AIM – we need to produce a tightly regulated cadmium sensor in our system which produces a signal in response
Need to design a promoter regulated by cadmium and responsive to the stochastic switch’s decision
The fate of a cell according to the environement
Feeds into the stochastic switch
Germination ability of spores
How do we build our cadmium sensor BioBrick:
Create an AND gate
Use metal sensors CzrA and ArsR
Cadmium Sensing: ArsR and CzrA
Both are metal sensitive repressors:
ArsR features in Arsenic Resistance operon
CzrA features in Cobalt zinc Resistance operon
Why use these two metal sensors?
For the same reason we are using the AND gate – metal sensitive promoters can sense MORE THAN ONE METAL! We know that the CadA promoter contains CzrA binding site Solution: engineer ArsR binding site next to CadA promoter to create cadmium sensing AND gate Metal Sensor Metals Sensed ArsR As(III) Ag(I) Cu Cd Metal Sensor Metals Sensed czrA Zn Co Ni Cd
Cadmium Sensing: BioBrick Construct AND gate BioBrick ArsR binding sites cadA promoter with CadA binding site RBS Metal Sensor
Cadmium Sensing: AND gate
Cadmium Sensing: Modelling Metal Sensor
Cadmium Sensing: Novelty and Importance
Need tightly-controlled cadmium metal sensor – single metal sensors not enough.
Usual metal-sensitive promoters are sensed by a single repressor protein only
Our metal-sensitive promoter is regulated by 2 different repressor proteins: CzrA and ArsR
Can be used in front of any gene, not only in our project but in different settings.
The concept of using two metal sensors in this way can be adapted for sensing other metals too.
Stochastic Switch: What is this sub-project?
Tuneable invertible promoter region which
Rate of Bacillus sporulation
Metal sponge expression
Germination ability of spores.
This subproject allows us to control key aspects of the Bacillus life cycle to suit our projects needs all in a tuneable manner.
Stochastic Switch: Novelty in design
The stochastic switch is the most novel part of our design
The switch regulates the decision to become a non-germinating metal container spore, or a spore that can go on to germinate as part of the normal life cycle
Hin invertase is used to switch on and off promoters.
We have added new bricks to our device to make the switch tuneable in three ways; 2 variable strength promoters and a degradation controller.
The switch controls 3 main aspects of the cell life cycle:
Metallothionein-cotC fusion expression
Stochastic Switch: BioBricks and Devices… EcoRI Xbal Biobrick Prefix Rfp AccIII BalI hixC hixC hin invertase sigA(pveg) pspac NaeI xlyA promoter NruI SpeI PstI Biobrick Suffix RBS + GFP Prefix (ATG) NheI Key Restriction site Invertase site Double terminator RBS ‘ Right facing’ promoter ‘ Left facing’ promoter
The device we had synthesised was for testing purposes an so contained IPTG and Xylose inducible promoters
The hin invertase has a degradation tag recognised by degradation controller ssbp from E.coli.
Sspb is placed under the control of an arabinose inducible promoter.
Stochastic Switch: Modelling
The device had to be modelled due to the many variables that contribute to the stochastic decision; these variables can be changed to give different pulse lengths of hin in order to give a net number of flips :
Stochastic Switch Stochastic Switch: Modelling hixC hixC hin Pveg hixC hixC hin Pveg
AIM – to render cadmium bio-unavailable by mopping it up using a metallothionein and moving it into spores
By wrapping a spore coat protein around cadmium ions, the ions become isolated from the environment (and humans) and no longer have harmful effects.
Metallothionein = SmtA; Spore coat protein = CotC
NOVELTY – moving cadmium into resilient spores has not been accomplished before.
Future teams could apply same principle for other toxic metals.
Cadmium Sequestration: What is this sub-project about?
Cadmium Sequestration: BioBrick Construct
Sporulation Tuning: Aims and Novelty
To adjust the natural sporulation system, instead of including something totally new.
Find out how the percentage of the population that sporulates, and does not sporulate, when the system is affected, by example, change in protein level.
Find the optimal percentage for the system in mind
Sin (sporulation inhibition) Operon Model (CellML)
Kin A Expression Model (COPASI)
Sporulation Induction using Kin A Model (COPASI)
Sporulation Tuning – the sporulation trigger
The transcription factor Spo0A is a master regulator for entry into sporulation in the bacterium Bacillus subtilis 
Sporulation can be triggered with high efficiency in cells in the exponential phase of growth in rich medium by artificial induction of the synthesis of any one of three histidine kinases that feed phosphoryl groups into the relay 
For our project, we are using kinA as:
- In Bacillus subtilis , KinA is a major histidine kinase responsible for activation the sporulation pathway 
The KinA Expression Model illustrates how KinA is expressed while the third model shows how KinA induces sporulation
It is important to note, that a gradual increase in the Spo0A protein and activity plays a critical role in triggering sporulation and requires the action of the phosphorelay 
Sporulation Tuning: Modelling
Sporulation Tuning: Lab Work and Characterisation
 Fujita, M. and Losick, R. 2005. Evidence that entry into sporulation in Bacillus subtilis is governed by a gradual increase in the level and activity of the master regulator Spo0A. Genes & Development 19: 2236-2244
 Eswaramoorthy, P., Guo, T. and Fujita, M. 2009. In Vivo Domain-Based Functional Analysis of the Major Sporulation Sensor Kinase, Kin A, in Bacillus subtilis . Journal of Bacteriology p. 5358-5368
Sin (sporulation inhibition) Operon Model
Controls the production and activity of the repressor SinR
SinR in its active tetrametric form inhibits sporulation by repressing stage II and Spo0A promoters
Phosphorylation induces Spo0A to form active dimers which activate trascription from the P1 promoter
Population Modelling: Aims and Novelty
What is the affect of modifying the bacteria's life cycle?
Does the population die, if we reduce germination?
How much of our bacteria will be needed to clean up an area?
A shared environment.
Independent cells of bacteria making decisions for their life.
Each cell runs cellular models, using it's own parameters.
Population Modelling: How does it work?
Programmed in Java, uses, Jsim, and other models written in CellML and SBML while integrating agent based models and biological models.
Due to each bacterial cell running independently as a thread, it used a lot of CPU power and RAM.
Powerful machines (16 CPUs) were used.
Population Modelling: Distributed Computing
The solution – Distributed Computing – By using multiple computers to spread the load.
Using Microbase – It now runs on university computers and the Amazon Elastic Compute Cloud.