Supplement: Biomolecular Feedback Systems

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Domitilla Del Vecchio (U. Michigan/MIT) and Richard M. Murray (Caltech)


This supplement is intended for researchers interested in the application of feedback and control to biomolecular systems. The material has been designed so that it can be used in parallel with Feedback Systems as part of a course on biomolecular feedback and control systems, or as a standalone reference for readers who have had a basic course in feedback and control theory. The supplement is being written by Domitilla Del Vecchio and Richard Murray based on a variety of presentations, lectures and notes.

This page contains working notes for the material that is being prepared for the supplement. A set of lectures were given at the ASCC 2009 Workshop that provide a partial collection of material on this topic.

Audience

The supplement is intended to be useful to three distinct audiences:

  • First year PhD students in biology and bioengineering interested in feedback and control mechanisms in cells
  • Juniors through graduate students in engineering who are interested in biological circuit design
  • More senior researchers in the biological sciences who want to understand the application of principles and tools from feedback systems as applied to biomolecular systems

The pre-requisites for the supplement are a basic understanding of probability, differential equations and some linear algebra at an undergraduate level, along with a first course in feedback and control systems at the level of the main text (can be taken concurrently).

Outline

The current plan for the supplement is based on the following draft outline. Comments are welcome on topics that are missing.

Review of Core Processes

The goal of this chapter is to describe basic biological mechanisms in a way that can be translated to a variety of different types of models (low order ODEs, mass action, stochiometry matrix/rate vector, stochastic descriptions [CME, CLE], etc)

  • Modeling of transcription and translation
  • Transcriptional Regulation
  • Post-transcriptional and post-translational regulation
  • Intracellular sensing (membrane receptors, ligand binding, G-proteins)
  • Intracellular communication (MAPK cascades)
  • Intracellular computation (integral feedback, logical operations)

Dynamic behavior

In this chapter, we describe the relevant tools from control and dynamical systems that will be used in the rest of the text to analyze and design biological circuits, building on tools already described in AM08.

  • Steady state analysis (log/log diagrams, ala Savageau)
  • Phase plane analysis
  • Bifurcations: Hopf, saddle-node, etc
  • Time response
  • Limit cycles, including stability
  • Singular perturbation theory
  • Sensitivity analysis
  • Chemical reaction networks, monotone network theory

Stochastic behavior

  • Noise modeling (intrinsic/extrinsic), spectrum
  • Stochastic simulation analysis (SSA)
  • Linearized modeling and analysis: include disturbance attenuation in freq domain (?)
  • Markov modeling and analysis

Feedback examples

  • Lactose metabolism
  • Heat shock
  • Chemotaxis
  • Yeast mating response
  • Circadian rhythm

Biological circuit components

The goal of this chapter is to describe the basic approaches to biological circuit designs and give examples of basic modules (inverters, oscillators, toggle switches, etc).

  • DNA-protein, vs protein-protein, etc - different ways of implementing feedback, eg transcription regulation versus sequestration versus allosteric effects
  • Feedforward loops
  • Oscillators, including tools to characterize when you get oscillations. Focus on circuits that have been built
  • PI(D?) in biocircuits
  • Logical functions: AND, switches, inverters, toggles, etc. - focus on things that have been built

Interconnecting components

  • Modularity and retroactivity
  • Insulating devices
  • Effect of retroactivity on frequency response (eg, pole location)
  • Crosstalk between components (including links to retroactivity)
  • Managing shared resources (RNA polymerase, ribosomal loading, ATP?)
  • Autocatalysis effects (and fundamental limitations)

Design examples

  • Biobricks style circuits (from iGEM) - ignore construction details, but include design choices (eg, terminators)
  • (Design of feedback-enabled device - bio-recorder? bio-counter? programmed chemotaxis?)

Robustness and evolvability

  • Activation vs repression (demand theory)

Multicellular systems

  • Additional core processes
  • Quorum Sensing
  • Development

Examples

The material above is intended to be useful in analyzing a large variety of natural and engineered biomolecular feedback systems. Some of these examples will be included in the text, while others may be mentioned in exercises or simply left for the reader to explore on their own.

  • Repressilator: eqs, simulations, limit cycle stability using PB+ and Hastings
  • Toggle switch: eqs, nullclines and equilibria, stability, Lyapunov
  • Self repression: frequency response, time response
  • Self activation: dynamics plus Lyapunov
  • Activator-repressor clock (two implementations): eqs, simulations, PB for the 2D model reduction, Hopf Bifurcation for the 4D (singular perturbation for the 2D reduction?)
  • Chemotaxis: integral feedback (Iglesias), modeling (incl methylation)
  • Input adaptation: MAPK with feedback? (Kholodenko?)
  • Biochemical circuits (in vitro)
  • Development/differentiation: signal switching (Tomlin, Ashthagiri)
  • Sporalation, fate choice (Elowitz)
  • MAPK cascades (Sauro, Kholodenko)
    • Possible in vitro phosphorilation (Ninfa)
  • Quorum Sensing (Weiss, ?), synchronization
  • Methabolic Pathways/engineering (yeast demand analysis, Hoffmeyer)
  • Hemoglobin (Kirshner) for allostery feedback?
  • Glucose/Lactose (Jacob and Monod)
  • Anti-sense switches (Smolke)
  • FFL (Alon)?
  • Heat Shock (El Samad)
  • Calcium Regulation (Khammash)

Additional Information