applications for such systems have been envisioned, for example biomedical, catalysis, molecular sensing, gas adsorption and separation [2–4].

These molecular polyhedra are assembled by mixing two types of building-blocks in solution. The vertices of these polyhedra are metal atoms that can coordinate to other molecular building-blocks (linkers) that span the faces [5]. The shape of the linker can influence what type of polyhedron is formed. One example is where a rectangular linker is twisted along one of the axes, leading to a non-planar face. In the lab, we observed that a new type of interesting, non-convex topology with tetrahedral symmetry could be formed from these ‘twisted’ linkers [6]. We would like to investigate whether other types of topologies based on flexible, non-planar faces can be made on a molecular scale.

The aim of this project is to construct a set of new polyhedra based on ‘twisted’ rectangular faces. Specifically, we would like to understand how the dimensions (e.g. length, width and degree of twisting) can lead to different topologies. We hypothesize that a topological series can be made for these types of cages with increasing size, similar to what has been seen for other molecular polyhedra [7,8]. The majority of the work will likely be done using Mathematica or another program for constructing and investigating topologies. Overall, the project is open-ended and the next steps are determined by what we learn along the way.

Based on the results, we would like to design a new set of molecular building-blocks that correspond to the variables drawn from the constructed polyhedra. Follow-up experiments done after this project will elucidate whether these building-blocks will indeed assemble into the predicted polyhedral structures in the lab.

The student will work closely together with a chemistry PhD candidate (Paula Teeuwen), who is part of a lab in the chemistry department. A discussion at the beginning of the project will take into account the students working habits and preferences. The student may work remotely, however they are welcome to take up a place in our office and be surrounded by our group-members. A hybrid option is also possible. Core lab-hours are from 9.00-17.00, however flexible working hours can be discussed at the start of the project as the student will not be doing any lab-work.

The student is expected to join our weekly group-meetings on Wednesday mornings. Regular meetings with the PhD student will make sure the student becomes familiar with the chemical background and can ask any relevant questions. In these meetings the progress is also monitored and new plans are made for the student to work on. The student is encouraged to also talk to the other group-members (postdocs, PhD students etc.) about the project. During scheduled meetings with the group-leader (professor Jonathan Nitschke) the student can present any interesting results together with the PhD candidate.

Friend, A.D., Eckes-Shephard, A.H. and Tupker, Q., 2022. Wood structure explained by complex spatial source-sink interactions. Nature Communications, v. 13, p.7824-. doi:10.1038/s41467-022-35451-7.
Friend, A.D., Eckes-Shephard, A., Fonti, P., Rademacher, T.T., Rathgeber, C., Richardson, A.D. and Turton, R.H., 2019. On the need to consider wood formation processes in global vegetation models and a suggested approach. Annals of Forest Science, doi:10.1007/s13595-019-0819-x.
Eckes-Shephard, A.H., Ljungqvist, F.C., Drew, D.M., Rathgeber, C.B.K. and Friend, A.D., 2022. Wood Formation Modeling – A Research Review and Future Perspectives. Frontiers in Plant Science, v. 13, p.837648-. doi:10.3389/fpls.2022.837648
https://gtr.ukri.org/projects?ref=NE%2FW000199%2F1

The early prediction of drug adverse effects is of great interest to pharmaceutical industry, as toxicity is one of the leading reasons for drug attrition. Analysing publicly available big data to understand the cell signalling and regulatory pathways affected by a drug candidate can greatly benefit the study of drug toxicity. Novel computational approaches to predict the toxicity and adverse effects of drug candidates in the pre-clinical stage are critical to improving human safety during clinical trials. In a recent industrial-academic collaborative project carried out within the EU IMI Translational Quantitative Systems Toxicology (TransQST) consortium (ww.transqst.org), we developed a big data analytics tool that predicts pathways affected by drugs and used it to explore the toxic effect of drugs [1,2].

Our computational technique employs the propagation of drug-protein interactions to connect compounds to biological pathways. Target profiles for drugs were built by retrieving drug target proteins from public repositories such as ChEMBL, DrugBank, IUPHAR, PharmGKB, and TTD. Subsequent enrichment tests of the protein pool using Reactome database revealed potential pathways affected by the drugs. Furthermore, an optional tissue filter utilizing the Human Protein Atlas was applied to identify tissue-specific pathways. The analysis pipeline was implemented in an open-source KNIME workflow called Path4Drug to allow automated data retrieval and reconstruction for any given drug present in ChEMBL. The pipeline was applied to withdrawn drugs and cardio- and hepatotoxic drugs with black box warnings to identify biochemical pathways they affect and to find pathways that can be potentially connected to the toxic events.

