๐ Official Curriculum & Master Syllabus
๐งฎ Computational and Systems Biology: Syllabus & Course Map
Equip biology and computational students with the mathematical modeling, network graph theory, constraint-based flux analysis, stochastic simulation, and synthetic genetic circuit design principles required to decode living systems as integrated dynamical networks.
10
Syllabus Modules
50
Mapped Topics
10
Chapter Pages
CURRICULUM ROADMAP
Interactive Syllabus & Modular Structure
Official curriculum mapped to university degree syllabi and competitive examinations. Each module links directly to its dedicated study notes page and relevant subtopics.
10 Modules
Module 02
Notes →
Biological Network Topology & Graph Theory
Graph Foundations: Nodes, Directed/Undirected Edges & Adjacency Matrices
Degree Distributions & Scale-Free Networks (Power Law P(k) ~ k^-ฮณ)
Small-World Effect & High Clustering Coefficients
Centrality Metrics: Degree, Betweenness, Closeness & Eigenvector
Modularity, Community Detection & Functional Biological Modules
Module 03
Notes →
Transcriptional Regulatory Networks & Network Motifs
Transcriptional Regulatory Networks & Directed Interaction Graphs
Autoregulation: Negative Autoregulation Accelerates Response, Positive Causes Bimodality
Coherent Feed-Forward Loops (FFL) as Sign-Sensitive Delay Filters
Incoherent Feed-Forward Loops (I-FFL) as Pulse Generators & Response Accelerators
Single-Input Modules (SIM), Multi-Input Modules (MIM) & Topological Robustness
Module 04
Notes →
Metabolic Flux Analysis & Constraint-Based Modeling
Genome-Scale Metabolic Models (GEMs) & Reaction Reconstructions
Stoichiometric Matrix (S) & The Steady-State Mass Conservation S ยท v = 0
Flux Balance Analysis (FBA) & Linear Programming Optimization
Flux Variability Analysis (FVA) & Shadow Prices
In Silico Gene Deletions, Essentiality Predictions & Metabolic Engineering
Module 05
Notes →
Stochasticity, Noise & Gillespie Simulations
Low Molecule Copy Number Regimes & Breakdown of Deterministic ODEs
Intrinsic vs. Extrinsic Noise & The Dual-Reporter Fluorophore Assay
Chemical Master Equation (CME) & Poisson/Burst Probability Distributions
Gillespie Stochastic Simulation Algorithm (SSA & Direct Method)
Phenotypic Switching, Bimodal Cell States & Bacterial Persistence
Module 06
Notes →
Dynamic Signaling Circuits & Bistability
Signal Transduction Cascades & The MAPK/ERK Phosphorylation Relay
Ultrasensitivity & Goldbeter-Koshland Zero-Order Kinetic Switching
Positive Feedback, Bistability, Hysteresis & Epigenetic Cellular Memory
Negative Feedback Oscillations: Circadian Clocks, p53-Mdm2 & NF-ฮบB
Reaction-Diffusion Systems & Turing Morphogen Pattern Formation
Module 07
Notes →
Synthetic Biology & Genetic Circuit Engineering
Standard Biological Parts, BioBricks & Synthetic Gene Promoters
The Gardner Genetic Toggle Switch: Mutual Repression & Bistable Memory
The Elowitz Repressilator: Synthetic Cyclic Transcriptional Oscillations
Cellular Logic Gates (AND, OR, NAND, NOR) & Multi-Input Boolean Circuits
CRISPR-Cas Based Transcriptional Repressors (CRISPRi) & Activators (CRISPRa)
Module 08
Notes →
Single-Cell Systems Biology & Omics Integration
Single-Cell RNA Sequencing (scRNA-seq) Architectures, UMIs & Technical Dropouts
Dimensionality Reduction (PCA, UMAP, t-SNE) & Graph Clustering (Leiden)
Pseudotime Trajectory Inference & Lineage Branching Bifurcations
RNA Velocity: Modeling Spliced vs. Unspliced mRNA Directional Dynamics
Multi-Omics Single-Cell Integration: Spatial Transcriptomics & scATAC-seq
Module 09
Notes →
Multi-Scale Whole-Cell Modeling & Physical Simulations
Integrating Heterogeneous Cellular Processes across Disparate Timescales
The Karr Whole-Cell Model of Mycoplasma genitalium Architecture
Spatial Compartmentalization, Macromolecular Crowding & Diffusion Constraints
Hybrid Multi-Algorithmic Simulation (Coupling ODEs, FBA & Stochastic SSA)
Resource Balance Analysis (RBA) & Proteome Allocation Partitioning
