Team:ETH Zurich/Project/Conclusions

Conclusions

In this iGEM project we addressed the question of how the minimal genome of a particular organism, E.coli, could be identified and made available in the form of minimal strain to researchers. The motivations that made the finding of a minimal genome strain appealing were two folds: the indication that in essentiality are hidden some of fundamental yet missing biological properties and the desire of providing a convinient chassis for synthetic biology. In this contest we required our ideal minimal genome to have two properties:

• to be as simple as possible, meaning as much reduced in genome size and gene content as possible
• to be vital, meaning that we were aiming for strains able to provide a reactive and productive background on which to add synthetic functionalities.

The approach we proposed was based on two main considerations. First, that the space solution of possible minimal genomes is huge and untractable without taking an heuristic approach. Second, that evolution probably worked in the contrary sense, by constructing complex organism starting from a very minimal set of genes. Combining the two concepts, we decided to take an evolutionary synthetic reductive approach. In order to do so, we had to invert two main biological mechanisms. First, losing part of the genome should made possible, while cells (for the evolutionary motivations discussed before) are indeed more equipped for uptaking chromosomal parts. Second, to give a fitness advantage to the cell that has a reduced genome, things that to our knowledge have never been showed before. Moreover, these two mechanisms had to be implemented in a framework that permitted the sequential application of a mutation phase (reduction) and selection phase (fitness function) in order to form the cycle that is proper of an evolutionary algorithm. By bringing out the concept that cells are natural carriers of our possible solutions (they indeed carry a chromosome that can be reduced), we investigated the following solutions:

• reduction of the chromosome could be achieved by controlled expression of restriction enzymes and ligase by using a genetic circuit.
• reduced strain can be made fitter by penalizing large chromosomal size through a nucleotide limitation in the feeding.
• populations of our solutions (cells) can be repetitevly subjected to reduction and selection phases by using a chemostat machinery.

Our efforts were spent in trying to prove the feasibility of our assumption from the experimental side (when possible) and using modelling techniques (when convinient). Here we report a brief summary of what we achieved with links to the detailed description.

Wet laboratory experimental results:

• we designed and sent to be synthetized a novel proof of concept construct regarding to the restriction enzyme in-vivo activity. Moreover we designed experiments that would reveal key parameters of restriction enzyme in-vivo efficiency (link).
• we succesfully performed knockouts of thymidylate synthase as first step for selection method evaluation (link).
• we showed evidence that is possible to control growth rate by constraining thymidine feeding, thus validating our proposed selection mechanism (link).
• we designed and brought to late step of cloning a novel genetic switch circuit, able to control the expression of in-vivo restriction enzymes (link).
• we construct and characterized a new biobrick, able to stop ribosomal functionality upon induction, as a spin-off from our main stream project (link).

Dry Laboratory (modelling) results:

• we applied statistical analysis to evaluate optimality for our purpose of a large population of restriction enzymes (link)
• we used the state-of-the-art genome scale model for E.coli has framework for testing the single cell response to our selection mechanism. We performed modification and simulation that permited to quantitatively validate the feasibility of our selection method and to predict a reduction up to 71% of E.coli genome with our method (link).
• we modelled the mechanical behaviour of the chemostat machinary and evaluated parameters and feasibility range using Ordinary Differential Equation modeling (link).
• we proposed the Ordinary Differential Equation modeling of the genetic switch circuit, taking paramters values from literature and performing sensitivity analysis in order to analyse key variables in the model.