Pablo Carbonell

Pablo Carbonell is Reader in Computational Biology at the ai2 Institute, Universitat Politècnica de València. My research interests are in automated design for metabolic engineering and synthetic biology. We develop machine learning and automation Design-Build-Test-Learn (DBTL) engineering solutions for sustainable bioeconomy-ready biofoundries.
Some of my other interests are hiking trails, food, eclectic music, fiction, philosophy, and math podcasts.

Joan de la Concepcion

My project is about modeling the Design-Build-Test-Learn (DBTL) process of a biofoundry. The project will focus on the development of tools for simulation, and prediction based on the experimental protocols usually found in the Design-Build-Test-Learn (DBTL) cycle of biofoundries. We will use machine learning to predict new iterations of the DBTL based on experimental data.

Vicente Pina

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.