Oana Carja
Carnegie Mellon University, PA·Ecology & Evolution·Early-career
Cancer cells, microbial colonies and human populations all live in space. Each individual interacts with a particular set of neighbors rather than with everyone.
- Fields
Ecology & Evolution
Mathematics & Statistics
Computer Science
- Topics
evolutionary dynamics
population genetics
spatial population structure
- Grants
- 2 known grants, through Aug 2030
What they work on
- Model how heterogeneous spatial population structure shapes competition between beneficial mutations, the crossing of rugged fitness landscapes and genetic hitchhiking of neutral mutations, using new mathematical and computational frameworks.
- Develop metrics for how the topology of a population's structure amplifies or suppresses natural selection, and use them to build tools that infer evolutionary forces from genetic data.
- Create open-source algorithms and visualization tools to compare, compress and visualize spatial population structures for interpreting emerging spatial biology datasets.
- Link theoretical population-genetic models to high-resolution molecular and cellular imaging datasets to identify spatial features that amplify or suppress selection on new mutations.
- Investigate how cellular and molecular spatial topologies arise and stay stable, and how they shape evolutionary trajectories, using computer vision analysis of imaging data.
More about the research
Most theories of evolution ignore this spatial structure. These grants ask how the "shape" of a population, meaning its patterns of interaction and reproduction, speeds up or slows down evolution. They combine mathematical theory, computational tools and image data from real biological systems.
Why it matters
Understanding how spatial structure shapes evolution could help researchers interpret detailed spatial data on cancer progression, aging and microbial communities. Using microfluidics and organoid technologies, scientists could someday build population structures that boost selection for chosen mutations. Such structures could help find better protein complexes for medicine or industry, or screen for faster-replicating pathogen variants.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Benjamin Good
Stanford University, CA·Ecology & Evolution
Develop time-series methods that track linked mutations in human gut metagenomes sampled repeatedly over time. These will measure the strength and duration of natural selection on sweeping variants.
Andrew D Kern
University of Oregon, OR·Ecology & Evolution
Develop methods to infer dispersal, how far individuals move, directly from population genomic data, and assess how spatial structure affects GWAS, genome-wide studies linking variants to traits, with Anopheles gambiae data in mind.
Sergey Kryazhimskiy
University of California-San Diego, CA·Ecology & Evolution
Develop mathematical theory for how the biochemical architecture of cells, starting with complex metabolic networks, constrains the shape of fitness landscapes and how mutations move organisms across them.
The school
Carnegie Mellon University: 28 labs on PI Pages, $505M a year on research, 346 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.