Darren Wilkinson is Professor of Stochastic modelling at Newcastle University in the United Kingdom. He was educated at the nearby University of Durham, where he took his first degree in Mathematics followed by a PhD in Bayesian statistics which he completed in 1995. He moved to a Lectureship in Statistics at Newcastle University in 1996, where he has remained since, being promoted to his current post in 2007. Professor Wilkinson is interested in computational statistics and Bayesian inference and in the application of modern statistical technology to problems in statistical bioinformatics and systems biology. He is also interested in some of the “big data” challenges that arise in bioscience and more generally. He serves on Biotechnology and Biological Sciences Research Council’s Strategy Advisory Panel for Exploiting new ways of working and is co-Director of Newcastle’s Engineering and Physical Sciences Research Council Centre for Doctoral Training in Cloud Computing for Big Data. He is also a Fellow of the Alan Turing Institute for data science and artificial intelligence.

"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the author’s webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering."- Chitaranjan Mahapatra, Appeared in ISCB News, January 2020

• Introduction to biological modellingRepresentation of biochemical networksProbability modelsStochastic simulationMarkov processesChemical and biochemical kineticsCase studiesBeyond the Gillespie algorithmSpatially extended systemsBayesian inference and MCMCInference for stochastic kinetic modelsConclusionsAppendices

Darren Wilkinson is Professor of Stochastic modelling at Newcastle University in the United Kingdom. He was educated at the nearby University of Durham, where he took his first degree in Mathematics followed by a PhD in Bayesian statistics which he completed in 1995. He moved to a Lectureship in Statistics at Newcastle University in 1996, where he has remained since, being promoted to his current post in 2007. Professor Wilkinson is interested in computational statistics and Bayesian inference and in the application of modern statistical technology to problems in statistical bioinformatics and systems biology. He is also interested in some of the “big data” challenges that arise in bioscience and more generally. He serves on Biotechnology and Biological Sciences Research Council’s Strategy Advisory Panel for Exploiting new ways of working and is co-Director of Newcastle’s Engineering and Physical Sciences Research Council Centre for Doctoral Training in Cloud Computing for Big Data. He is also a Fellow of the Alan Turing Institute for data science and artificial intelligence.

"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the author’s webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering."- Chitaranjan Mahapatra, Appeared in ISCB News, January 2020

• Introduction to biological modellingRepresentation of biochemical networksProbability modelsStochastic simulationMarkov processesChemical and biochemical kineticsCase studiesBeyond the Gillespie algorithmSpatially extended systemsBayesian inference and MCMCInference for stochastic kinetic modelsConclusionsAppendices

Darren Wilkinson is Professor of Stochastic modelling at Newcastle University in the United Kingdom. He was educated at the nearby University of Durham, where he took his first degree in Mathematics followed by a PhD in Bayesian statistics which he completed in 1995. He moved to a Lectureship in Statistics at Newcastle University in 1996, where he has remained since, being promoted to his current post in 2007. Professor Wilkinson is interested in computational statistics and Bayesian inference and in the application of modern statistical technology to problems in statistical bioinformatics and systems biology. He is also interested in some of the “big data” challenges that arise in bioscience and more generally. He serves on Biotechnology and Biological Sciences Research Council’s Strategy Advisory Panel for Exploiting new ways of working and is co-Director of Newcastle’s Engineering and Physical Sciences Research Council Centre for Doctoral Training in Cloud Computing for Big Data. He is also a Fellow of the Alan Turing Institute for data science and artificial intelligence.

"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the author’s webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering."- Chitaranjan Mahapatra, Appeared in ISCB News, January 2020

• Introduction to biological modellingRepresentation of biochemical networksProbability modelsStochastic simulationMarkov processesChemical and biochemical kineticsCase studiesBeyond the Gillespie algorithmSpatially extended systemsBayesian inference and MCMCInference for stochastic kinetic modelsConclusionsAppendices