Post-doctoral positions on the Origin of Life: RNA biochemistry, molecular biology, bioinformatics, biophysics
We are seeking postdoctoral scholars to conduct innovative and multi-disciplinary research on the origin of life. The project is funded by the Human Frontier Science Project and involves labs with complementary expertise, located in four countries (USA, Germany, India and France). Funding is available for up to 3 years pending satisfactory progress. Sufficient travel funds are also available to facilitate international collaboration and training. Starting date as soon as possible given the current international situation.
How probable is the emergence of self-reproduction in the prebiotic world? Recent work has demonstrated that RNA catalysts (ribozymes) can build copies of themselves by the recombination of smaller RNA fragments. A well-studied system has been engineered from a specific ribozyme (a group I intron). Traditional low-throughput engineering approaches have prevented a thorough investigation of the probability of this type of reproduction contributing to life’s origins. This project aims to develop high-throughput engineering approaches to fill this knowledge gap.
A large diversity of RNA sequences will be designed and tested for their ability to build copies of themselves or to build networks of cooperative reproduction. The approach will use 1) bioinformatics and biophysics to mine naturally occurring ribozyme diversity and predict RNA sequence candidates; 2) High-throughput laboratory approaches (SELEX, droplet microfluidics, Next Generation sequencing) to study these candidates; 3) Advanced data analysis (bioinformatics, machine learning, statistical physics) to understand the experimental results in the context of evolutionary and structural constraints; and 4) biochemical characterization (kinetics, secondary structure probing, crystallization) of exciting candidates. Postdocs will be expected work on one or several of these aspects.
The research team includes:
- Biochemistry Lab at ESPCI Paris (Ecole Supérieure de Chimie et Physique Industrielles), France, microfluidics, molecular biology, biophysics, www.nghe.net
- Ramesh lab at NCBS (National Center for Biological Sciences), Bangalore, India, RNA biochemistry, RNA biophysics, https://aratirameshlab.weebly.com
- Structure of Evolution group at the Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany; molecular evolutionary theory, biomathematics, ecology; www.smerlak.group
- Molecular Biology Lab at Boise State University, Boise, USA; in vitro selection, high-throughput sequencing, https://haydenlab.gitlab.io/web/
We are looking for candidates highly motivated by fundamental science, interdisciplinarity and cutting edge approaches in RNA biochemistry, molecular biology, bioinformatics, or biophysics. Time can be shared between the teams. Applicants must provide a motivation letter specific to the project, two contacts for recommendations (or recommendation letters), and a CV with several publications including a first author one and a high-impact one.
If you are interested in working in a team, or between teams, you should send your application to the following address, specifying in the subject the main team to which you apply:
Selection of publications from the teams
Arsène, S., Ameta, S., Lehman, N., Griffiths, A. D., & Nghe, P. (2018). Coupled catabolism and anabolism in autocatalytic RNA sets. Nucleic acids research, 46(18), 9660-9666.
Matsumura, S., Kun, Á., Ryckelynck, M., Coldren, F., Szilágyi, A., Jossinet, F., Nghe P., Szathmary E, Griffiths, A. D. (2016). Transient compartmentalization of RNA replicators prevents extinction due to parasites. Science, 354(6317), 1293-1296.
Bendixsen, D.P., Collet, J., Østman, B., and Hayden, E.J. (2019). Genotype network intersections promote evolutionary innovation. PLoS Biol. 17, e3000300.
Smerlak M. 2020. Localization of neutral evolution: selection for mutational robustness and the maximal entropy random walk, bioRxiv: 10.1101/2020.01.28.922831
Smerlak M. & Youssef A. 2017. Limiting fitness distributions in evolutionary dynamics. J. Theor. Biol. 416: 68–80