Stochastic Models in Biology: Probability, Uncertainty, and Data-Driven Approaches

Stochastic Models in Biology explores the essential role of randomness, probability, and uncertainty in biological systems. This book provides a clear and rigorous introduction to stochastic modeling techniques and their increasingly important applications in modern biology.

Readers will gain a solid foundation in core probabilistic concepts and learn how to build and analyze data-driven stochastic models for a wide range of biological phenomena — from population dynamics and gene expression to epidemiology and evolutionary processes. The text bridges traditional mathematical theory with contemporary computational and statistical approaches, emphasizing practical implementation and real-world biological relevance.

Key topics include:

• Fundamental probability theory for biological systems
• Stochastic processes and Markov models
• Uncertainty quantification and sensitivity analysis
• Data-driven modeling using modern statistical methods
• Simulation techniques and numerical methods
• Applications to molecular biology, ecology, and medicine

Written for upper-level undergraduate students, graduate students, and researchers in biology, bioinformatics, biostatistics, and applied mathematics, this book offers an accessible yet mathematically sound treatment of stochastic modeling. It equips readers with both the theoretical understanding and practical tools needed to incorporate randomness and uncertainty into biological research.

Whether you are new to stochastic methods or looking to strengthen your modeling skills with data-driven techniques, this volume serves as a valuable resource for navigating the probabilistic nature of living systems.