Cross-disciplinary post-doctoral fellow training scheme in quantative biomedicine (XDF) - 8 x 4 year post-docs
Lead Research Organisation:
University of Edinburgh
Department Name: UNLISTED
Abstract
2018 Intake:
Dr. Andrew Papanastasiou - final project title: Uncovering the role and regulation of transcriptional variability in the developing eye
Dr Chantriolnt-Andreas Kapourani - final project title: Spatial modelling of transcriptomic changes in tissue pathology
Dr Mattia Marenda - final project title: Understanding how nuclear meshes of RNA and proteins regulate the structure of transcriptionally related protein complexes
Dr Ava Khamseh - final project title: Recurrent clonal expansion trajectories and mutational competition in a model of very early oncogenesis
2019 Intake:
Dr Vanessa Smer Barreto - final project title: Discovery of new senolytics for anticancer therapy via machine learning
Dr Lyndsay Kerr - final project title: Understanding the Dynamics of DNA Methylation in Cancer Using Mathematical Models
Dr Eric Latorre Crespo - final project title: Time evolution of age-related clonal hematopoiesis – Mathematical modelling and deep learning approaches to uncover disruptive genetic and epigenetic patterns
2020 Intake:
Dr Didier Devaurs - final project title: TBC
Dr. Andrew Papanastasiou - final project title: Uncovering the role and regulation of transcriptional variability in the developing eye
Dr Chantriolnt-Andreas Kapourani - final project title: Spatial modelling of transcriptomic changes in tissue pathology
Dr Mattia Marenda - final project title: Understanding how nuclear meshes of RNA and proteins regulate the structure of transcriptionally related protein complexes
Dr Ava Khamseh - final project title: Recurrent clonal expansion trajectories and mutational competition in a model of very early oncogenesis
2019 Intake:
Dr Vanessa Smer Barreto - final project title: Discovery of new senolytics for anticancer therapy via machine learning
Dr Lyndsay Kerr - final project title: Understanding the Dynamics of DNA Methylation in Cancer Using Mathematical Models
Dr Eric Latorre Crespo - final project title: Time evolution of age-related clonal hematopoiesis – Mathematical modelling and deep learning approaches to uncover disruptive genetic and epigenetic patterns
2020 Intake:
Dr Didier Devaurs - final project title: TBC
Technical Summary
A post-doctoral level Programme for physicists, chemists, mathematicians, statisticians, engineers, computer scientists etc. seeking training to become leaders in Quantitative Biomedicine.
Cross Disciplinary Fellowships
Background
The beginning of the 21st century has seen enormous advances in science and technology. With the completion of the Human Genome Project and implementation of multiple “Big Data” approaches in biomedical sciences, there is now a pressing need to train a new generation of mathematically-minded biomedical scientists who will be able to bridge the gap between life sciences and mathematics/physics/informatics, and efficiently link modern biomedical research with big data research technologies. To address this need a pioneering Cross-Disciplinary Post-Doctoral Fellowships programme (XDF) has been initiated at the University of Edinburgh with matching financial support from the Medical Research Council.
The University of Edinburgh is one of the world leading research universities (ranked 4th in UK for its research power) and is at the forefront of both computational sciences and health sciences. Informatics is the largest and strongest computer science department in the UK (1st for research power according to REF2014), with particular strengths in data science and computational biology. Clinical medicine has been ranked 4th in the UK (research power) with the MRC Institute of Genetics and Molecular Medicine (IGMM) being one of the biggest biomedical research establishments in the country. The XDF Programme lead, Professor Ponting, was trained first in particle physics before pursuing a successful career in biomedicine, so knows first-hand the skills necessary for Fellows to transition into “Big Data Biomedicine”. The programme is led by a Board of Directors, including investigators from the Institute of Genetics & Molecular Medicine and the School of Informatics, who provide Fellows with diverse perspectives.
