Genetics + evolution + systems biology¶

Open Frontier

What you will develop¶

Reason about inheritance, variation, population change, gene circuits, network motifs, stochastic expression and evolutionary robustness.

Before you begin¶

Recorded prerequisites:

• Scientific computing

Study and practice¶

Progress by the work you can do, not by a fixed calendar.

Practice sequence 37¶

Spine: OpenStax Biology 2e

Reading: Ch. 11 meiosis; Ch. 12 Mendel; Ch. 13 inheritance; Ch. 19 population genetics

Know: Reason about segregation, recombination, linkage, genotype-phenotype uncertainty and allele-frequency dynamics.

Reconstruct: Derive Hardy-Weinberg equilibrium and basic selection update.

Do: Simulate drift vs selection in finite populations and quantify fixation variability.

Defend: Why can a beneficial allele fail to fix?

Gate: Pass: distinguish deterministic selection from stochastic drift and linkage.

Source: source

Practice sequence 38¶

Spine: MIT Systems Biology + Alon reference

Reading: Sessions 2-7: input functions, autoregulation, bistability, oscillators, motifs

Know: Model gene circuits with Hill/Michaelis-like functions, feedback and network motifs.

Reconstruct: Derive fixed points of a self-regulating gene and conditions for bistability qualitatively.

Do: Simulate toggle switch and oscillator; perturb parameters/noise and map robust operating regions.

Defend: When is a biological circuit 'designed' by evolution versus merely describable as a circuit?

Gate: Pass: network behavior predicted from equations and tested under perturbation.

Source: source

Practice sequence 39¶

Spine: MIT Systems Biology

Reading: Sessions 8-17: stochastic expression, master equation/Gillespie, robustness, evolution

Know: Model stochastic gene expression and evolutionary adaptation under finite populations.

Reconstruct: Reconstruct Gillespie-event selection logic and selection coefficient/fixation intuition.

Do: Implement stochastic gene-expression or mutation-selection simulation and compare mean-field ODE prediction.

Defend: When does averaging erase the phenomenon you care about?

Gate: Pass: explain when deterministic and stochastic models disagree.

Source: source

Practice sequence 40¶

Spine: MIT Systems Biology

Reading: Sessions 18-24 games, fluctuating environments, interactions, ecosystem stability/spatial dynamics

Know: Analyze eco-evolutionary feedback, games, predator-prey/interactions, resilience and critical transitions.

Reconstruct: Derive replicator equation for a two-strategy game or Lotka-Volterra fixed points.

Do: Design an artificial-ecosystem simulation with resource constraints, mutation/adaptation and perturbation recovery.

Defend: What makes an engineered ecosystem robust rather than merely stable at one equilibrium?

Gate: Module defense: proposed biological intervention includes evolutionary escape/adaptation scenario.

Source: source

Exit gate¶

Closed-book: 120 min: inheritance/pop-gen, gene circuits, stochastic expression, selection/drift, games/ecosystem dynamics.

Novel problem: Design an engineered biological function and then attack it with mutation, drift, noise and ecological feedback.

Artifact: Stochastic/evolutionary simulation with escape scenarios.

Defend: Defend robustness definition and how evolutionary adaptation changes the design objective.

Pass criterion: Pass if at least one plausible evolutionary failure is quantified and mitigated or accepted.

Transfer problems¶

Try these before consulting solutions or asking for the complete answer.

1. Mendel: Compute genotype/phenotype probabilities with linkage or incomplete dominance.

2. Population genetics: Simulate Hardy-Weinberg departure under selection.

3. Drift: Compare fixation variability at population sizes 20, 200 and 20,000.

4. Mutation-selection: Find equilibrium mutation load in a simple model.

5. Toggle switch: Find fixed points and bistability region in a toy gene circuit.

6. Oscillator: Perturb a synthetic oscillator and measure period/amplitude robustness.

7. Noise: Compare stochastic and deterministic expression models at low copy number.

8. Evolutionary game: Analyze ESS/replicator dynamics for two strategies.

9. Eco feedback: Model predator-prey or resource competition and perturbation recovery.

10. Escape: Design an evolutionary escape scenario for an engineered organism and a containment response.

Textbooks¶

See the five-book resource page.

Reading options¶

Assigned readings, where available, appear in the practice sequence above. These additional recommendations are not all required reading.

• Genetics: A Conceptual Approach

Applications¶

These are editorial study connections, not compulsory prerequisites.

• Regenerative medicine: Use genetics and systems biology to investigate cell-state control and unintended growth.

Continue¶

Connections above reflect recorded prerequisites, not a compulsory calendar.

Integrated practice | Choose a study route