Dry Lab Tutorial UCCKE & HKUST
The iGEM team of Hong Kong University of Science and Technology (HKUST) had offered us a privileged opportunity to teach us the operation of dry lab, and we gained invaluable insights during the tutorial. HKUST provided various guidance on the main roles of dry lab, including different types of model. Based on our project, HKUST suggested we could approach kinetic modeling to predict the growth rate of E. coli. HKUST introduced us to the use of Elastic Network Regression to refine data in order to produce an accurate growth curve. On top of that, they also answered questions regarding how the growth curve could be generated from limited data, in which OptKnock, a programming framework, could be used to generate a maximum growth value.
We are very honored to collaborate with HKUST and receive precious feedback regarding the role of dry lab.
Generation of anaerobic E. coli growth curve
With the valuable dry lab knowledge from HKUST, we attempted to generate the anaerobic growth equation of E. coli.
Referring to the logistic growth equation:
Given that N = population size, t = growth time, K = carrying capacity, and r = growth rate constant, we could derive the equation into the following form:
Which Nt = population size of E. coli at t, N0 = initial population size of E. coli at t = 0, K = carrying capacity, and r = growth rate constant.
even though we could find the growth constant, we lack the carrying capacity. The carrying capacity may vary with different growth mediums and repeats, therefore it would be difficult to compare with data from our team’s experiments.
In conclusion, although our team’s attempt to generate a model growth curve may be unsuccessful, it is a great opportunity for us to learn from mistakes, and strive to develop a better model in future projects. Still, we are thankful for the invaluable advice on dry lab by HKUST.
Reference
[1]Monk JM, Koza A, Campodonico MA, Machado D, Seoane JM, Palsson BO, Herrgård MJ, Feist AM. Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes. Cell Syst. 2016 Sep 28;3(3):238-251.e12. doi: 10.1016/j.cels.2016.08.013. Epub 2016 Sep 22. PMID: 27667363; PMCID: PMC5058344.