Purpose
The fellowships are aimed at early-career quantitatively trained scientists, whose ambition is to achieve an independent career in data-driven computational biomedicine. Fellows follow a personalised training and research programme to become truly cross-disciplinary leaders in quantitative biomedicine. Fellows are expected to gain analytical and computational expertise, and an in-depth appreciation of biomedical and health research. They are motivated to address biomedical questions, to apply and train others in their previously acquired analytical/computational skills, and to learn the strengths and limitations of biomedical science methods. Fellows propose a well-developed, important and innovative biomedical project only after substantial relevant training.
Fellowships are funded jointly by the MRC and the University of Edinburgh (with subsequent incorporation of other funds). Fellows receive mentorship from both computational and biomedical scientists, and can use office space in both Informatics and IGMM. Where appropriate, the research may also be conducted in collaboration with an industrial partner and/or the NHS. After their initial year, fellows focus on original research and produce material for peer-reviewed publications and for dissemination at national and international level.
Cross Disciplinary Fellowships
Background
The beginning of the 21st century has seen enormous advances in science and technology. With the completion of the Human Genome Project and implementation of multiple “Big Data” approaches in biomedical sciences, there is now a pressing need to train a new generation of mathematically-minded biomedical scientists who will be able to bridge the gap between life sciences and mathematics/physics/informatics, and efficiently link modern biomedical research with big data research technologies. To address this need a pioneering Cross-Disciplinary Post-Doctoral Fellowships programme (XDF) has been initiated at the University of Edinburgh with matching financial support from the Medical Research Council.
The University of Edinburgh is one of the world leading research universities (ranked 4th in UK for its research power) and is at the forefront of both computational sciences and health sciences. Informatics is the largest and strongest computer science department in the UK (1st for research power according to REF2014), with particular strengths in data science and computational biology. Clinical medicine has been ranked 4th in the UK (research power) with the MRC Institute of Genetics and Molecular Medicine (IGMM) being one of the biggest biomedical research establishments in the country. The XDF Programme lead, Professor Ponting, was trained first in particle physics before pursuing a successful career in biomedicine, so knows first-hand the skills necessary for Fellows to transition into “Big Data Biomedicine”. The programme is led by a Board of Directors, including investigators from the Institute of Genetics & Molecular Medicine and the School of Informatics, who provide Fellows with diverse perspectives.
Purpose
The fellowships are aimed at early-career quantitatively trained scientists, whose ambition is to achieve an independent career in data-driven computational biomedicine. Fellows follow a personalised training and research programme to become truly cross-disciplinary leaders in quantitative biomedicine. Fellows are expected to gain analytical and computational expertise, and an in-depth appreciation of biomedical and health research. They are motivated to address biomedical questions, to apply and train others in their previously acquired analytical/computational skills, and to learn the strengths and limitations of biomedical science methods. Fellows propose a well-developed, important and innovative biomedical project only after substantial relevant training.
Fellowships are funded jointly by the MRC and the University of Edinburgh (with subsequent incorporation of other funds). Fellows receive mentorship from both computational and biomedical scientists, and can use office space in both Informatics and IGMM. Where appropriate, the research may also be conducted in collaboration with an industrial partner and/or the NHS. After their initial year, fellows focus on original research and produce material for peer-reviewed publications and for dissemination at national and international level.
Publications
Aitken SJ
(2020)
Pervasive lesion segregation shapes cancer genome evolution.
in Nature
Argelaguet R
(2019)
Multi-omics profiling of mouse gastrulation at single-cell resolution.
in Nature
Beentjes SV
(2020)
Higher-order interactions in statistical physics and machine learning: A model-independent solution to the inverse problem at equilibrium.
in Physical review. E
Behring A
(2019)
Higher Order Corrections to Spin Correlations in Top Quark Pair Production at the LHC.
in Physical review letters
Conev A
(2022)
3pHLA-score improves structure-based peptide-HLA binding affinity prediction
in Scientific Reports
Conev A
(2023)
EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.
in Briefings in bioinformatics
Cossu G
(2019)
Machine learning determination of dynamical parameters: The Ising model case
in Physical Review B
Crofts S
(2023)
DNA methylation rates scale with maximum lifespan across mammals
Crofts SJC
(2024)
DNA methylation rates scale with maximum lifespan across mammals.
