A key missing capability in current cancer research is the ability to predict how a particular cancer cell will respond to a perturbation (e.g., drug inhibition or gene knockdown). However, building computational models of complex, large-scale, and incompletely understood systems remains extremely challenging. To address this issue, we recently developed the SPARCED pipeline to convert structured lists of species, parameters, and reaction types into an SBML (Systems Biology Markup Language) model file, and we created a model based on one of the largest pan-cancer signaling models in the literature.
SPARCED is compatible with high-performance and cloud computing, can simulate thousands to millions of single-cell trajectories, is easily expandable with new pathways, and is retrainable with new omics data for new cellular contexts. In this project, we will further develop the SPARCED pipeline for: (i) even larger-scale model construction, (ii) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies.
The postdoctoral fellow will have the opportunity to:
-Learn about computational modeling pipelines for human intracellular signaling pathways
-Develop our SPARCED pipeline further
-Construct mechanistic models to study in silico drug combination responses in cancer cells
-Construct mechanistic models to replicate 2D-3D tissue architectures
In our lab, we are committed to fostering a collaborative and inclusive environment that promotes reproducible and rigorous research practices. As a postdoctoral fellow in our team, you will benefit from a safe and respectful research environment, receive dedicated mentorship, and have the freedom to pursue innovative ideas. This scholarship is ideal for researchers eager to develop and use computational tools to study in silico cellular responses and who would like to grow in a supportive environment.
-A strong track record in a relevant field of research, documented by first authorships in peer-reviewed original publications
-Comprehensive skills in data analysis and bioinformatics
-Proficiency in programming with Python
-Proficiency in version control (Git, GitHub)
Meriting criteria are:
-Experience in mechanistic and ODE-based computational modeling
-Experience in SBML file handling
-Experience in Docker or other container-based platforms
-Basic understanding of wet-lab work and cell culture
We are looking for candidates with a strong interest in large-scale computational modeling and cancer research. Candidates should be team players, engaging in scientific discussions and exchanging ideas.