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?

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?

✍️ @julipereto.bsky.social i @pablocarb.bsky.social (@i2sysbio.es @uv.es @csic.es)

‼️New PhD position at the I2SYSBIO within the MSCA-COFUND SYNBIO-CSIC doctoral network for advanced training in Synthetic and Engineering Biology.

🗓️Apply by February 26, 2026.

www.i2sysbio.es/position/15/

@pablocarb.bsky.social @dicv.csic.es @uv.es @csic.es

BiosInt: Biosensor-based smart design of pathway dynamic regulation for industrial biomanufacturing www.biorxiv.org/content/10.1101/2025.11....

I am happy to announce the launch of our seminar series “Microbial Biotechnology: Developing the Conceptual Framework of the DBTL Cycle.” We invite you to join us and engage in shaping the future of biomanufacturing! researchseminars.org/seminar/Micr...

Yesterday, my book on biological agency, written with philosopher Álvaro Moreno, was published. Coincidentally, today an article appears in collaboration with my colleague @pablocarb.bsky.social @i2sysbio.es in which we explore a solution to a classic problem in the study of the origin of life.

Thrilled to see our published work featured in the news! AI may help bridge the gap between life’s origin and the last universal common ancestor, with @julipereto.bsky.social www.csic.es/es/actualida... @i2sysbio.es #AI #LUCA

Excited to see our perspective published: How #AI may help bridge the gap between life’s origin and the last universal common ancestor #LUCA, with @julipereto.bsky.social @royalsociety.org @i2sysbio.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...

Among the selected projects for Major European Research Infrastructures 🇪🇺🧪 awarded by CSIC is the Spanish node of Innovation in Industrial Biotechnology and Synthetic Biology #IBISBA, led by Irene Otero from #I2SysBio (@csic.es–@uv.es). Congratulations! 🏆👏
More info: 🌐 i2sysbio.es/communicatio...

Alguien se ha planteado asignar el CSIC al ministerio de defensa? Cumpliríamos con creces los acuerdos de la OTAN

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.

Just published: our new work on biology-guided AI approaches for context-aware biosensor dynamics modeling. Check it out! @i2sysbio #AI #SciML #biosensor pubs.acs.org/doi/10.1021/...

Looking forward to a great day filled with insightful discussions on #AI in #Biology!

Thrilled to take part in this event and share ideas on #AI in #biology. Thank you @angelgm.bsky.social for organising a great workshop.

Thrilled to take part in this event and share ideas on #AI in #biology. Thank you @angelgm.bsky.social for organising a great workshop.

Looking forward to this timely workshop on #AI in #Biology at @cnb-csic.bsky.social (Feb 14)! Excited to hear from experts like @hsalis.bsky.social , @alfonsovalencia.bsky.social , @noeliaferruz.bsky.social , @pablocarb.bsky.social , and others. (Note: Won’t be streamed.) 🧬🤖 #Science #synbio

Highly multiplexed design of an allosteric transcription factor to sense new ligands
doi.org/10.1038/s414...