in Nature aging
Devaurs D
(2022)
Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data.
in Journal of the American Society for Mass Spectrometry
Fernández-Duran I
(2022)
Cytoplasmic innate immune sensing by the caspase-4 non-canonical inflammasome promotes cellular senescence.
in Cell death and differentiation
Hall-Swan S
(2021)
DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.
in Computers in biology and medicine
Hernandez-Moran BA
(2022)
Robust Genetic Analysis of the X-Linked Anophthalmic (Ie) Mouse.
in Genes
Higham J
(2022)
Local CpG density affects the trajectory and variance of age-associated DNA methylation changes.
in Genome biology
Kapourani CA
(2021)
scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution.
in Genome biology
Kapourani CA
(2019)
Melissa: Bayesian clustering and imputation of single-cell methylomes.
in Genome biology
Kerr L
(2022)
Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns.
in Journal of the Royal Society, Interface
Marenda M
(2021)
Parameter-free molecular super-structures quantification in single-molecule localization microscopy.
in The Journal of cell biology
Marenda M
(2022)
The role of SAF-A/hnRNP U in regulating chromatin structure.
in Current opinion in genetics & development
Martin L
(2023)
Modelling the dynamics of senescence spread.
in Aging cell
Michieletto D
(2022)
Rheology and Viscoelasticity of Proteins and Nucleic Acids Condensates.
in JACS Au
Michieletto D
(2022)
Rheology and Viscoelasticity of Proteins and Nucleic Acids Condensates
Nicholson MD
(2023)
Sequential mutations in exponentially growing populations.
in PLoS computational biology
Nicholson MD
(2021)
Response to comment on "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2".
in Science translational medicine
Owen LJ
(2023)
Characterization of an eye field-like state during optic vesicle organoid development.
in Development (Cambridge, England)
Ponting CP
(2020)
Genetics Needs Non-geneticists.
in Trends in genetics : TIG
Reijns MAM
(2022)
Signatures of TOP1 transcription-associated mutagenesis in cancer and germline.
in Nature
Robertson N
(2022)
Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects
in Nature Medicine
Simpson DJ
(2023)
Region-based epigenetic clock design improves RRBS-based age prediction.
in Aging cell
Smer-Barreto V
(2023)
Discovery of senolytics using machine learning.
in Nature communications
Taglini F
(2024)
DNMT3B PWWP mutations cause hypermethylation of heterochromatin.
in EMBO reports
Travnickova J
(2019)
Zebrafish MITF-Low Melanoma Subtype Models Reveal Transcriptional Subclusters and MITF-Independent Residual Disease.
in Cancer research
Uttley K
(2023)
Unique activities of two overlapping PAX6 retinal enhancers.
in Life science alliance
Uttley K
(2022)
Unique functions of two overlapping PAX6 retinal enhancers
Waddell SH
(2023)
A TGFß-ECM-integrin signaling axis drives structural reconfiguration of the bile duct to promote polycystic liver disease.
in Science translational medicine
Yao Y
(2022)
Comparative transcriptome in large-scale human and cattle populations.
