Les raons de per què encara costa unir les peces de l'origen de la vida

Quan pensem en l’origen de la vida, hi ha un gran salt entre les primeres molècules i el famòs LUCA o primer ancestre universal. Però, què passa enmig de tot això i com ho unim?

theconversation.com

i2sysbio - Position

i2sysbio.es

researchseminars.org - View series

Welcome to researchseminars.org, a list of research seminars and conferences!

researchseminars.org

Un método basado en inteligencia artificial busca llenar el vacío entre el origen de la vida y el ancestro común universal | Consejo Superior de Investigaciones Científicas

Juli Peretó y Pablo Carbonell, investigadores del Instituto de Biología Integrativa de Sistemas (I2SysBio), centro mixto de la Universitat de València (UV) y del Consejo Superior de Investigaciones Ci...

csic.es

An I2SysBio project promotes the CSIC's presence in major European R&D platforms

<p><strong>The Spanish National Research Council (CSIC) has awarded grants for Major European Research Infrastructures for 2025, which seek to position the organisation in European and international c...

i2sysbio.es

Context-Aware Biosensor Design Through Biology-Guided Machine Learning and Dynamical Modeling

Addressing the challenge of achieving a global circular bioeconomy requires efficient and robust bio-based processes operating at different scales. These processes should also be competitive replacements for the production of chemicals currently obtained from fossil resources, as well as for the production of new-to-nature compounds. To that end, genetic circuits can be used to control cellular behavior and are instrumental in developing efficient cell factories. Whole-cell biosensors harbor circuits that can be based on allosteric transcription factors (TFs) to detect and elicit a response depending on the target molecule concentrations. By modifying regulatory elements and testing various genetic components, the responsive behavior of genetic biosensors can be finely tuned and engineered. While previous models have described and characterized the behavior of naringenin biosensors, additional data and resources are required to predict their dynamic response and performance in different contexts, such as under various gene expression regulatory elements, media, carbon sources, or media supplements. Tuning these conditions is pivotal in optimizing biosensor design for applications operating in varying conditions, such as fermentation processes. In this study, we assembled a library of FdeR biosensors, characterized their performance under different conditions, and developed a mechanistic model to describe their dynamic behavior under reference conditions, which guided a machine learning-based predictive model that accounts for context-dependent dynamic parameters. Such a Design-Build-Test-Learn (DBTL) pipeline allowed us to determine optimal condition combinations for the desired biosensor specifications, both for automated screening and dynamic regulation. The findings of this work contribute to a deeper understanding of whole-cell biosensors and their potential for precise measurement, screening, and dynamic regulation of engineered production pathways for valuable molecules.

pubs.acs.org