Predicting evolution
Summary
To treat bacterial infections, we use antibiotic treatments that prevent bacteria from replicating. Cancer is treated by targeting cancerous cells using agents that prevent their proliferation and kills them. Although these treatments may be very effective at the beginning, they can, unfortunately, lose their effectivity over time. This reveals something remarkable about living systems: through the process of evolution, cellular components that appeared to be essential for survival or proliferation can become dispensable.
To understand evolution we look at cells from the molecular perspective: Underlying the ability of cells to change their characteristics are molecular networks that either alone or together regulate cellular functions (traits). Typically, these networks are robust, allowing them to reorganize in response to perturbations (such as the deletion of components). Although the possibilities for evolution might seem limitless, there is evidence that adaptive pathways are guided by certain ‘constraints’ that emerge from the interactions between the different components in a network. We are interested in understanding how the architecture of these interaction networks determines what these constraints look like and how these interaction networks can change their structure to accommodate different sets of essential genes with the goal to predict and eventually control evolution.
Lab members
Ir. Leila Inigo de la Cruz
Collaborators
Prof dr Benoit Kornmann, Oxford University, UK
Prof dr. Sander Tans, AMOLF, Amsterdam
Dr. Katerina Stankova, TPM, TU Delft
Dr. Marianne Bauer, Bionanoscience, TU Delft
Dr Jasmijn Baaaijens, EEMCS, TU Delft
Dr. Anne-Florence Bitboll, EPFL, Switserland
Dr. Nikolina Sostaric, Bionanoscience, TU Delft
Papers
E Kingma, F Dolsma, L M Iñigo De La Cruz, L Laan (2023)
Saturated Transposon Analysis in Yeast as a One-step Method to Quantify the Fitness Effects of Gene Disruptions on a Genome-Wide Scale
BioRxiv, https://doi.org/10.1101/2023.09.08.556793
Enzo Kingma, Eveline T. Diepeveen, Leila M. Inigo de la Cruz and Liedewij Laan (2023)
Pleiotropy drives evolutionary repair of the responsiveness of polarized cell growth in Saccharomyces cerevisiae to environmental cues.
Frontiers in Microbiology Volume 14 - 2023 | doi: 10.3389/fmicb.2023.1076570
Meike Wortel, ….Liedewij Laan,…., Pleuni Pennings (2023)
Towards evolutionary predictions: Current promises and challenges.
Evolutionary applications 16 (1), 3-21
WKG Daalman, E Sweep, L Laan (2023)
A tractable physical model for the yeast polarity predicts epistasis and fitness.
Philosophical Transactions of the Royal Society B 378 (1877), 20220044
Werner Karl-Gustav Daalman, Els Sweep, Liedewij Laan (2020)
The Path towards Predicting Evolution as Illustrated in Yeast Cell Polarity.
Cells 2020, 9(12), 2534; https://doi.org/10.3390/cells9122534
Philippe Nghe*, Marjon de Vos*, Enzo Kingma*, Manjunatha Kogenaru, Frank Poelwijk, Liedewij Laan, Sander Tans (2020)
Predicting evolution using regulatory architecture.
Annu Rev Biophys , Feb 4. doi: 10.1146/annurev-biophys-070317-032939 (*equal contribution)