Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthet...

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Main Authors: Miller, Miles Aaron (Contributor), Hafner, Marc (Contributor), Sontag, Eduardo (Author), Davidsohn, Noah Justin (Contributor), Subramanian, Sairam (Author), Purnick, Priscilla E. M. (Author), Lauffenburger, Douglas A. (Contributor), Weiss, Ron (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Public Library of Science, 2012-08-29T17:20:26Z.
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Online Access:Get fulltext
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042 |a dc 
100 1 0 |a Miller, Miles Aaron  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Biological Engineering  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Weiss, Ron  |e contributor 
100 1 0 |a Miller, Miles Aaron  |e contributor 
100 1 0 |a Hafner, Marc  |e contributor 
100 1 0 |a Davidsohn, Noah Justin  |e contributor 
100 1 0 |a Lauffenburger, Douglas A.  |e contributor 
100 1 0 |a Weiss, Ron  |e contributor 
700 1 0 |a Hafner, Marc  |e author 
700 1 0 |a Sontag, Eduardo  |e author 
700 1 0 |a Davidsohn, Noah Justin  |e author 
700 1 0 |a Subramanian, Sairam  |e author 
700 1 0 |a Purnick, Priscilla E. M.  |e author 
700 1 0 |a Lauffenburger, Douglas A.  |e author 
700 1 0 |a Weiss, Ron  |e author 
245 0 0 |a Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity 
260 |b Public Library of Science,   |c 2012-08-29T17:20:26Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/72426 
520 |a Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated. 
520 |a National Institutes of Health (U.S.) (NIGMS) (Grant number R01GM086881) 
520 |a National Science Foundation (U.S.). (Award number 1001092) 
520 |a National Science Foundation (U.S.). Graduate Research Fellowship Program 
520 |a SystemsX.ch initiative (IPhD project) 
546 |a en_US 
655 7 |a Article 
773 |t PLoS ONE