Alfonso Jaramillo

Alfonso Jaramillo  received a Ph.D. in theoretical physics from the University of Valencia (1999) and a Habilitation in biology from the U. Paris Sud XI (2007). He conducted postdoctoral research in computational biology at the Universite Libre de Bruxelles (1999-2002), Universite Louis Pasteur in Strasbourg (2002) and Harvard University (2003). In September 2003 he joined the biochemistry faculty of the Ecole Polytechnique as assistant professor, where he got tenured in 2005. He got the qualification for French full Professorship in Biophysics and in Molecular Biology sections by the French ministry in 2007. He is now a group leader at the Institute of Systems and Synthetic Biology (Genopole-UEVE-CNRS), where he holds a CNRS tenured senior researcher position since 2009.


"We have been working for many years on the computational design of proteins, using molecular modeling techniques. Afterwards we extended our computational optimization methods to other biological systems involving metabolic networks, transcriptional networks, or RNA networks. In particular we devoted more focus to the transcriptional and RNA networks, which are the most important regulatory systems in the cell. In 2009, we started our experimental microbiology lab with the aim of validating experimentally our computational designs without relaying on external collaborations. This allowed us to test our synthetic regulatory systems and we started moving towards the aim of developing novel information processing devices in living cells.

Until now, the engineering of the regulation of gene expression has been more of a crafting manufacturing than a systematic endeavor. This is due to the complexity and unreliability of biological systems in living cells. One obvious approach against complexity is to use computational power, but it is unknown weather our current biological knowledge is sufficient to engineer anything using automated design techniques. To try to get some plausible positive answers to this question, we notice that: i) most engineering attempts rely on the use of design principles that could already be incorporated into an optimization algorithm, ii) many behaviors of interest are robust, iii) researchers do actually succeed sometimes to engineer the targeted behavior in living cells despite after a few tries. In addition, we could better spot misleading quantifications when we try to incorporate the feedback from experiment into the algorithm. For instance, growth rate has a devastating effect in the quantification of long-lived proteins (such as the fluorescent proteins) and this is most often overlooked.

The engineering of synthetic gene sequences by rational design usually require several years to obtain an functional sequence able to work as expected in a living cell. In fact, the complexity of the combinatorial interactions difficult the design, as it happens in RNA systems where a single nucleotide mutation may lead to a completely unrelated structure. Another example is in the design of genomes, where the many gene interactions make unpractical the design of a whole genome sequence. They exemplify a major roadblock that faces Synthetic Biology in the years to come: our capability to construct exceeds our ability to design. Even the introduction of high-throughput characterization techniques to screen large libraries cannot solve such a problem, and we require improving dramatically our design strategy. Here it is where the computational approaches to automate the invention will enter. Such approaches have already been used in the last decades to develop software able to program computers or even to provide novel designs of patents (in the computer science discipline of “genetic programming”). Our research is focused on the development of higher-order information processing systems by the use of automated approaches. We don’t want to automate just the design step, but also the construction and characterization phases. To automate the design we use software that implements an optimization algorithm. To automate the construction we are developing methods to synthesize large fragments of DNA, for instance using directed evolution. To automate the characterization we use microfluidics devices and automated image treatment."



G. Rodrigo, B. Kirov, S. Shen and A. Jaramillo. Theoretical and experimental analysis of the forced LacI-AraC oscillator with a minimal gene regulatory model. Chaos (2013) in press. 

M. Suárez-Diez, A. M. Pujol, M. Matzapetakis, A. Jaramillo, O. Iranzo. Computational protein design with electrostatic focusing and experimental characterization of a conditionally folded helical protein domain with a reduced amino acid alphabet. Biotechnology J. (2013) in press.

G. Rodrigo and A. Jaramillo. AutoBioCAD: Full bio-design automation of genetic circuits. ACS Synth Biol (2012) in press. Doi:
G. Rodrigo, T.E. Landrain and A. Jaramillo. De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells. Proc. Natl. Acad. Sci. (2012) in press. Doi: 10.1073/pnas.1203831109

Phys.Org : Feature Stories (2012) Cell & Microbiology. From vitro to vivo: Fully automated design of synthetic RNA circuits in living cells

CNRS-INSB press: Vers une automatisation de la biologie de synthèse
Biofutur: La conception du vivant s’automatise (PDF)

J. Carrera, S.F. Elena and A. Jaramillo. Computational design of genomic transcriptional networks with adaptation to varying environments. Proc. Natl. Acad. Sci. (2012) in press
. Doi: 10.1073/pnas.1200030109

: La conception du vivant s’automatise 
J. Carrera, A. Fernandez del Carmen, R. Fernandez-Muñoz, J.L. Rambla, C. Pons, A. Jaramillo, S.F. Elena, and T. Granell. Fine-tuning tomato agronomic properties by computational genome redesign. Plos. Comp. Biol. (2012). 8(6): e1002528. in press. Doi: 10.1371/journal.pcbi.1002528

G. Rodrigo, J. Carrera and A. Jaramillo, Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors. Nucl. Acids Res. (2011). 39(20): e138
. doi: 10.1093/nar/gkr616
G. Rodrigo, A. Jaramillo, M.A. Blazquez. Integral control of plant gravitropism through the interplay of hormonal signaling and gene regulation. Biophys J. (2011) Aug 17; 101(4):757-63
. doi: 10.1016/j.bpj.2011.06.047

J. Carrera, G. Rodrigo, V. Singh, B. Kirov, A. Jaramillo. Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate. Biotechnol. J. (2011) Jul;6(7):773-83
. doi: 10.1002/biot.201100084.

D.J. Glykys, G.R. Szilvay, P. Tortosa, M. Suarez, A. Jaramillo and S. Banta. Pushing the Limits of Automatic Computational Protein Design: Design, Expression, and Characterization of a Large Synthetic Protein based on a Fungal Laccase Scaffold. Syst Synth Biol. (2011), pp. 1-14
. DOI: 10.1007/s11693-011-9080-9

G. Rodrigo, J. Carrera, A. Jaramillo, and S.F. Elena. Optimal viral strategies for bypassing RNA silencing. J. R. Soc. Interface. (2010)
. DOI: 10.1098/rsif.2010.0264

G. Rodrigo, J. Carrera, S.F. Elena and A. Jaramillo. Robust Dynamical Pattern Formation from a Multifunctional Minimal Genetic Circuit. BMC Systems Biology (2010)
. DOI:10.1186/1752-0509-4-48

M. Suarez, P. Tortosa, M.M. Garcia-Mira, D. Rodríguez-Larrea, R. Godoy-Ruiz, B. Ibarra-Molero, J.M. Sanchez-Ruiz, A. Jaramillo. Using multi-objective computational design to extend protein promiscuity. Biophys Chem. 2010 Mar;147(1-2):13-9
. DOI:10.1016/j.bpc.2009.12.003

J. Carrera, G. Rodrigo, A. Jaramillo, and S.F. Elena. Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biol. 2009;10(9):R96
. DOI:10.1186/gb-2009-10-9-r96

M. Suarez, P. Tortosa and A. Jaramillo. PROTDES: CHARMM toolbox for computational protein design. Syst Synth Biol. 2009
. DOI : 10.1007/s11693-009-9026-7.


CR1 CNRS (Head of Synth-Bio team)

Phone: +33(0)1 69 47 44 44