Tutorials

• Preface
  • Philosophy and scope of BoolForge
  • Structure of the tutorials
• Working with Boolean Functions
  • What you will learn
  • Setup
  • Create a Boolean function
    • Create Boolean functions from a truth table
    • Create Boolean functions from text
    • Combining BooleanFunction objects
  • Attributes of BooleanFunction
  • Basic properties of Boolean functions
  • Logical and polynomial representations
  • Summary
    • Boolean functions
    • Representations of Boolean functions
    • Variable names and ordering
    • Basic properties of Boolean functions
    • Conversions and interoperability
  • Frequently Asked Questions
    • Why does the order of variables matter?
    • How do I choose between defining a function via a truth table or via an expression?
    • What is the difference between get_type_of_inputs() and monotonicity?
    • Quick Reference
• Advanced Concepts for Boolean Functions
  • What you will learn
  • Setup
  • Symmetries in Boolean functions
  • Degenerate functions
  • Activities and sensitivities
    • Activity
    • Average sensitivity
    • Exact vs Monte Carlo computation
    • Computing activities and sensitivities
  • Summary
• Canalization
  • What you will learn
  • Setup
  • Canalizing variables and layers
    • Canalizing depth and nested canalization
    • Canalizing layer structure
  • Collective canalization
    • Canalizing strength
    • Distribution of canalizing strength
  • Canalization as a measure of input redundancy
  • Summary
• Random Boolean function generation
  • What you will learn
  • Setup
  • Generating random Boolean functions
  • Parity functions
  • Functions with prescribed canalizing properties
    • Functions with prescribed canalizing layer structure
    • Functions with prescribed canalizing depth
  • Allowing degenerate functions
  • Functions with prescribed Hamming weight
  • Biased and absolutely biased functions
  • Summary
  • Common pitfalls
• Ensemble experiments with random Boolean functions
  • What you will learn
  • Setup
  • Prevalence of canalization
    • Restricting to canalizing functions
  • Collective canalization vs degree
    • Stratification by canalizing depth
    • Correlation between canalizing strength and input redundancy
  • Correlation between canalization and bias
    • Degeneracy vs bias
  • Analyzing functions with specific canalizing layer structure
  • Summary
• Working with Boolean Networks
  • What you will learn
  • Setup
  • Boolean network theory
  • Wiring diagrams
    • Example with constants and unequal degrees
  • Creating Boolean networks
    • Creating networks from strings
    • Interoperability with CANA
  • Types of nodes in Boolean networks
  • Boolean network properties
  • Manipulation and control of Boolean networks
    • Node controls
    • Edge controls
  • Outlook
• Dynamics of Boolean Networks
  • What you will learn
  • Setup
  • State space of a Boolean network
  • Dynamics of synchronous Boolean networks
    • Exact computation
    • Exhaustive attractor computation
    • Monte Carlo simulation
  • Dynamics of asynchronous Boolean networks
    • Attractors under general asynchronous update
    • Monte Carlo approximation
    • Sampling from a fixed initial condition
  • Summary
• Perturbation and sensitivity analysis of Boolean networks
  • What you will learn
  • Setup
  • Exact attractors and robustness measures
  • Network-, basin- and attractor-level robustness in synchronous networks
  • Network-, basin- and attractor-level robustness in asynchronous networks
  • Approximate robustness for larger synchronous networks
  • Derrida value: dynamical sensitivity
  • Summary
• Random Boolean network generation
  • What you will learn
  • Setup
  • Generating random wiring diagrams
    • Allowing self-regulation
    • Poisson in-degree distributions
    • Avoiding output nodes
    • Strong connectedness
    • Fixed wiring diagrams
  • Specifying functional constraints
    • Parity functions
    • Canalizing functions
    • Biased functions
  • Summary
• Ensemble experiments with random Boolean networks
  • What you will learn
  • Setup
  • NK Kauffman networks
  • BoolForge philosophy: regulatory functions are non-degenerate
  • Random networks with prescribed canalization
  • Summary and outlook
• Curated biological Boolean networks and null models
  • What you will learn
  • Setup
  • Loading model repositories
  • Analyzing model repositories
  • Null models
    • Randomization of the wiring diagram
    • Randomization of the update functions
  • Example use case: high coherence of biological networks
  • Summary
• Dynamics Decomposition
  • What you will learn
  • Setup
  • Trajectories
    • Computing trajectories
    • Plotting trajectories
    • Computing the product of trajectories
  • Examples
    • Example 2.8
    • Example 2.9
    • Example 2.10
    • Example 3.2
    • Figure 11