ECE 471
Systems Biology for Engineers

Course Prerequisites Credit in CHEM 102
Credit in MATH 286
Course Directors Gregory L Timp
Detailed Description and Outline


  • Introduction to molecular/cell biology
  • A review of network concepts: properties and modeling of feedback/feed-forward systems
  • Introduction to molecular biology (molecular recognition, proteins, DNA, repressors/promoters/ Hill functions)
  • transcription networks (timescales, introduction to gene regulation)
  • Transcription networks revisited (multi-d input functions, dynamic response in gene regulation)
  • Autoregulation (AR) (negative for fast response time and robust stable production in gene circuits)
  • Autoregulation (positive AR slows response and leads to bi-stability
  • Feed-forward Loop (FFL) Network Motif (Dynamics of coherent FFL with AND logic)
  • FFL is a sign-sensitive delay element
  • Incoherent FFL (dynamics—pulse generator; response acceleration)
  • Network Motifs in development transcription networks (positive feedback loops for making decisions; regulating feedback; developmental timing; interlocked feed-forward loops in B. subtilis)
  • Information processing using Multi-layer perceptrons.
  • Network Motifs in neuronal networks (An example: C. elegans)
  • Network Motifs: negative feedback and oscillator motifs
  • Protein circuits (a review of protein biochemistry)
  • Protein circuits (an example: bacterial chemotaxis in E. coli)  
  • Two models for Adaptation: 1. Robust and 2. Fine-tuned
  • The Robust Adaptation (Barkai-Leibler) and Integral Feedback.
  • Linearization of nonlinear systems--linear system response
  • Stability— Routh criterion, Nyquist criterion, root locus techniques,
  • Circadian rhythms—how to build an oscillator; represillator
  • Buzzers, Toggles, sniffers, and oscillators
  • Kinetic Proofreading (proofreading the genetic code to reduce error rates of molecular recognition)
  • Recognizing Self and Non-self by the Immune system
  • Kinetic Proofing and T-cell recognition
  • Gene Circuit Design I (optimal expression of a protein in a constant environment)
  • Gene Circuit Design II(optimal regulation in a variable environment)
  • The Savageau Demand Rule: e.g. the demand rule in E. coli
  • Rules for gene regulation (based on minimal error load or selection repression; multiregulator systems)

Grade is determined by a mid-term (40%), final exam (40%), and homeworks + class participation in analysis of current research (20%).

Last updated: 2/13/2013