Team:LUBBOCK TTU/Description

Description

(Content)

Our main focus this year was to work on the optimal calcium condition using models to direct our wet lab work. Below you can find a list that outlines the logic we went through to arrive at the work for this year’s iGEM competition.
1. In-Silico Control
2. EM Wave TRPV1-ferritin stimulation
3. TRVP1 thermal gating
4. Optimal calcium condition

You can find a more detailed description in the paragraphs below.

Inspiration for our project

To ensure the reliability, predictability, and robustness of genetic circuits to perturbations, the implementation of control methods improves the design and regulation of synthetic biological systems for scalability [1-4]. Control theory has found application in many other engineering disciplines to predict and regulate the system dynamics of a process [5-7]. These methods help to maintain the desired state of a process that experiences fluctuations from external influences. Along with the development of orthogonal genetic devices that experience little cross-talk, the application of control methods are critical to the upscale of genetic circuits as the system complexity increases. Most genetic circuits regulate themselves inside the cell where control is susceptible to noisy interactions that can corrupt the reliability and functionality of the genetic circuit. However, recent movements have seen improvement in the construction of robust feedback systems by removing the controller component of the genetic process from the cell and regulating the process outside of the cell. This method of control is known as In Silico feedback and allows for the system to function in real time with greater control, reliability, and robustness to perturbations.

Several independent studies have shown that electromagnetic (EM) fields can non-invasively regulate blood glucose levels in mice and activate the stimulation of neuronal activity [8-10]. This stimulation has the potential to overcome the disadvantages of traditional input signals and offer a responsive and versatile In Silico control method for feedback regulation of cellular systems. The application of EM waves to stimulate gene expression, also known as magnetogenetics, currently relies on the tethering of a ferritin nanoparticle with an iron oxide core to a thermal sensitive cation selective channel of the TRP Family such as TRPV1 [11-14]. Although the known biophysical mechanism that allows for gating by the production of sufficient heating from ferritin nanoparticles in response to EM waves has received criticism [15, 16], there are efforts to explain this phenomenon [17]. There is experimental evidence from independent studies [11-14] that provides empirical data, which demonstrates calcium influx from EM stimulation by using calcium fluorescence imaging and blood glucose regulation in mice via the activation of a calcium sensitive promoter that induces the expression of a synthetic insulin gene. Considering the possibility of TRPV1 as a thermal actuator for genetic circuit regulation, our team decided explore the optimal calcium influx conditions due to TRPV1 that could be used to regulate gene expression in S. cerevisiae. Relevant pathway

To learn more about the pathway, from TRPV1 gating to gene expression, view our design page.

What we are working on for this year’s project

For our project we plan on using S. cerevisiae to establish the extracellular calcium concentration that would be optimal to regulate gene expression at the activation threshold temperature of TRPV1. Considering the native cytosolic calcium concentration ranges between 50 pM to 200 pM we worked on models that would predict our reporter gene expression under conditions near the native cytosolic concentration and far greater than the native calcium concentration. These models allowed us to decide on a low sensitivity promoter to activate our reporter genes known as PMC1 which we worked to construct in the wet lab.

References
[1] Prescott TP, Papachristodoulou A (2014) Synthetic biology: A control engineering perspective. In: Proceedings of the European Control Conference (ECC). pp. 1182–1186.
[2] He F, Murabito E, Westerhoff HV. 2016 Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering. J. R. Soc. Interface 13, 20151046.
[3] Harris et al., 2015 A.W.K. Harris, J. Dolan, C. Kelly, J. Anderson, A. Papachristodoulou. Designing genetic feedback controllers IEEE Trans. Biomed. Circuits Syst., 9 (2015), pp. 475–484.
[4] T.P. Prescott, A.W.K. Harris, J. Scott-Brown, A. Papachristodoulou. Designing feedback control in biology for robustness and scalability. IET/SynbiCITE Engineering Biology Conference, 2016 page 2.
[5] J.W. Eaton, J.B. Rawlings. Model predictive control of chemical processes. Chem. Eng. Sci., 47 (1992), pp. 705-720.
[6] T.C. Bulgrin, T.H. Richards Application of advanced control theory to enhance molding machine performance IEEE Transactions on Industrial Applications, 31 (6) (1995), pp. 1350-1357.
[7] S.J. Qin, T.A. Badgwell. A survey of industrial model predictive control technology. Control Eng. Pract., 11 (2003), pp. 733–764.
[8] J.W. Eaton, J.B. Rawlings. Model predictive control of chemical processes. Chem. Eng. Sci., 47 (1992), pp. 705-720.
[9] T.C. Bulgrin, T.H. Richards Application of advanced control theory to enhance molding machine performance IEEE Transactions on Industrial Applications, 31 (6) (1995), pp. 1350-1357.
[10] S.J. Qin, T.A. Badgwell. A survey of industrial model predictive control technology. Control Eng. Pract., 11 (2003), pp. 733–764.
[11] Stanley SA, Gagner JE, Damanpour S, Yoshida M, Dordick JS, Friedman JM. Radio-wave heating of iron oxide nanoparticles can regulate plasma glucose in mice. Science. 2012;336:604–8.
[12] M.A. Wheeler, C.J. Smith, M. Ottolini, B.S. Barker, A.M. Purohit, R.M. Grippo, R.P. Gaykema, A.J. Spano, M.P. Beenhakker, S. Kucenas, et al. Genetically targeted magnetic control of the nervous system Nat. Neurosci. (2016).
[13] Long, X., Ye, J., Zhao, D. & Zhang, S.J. Magnetogenetics: remote non-invasive magnetic activation of neuronal activity with a magnetoreceptor. Sci. Bull. (Beijing) 60, 2107–2119 (2015).
[14] Stanley, S.A., Sauer, J., Kane, R.S., Dordick, J.S. & Friedman, J.M. Remote regulation of glucose homeostasis in mice using genetically encoded nanoparticles. Nat. Med. 21, 92–98 (2015).
[15] Meister, M. Physical limits to magnetogenetics. Elife 5, 1689–1699 (2016).
[16] Anikeeva, P. & Jasanoff, A. Problems on the back of an envelope. Elife 5, e19569 (2016).
[17] Guillaume Duret, Sruthi Polali, Erin D. Anderson, A. Martin Bell1, Constantine N. Tzouanas, Benjamin W. Avants, & Jacob T. Robinson. Magnetic entropy as a gating mechanism for magnetogenetic ion channels. bioRxiv preprint first posted online Jun. 11, 2017.