Synthetic Biology is an emerging area of Bioengineering that aims at constructing novel biological functions to carry out specific user-defined tasks. The design rules are inspired by the engineering world: as an electronic circuit is constructed by connecting resistors, capacitors or diodes, a genetic program can be assembled by ligating DNA sequences, like genes, promoters, transcriptional terminators, ribosome binding sites or other regulatory elements.The ideal Synthetic Biology paradigm can be summarized as follows:
choose biological parts from a library of well-characterized standard DNA components;
assemble them together to obtain a genetic program that encodes the desired function;
incorporate it in a living (artificial or natural) organism to complete the job.
This process can be strongly supported by mathematical models able to guide the designer in the choice of the suitable components.
Synthetic Biology could potentially yield applications of remarkable importance, like bioremediation or production of renewable fuels, new biomaterials and therapeutic molecules. However, its success depends on the definition of the working boundaries in which biological functions can be predictable, in order to enable the rational design of customized systems.
As it happened in all the areas of engineering, physical standardization was introduced to facilitate the assembly of components by defining the concept of BioBrick, i.e. DNA parts with specific sequence and function that can be assembled through an easily reproducible process thanks to their common physical interface (http://parts.igem.org/Assembly:Standard_assembly). The MIT Registry of Standard Biological Parts has been the first repository for standard components and it currently includes a wide number of BioBrick parts. However, in order to consider it as a real library of standard elements, the quantitative characterization of components is required. Although standard measurement approaches have also been recently proposed to quantify the characteristics of biological parts, the bottom-up construction of predictable biological systems is currently a major challenge. Incompatibility among components, context-dependent behavior of parts, intrinsic noise of biological processes and nonlinearities in gene expression are some of the limiting factors that contribute to the unpredictability of bottom-up-composed systems.
Our activities mainly include:
modularity of biological parts and devices:we aim to investigate the predictability boundaries of biological parts that are quantitatively characterized and re-used in different contexts; this is carried out by designing and constructing increasingly complex model systems, measuring their quantitative behaviour and exploiting mathematical models to describe and predict their output. In particular we are now trying to implement proper mathematical models in order to describe unpredictability sources in rational design such as metabolic burden (i.e. metabolic load carried by the host cell given by the expression of recombinant proteins encoded in synthetic circuits) and DNA copy number variations.
biofuel production: microorganisms can be engineered to implement optimized non-native metabolic pathways in order to produce industrially relevant bioproducts; we are engineering E. coli to produce sustainable biofuels from dairy industry waste.
quorum sensing re-engineering: communication in bacteria is performed through signalling molecules. One of the mostly studied and known processes for the regulation of specific pathways is quroum sensing. We use different genetic elements involved in quorum sensing to design novel engineering-inspired biological systems. We test the bottom-up design approach via mathematical modelling and experimental characterization of such systems.
genetic and computational tools for Synthetic Biology: in order to support and simplify the construction and characterization of biological systems, we develop novel computational tools and genetic solutions. In this framework, datasheets of biological parts are produced and deterministic, stochastic, empirical or mechanistic mathematical models are used to support part selection and re-use, while standardized genetic tools are used to rapidly generate recombinant strains.
metabolic engineering: simulation of the metabolic behavior of microorganisms under any perturbations, for example genetic modifications and/or environmental changes, through the constraint-based reconstruction and analysis (CoBRA) method. We aim to predict the optimal conditions for the overproduction of biomolecules in the desired chassis using their metabolic models.
CRISPR technology: we are starting to explore the possibilities of this new genetic tool, from genome editing to gene expression regulation. In particular we are now focusing on the mathematical modeling of the CRISPR interference (CRISPRi) in order to efficiently use this technique (as well as RNAi) in rational design of complex genetic circuits, with a special focus on microbiome-related applications.
The working group:
PhD students: Davide De Marchi, Angelica Frusteri Chiacchiera
Research fellows/collaborators: Massimo Bellato, Michela Casanova
Lab Head: Lorenzo Pasotti
Lab Director and Supervisor: Paolo Magni