Quantitative Biology

Nyhet

Häftad, Engelska, 2026

1 219 kr

Kommande

Quantitative Biology introduces and implements quantitative and data-driven approaches for analyzing biological and bio-inspired systems, covering the foundations of mathematical modeling, analysis, and computation. The book presents a practical mix of both theory and computation for a variety of biological applications, with tied-in, engaging project activities, instruction, programming language, and technological tools. Modeling approaches in the book combine mathematical foundations, statistical reasoning, and computational thinking, with application in compartmental, agent-based, bio image, biological interaction, and neural network modeling, as well as machine learning, parameter identification, and more, with a later chapter considering applications across societal challenges. Each chapter includes exposure to models and modeling, a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style, helping readers go beyond replication models and into prediction and data-driven discovery. A companion website also features interactive code to accompany projects across each chapter.

Introduces and demonstrates mathematical modeling, analysis, and computation for biological and bio-inspired systems
Presents and instructs in computation for a variety of biological applications via engaging project activities, benchmark examples, and technology tools
Offers insights into replicative models for biological systems, empowering prediction and data-driven discovery
Includes a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style across all chapters
Features a companion webpage with interactive code to accompany chapter projects

Produktinformation

Utgivningsdatum2026-05-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor350
FörlagElsevier Science
ISBN9780443274527

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Alonso Oliva Ogueda holds a Master’s degree in Mathematics from the Universidad Técnica Federico Santa María (2021) and a Mathematical Engineering degree from Universidad Técnica Federico Santa María (2019). He has worked on a variety of projects involving development of mathematical/statistical algorithms, data analysis, data science and engineering and Cloud computing. Dr. Padmanabhan Seshaiyer is a tenured Professor of Mathematical Sciences at George Mason University and serves as the Director of the STEM Accelerator Program in the College of Science as well as the Director of COMPLETE (Center for Outreach in Mathematics Professional Learning and Educational Technology) at George Mason University in Fairfax, Virginia. His research interests are in the broad areas of computational mathematics, computational data science, scientific computing, computational biomechanics, design and systems thinking, entrepreneurship and STEM education. During the last decade, Dr. Seshaiyer initiated and directed a variety of educational programs including graduate and undergraduate research, K-12 outreach, teacher professional development, and enrichment programs to foster the interest of students and teachers in STEM at all levels.

1. Computational Thinking for Mathematical Biology2. Modeling and Computation for Biological Interactions3. Understanding Spread of Infection and Epidemic Dynamics4. Modeling, Analysis and Computation in Epidemiology5. Foundations of Optimal Control Theory for Biological Systems6. Incorporating spatial dynamics into biological systems7. From Deterministic to Predictive Modeling8. Data-Driven Classification for Biological Applications through Machine Learning9. Physics Informed Neural Networks for Predicting Biological Dynamics