Bring computation into biological sciences research, courses, and labs, helping trainees learn coding through real biological questions and data.
With MATLAB and Simulink, biologists and life scientists can:
- Teach consistent and connected workflows for acquiring, processing, visualizing, modeling, and interpreting biological data
- Use real-world datasets and common formats from genomics, microscopy, neuroscience, physiology, ecology, and related fields
- Introduce programming gradually with interactive apps, live scripts, and project-based activities
- Connect statistical methods, image and signal processing, mathematical modeling, and machine learning to biological questions
- Make analyses reproducible and share code, data, apps, and reports
- Build on existing Python, C/C++, and community tools rather than recreating workflows from scratch
Working with Biological Data and Statistics
Import, organize, visualize, and analyze scientific datasets using statistical methods such as hypothesis testing, correlation, and regression. Then, combine these skills into reproducible workflows that connect computation to biological questions.
Featured Resources
Image Processing and Microscopy
Import, enhance, register, segment, label, and quantify biological images. Interactive apps provide accessible entry points for exploring images, identifying cells and structures, and testing segmentation methods. Generate or extend code for batch processing, reproducibility, and larger datasets, including multidimensional and whole-slide images.
Bioinformatics and Genetics
Analyze DNA and protein sequences, examine genetic variation, and build reproducible next-generation sequencing workflows. The Biopipeline Designer app supports interactive construction of complete pipelines with built-in blocks for established NGS libraries and custom blocks for community tools. Students can develop pipelines visually or programmatically and scale through parallel or batch execution.
AI and Machine Learning for Biosciences
Use interactive apps to train and validate machine learning models, then explore the underlying code and apply their models to new data. Applications or teaching examples can span classification of biological measurements, cell and tissue segmentation, interpretation of biological signals, and model-assisted discovery. MATLAB also supports importing existing models and interoperability with Python and other languages. These capabilities connect AI instruction with established scientific workflows.
Featured Resources
Modeling Biological Systems
Translate biological mechanisms into quantitative models, explore system behavior, and compare simulations with experimental observations. MATLAB and Simulink support workflows, including ordinary and partial differential equation models, population dynamics, and system-level simulation. SimBiology extends these workflows to pharmacological modeling, dose studies, and quantitative systems pharmacology applications.
