SURF 2013: The costs and benefits of various designs of biochemical 'decision engines'

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2013 SURF project description

  • Mentor: Richard Murray
  • Co-mentor: Dan Siegal-Gaskins

The experimental and theoretical study of 'decision making' in biological systems has led to the identification of fundamental biochemical circuits and networks with interesting dynamical behavior. Switch-like (or bistable) networks may be be particularly important as rudimentary 'decision engines' [1]. Indeed, bistability has already been found in a wide range of regulatory networks involved in decision making (e.g., [2, 3]).

This project involves exploring the costs and benefits of various bistable circuit architectures using a combination of theoretical and experimental tools. Cost-benefit-type analyses have been used previously in the study of biochemical systems [4] and have significant potential for understanding bistability and decison making in particular. The relevant costs and benefits may include energetic costs, circuit response time, stability, and cellular resource utilization (e.g., RNA polymerase and ribosomes). This work will form the basis for a general theory that could account for the large diversity of bistable network topologies found in nature and could direct the design of novel synthetic biocircuits.

At least two different bistable circuits will be explored using computational and experimental techniques. One possible approach involves developing differential equation models that capture energy usage, mean time to transition and robustness to concentration of transcriptional machinery. Simulations may be run using a variety of switch parameters to explore whether one topology or the other is superior across relevant (plausible) parameter ranges. Switch circuits will also be tested experimentally, taking advantage of the Murray Lab's expertise working with cell-free transcriptional circuits.


  1. Waldherr, S., Wu, J. & Allgöwer, F. Bridging time scales in cellular decision making with a stochastic bistable switch. BMC Syst Biol 4, 108 (2010).
  2. Chang, H. H., Oh, P. Y., Ingber, D. E. & Huang, S. Multistable and multistep dynamics in neutrophil differentiation. BMC Cell Biol 7, 11 (2006).
  3. Legewie, S., Blüthgen, N. & Herzel, H. Mathematical modeling identifies inhibitors of apoptosis as mediators of positive feedback and bistability. PLoS Comput Biol 2, e120 (2006).
  4. Kalisky, T., Dekel, E. & Alon, U. Cost-benefit theory and optimal design of gene regulation functions. Phys Biol 4, 229–245 (2007).