Module 10
Notes →
Machine Learning, Deep Learning & AI in Systems Biology
Supervised & Unsupervised Machine Learning in Multi-Omics Profiling
Deep Variational Autoencoders for Single-Cell Latent Embeddings (scVI)
Graph Neural Networks (GNNs) for Molecular Interaction Networks
Large Foundation Models in Genomics & Cellular State Embeddings (Geneformer, scGPT)
Physics-Informed Neural Networks (PINNs) & Generative In Silico Perturbation
Core Learning Outcomes & Competencies
Upon mastery of this comprehensive syllabus, students and researchers are expected to:
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Formulate ordinary differential equation (ODE) models of biochemical reaction cascades, applying the law of mass action and Michaelis-Menten / Hill kinetics.
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Calculate network graph topological properties including node degree distribution, clustering coefficient, path length, and centrality metrics across biological networks.
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Classify transcriptional network motifsโincluding coherent/incoherent feed-forward loops (FFLs) and negative/positive autoregulatory loopsโand analyze their dynamical signal-filtering properties.
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Construct stoichiometric matrices (S) for genome-scale metabolic networks and solve Flux Balance Analysis (FBA) linear optimization problems to predict growth phenotypes and metabolic fluxes.
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Distinguish intrinsic from extrinsic noise in isogenic cell populations and execute the Gillespie Stochastic Simulation Algorithm (SSA) for low-copy-number molecular regimes.
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Analyze dynamic signaling cascades for bistability, hysteresis, and ultrasensitivity, contrasting Goldbeter-Koshland zero-order kinetics with multistable toggle circuits.
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Design synthetic biological devicesโincluding the Gardner toggle switch, the Elowitz repressilator, and digital logic gatesโusing standardized BioBrick and CRISPRi/a parts.
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Process single-cell RNA sequencing (scRNA-seq) matrices through dimensionality reduction (PCA, UMAP), graph clustering (Leiden), and compute RNA velocity vector fields.
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Describe multi-scale whole-cell computational models integrating heterogeneous cellular sub-models into unified physical simulations.
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Apply graph neural networks (GNNs), deep generative autoencoders, and foundation models to predict cell states, gene perturbation responses, and macromolecular interactions.
CHAPTER DIRECTORY
Dedicated Chapter & Lecture Pages (10)
Browse every individual chapter, experimental protocol, and specialized topic module published in this discipline.
10 Pages
Computational and Systems Biology: Chapter 1 โ Foundations of Systems Biology & Mathematical Modeling
Computational and Systems Biology: Chapter 2 โ Biological Network Topology & Graph Theory
Computational and Systems Biology: Chapter 3 โ Transcriptional Regulatory Networks & Network Motifs
Computational and Systems Biology: Chapter 4 โ Metabolic Flux Analysis & Constraint-Based Modeling
Computational and Systems Biology: Chapter 5 โ Stochasticity, Noise & Gillespie Simulations
Computational and Systems Biology: Chapter 6 โ Dynamic Signaling Circuits & Bistability
Computational and Systems Biology: Chapter 7 โ Synthetic Biology & Genetic Circuit Engineering
Computational and Systems Biology: Chapter 8 โ Single-Cell Systems Biology & Omics Integration
Computational and Systems Biology: Chapter 9 โ Multi-Scale Whole-Cell Modeling & Physical Simulations
Computational and Systems Biology: Chapter 10 โ Machine Learning, Deep Learning & AI in Systems Biology