in Genome biology
Related Projects
Project Reference | Relationship | Related To | Start | End | Award Value |
---|---|---|---|---|---|
MC_UU_00009/1 | 31/03/2018 | 30/03/2023 | £446,000 | ||
MC_UU_00009/2 | Transfer | MC_UU_00009/1 | 31/03/2018 | 30/03/2023 | £500,700 |
Title | Additional file 2 of Local CpG density affects the trajectory and variance of age-associated DNA methylation changes |
Description | Additional file 2: Supplementary tables Table S1-7. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://springernature.figshare.com/articles/dataset/Additional_file_2_of_Local_CpG_density_affects_... |
Title | Additional file 2 of Local CpG density affects the trajectory and variance of age-associated DNA methylation changes |
Description | Additional file 2: Supplementary tables Table S1-7. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://springernature.figshare.com/articles/dataset/Additional_file_2_of_Local_CpG_density_affects_... |
Description | X-Net: A UK-wide Cross-Disciplinary Training Network (Oxford) |
Organisation | University of Oxford |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | The MRC HGU is leading this UK-wide Cross-Disciplinary Training Network initiative, bringing together partners from across academia and industry to dismantle obstacles currently impeding the career progression of young cross-disciplinary researchers. |
Collaborator Contribution | The University of Oxford is a key partner in X-Net. |
Impact | Overcoming Barriers to Cross-Disciplinary Research WEDNESDAY 06 JULY 2022 - 1:00 PM - 4:00 PM Final Survey Report https://era.ed.ac.uk/handle/1842/39337 |
Start Year | 2022 |
Description | Overcoming Barriers to Cross-Disciplinary Research - Workshop and Online Survey |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | The workshop consisted of interactive and discussion focused activities to find common barriers and start to find solutions to these in groups. Using pre-workshop anonymous survey data, the group discussed and defined what some of the common barriers are. Using case study stories, the groups then collectively started to think about what some of the solutions might be. Finally, there was time for individuals to start thinking about specific actions that will help overcome personal barriers through a short action learning activity. |
Year(s) Of Engagement Activity | 2022 |
URL | https://era.ed.ac.uk/handle/1842/39337 |
Description | Preparing the roadmap: prioritising cross-disciplinary training needs with industry |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | This workshop provided extensive evidence how academia can improve the training of skilled cross-disciplinary scientists ahead of them joining industry. Knowledge, mindset and skill gaps were identified, and the lack of porosity between academia and industry identified. |
Year(s) Of Engagement Activity | 2023 |
URL | https://x-net.bio/ |
Description | Thurs 10 February 2022, 11am - 1pm. |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Didier attended IGC PE Training Programme 2022 - Storytelling for Researchers - tell them a tale. External Trainer: Duncan Yellowlees, in preparation for his SULSA ECR application |
Year(s) Of Engagement Activity | 2022 |
Description | Thurs 13 January 2022, 3-5pm |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Didier attended IGC PEC Training Programme 2022 - Presenting Skills. External Trainer: Dr Steve Cross, , in preparation for his SULSA ECR application |
Year(s) Of Engagement Activity | 2022 |
Description | Thurs 24 Feb 2022, Dr Didier Devaurs won SULSA ECR PRIZE |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | SULSA award for outstanding early career scientists whose work shows excellent potential to make an impact in the field of life sciences. This prize includes • a fully-funded tour of 3 Scottish Universities where the awardee will deliver a seminar • £2,000 of flexible funding to be used at their discretion "One of the key things that made the jury choose me is how accessible my presentation was to a broad audience. Without your help, I would have not been able to reach that level." Didier Devaurs |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.ed.ac.uk/institute-genetics-cancer/news-and-events/news-2022/early-career-prize-for-didi... |
Description | Weds 11 May 2022, 7.30-9.30pm |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Public/other audiences |
Results and Impact | Dr Lucy Martin, XDF, presented a Pint of Science 2022 event - "Cancer & Chronic Health - Two Health Emergencies" "Cancer - reasons to diagnose" - Dr. Lucy Martin, Cross-Disciplinary Research Fellow, MRC HGU, IGC Cancer is often a life-changing experience, irrespective of a patient's age or treatment options. While a cancer diagnosis is never good news, the words "brain cancer" are particularly terrifying. Researchers in Edinburgh are studying brain cancers to understand their biology better, in the hope of finding new treatments. Tonight I'll talk about some of the most interesting questions in cancer biology. What is cancer? Why does it occur? Why is brain cancer so difficult to treat? How do we study these cancers in the lab? And finally, how are we trying to improve treatments for patients? AUDIENCE: Pre-booked: 39 - Actual 40 |
Year(s) Of Engagement Activity | 2022 |
URL | https://pintofscience.co.uk/event/cancer-and-chronic-stress-two-health-emergencies |