Cover of Mathematical models in biology

Mathematical models in biology

Focusing on discrete models across a variety of biological subdisciplines, this introductory textbook includes linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction from DNA sequence data, genetics, and infectious disease models. Assuming no knowledge of calculus, the development of mathematical topics, such as matrix algebra and basic probability, is motivated by the biological models. Computer research with MATLAB is incorporated throughout in exercises and more extensive projects to provide readers with actual experience with the mathematical models.

Audience
adult
Length
medium · 384 pages

Click a tag to see books like this one that share it. Point at it (on a phone, tap it) for less of it instead.

Read it? Sign in to help tag it.

Liked Mathematical models in biology? Here’s where I’d start.

These suit anyone who liked Mathematical models in biology. Sign in and mark a few books for picks shaped around your own taste.

  1. Functional analysis Shares science mathematics, mathematics
  2. Applied regression analysis and other multivariable methods Shares science mathematics, mathematics
  3. Mathematical methods for physics and engineering Shares science mathematics, mathematics
  4. Time Series Analysis Shares science mathematics, mathematics

“If you want more of the same (science), start with these.”

  1. Mathematical Mysteries: The Beauty and Magic of Numbers
  2. The Fabric of the Heavens: The Development of Astronomy and Dynamics
  3. Gravity: An Introduction to Einstein's General Relativity
  4. The Mathematical Mechanic: Using Physical Reasoning to Solve Problems

“If it was the science mathematics that hooked you, try one of these.”

  1. Marine biology
  2. Biochemistry
  3. Organic reaction mechanisms
  4. Linear Algebra with Applications

“These start from a similar idea, even if they go somewhere else with it.”

  1. Mathematics for Machine Learning
  2. Linear and Geometric Algebra
  3. Introductory Linear Algebra with Applications
  4. The Analysis of Biological Data