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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für höhere Schule und Studium
Maße
Höhe: 235 mm
Breite: 191 mm
Gewicht
ISBN-13
978-0-443-27452-7 (9780443274527)
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Schweitzer Klassifikation
Alonso Oliva Ogueda holds a Master's degree in Mathematics from the Universidad Tecnica Federico Santa Maria (2021) and a Mathematical Engineering degree from Universidad Tecnica Federico Santa Maria (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.
Autor*in
George Mason University, USA
Professor of Mathematical Sciences, George Mason University, USA
1. Computational Thinking for Mathematical Biology
2. Modeling and Computation for Biological Interactions
3. Parameter identification for Biological Systems
5. From Deterministic to Predictive Modeling
6. Classification Algorithms for Modeling Biological Systems
7. Data-driven approaches for Bioimage Processing
8. Physics Informed Neural Networks for Biological Dynamics
9. Data-driven Optimal Control in Mathematical Biology
10. Data-driven approaches for real-world Societal Challenges
