Cybergenetics: Control systems for living cells

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Cell feedback

In his 1948 book, Cybernetics, Norbert Wiener presented a vision where the study of control and communication in the animal and the machine are unified. The field of Cybernetics (art of steering) was born. Predating the discovery of the structure of DNA and the ensuing molecular biology revolution, cybernetic applications in the life sciences at the time were limited. More than 60 years later, the confluence of modern genetic manipulation techniques, powerful measurement technologies, and advanced analysis methods is enabling a new area of research in which systems, communications, and control theory notions are used for synthetically regulating cellular processes at the gene level. We refer to this nascent field as Cybergenetics.

We are developing novel analytical and experimental methods for the analysis and design of cybergenetic systems. Our current work demonstrates how de novo control systems can be interfaced with living cells and used to control their dynamic behavior in real time.

Cybergenetics: In silico control 

In silico control  
In silico control

One way to realize cybergenetic control is by interfacing the living cells with a digital computer which implements the control system. The interface is achieved using light (optogenetics) or chemical inducers. In the case of light, the cells are genetically engineered to host a light responsive circuits which activates a biological process of interest, e.g. gene expression.  The output of this biological process, e.g. protein concentration, is sensed in real time, typically by measuring flurorescene intensity, and the measured values are fed to the computer. The computer implements a control system whose commands are translated into light signals that are delivered to the cells through an LED, thereby closing the feedback loop. This in silico control configuration enables flexible and fast implmentation of sophisticated control systems that can be rapidly modified and optimized.  

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A. Milias-Argeitis, M. Rullan, S. Aoki, P. Buchmann, and M. Khammash. "Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth," Nature Communications, 2016. doi:10.1038/ncomms12546  Article

J. Ruess, F. Parise, A. Milias-Argeitis, M. Khammash, and J. Lygeros. "Iterative experiment design guides the characterization of a light-inducible gene expression circuit," Proceedings of the National Academy of Sciences, 2015. doi:10.1073/pnas.1423947112  Article

A. Milias-Argeitis, S. Summers, J. Stewart-Ornstein, I. Zuleta, D. Pincus, H. El-Samad, M. Khammash, and J. Lygeros. "In silico feedback for in vivo regulation of a gene expression circuit," Nature Biotechnology, 2011. doi:10.1038/nbt.2018  Article

C. Briat and M. Khammash, "Computer control of gene expression: Robust setpoint tracking of protein mean and variance using integral feedback," IEEE Conference on Decision and Control, 2012, pp. 3582-3588. doi:10.1109/CDC.2012.6426720  Article

C. Briat and M. Khammash, "Integral population control of a quadratic dimerization process," IEEE Conference on Decision and Control, 2013, pp. 3367-3372. doi:10.1109/CDC.2013.6760398 Article    


Cybergenetics: In vivo control

In vivo control  
In vivo control

Here the control system is engineered into the living cell so that it is implemented entirely using biomolecular components. Sensing, actuation, and computation take place within each cell using biomolecular reactions of cellular species. The control system must contend with the noisy environment of the cell along with the noise generated from its own molecular reactions. The in vivo feedback control systems implemented thus are autonomous, highly dynamic, and generally stochastic in nature.  We are currently developing the control theory needed to analyze and design such biomolecular controllers. In the lab, we are constructing these controllers in  E. coli, yeast, and mammalian cells for biotechnology and therapeutic applications.

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C. Briat, C. Zechner, and M. Khammash. "Design of a Synthetic Integral Feedback Circuit: Dynamic Analysis and DNA Implementation,"  ACS Synthetic Bioliogy, 2016. doi:10.1021/acssynbio.6b00014  Article

C. Zechner, G. Seelig, M. Rullan, and M. Khammash. "Molecular circuits for dynamic noise filtering," Proceedings of the National Academy of Sciences of the United States of America, 2016. doi:10.1073/pnas.1517109113  Article  

C. Briat, A. Gupta, and M. Khammash. "Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Bimolecular Networks," Cell Systems, 2016. doi:  Article  

A. Gupta, C. Briat, and M. Khammash, "A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks," PLoS Computational Biology, 2014. doi:  Article 

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