Akute leukemia is a blood cancer that usually starts displaying symptoms in a late stage, making its
treatment difficult and often unsuccessful. The disease is caused by carcinogenic mutations in
hematopoietic stem cells (HSCs). They cause the cells to proliferate instead of differentiating further
towards mature blood cells. Recently, it has been observed that certain mutations are present in
patients already a few decades before the onset of leukemia, a property called clonal hematopoiesis
of indeterminate potential (CHIP) [1,2].
In the Marr lab at Helmholtz Munich, and at the Schumacher lab at the University of Edinburgh, we
work on understanding hematopoiesis [3,4], the kinetics of CHIP , and the role of the immune
system in the regulation of the CHIP mutated single cells. This could help us prevent the development
of leukemia, or lead to the development of less invasive treatments than chemotherapy and bone
In the PhD project, the student will address the question of how the immune system deals with CHIP
from two different perspectives, using contemporary analytical tools. The first level will involve
mathematical modeling of the kinetics of CHIP mutations and the blood production system. The goal
here is to use data from large cohorts to find phenotypic evidence and properties of the immune
system activity. This will include stochastic modeling, ordinary differential equations and statistical
inference to describe hematopoiesis. The second level will be understanding the molecular and
epigenetic mechanisms of immune regulation of CHIP. The candidate will analyze data from blood and
bone marrow smears, single cell RNA sequencing, single cell ATAC sequencing, chromatin
immunoprecipitation, and mass spectroscopy with bioinformatic techniques and machine learning
models to understand the signaling pathways between CHIP cells and immune cells.
The student will enter a 4 year program leading towards a doctoral degree. The research work will be
conducted at Helmholtz Munich with an expected 6-12 months visit to Edinburgh. The research will be
conducted in an interdisciplinary team involving mathematicians, bioinformaticians, clinicians,
physicists and artificial intelligence experts.
The successful candidate will have a background in bioinformatics, computer science, mathematics,
physics or other quantitative fields. Alternatively, the candidate will have a background in biology or
medicine with strong analytical and programming skills. The candidate needs a passion for biomedical
research and enjoys working in an interdisciplinary environment.
1. Genovese G, Kähler AK, Handsaker RE, Lindberg J, Rose SA, Bakhoum SF, et al. Clonal
hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371:
2. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, et al. Age-related clonal
hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371: 2488–2498.
3. Bast L, Buck MC, Hecker JS, Oostendorp RAJ, Götze KS, Marr C. Computational modeling of
stem and progenitor cell kinetics identifies plausible hematopoietic lineage hierarchies. iScience.
4. Buggenthin F, Buettner F, Hoppe PS, Endele M, Kroiss M, Strasser M, et al. Prospective
identification of hematopoietic lineage choice by deep learning. Nat Methods. 2017;14: 403–406.
5. Robertson NA, Latorre-Crespo E, Terradas-Terradas M, Lemos-Portela J, Purcell AC, Livesey BJ,
et al. Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects. Nat Med.