Data representation
Encode sequences, structures, expression profiles, images and populations in forms appropriate to the biological question.
Side 206Side Studies / Research
Subject
Computational Biology
Purpose
Biological questions studied through algorithms, statistical models, simulation and large-scale molecular, cellular and population data.
Structure
05 movesSystem mapV0
Components → constraints → flows → control → failure
Let computation serve a biological question.
Computational biology becomes rigorous when model choice, data-generating process and biological interpretation remain connected rather than treating analysis as a detached pipeline.
Algorithms & models
Choose statistical, optimization or machine-learning methods whose assumptions match the structure and scale of the data.
Uncertainty & validation
Separate training performance from generalization and quantify uncertainty when data are sparse, biased or batch-structured.
Simulation
Use computational experiments to explore mechanisms, parameter regimes and hypotheses that are difficult to isolate experimentally.
Biological interpretation
Translate computational output back into mechanisms, testable predictions and limits that domain experts can challenge.
Keep the boundaries visible.
prediction accuracy ≠ biological explanation
large dataset ≠ representative dataset
association ≠ mechanism
Questions that organize the Side.
How does the data-generating process constrain what an algorithm can learn?
Which validation split matches the biological generalization being claimed?
What new experiment would distinguish a useful computational hypothesis from an attractive pattern?
What should carry weight here?
Benchmarking, external validation and biological replication matter more than model complexity; batch effects, leakage and sampling bias must be actively tested.