Molecular biology workflow relies on the growth and maintenance of microbes...

...but the lack of control over how fast microbes grow can hinder some areas of research...

microbial co-cultures are highly unstable, protein production is held back by energetic flux shunted towards growth and giving directed evolving microbes an arbitrary growth advantage is nearly impossible!

Can microbial growth be controlled?

Yes, but not predictably. If a precise growth rate is required, changes to nutrients, pH, and temperature may not provide the fidelity with which to program it.

Can we predictably control bacterial growth?

Now you can! The Sheffield iGEM Fairies are back this year to bring you PARSE: PhAge deRived growth SlowErs - a set of genetic parts that allow for the tuning of bacterial growth with a range of potential inducers, alongside a bespoke turbidostat to ease the required experiments

This is why we built PARSE

Sheffield iGEM 2022, amongst other iGEM teams, have unearthed a key problem within the field of microbiology: how do we predictably control microbial growth? To tackle this, the science fairies from Sheffield iGEM 2023 have taken the first steps into granting this wish with PARSE. This year we’ve combined:

Biology

• Characterised a set of seven growth slowing genes with a variety of targets to assess their validity and ability to control bacterial growth in response to IPTG induction.
• Created a protocol whereby more fairies can characterise their own growth slowers with a biosensing promoter of their choice
• Characterised and standardised a new GFP - vsfGFP - for good measure (you get three wishes after all!)

Hardware modelling and Systems Design and Engineering

• Created a Simulink model with the integration the different subsystems of a physical bioreactor. The integrated model was used to to illustrate how different subsystems interact and control microbial growth within a turbidostat.
• Designed schematics and preliminary component prototypes for the proposed system. This system is intended for growth modulation experiments across various scales, offering a more streamlined and cost-effective alternative to intricate and expensive microplate spectrophotometers.
• The Simulink model supported the design and test of the control algorithms to refine the logic during prototyping. The system’s design worked together alongside hardware modelling in order to optimise the design of the prototype.

Together we have taken the once wish of biologists' wildest dreams, and begun to make it a reality.