Establishment of proteome-to-phenome relationships by network analysis
Explore proteomic data for biological interpretation using systems biology-based approaches to establish proteome-to-phenome relationships, and develop tools to integrate proteomics with other diverse data sets for clinical applications.
Mathematical network-based modeling currently in place in ANAXOMICS Biotech S.L. will be optimized and new analytical features added. The modeling is based on a human protein functional network assembled from individual literature-supported relationships. Stochastic optimization algorithms (stochastic hill climbing, genetic algorithms, simulated annealing) are used to identify plausible protein interactions networks learning from sets of diffuse evidence and prior knowledge. The algorithms construct and analyse the regularities of the sampling of different plausible solutions (non-canonical pathways). This information is used to construct feature vectors descriptive of the most probable protein network interaction structure and network activation signal flow derived from the space of plausible protein interaction solutions. The feature vectors are further used as input to machine learning supervised methods such as for ensembles of classifiers that allows us to infer new clinical and protein level knowledge.
The candidate should hold a Master`s degree or equivalent and a strong background in bioinformatics, bioengineering, computer science, mathematics, physics or similar with a strong interest in molecular biology
Experience programming in Matlab
Basic biostatistics knowledge
Hands on experience in machine learning
Fluent spoken and written English skills
In addition, the following experience would be helpful, but not essential:
Programming in Python, R
Experience in systems biology or systems medicine
Understand and interpret protein data sets for their biological and functional significance. Identify uncharacterized or missing network proteins based on functional predictions after analysing proteomics sets. Optimized and automatized network analysis algorithms and new graphical visualization tools to be incorporated in the ANAXOMICS Biotech S.L. analysis pipeline.
Host: FGCZ (R. Schlapbach), Duration: 1 Month; When: Year 2; Goal: Learn proteomics procedures and raw data analysis.
Host: TAU (J. Hamari), Duration: 1 Month; When: Year 3; Goal: Dissemination of functional proteomics by gamification.
1. Artigas, L., Coma, M., Matos-Filipe, P., Aguirre-Plans, J., Farrés, J., Valls, R., Fernandez-Fuentes, N., de la Haba-Rodriguez, J., Olvera, A., Barbera, J., Morales, R., Oliva, B., & Mas, J. M. (2020). In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm. PLOS ONE, 15(10), e0240149. https://doi.org/10.1371/journal.pone.0240149
2. Moncunill, G., Scholzen, A., Mpina, M., Nhabomba, A., Hounkpatin, A. B., Osaba, L., Valls, R., Campo, J. J., Sanz, H., Jairoce, C., Williams, N. A., Pasini, E. M., Arteta, D., Maynou, J., Palacios, L., Duran-Frigola, M., Aponte, J. J., Kocken, C. H. M., Agnandji, S. T., … Dobaño, C. (2020). Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization. Science Translational Medicine, 12(543), eaay8924. https://doi.org/10.1126/scitranslmed.aay8924
3. Jorba, G., Aguirre-Plans, J., Junet, V., Segú-Vergés, C., Ruiz, J. L., Pujol, A., Fernández-Fuentes, N., Mas, J. M., & Oliva, B. (2020). In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan. PLOS ONE, 15(2), e0228926. https://doi.org/10.1371/journal.pone.0228926