Jaramillo Lab

<), but instead of looking for a postdoctoral position where he could have further developed such new methodologies, he decided instead to look for new horizons in biology.

On the next working day after AJ’s PhD dissertation, he started to work on structural molecular biology with the co-development of the first automated computational protein design program based on first principles called Designer software. As a postdoc of Prof. Wodak (ULB) and later as a postdoc of Prof. Karplus (ULP & Harvard), AJ combined physicochemical principles (CHARMM force field with almost no extra parameters and no fine-tuning) with protein structure to design proteins that were tested experimentally by collaborators (Jaramillo et al., PNAS, 2002). He later extended the computational protein design methodology, publishing several papers in the best journals in the discipline such as Biophys J. and JCC.

AJ extended his work on protein design to other inverse problems in biology such as the gene-network design problem. He directed his group to develop the first methodology for automated genetic network design, implemented in the software Genetdes (Rodrigo et al. 2007, 2011), which was later extended in 2009 to the genomic scale by inferring the global transcriptional network for an organism using transcriptomic data (software InferGene). For this, they used a model consisting of more than 4,500 coupled ordinary differential equations (ODEs) using steady-state transcriptomic & signaling measurements (Carrera et al. 2009, 2009b, 2011b). However, this effort was limited by the lack of using more precise techniques such as single-cell time-lapse microscopy. They approached the problem of the de novo design of a bacterial genome for the first time by showing that the Escherichia coli genome could be refactored to a more simplified regulatory transcriptional regulatory structure (Carrera et al. 2012). To monitor the gene expression dynamics of his synthetic oscillators, they constructed a single-cell microfluidic platform using an automated microscope for live-cell imaging. Recently, they published a quantitative model of cell growth by fitting the model with custom plasmids expressing tunable gene reporters, a fluorometer and microfluidic measurements (Carrera et al. 2011a). Work on the automated image treatment of long time-lapse microfluidics measurements was presented at the SPIE Medical Imaging world conference (San Diego, 2012) (published at Fetita et al. 2012). AJ also guided his group to develop the first automated computational design program of RNA networks with targeted allosteric interactions. This project implemented AJ’s former inverse-folding concepts together with evolving biochemical reactions to RNA interactions, where they have already designed and experimentally validated the synthetic riboregulators (Rodrigo et al. 2012).

AJ’s group has created several software for Synthetic Biology: Designer2 (2006), Genetdes (2007), Protdes (2008), Asmparts (2007), Infergene (2009), Desharky (2008), RNAdes (2011), AutoBioCAD (2012) and Ribomaker (2012). All of them consist on fully automated methodologies for computational design of biological systems. AJ made a first major move of discipline (although his work as engineer in structural analysis was already a big move from particle physics) just after receiving his PhD: From theoretical physics and mechanical engineering to biology (1999). He made a second move 9 years later: Then, he started his own microbiology wet lab from scratch (the current Synth-Bio group, or Jaramillo Lab). AJ stills moves across disciplines by developing and integrating in his research techniques from disciplines such as microfluidics, millifluidics, chemical engineering, image analysis or artificial intelligence. In fact, it is quite limiting to think about disciplines in our days.

Our group is affiliated to:

Societe de Biologie Cellulaire de France

NanoSciences Ile-de-France (section NanoBioSciences)

Reseau National des Systems Complexes

CNRS-MPG European Network in Systems Biology

MATEs BBSRC Synthetic Biology Network

SynBioNT BBSRC Synthetic Biology Network for Modelling and Programming Cell-Chell Interactions.

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