Systems Biology Course

Lesson 1 • Data Types in Systems Biology

Surveys genomic, transcriptomic, proteomic, and metabolomic data and their roles in systems-level analysis. Students learn how each omics layer contributes to a holistic biological model.

Lesson 2 • What Is Systems Biology

Defines systems biology as the study of emergent biological behaviour through component interactions. Grounds students in the paradigm shift from gene-centric to network-centric thinking.

Lesson 3 • Quantitative Thinking in Biology

Builds comfort with mathematical reasoning applied to biological systems, including rates, concentrations, and steady states. Prepares students for modelling chapters ahead.

Lesson 1 • Topological Properties of Networks

Examines clustering coefficients, shortest paths, diameter, and centrality measures. Students interpret these metrics in the context of biological function and robustness.

Lesson 2 • Network Visualization and Tools

Introduces software platforms for constructing, visualising, and analysing biological networks. Students produce publication-quality network figures and interpret visual layouts.

Lesson 3 • Scale-Free and Small-World Networks

Analyses power-law degree distributions and small-world properties observed in biological networks. Connects topology to evolutionary pressures and network resilience.

Lesson 4 • Graph Theory Essentials

Covers directed and undirected graphs, adjacency matrices, and degree distributions as applied to biological data. Establishes the mathematical language for all subsequent network analysis.

Lesson 1 • Ordinary Differential Equations in Biology

Introduces ODEs as the primary tool for modelling time-dependent biological processes. Students formulate and solve simple ODE systems representing gene expression and population dynamics.

Lesson 2 • Oscillations and Bistability

Examines conditions producing limit cycles and bistable switches in biological circuits. Students connect mathematical criteria to biological phenomena such as cell-cycle oscillations.

Lesson 3 • Sensitivity and Robustness Analysis

Quantifies how model outputs respond to parameter perturbations using sensitivity coefficients. Students assess model robustness and identify critical parameters for experimental targeting.

Lesson 4 • Steady-State and Stability Analysis

Applies phase-plane analysis and linearization to determine fixed points and their stability. Students classify biological equilibria and predict system behaviour near steady states.

Lesson 1 • Langevin and Fokker-Planck Approaches

Introduces continuous stochastic differential equations and their probability density evolution. Students apply these approximations to systems where the CME is computationally intractable.

Lesson 2 • Sources of Biological Noise

Distinguishes intrinsic noise from molecular fluctuations and extrinsic noise from cell-to-cell variability. Motivates the need for stochastic approaches beyond deterministic ODE models.

Lesson 3 • Noise Propagation in Networks

Analyses how noise propagates through signalling cascades and gene regulatory networks. Students use linear noise approximation to predict variance in network outputs.

Lesson 4 • Chemical Master Equation

Derives the chemical master equation (CME) for discrete stochastic reaction systems. Students understand the probability distribution evolution of molecular species over time.

Lesson 1 • Preprocessing and Normalization

Covers quality control, batch correction, and normalisation strategies for high-throughput omics data. Ensures students can prepare raw data for downstream systems-level analysis.

Lesson 2 • Network-Based Data Integration

Overlays omics data onto biological networks to identify active subnetworks and key regulators. Students use network propagation and prize-collecting Steiner tree approaches.

Lesson 3 • Differential Expression Analysis

Applies statistical models to identify genes and proteins that change significantly across conditions. Students interpret fold changes, p-values, and false discovery rate corrections.

Lesson 4 • Pathway and Enrichment Analysis

Maps differentially expressed molecules onto curated biological pathways and gene ontology terms. Students perform and interpret over-representation and gene set enrichment analyses.

Lesson 1 • Correlation and Mutual Information Methods

Applies Pearson correlation, Spearman correlation, and mutual information to infer co-expression relationships. Students understand the limitations of correlation-based inference.

Lesson 2 • Benchmarking and Validation

Evaluates inferred networks using AUROC, AUPR, and comparison to curated gold-standard datasets. Students apply DREAM challenge benchmarking frameworks to assess method performance.

Lesson 3 • Regression-Based Network Inference

Uses LASSO, ridge regression, and random forests to predict regulatory interactions from expression data. Students compare regression methods for accuracy and interpretability.

Lesson 4 • Regulatory Network Concepts

Defines transcription factors, cis-regulatory elements, and the logic of gene regulation at a systems level. Provides the biological foundation for computational inference methods.

Lesson 1 • Genome-Scale Metabolic Reconstruction

Covers the pipeline for reconstructing genome-scale metabolic models from annotated genomes. Students use automated tools and manual curation to produce a draft model.

Lesson 2 • Stoichiometric Modeling Fundamentals

Introduces the stoichiometric matrix and steady-state flux constraints as the basis for metabolic modelling. Students construct small metabolic networks and define feasible flux solution spaces.

Lesson 3 • Advanced Constraint-Based Methods

Extends FBA with parsimonious FBA, flux variability analysis, and context-specific modelling using omics data. Students integrate transcriptomic constraints to generate condition-specific models.

Lesson 4 • Gene Essentiality and Knockout Analysis

Simulates single and double gene knockouts to predict essential genes and synthetic lethality. Students compare computational predictions to experimental essentiality screens.

Lesson 1 • Drug Target Identification

Uses network centrality, essentiality, and druggability criteria to prioritise therapeutic targets. Students integrate network analysis with pharmacological databases to rank candidates.

Lesson 2 • Personalized Systems Medicine

Integrates patient-specific omics data into network models to generate individualised disease profiles. Students construct patient-specific subnetworks and propose tailored intervention strategies.

Lesson 3 • Disease as Network Perturbation

Frames disease as a disruption of normal network states caused by genetic, epigenetic, or environmental factors. Students map disease mutations onto biological networks to identify perturbed modules.

Lesson 4 • Drug Repurposing Strategies

Applies network proximity and signature-matching methods to identify new uses for approved drugs. Students evaluate repurposing candidates using disease network and transcriptomic evidence.