Networked Feedback Systems in Biology

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This is a joint project with John Doyle, funded by the ARO Institute for Collaborative Biotechnologies. This page primarily describes the work done in Richard Murray's group.

Current participants:

  • Vanessa Jonsson (PhD student, CDS)
  • Ophelia Venturelli (PhD student, BMB)
  • Enoch Yeung (PhD student, CDS)
  • Dan Siegal (-Gaskins) (postdoc, BE → Schafer Corp)

Previous participants:

  • Dionysios Barmpoutis
  • Mary Dunlop
  • Per-Ola Forsberg
  • Elisa Franco
  • Roman Galeev

  • Arthur Prindle
  • Henrik Sandberg
  • Johan Ugander
  • Kevin Welch



Candidate architecture for biomolecular control system. The feedback system consists of the process dynamics, an arrange of molecular sensing systems, an "networked" control system and a collection of actuation mechanisms. The networked control system makes use of an interconnection matrix <amsmath>L</amsmath>, a set of asynchronous delays (represented by the blocks labeled <amsmath>\tau</amsmath>) and nonlinear elements <amsmath>N(\cdot)</amsmath>. Internal feedback between the nonlinear block and the interconnection block allows a general set of dynamical systems to be formed from this simple structure.

This project explores the application of theoretical approaches to complex systems in the domain of biological networks. Our focus is twofold: on the development and application of analytical tools for the analysis and design of complex networks, and on the use of those techniques to construct novel biological circuits for feedback control of cellular processes. Specific activites include:

  • Design of programmable, RNA-based platforms for control in biological networks. We are exploring the use of RNA-based feedback circuits for regulation of biosynthetic pathways and developing methods that allow larger scale networks to be constructed in a modular fashion.
  • Modularity and composition of biological circuits and subsystems. In order to build useful models for the design of large-scale biological networks, it will be necessary to combine models of smaller subnetworks that interact with each other. While the theory for such interconnection is well developed in physical and information systems, such a theory is not present in biological systems, where coupling between the indi-vidual circuits is present through a variety of mechanisms.
  • Analysis and design tools for complex networked systems, driven by biological circuit design. Biology integrates computation, communications, control, thermodynamics and statistical mechanics in a way that does not allow current theories to be applied beyond small subsystems. Tools that can be used to understand and design biological control systems will require new methods that capture robustness, fragility, evolvability and modularity in new ways. We believe that new theoretical approaches, driven by our in-terets in biological circuit design, can provide useful tools for understanding large-scale, complex networks in a variety of application areas.