A New Improved and Extended Version of the Multicell Bacterial Simulator gro.

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

Laser heating tunability by off-resonant irradiation of gold nanoparticles.

Temperature changes in the vicinity of a single absorptive nanostructure caused by local heating have strong implications in technologies such as integrated electronics or biomedicine. Herein, the temperature changes in the vicinity of a single optically trapped spherical Au nanoparticle encapsulated in a thermo-responsive poly(N-isopropylacrylamide) shell (Au@pNIPAM) are studied in detail. Individual beads are trapped in a counter-propagating optical tweezers setup at various laser powers, which allows the overall particle size to be tuned through the phase transition of the thermo-responsive shell. The experimentally obtained sizes measured at different irradiation powers are compared with average size values obtained by dynamic light scattering (DLS) from an ensemble of beads at different temperatures. The size range and the tendency to shrink upon increasing the laser power in the optical trap or by increasing the temperature for DLS agree with reasonable accuracy for both approaches. Discrepancies are evaluated by means of simple models accounting for variations in the thermal conductivity of the polymer, the viscosity of the aqueous solution and the absorption cross section of the coated Au nanoparticle. These results show that these parameters must be taken into account when considering local laser heating experiments in aqueous solution at the nanoscale. Analysis of the stability of the Au@pNIPAM particles in the trap is also theoretically carried out for different particle sizes.

Synthetic approaches to study transcriptional networks and noise in mammalian systems.

Synthetic biology aims to build new functional organisms and to rationally re-design existing ones by applying the engineering principle of modularity. Apart from building new life forms to perform technical applications, the approach of synthetic biology is useful to dissect complex biological phenomena into simple and easy to understand synthetic modules. Synthetic gene networks have been successfully implemented in prokaryotes and lower eukaryotes, with recent approaches moving ahead towards the mammalian environment. However, synthetic circuits in higher eukaryotes present a more challenging scenario, since its reliability is compromised because of the strong stochastic nature of transcription. Here, the authors review recent approaches that take advantage of the noisy response of synthetic regulatory circuits to learn key features of the complex machinery that orchestrates transcription in higher eukaryotes. Understanding the causes and consequences of biological noise will allow us to design more reliable mammalian synthetic circuits with revolutionary medical applications.