Networks, Trees, and Landscapes
We use networks to organise our understanding of how different agents (individuals in a population, cells in an organism, proteins in a cell, …) interact and organise themselves.
Similarly we can trace relationships between individuals and cells over time and evolutionary or genealogical trees have been an organising principle in biology for a long time, predating Darwin and the theory of evolution by centuries.
Landscapes or manifolds allow us to visualise and think about dynamics of complex systems.
None of these conceptual frameworks by themselves provides an adequate representation through which we can understand the organisation, evolution, and changing nature of biological organisms. We are interested in the interplay and intersection of these organising principles and and how we can use them to make sense of living systems. In our work we are using networks, trees, and landscapes both as concepts and computational tools. The group has worked on both the mathematical foundations (e.g. of network sampling theory) and their biological uses (e.g. in the context of epigenetic landscapes in developmental biology).

Team
Adriana Zanca
Cell Fates, Cell States, and Cell MapsNetworks, Trees, and Landscapes
Léo Diaz
Algebraic Perspectives in BiologyNetworks, Trees, and Landscapes
Latest research
2019
Leander Dony; Fei He; Michael P.H. Stumpf
Parametric and non-parametric gradient matching for network inference: a comparison Journal Article
In: BMC Bioinformatics, vol. 20, no. 1, 2019, ISSN: 1471-2105.
Links | BibTeX | Tags: Networks | trees and cell structures
@article{Dony2019,
title = {Parametric and non-parametric gradient matching for network inference: a comparison},
author = {Leander Dony and Fei He and Michael P.H. Stumpf},
doi = {10.1186/s12859-018-2590-7},
issn = {1471-2105},
year = {2019},
date = {2019-12-00},
urldate = {2019-12-00},
journal = {BMC Bioinformatics},
volume = {20},
number = {1},
publisher = {Springer Science and Business Media LLC},
keywords = {Networks | trees and cell structures},
pubstate = {published},
tppubtype = {article}
}
2017
Thalia E. Chan; Michael P.H. Stumpf; Ann C. Babtie
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures Journal Article
In: Cell Systems, vol. 5, no. 3, pp. 251–267.e3, 2017, ISSN: 2405-4712.
Links | BibTeX | Tags: Networks | trees and cell structures
@article{Chan2017,
title = {Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures},
author = {Thalia E. Chan and Michael P.H. Stumpf and Ann C. Babtie},
doi = {10.1016/j.cels.2017.08.014},
issn = {2405-4712},
year = {2017},
date = {2017-09-00},
urldate = {2017-09-00},
journal = {Cell Systems},
volume = {5},
number = {3},
pages = {251--267.e3},
publisher = {Elsevier BV},
keywords = {Networks | trees and cell structures},
pubstate = {published},
tppubtype = {article}
}
2008
Michael P.H. Stumpf; Thomas Thorne; Eric de Silva; Ronald Stewart; Hyeong Jun An; Michael Lappe; Carsten Wiuf
Estimating the size of the human interactome Journal Article
In: Proc. Natl. Acad. Sci. U.S.A., vol. 105, no. 19, pp. 6959–6964, 2008, ISSN: 1091-6490.
Abstract | Links | BibTeX | Tags: Networks | trees and cell structures
@article{Stumpf2008,
title = {Estimating the size of the human interactome},
author = {Michael P.H. Stumpf and Thomas Thorne and Eric de Silva and Ronald Stewart and Hyeong Jun An and Michael Lappe and Carsten Wiuf},
doi = {10.1073/pnas.0708078105},
issn = {1091-6490},
year = {2008},
date = {2008-05-13},
urldate = {2008-05-13},
journal = {Proc. Natl. Acad. Sci. U.S.A.},
volume = {105},
number = {19},
pages = {6959--6964},
publisher = {Proceedings of the National Academy of Sciences},
abstract = {<jats:p>
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be ≈650,000. We find that the human interaction network is one order of magnitude bigger than the
<jats:italic>Drosophila melanogaster</jats:italic>
interactome and ≈3 times bigger than in
<jats:italic>Caenorhabditis elegans</jats:italic>
.
</jats:p>},
keywords = {Networks | trees and cell structures},
pubstate = {published},
tppubtype = {article}
}
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be ≈650,000. We find that the human interaction network is one order of magnitude bigger than the
<jats:italic>Drosophila melanogaster</jats:italic>
interactome and ≈3 times bigger than in
<jats:italic>Caenorhabditis elegans</jats:italic>
.
</jats:p>