Pablo Carbonell 

Pablo Carbonell leads the Automated Biofoundries and Smart Biomanufacturing research line at the ai2 Institute, Universitat Politècnica de València. Research interests are in automated design for metabolic engineering and synthetic biology. We develop machine learning and automated Design-Build-Test-Learn (DBTL) engineering solutions for sustainable biofoundries.
Some of my other interests are hiking trails, food, modern past music, fiction, philosophy, and math podcasts.

Jonathan Tellechea-Luzardo

Marie Skłodowska-Curie Fellow researcher, former Margarita Salas researcher starting at AI2 Institute, Universitat Politècnica de València. Master in Systems and Synthetic Biology from Univ. Paris-Saclay and PhD in Synthetic Biology from Newcastle University.

Martin Stiebritz

María Zambrano Fellow distinguished researcher at the AI2 Institute, Universitat Politècnica de València. Former researcher at University of California, Irvine. PhD in Computational Biology and Bioinformatics from Univ. of Erlangen, Germany and former lecturer/senior researcher at ETH Zurich.

Raúl Moreno López

My project is about developing a tool for translating lab protocols in a biofoundry into standardised instructions for robotic platforms. The objective of my project is the development and application of optimization strategies and machine learning techniques in the automated biomanufacturing environment. The TFM will focus on the development of machine learning tools based on experimental protocols of the learning phase of the Design-Build-Test-Learn (DBTL) cycle of biomanufacturing centers or biofoundries.

Hèctor Martin

The objective of my project is to develop a computational predictive tool that will help identify biomarkers of different cancer types (e.g.: colon, liver, pancreas) from human samples (e.g., blood plasma) analyzed by omics methods such as metabolomics. This project will help to develop a computational framework to interpret large omics datasets (big data) from cancer patients. With a final goal to identify new metabolic biomarkers that could help in better prevention and diagnosis of cancers.

María Camarena

My name is María Camarena. I am currently studying third year of Biomedical Engineering and working at the ai2 Institute, carrying out two different projects which are explained below:

The first project in which I am involved is the code development using Python in order to improve the selection enzyme tool named Selenzyme. Our main objective was to create a data extension (related with taxonomic distances) of what already the Selenzyme returns. Pablo and I have been working on it for a few months and it is nearly done.

The second main project can be considered as two different but related projects. These include practical plasmid assembly to terminate an existing plasmid library and its subsequent data processing with machine learning algorithms in order to find new plasmid sequences that combine different RBS, promoter, Per and Ori. The main objective is to find new plasmid sequences (that are not already assembled) which are supposed to translate more target protein.

Finally, my other interests includes swimming in a competition club; training at the gym; writing about feelings; learning about  metabolism, molecular interactions and the correct ways to feed the body.