Team:SUSTech Shenzhen/Model

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Overview

Optogenetics, a combination of light and genetic sciences, enable us to use light to achieve precise control of various cellular activities with high spatiotemporal resolution. However, precise control of a certain protein level remains difficult in the mammalian cell due to its complex multi-level regulation mechanisms such as various gene expression processes. Thus, it’s essential to simulate the whole process of gene expression (from transcription to secretion) by establishing fine-tuned mathematical models.

Q1: How to abstract these biological processes and establish the ordinary differential equations?

Q2: How to parameterize the models based on experimental data and simulate the process?

Also, to further achieve the goal of precise control of gene expression, we need a new control algorithm that can iteratively compute and simulate system output until the predicted output is as close as possible to the reference over a given time horizon.

Q3: How to develop a control algorithm which has excellent response dynamics and is easy to manipulate?

Q4: How to modify the control function or parameters according to a regulation test in a ‘close-feedback’ manner?

Although we have characterized and controlled the secretion process in previous models, it’s still essential to verify the biological activity of secreted protein which is proof of the effectiveness of regulation. And we choose Chemotaxis as our testing model which is described as the directed migration of cells towards a chemoattractant. We used IL8 as a chemoattractant because IL8 can promotes the activation and chemotaxis of neutrophils(a white blood cell). And we try to analyze the migration behavior of mammalian cells on a space-scale by calculating the relevant chemotaxis parameters using our cell tracking algorithm.

Q5: How to detect and track mammalian cells using the bright field imaging for extended period of time?

Q6: How to illustrate the tendency of mammalian cells response to cytokines gradient?

Conclusion

Through parameterizing these models according to our experiment data, it allows us to more specifically characterize and control the process of light-induced gene expression process in Mammalian Cells.

References

1.Olson, E. J. , Hartsough, L. A. , Landry, B. P. , Shroff, R. , & Tabor, J. J. . (2014). Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nature Methods,11(4), 449-455.

2.Milias-Argeitis, A. , Rullan, M. , Aoki, S. K. , Buchmann, P. , & Khammash, M. . (2016). Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nature Communications, 7, 12546.

3.Uhlendorf, J. , Miermont, A. , Delaveau, T. , Charvin, G. , Fages, F. , & Bottani, S. , et al. (2012). Long-term model predictive control of gene expression at the population and single-cell levels. Proceedings of the National Academy of Sciences, 109(35), 14271-14276.

4.Müller, K., Engesser, R., Metzger, S., Schulz, S., Kämpf, M. M., Busacker, M., … Weber, W. (2013). A red/far-red light-responsive bi-stable toggle switch to control gene expression in mammalian cells. Nucleic acids research, 41(7), e77. doi:10.1093/nar/gkt002

5.Ricard, J., & Ricard, K. (2005). Mathematical Models in Biology. Mathematical models in biology.