Team:NUS Singapore/Software

NUS iGEM 2019


Overview
Our E.co Grow software allows users to run hundreds of simulations in order to gain useful insights without carrying out elaborate experiments. The foundation of this software lies in our models of the HicA-HicB and SgrS systems which have been built using our characterization data. On the basis of a few inputs specified by users, our software can predict bacterial growth trends and offer experimental design recommendations to suit their purpose. By harnessing the power of modelling, our software aims to effectively reduce the number of experiments carried out by the wet lab to test and optimise the system.

Download our E.co Grow software here!
Growth Knob (SgrS)
The Growth Knob feature of the E.co Grow software allows users to simulate bacterial growth and glucose trends of our SgrS system by varying different parameters. Users have a choice of four parameters:
  • 1) Inducer (aTc) concentration for SgrS: A larger value will result in slower growth as SgrS has a repressive effect on growth.
  • 2) Glucose half saturation constant for growth: This represents the sensitivity of growth to glucose, and depends on the strain of bacteria used. A larger value represents a lower sensitivity of the bacteria’s growth to glucose, and will result in slower growth.
  • 3) Inducer half saturation constant for growth: This is the sensitivity of growth to the inducer for SgrS, and it depends on the type of inducer used. A smaller value represents a greater synthesis rate of SgrS, and will result in slower growth.
  • 4) Maximum rate of cell growth

These simulations can offer powerful insights to the wet lab such as the glucose level in the media required to achieve a certain level of OD600. It also offers the ability to fine-tune the growth rate of the bacteria by controlling the amount of glucose in the media.
OD simulation:


Glucose simulation:
Growth Switch (HicA-HicB)
The Growth Switch feature of the E.co Grow software allows users to simulate bacterial growth trends of our HicA-HicB system in different experiment design conditions. Users have a choice of four parameters:
  • 1) Inducer (IPTG) concentration for toxin: A larger value will result in a more pronounced growth arrest as the toxin has a repressive effect on growth.
  • 2) Inducer (arabinose) concentration for antitoxin: A larger value will result in a greater growth resumption as the antitoxin can rescue cells from a growth-arrested mode.
  • 3) Induction timepoint of toxin: This is the timepoint at which the inducer for toxin is added to the cells.
  • 4) Induction timepoint of antitoxin: This is the timepoint at which the inducer for antitoxin is added to the cells.
By running simulations, the wet lab team can better understand the parameters they need to tweak in order to achieve the desired trends. For example, if a user induced the toxin at 1hr then the different plots generated by the Growth Switch feature of the software informs them that they should induce the antitoxin by 10hr at the latest in order to achieve a significant growth resumption.
Surface plot
The Surface plot feature of the E.co Grow software allows users to identify the optimal combination of a pair of input parameters evaluated based on five criteria. Users can choose any 2 of the following parameters:
  • 1) Logistic coefficient (nOD): Accounts for the gradient of the growth curve. A larger value will result in slower growth as SgrS has a repressive effect on growth.
  • 2) Threshold toxin concentration for growth repression (Ktox): This represents the toxin concentration required to achieve half of the maximum repression of growth. A larger value implies a lower sensitvity of growth to toxin.
  • 3) Hill coefficient for growth repression due to toxin (ntox): This captures the nature of the response of growth to toxin. A larger value will result in a steeper repression as the toxin concentration is increased.
  • 4) Threshold antitoxin concentration for repression of toxin levels (Kb): This represents the antitoxin concentration required to achieve half of the maximum repression of the toxin level. A larger value implies a lower sensitivity of the toxin level to the antitoxin.
  • 5) Maximum synthesis rate of toxin (synt): A larger value of this parameter implies a greater rate of toxin production.
  • 6) Threshold IPTG concentration for toxin induction (Ki): This represents the IPTG concentration required to achieve half of the maximum toxin synthesis rate. A larger value implies a lower sensitivity of toxin production to IPTG.
  • 7) Degradation rate of toxin (degt): This parameter determines the rate at which the toxin is degraded. A larger value will imply a faster degradation.
  • 8) Maximum synthesis rate of toxin (syna): A larger value of this parameter implies a greater rate of antitoxin production.
  • 9) Threshold Arabinose concentration for antitoxin induction (Ka): This represents the Arabinose concentration required to achieve half of the maximum antitoxin synthesis rate. A larger value implies a lower sensitivity of antitoxin production to Arabinose.
  • 10) Degradation rate of antitoxin (dega): This parameter determines the rate at which the antitoxin is degraded. A larger value will imply a faster degradation.
  • 11) Inducer (IPTG) concentration for toxin: A larger value will result in a more pronounced growth arrest as the toxin has a repressive effect on growth.
  • 12) Inducer (arabinose) concentration for antitoxin: A larger value will result in a greater growth resumption as the antitoxin can rescue cells from a growth-arrested mode.
  • 13) Induction timepoint of toxin (ti): This is the timepoint at which the inducer for toxin is added to the cells.
  • 14) Induction timepoint of antitoxin (ta): This is the timepoint at which the inducer for antitoxin is added to the cells.
  • 15) Initial OD600 value(OD_init): This is the starting value of OD600 which can be controlled during experiments.

Upon the entry of 2 input parameters, the ODEs in the model are then integrated using the parameter sets and the output is evaluated using objective functions that account for five different evaluation criteria. The performance of the different parameter combinations evaluated based on the value of the objective functions is displayed on a heatmap. The user can also customize the importance assigned to the different evaluation criteria in the form of 'weights' which are numerical values. It is important to note that the weight assigned to the Arrest criteria can only be 0 or 1. The five evaluation criteria are as follows:
  • 1) Growth reduction: Accounts for the reduction in growth brought about by the induction of toxin.
  • 2) Growth arrest: Serves as an additional filter for the above mentioned criteria by penalising a growth of 10% or above in the gowth-arrested mode.
  • 3) Growth recovery: Accounts for the recovery in growth brought about by the induction of antitoxin.
  • 4) Minimum usage of IPTG: Accounts for minimizing the usage of valuable chemical inducers like IPTG.
  • 5) Minimum usage of Arabinose: Accounts for minimizing the usage of valuable chemical inducers like Arabinose.

Heatmap based on performance: