SPARCED currently has two interlinked modules, where the first part simulates protein signaling cascades and the second part simulates mRNA levels using stochastic methods. Doing so enables SPARCED to realistically represent mRNA numbers (or bursts), which are slightly different for each simulation, and thus a single-cell like behavior. However, SPARCED currently does not contain one key aspect: cellular metabolism. Recently, a metabolic atlas of human cells has been released, making it now possible to merge protein signaling models with metabolic network information.
In this project, the postdoctoral fellow will have the opportunity to:
-Curate the human metabolic atlas information into SPARCED format and incorporate them to generate SPARCED-Metabolome (SPARCED-Me) model.
-Curate other databases to incorporate required crosstalk interactions to construct a complete model.
-Help collect multi-omics (transcriptomic, proteomic, and metabolomic) data from different cancer cell lines to parametrize the SPARCED-Me model in various contexts.
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 processes and who would like to grow in a supportive environment.
This postdoctoral scholarship is financed by the Kempe Foundation and administered by Umeå University.
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 metabolic network modeling
-Experience in metabolomic data analysis and research
-Experience in 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.