Learning Objectives
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# Learning Objectives - Understand how biological networks evolve - Model network dynamics - Analyze robustness ## 1. Network Evolution **Gene Duplication:** Duplicate gains same interactions, then diverges.
Learning Objectives
- Understand how biological networks evolve
- Model network dynamics
- Analyze robustness
1. Network Evolution
Gene Duplication: Duplicate gains same interactions, then diverges. Subfunctionalization: Duplicates split original function. Neofunctionalization: Duplicate gains new function. Preferential attachment: New nodes connect to hubs -> scale-free.
2. Network Dynamics Models
Boolean Networks: Binary states (ON/OFF), logical update rules. Simple, captures regulatory logic. Differential Equations: Continuous concentrations, requires kinetic parameters. Detailed. Petri Nets: Tokens (concentrations), transitions (reactions).
3. Robustness
Biological networks robust to random failure (most nodes few connections), vulnerable to hub attacks (fragments network).
Mechanisms: Redundancy (parallel pathways), Feedback control (homeostasis), Modularity (contain damage).
Q1: How does gene duplication affect networks?Duplicate inherits original's interactions. Over time duplicates diverge: lose (drift) or gain (neofunctionalization) interactions. Drives network complexity evolution. Q2: Why robust to random failure?Most nodes few connections - removing random nodes unlikely to affect connectivity. Hubs few, unlikely to be randomly removed. But targeted hub removal rapidly fragments. Q3: What is a Boolean network?Binary node states (ON/OFF). Logical update rules (AND, OR, NOT). Simple but can model complex dynamics like cell cycle, differentiation. Q4: Redundancy vs robustness?Redundancy: duplicate components (genes, pathways). Robustness: maintain function despite perturbations. Redundancy is one mechanism for robustness. Q5: What is an attractor in Boolean networks?System state it settles to. Fixed point (steady state), limit cycle (oscillation), or chaotic. Cell types = attractors in gene regulatory networks. Q6: How does preferential attachment create scale-free?New nodes preferentially connect to high-degree nodes. Rich get richer. Over time, hubs emerge. Likely operates in biological network evolution. Join Discord PreviousNetwork Biology & DiseaseNextNetwork Visualization & Tools