Quantitative Biology, Single-Cell Omics, and Computational Modeling
Why biology now depends on computation
Data-rich biology
Modern biology routinely generates high-dimensional data from genomics, transcriptomics, proteomics, imaging, lineage tracing, and perturbation screens. Computational methods are essential for inference, integration, and causal modeling.
Practical frontier
The next challenge is not just describing cell states, but predicting how they change under perturbation.
Single-cell and spatial technologies
Resolution and integration
Single-cell RNA-seq, ATAC-seq, multiome assays, spatial transcriptomics, and imaging-based profiling expose cell-state diversity and tissue context. Analytical challenges include batch correction, label transfer, trajectory inference, and causal perturbation analysis.
What is a major analytical challenge in single-cell datasets?
Single-cell experiments are sensitive to technical variation, requiring careful normalization and integration.
Correct answer: Batch effects and technical noise
What is trajectory inference used for?
It estimates developmental or activation paths from snapshot measurements.
Correct answer: To reconstruct dynamic cellular transitions from static data
Modeling frameworks
Common modeling approaches
Ordinary differential equations
- Useful for continuous dynamics
- Requires explicit parameterization
Stochastic models
- Capture noise and burstiness
- Important for small copy-number systems
Which model class is best suited to capturing random fluctuations in low-copy-number molecular systems?
Stochastic approaches are needed when noise and discrete events materially affect behavior.
Correct answer: Stochastic models
What is a key goal of causal perturbation screens?
Perturbations such as CRISPR can reveal which components drive observed phenotypes.
Correct answer: To identify mechanisms rather than correlations