Biological World Models0%
02 · Weekly Lessons

24 weeks of technical translation.

Each week converts a slice of CS, AI, math, or physics into biological modeling practice. Mark complete as you go and capture notes that feed the capstone.

WEEK 01 · MONTH 1

Biology for Model Builders

  • ·Understand cells as stateful systems
  • ·Learn the hierarchy: DNA → RNA → protein → pathway → phenotype
  • ·Translate biological terms into state variables and interventions
WEEK 02 · MONTH 1

Molecular Biology and Regulation

  • ·Learn transcription, translation, regulation, chromatin, splicing, and protein modification
  • ·Understand why gene expression is not the same as biological function
WEEK 03 · MONTH 1

Cell Biology and Disease States

  • ·Learn cell cycle, apoptosis, senescence, stress response, organelles, and cell identity
  • ·Understand disease as a state transition or attractor
WEEK 04 · MONTH 1

Pharmacology for AI/Physics Thinkers

  • ·Learn target, ligand, potency, selectivity, exposure, toxicity, PK/PD, and therapeutic index
  • ·Understand why binding affinity alone is not a drug
WEEK 05 · MONTH 2

Perturbation Biology

  • ·Learn perturbation types: CRISPR knockout, knockdown, overexpression, drug, ligand, dose, time
  • ·Understand perturbation-response matrices
WEEK 06 · MONTH 2

Single-Cell and Omics Data

  • ·Learn single-cell RNA-seq concepts
  • ·Understand cells as distributions over states
  • ·Learn batch effects, embeddings, clustering, and differential expression
WEEK 07 · MONTH 2

Baselines and Benchmarks

  • ·Build linear and nearest-neighbor baselines
  • ·Learn leakage-aware evaluation
  • ·Learn why simple baselines matter
WEEK 08 · MONTH 2

Neural Perturbation Models

  • ·Learn conditional prediction of cell state after intervention
  • ·Compare neural models to baselines
  • ·Track uncertainty and failure modes
WEEK 09 · MONTH 3

Pathways as Graphs

  • ·Learn directed, signed, typed biological graphs
  • ·Understand pathway databases as priors, not complete truth
WEEK 10 · MONTH 3

Dynamical Systems in Biology

  • ·Learn ODEs, SDEs, mass action, feedback, and steady states
  • ·Understand attractors and bifurcations in biological systems
WEEK 11 · MONTH 3

Hybrid Mechanistic-Neural Models

  • ·Combine known pathway structure with learned residuals
  • ·Learn where mechanistic models fail and where neural models help
WEEK 12 · MONTH 3

Control and Intervention

  • ·Learn controllability, observability, state estimation, and intervention design
  • ·Translate drug dosing into control inputs
WEEK 13 · MONTH 4

Causal Graphs for Biology

  • ·Learn causality, confounding, interventions, and counterfactuals
  • ·Understand why observational omics is not enough
WEEK 14 · MONTH 4

Counterfactual Prediction

  • ·Predict what would happen if… under unseen perturbations
  • ·Learn invariance and domain shift
WEEK 15 · MONTH 4

Experimental Design

  • ·Learn expected information gain, uncertainty sampling, and active learning
  • ·Choose experiments that reduce model uncertainty
WEEK 16 · MONTH 4

Combination Therapy and Optimization

  • ·Understand drug synergy, antagonism, resistance, and adaptive therapy
  • ·Learn multi-objective optimization for efficacy and safety
WEEK 17 · MONTH 5

Protein Structure and Binding

  • ·Learn protein folding, structure, active sites, conformational states, and binding
  • ·Understand what structure prediction can and cannot solve
WEEK 18 · MONTH 5

Molecular Docking and Selectivity

  • ·Learn docking intuition, binding pockets, off-targets, and selectivity
  • ·Understand why docking scores are not final truth
WEEK 19 · MONTH 5

Allostery and Dynamic Proteins

  • ·Learn proteins as ensembles, not static objects
  • ·Understand allosteric regulation and conformational switching
WEEK 20 · MONTH 5

Structure-to-Cell Bridge

  • ·Connect target inhibition to pathway response
  • ·Understand mechanism of action at multiple scales
WEEK 21 · MONTH 6

Building the Mini Virtual Cell

  • ·Define state variables, interventions, context variables, and outputs
  • ·Integrate graph priors, perturbation data, and dynamic models
WEEK 22 · MONTH 6

Disease-State Reversal

  • ·Define disease signatures and healthy signatures
  • ·Search for interventions that reverse disease states
WEEK 23 · MONTH 6

Validation, Uncertainty, and Failure Modes

  • ·Add uncertainty estimates
  • ·Identify what the model cannot know
  • ·Create falsifiable predictions
WEEK 24 · MONTH 6

Capstone Presentation

  • ·Present the mini virtual cell
  • ·Explain model assumptions, data sources, predictions, and limitations
  • ·Propose the next experiment
Study session generator

Plan the next 45 minutes.

We pick your next unfinished week and propose a focused session: a coding task, a biology reading, and a reflection question — tied back to the capstone.