In this internship, you will implement the workflow in python and apply the analysis on all approved drugs available in the market. You will also identify toxicity-specific pathway fingerprints through clustering analysis and apply a supervised machine-learning approach to predict the toxicity class of new drug candidates. This tool will be implemented in the Reactome database. There are also opportunities to co-author and publish the manuscript on this work.

Our previous interns have co-authored manuscripts with us (e.g., PMID: 31701150, PMID: 33620773) and have secured PhD positions in top organizations including Harvard Medical School, Cambridge, Oxford, EMBL. A fixed monthly allowance is provided to help towards living costs if no other funding is available to the candidates. Support for a UK visa will be offered to the selected candidate if needed.

The Goldman group at EMBL-EBI can offer a number of projects to best fit the interests and skills of the students.

One of our main areas of research is sequencing technologies, which can read DNA and amino acid sequences. Nanopore sequencing is one of the most promising of these technologies. A useful feature of nanopore sequencing is that it allows selective sequencing: we can reject a DNA molecule if deemed uninteresting after reading only a small portion of it. This allows users to save time and reagents and to gather more useful information from the sequencing process. We have recently developed BOSS-RUNS, a mathematical model and algorithmic framework based on information theory principles that can dynamically optimise the sequencing process using the information collected so far by the sequencing machine, see https://www.nature.com/articles/s41587-022-01580-z.

We are currently expanding the applicability and features of this method, and possible projects in this context could involve conducting simulations of the sequencing process with the proposed method and quantitative analysis of the resulting assemblies. For this project some knowledge of python and UNIX is desirable.

While nanopore sequencing is often applied to DNA, we are also collaborating with a project aimed at developing nanopore sequencing technologies for proteins, based on single molecule surface enhanced Raman spectroscopy. In brief, a protein translocating through the nanopore is optically excited. A subsequent transition to the ground state results in Raman emission of photons. These photons are characteristic of the amino acid in the nanopore. The aim of a possible project in this area would be to develop a neural network which takes those photons and decodes it back to the protein sequence. Initially, the neural network can be trained/tested/validated using a nanopore simulator developed by a previous CMP student. Further hyperparameter optimisation, warm/cold start of the trained model can be done using new experimental data.

Lastly, a long term research interest in our group is algorithms and mathematical models in molecular phylogenetics, that is, the reconstruction of evolutionary histories using DNA sequences. While advancements in sequencing technologies make DNA sequence data ever more affordable and ubiquitous, our algorithms and models to study this data are not efficient enough for analysing large datasets. This has been made painfully obvious during the COVID-19 pandemic: while millions of SARS-CoV-2 genomes have been sequences, traditional phylogenetic methods can only study a few thousands of them at the time. For this reason we have been developing new phylogenetic algorithms and mathematical models that allow the study of millions of closely related genomes, see https://doi.org/10.1101/2022.03.22.485312 . A possible project in this area could involve extending the functionalities and models of our methods, improving the algorithms, or running simulations and benchmark analyses. Useful skills for these projects are familiarity with Python, basic probability concepts, and algorithmic skills.

The advent of single molecule localization microscopy enabled the improvement of the resolution by an order of magnitude beyond the diffraction limit. For the first time biological structures and processes could be observed via fluorescence microscopy at sub-diffraction scales. However, the reconstruction are not an images in a classical sense, but noisy point clouds. Dedicated approaches are necessary to extract the relevant biological information from the collected data, disentangling them from the characteristics and limitations of the technique.

This project aims at developing a rigorous mathematical model to capture the behaviour of soils when subjected to earthquake loads. This is essential to the assessment of wind turbine behaviour under earthquake loads, and to the imminent need for offshore wind deployment in East Asia and North America.

The model will be developed within the hyperplasticity framework. The hyperplasticity framework is a rigorous mathematical framework that brings together thermodynamics and mechanics, enabling to formulate models for mechanical applications that obey the fundamental principles of thermodynamics. The framework is highly mathematical and would benefit from the rigorous grounding of a maths student. The project will also require implementation of the model, and therefore strong coding skills.

The project will aim at (i) performing a thorough literature review on the methods that could be used to capture this specific behaviour, (ii) exploring formulations and implementing them, and (iii) implementing a model for offshore wind turbine monopile design under earthquake loads. The outcome of the project will enable advances in modelling of foundations for offshore wind turbines, both current ones and floating wind.

This project is ideally suited to an applied mathematician interested in engineering who is comfortable with mathematical (convex) analysis, in particular differentiation and integration, as well as numerical implementation. Basic knowledge of mechanics and thermodynamics is also desirable. The selected students will learn aspects of civil engineering for foundation design for offshore wind turbines, and hyperplasticity.