From OpenWetWare
Jump to: navigation, search

Home        Contact        Lab Members        Publications        Research        Internal        Protocols        Data       

  • Modularity and predictability of biological parts and devices.

A key concept in synthetic biology is the composition of predictable systems from a set of reusable, well characterized parts. This paradigm has been successful in all the fields of engineering and, similarly, it could enable the design of customized biological systems without following trial-and-error approaches. However, predictable design approaches are needed to accomplish this task, in which parts can be individually characterized and assembled in a complex system that will exhibit predictable function. Towards this goal, we are now investigating the predictability boundaries of biological components to disclose the modularity limits of several parts and devices when tested in different conditions (e.g. chassis, copy number, media) and assembled in increasingly complex circuits in prokaryotes. Synthetic circuits are composed by transcriptional and post-transcriptional regulators: activator/repressor proteins, small RNAs, CRISPRi gRNAs.

  • Biofuel production

The foundational research studies on biological parts are exploited to optimize a recombinant metabolic pathway, including a pyruvate decarboxylase and an alcohol dehydrogenase, for ethanol production from lactose fermentation in E. coli. Lactose is an abundant sugar in dairy industry waste (cheese whey and whey permeate) that can be considered as a free raw material for biofuel production. The expression of recombinant genes is optimized, in terms of product yield and phenotype stability, via orthogonal well-characterized regulatory parts and codon optimization. The engineered microbes are a starting point for the development of a cost-effective industrial valorization process of whey and also for the production of other fuels with synthetic biology.

  • Metabolic engineering of B. subtilis for biopolymer production

Genome-scale metabolic models and predictable design of complex pathway in synthetic biology are adopted in Bacillus subtilis to enhance poly-gamma-glutamic acid (gamma-PGA), a polymer with a number of applications, e.g., bioremediation, cosmetics and medicine. Following these approaches, competing pathways are deleted and the biosynthetic pathway is optimized to enable the conversion of glycerol, a biodiesel industry-derived waste, into biopolymer at high yield.

  • Quorum sensing re-engineering

Quorum sensing elements are used to engineer a genetic circuit that implements a closed-loop control system. Mathematical models are used to predict the static and dynamic behaviour of the designed circuit and its variants. The final system must be able to mimic in a predictable fashion the key features of a traditional closed-loop regulated engineering device, e.g. steady state regulation, disturbance rejection and robustness. We intend to carry out these goals by following a rigorous bottom-up procedure where sub-circuits are quantitatively characterized to identify the main model parameters, the whole system behaviour is tested in silico and finally it is experimentally validated to compare real vs simulated results.

  • Genetic and computational tools for synthetic biology.

Physical and measurement standards are some of the key concepts introduced by engineers to facilitate the rational design and construction of biological systems in synthetic biology. We aim to develop 1) user-friendly genetic tools to solve recurrent problems in the generation of engineered strains, e.g., BioBrick integrative vectors (pBBintPhi and pBBknock) that can be specialized to target the desired genomic locus, or the construction of orthogonal low-burden inducible systems for gene expression that respond to different stimuli; 2) detailed datasheets of industrially-attractive biological parts/devices, e.g. a synthetic bacterial self-destruction device for recombinant protein release; 3) mathematical studies that support the design of complex systems in a predictable way, e.g. models that are able to predict the effects of copy number variations of promoters and transcription factors.