DBDL team

Pablo Carbonell

DBDL scientific director

Pablo Carbonell leads the Automated Biofoundries and Smart Biomanufacturing research line at the ai2 Institute, Universitat Politècnica de València, and is a visiting scientist at I2SysBio (CSIC-UV). His research interests focus on automated design for metabolic engineering and synthetic biology. He develops machine learning and automated Design-Build-Test-Learn (DBTL) engineering solutions for sustainable biofoundries. Some of his other interests are hiking trails, food, symbolist music, idealism philosophy, and math podcasts.

ORCID

Jonathan Tellechea-Luzardo

Marie Skłodowska-Curie Fellow postdoctoral fellow researcher

Jonathan is a Marie Skłodowska-Curie Fellow researcher with a PhD in Synthetic Biology from Newcastle University. He is currently working on BioCircus, which aims to create autonomously controlled microbial strains for bioproduction, using naringenin as a proof-of-principle production molecule. Jonathan is focused on constructing biosensor and dynamic regulation circuits that measure and control the production of the target molecule, to improve the efficiency and scalability of the strain engineering process.

Hèctor Martín

PhD candidate SmartBioFab

Hèctor is studying a master in automation and industrial computing at Universitat Politècnica de València. (UPV). His PhD Thesis aims to expand the design space of biomanufacturing processes to encompass virtualization, machine learning-based exploration, and automated workflow development. The Thesis explores approaches aimed at replacing current chemical industrial processes through the rational and sustainable use of biodiversity resources by means of automated processes using robotic lab platforms and rational design.

Christian Fernández

PhD candidate

After finishing his undergraduate degree in Mathematics and his master’s degree in industrial mathematics, Christian is currently working on his PhD project in Mathematical Methods and Numerical Simulation. His research consists of developing, by means of control theory methods, nonlinear analysis and PIDE models, analysis and control strategies for biomolecular networks and biocircuits in presence of high molecular noise, understanding the emergent properties in presence of noise, and controlling cell populations in different scenarios of interest in systems and synthetic biology.

Guillermo Climent

Master thesis Data science UV

Guillermo holds a bachelor’s degree in biotechnology from the Universitat Politècnica de València (UPV) and a master’s degree in bioinformatics from the Universitat de València (UV). He has multiple years of working experience in the private sector as a bioinformatician and data scientist. Currently, Guillermo is completing his master’s thesis to obtain a master’s degree in data science (UV) by bridging his interest in Astrobiology with his data analytics and machine learning skills. Concisely, his current research is focused on exploring metabolic capabilities that can be useful for setting up a biofoundry on Mars from a data-driven perspective.

Lorena Martínez España

Research assistant BioEcoDBTL

After graduating as a biotechnologist in 2021, specialising in Systems Biology, Protein Technology and Bioinformatics, Lorena started a master’s degree in bioinformatics at the University of Valencia, specialising in mathematical modeling in model organisms. She is currently working in the BIOECODBTL project in Dynamics BioDesign Lab. Her tasks are based on the use of bioinformatics tools and databases to study the metabolic space, with the aim of obtaining more information about molecules with industrial interest, aiming at the discovery of new molecules that can be useful in fields such as Pharmacy, Cosmetics or Agrifood.

Ricardo Marín

Research associate BioEcoDBTL

Ricardo Marín has a degree in industrial and automatic electronic engineering and in business administration and management, as well as a master’s degree in biomedical engineering from Universitat Politècnica de València (UPV). He has carried out academic research work related to computational modeling of biological processes associated with areas such as electrocardiography and neurodegenerative diseases. He has recently joined the Automated Biofoundries and Smart Biomanufacturing research line at AI2  Institute, Universitat Politècnica de València, to contribute to the development of machine learning and automated Design-Build-Test-Learn (DBTL) engineering solutions for sustainable biofoundries. His main professional and research interest is the application of mathematical and computational formalisms to the understanding and improvement of biochemistry, synthetic biology, and metabolic processes. Additional interests include reading fiction and non-fiction, popular science podcasts, sports, music, philosophy, films, and video game programming and design.

Martin Stiebritz

María Zambrano postdoctoral fellow

Martin is a 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

AI2 industrial internship

Raúl is completing his master’s thesis consisting of developing a tool for translating lab protocols in a biofoundry into standardised instructions for robotic platforms. The objective of his project is to develop and apply optimization strategies and machine learning techniques in the automated biomanufacturing environment. The master’s thesis 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.

María Camarena

AI2 industrial internship

María is currently studying at University of Amsterdam. The first project in which she was involved was focused on improving the enzyme selection tool Selenzyme. Their main objective was to create a data extension (related to taxonomic distances) of what Selenzyme already returns. The second project included plasmid assembly of an existing library and its subsequent data processing with machine learning algorithms in order to find new plasmid sequences that combine different RBS, promoter, plasmid, and origin of replication.