BIOLOGY-12 · Advanced · ENGLISH · PLANNED
Zoom from organism to mechanism, then evaluate the biological evidence.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Biology | Quantitative Biology and Bioinformatics
Playlist title reserved in the plan; no YouTube playlist or upload has been created yet.
Sign in to save this learning pathPractical outcome: Analyse a supplied experiment with replicates and effect-size estimates.
Explain experimental units using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Explain biological versus technical replicates using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Explain batch effects using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Explain effect sizes using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Use the first concepts from this section. Analyse a supplied experiment with replicates and effect-size estimates. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain multiple-testing correction using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Explain power in experiments using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Explain count data models using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Explain survival analysis concept using a visual and one worked real-world example. Context: Analyse a supplied experiment with replicates and effect-size estimates. End with a prediction or check-for-understanding.
Analyse a supplied experiment with replicates and effect-size estimates. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Experimental units, Biological versus technical replicates, Batch effects and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare sequence similarity with a justified scoring scheme.
Explain sequence formats using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Explain pairwise alignment using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Explain dynamic programming for alignment using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Explain multiple sequence alignment using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare sequence similarity with a justified scoring scheme. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain phylogenetic inference using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Explain protein structure prediction principles using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Explain gene-set analysis using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Explain reproducible notebooks using a visual and one worked real-world example. Context: Compare sequence similarity with a justified scoring scheme. End with a prediction or check-for-understanding.
Compare sequence similarity with a justified scoring scheme. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Sequence formats, Pairwise alignment, Dynamic programming for alignment and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Fit a small regulatory-network model and test identifiability.
Explain network motifs using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Explain differential-equation models using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Explain parameter estimation using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Explain identifiability using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Use the first concepts from this section. Fit a small regulatory-network model and test identifiability. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain sensitivity analysis using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Explain stochastic gene expression using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Explain metabolic networks using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Explain model validation using a visual and one worked real-world example. Context: Fit a small regulatory-network model and test identifiability. End with a prediction or check-for-understanding.
Fit a small regulatory-network model and test identifiability. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Network motifs, Differential-equation models, Parameter estimation and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Fit a small regulatory-network model and test identifiability. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Quantitative Biology and Bioinformatics, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Annotated systems, sequence analysis and observational investigations. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
Open the interactive practice sampler →