ECE 471
ECE 471 - Data Science Analytics using Probabilistic Graph Models
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Subject Area
- AI: AI Software and Hardware Systems
Description
The goal of this course is to highlight elementary design principles of biological systems. Many of the underlying principles that govern the biochemical interactions within a cell can be related to networks consisting of basic building-block circuits with multiple inputs/output, feedback and feedforward etc. This course draws on control theory and simple biology to provide a mathematical framework to understand these biological networks. The course is intended for advanced undergraduates or graduate students.
Notes
Grade is determined by a mid-term (40%), final exam (40%), and homeworks + class participation in analysis of current research (20%).
Topics
- 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)
Detailed Description and Outline
Topics:
- 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