Inducible promoters such as the GAL1 promoter in yeast activated by galactose, or synthetic tetracycline-inducible promoters in mammalian cells, are the “staple food” of molecular biology and are used to either switch ON or OFF the expression of a gene of interest and to study the resulting phenotype to gain insight into the biological processes in which the gene is involved. One of the biggest limitations of inducible promoters is that it is very difficult to obtain an intermediate expression of the gene by titrating the inducer molecule (i.e. galactose, tetracycline, etc.). Indeed, a threshold value exists for the inducer molecular concentration, above which the promoter is fully activated (or repressed) and below which the promoter is fully repressed (or activated). Hence, inducible promoters behave like toggle-switches, where gene expression can either be turned ON or OFF, whereas a dimmer would be much more useful to finely regulate the expression of the gene at any intermediate level.
Such limitation prevents these promoters from being used for probing biological processes where gene dosage is key to understand their mechanisms. Moreover, to infer mathematical quantitative models of biological processes, it is not sufficient to express a gene of interest, but it is necessary to change its concertation over time in order to characterise the dynamic response of the biological process being investigated. We applied Control Engineering approaches to drive gene expression from inducible promoter in order to express any desired time-varying concentration of a protein of interest.
Control Engineering aims at driving a physical system in order to reach a specific value of a quantity of interest (such as a boiler that needs to warm water to a desired temperature, or a car cruise-control maintaining a constant speed) despite the presence of disturbances. This is achieved by appropriately varying its inputs (switch on or off a heater in the case of the boiler, or accelerating or braking in the cruise- control) as a function of the difference between the measured value of the output and its desired target value (control error). At the core of most control schemes lies a negative feedback loop, where the quantity being controlled is measured and then compared to its desired value, yielding an error, which is minimized by a computer by perfoming the right control action.
We built a completely automated microfluidic platform, with the help of Prof. Jeff Hasty, to control in real-time gene expression in yeast and mammalian cells where a computer runs the control algorithm, which at each sampling interval (5 min for yeast and 15 min for mammalian cells): (i) processes images acquired by the microscope to estimate the fluorescence; (ii) decides whether to provide the inducer molecular and for how long by comparing the actual fluorescence to the desired fluorescence (iii) controls the automated syringes to provide the inducer molecule to the cells.
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We are currently using this platform to quantitatively investigate the function of Hes1 ultradian oscillator in mouse progenitor cells.
References
[1] Automatic Control of Gene Expression in Mammalian Cells. Fracassi C, Postiglione L, Fiore G, di Bernardo D. ACS Synth Biol. 2016 Apr 15;5(4):296-302. doi: 10.1021/acssynbio.5b00141. Epub 2015 Oct 6. PMID: 26414746.
[2] In Vivo Real-Time Control of Gene Expression: A Comparative Analysis of Feedback Control Strategies in Yeast. Fiore G, Perrino G, di Bernardo M, di Bernardo D. ACS Synth Biol. 2016 Feb 19;5(2):154-62. doi: 10.1021/acssynbio.5b00135. Epub 2015 Dec 4. PMID: 26554583.