Book recommendations for algorithms used in evolutionary biology

Question

Do you have recommendations for a book that presents the different algorithm used in theoretical evolutionary biology?

I don't mean evolutionary or genetic algorithms (otherwise this question would not be a good fit for Biology.SE) but algorithms applied to evolutionary biology. I am not interested in statistical procedures and algorithms to reconstruct phylogenetic trees, to annotate DNA sequences or to find out synonymous changes by comparing sequences of closely related species. I am not interested in introductory book on programming.

I am interested to computational modeling applied population genetics, kin selection, game theory, population range expansion, simulating sexual reproduction, selection for different sex determination system, evolution for robustness/evolvability, evolution of codon usage, evolution of genetic code, evolution of cognition, evolution of multicellularity, …

I don't quite know if one book that encompass all these subjects exist. If not, I wold welcome suggestions of book that present the algorithmic used in some but not all of these subjects.

Searching a bit in amazon, I easily found tons of book but don't quite know if they fit my expectations. Below are some examples

Evolution as computation

Individual based modeling and ecology

Models and algorithm for genome evolution

modelling for field biologists

Practical computing for biologist

Evolution as computation

Genetic and evolutionary computation

Genetic algorithms+ data structures = evolution programs

The missing algorith

Cellular Model: Mathematical and theoretical biology, Algorithm, Data structure, Bioinformatics, Computational biology, Artificial life, Computer simulation

Natural-Artificial-Models-Computation-Biology

An-Introduction-Genetic-Algorithms-Paper

Answer 1

What you are describing usually falls under the category of computational biology or just mathematical biology. Unfortunately, the biggest part of this field is bioinformatics, or the application of statistical and/or dynamical programming techniques to sequence data. You exclude this in your question, and I would agree with you that it is a "boring" topic from a theoretical biologist's perspective, because it mostly uses computer science in a very standard way as a tool for experimental biologists.

As you noticed, it is also standard to use computational models in theory papers as a way of simulating things. I think this is the sort of resources you are asking for. Unfortunately, specific algorithms are seldom standardized or reused in this area. Typically, each paper (or sequence of related papers) uses their own models.^[1] As you read a lot of papers, you will find some common themes, but these are just standard ideas of theoretical biology expressed as simulation. I doubt that comprehensive books exist, and even if some do their utility is not clear to me due to a lack of specific algorithm (re)use.^[2] However, the most commonly used techniques are to either to solve differential equations numerically (which most would classify as mathematical modeling) or run agent-based or population-based models. General resources on these exist, and here are some discussions for the latter other SEs:

• Simulation modeling of diseases.
• Sources for Algorithmic Evolutionary Game Theory.
• Canonical reference on agent-based computing.

Finally, do not confuse 'computational modeling in theoretical biology' with 'algorithmic biology'. Algorithmic biology is a new field that views ecological and evolutionary dynamics as computatioanl proccesses. Instead of using mathematical tools borrowed from physics (as is standard with the dynamic systems approaches to mathematical biology), it uses the tool of theoretical computer science (note that this is a type of math that has very little to do with programming the laptop infront of you). I know of only two books in this field:

• Gregory Chaitin "Proving Darwin: Making biology mathematical" - Although Chaitin had contributed significant and original thoughts to computer science in the past, I would strongly advice against this book because it misses the point from both the biological and computer science point of view.

• Leslie Valiant "Probably Approximately Correct: Nature's algorithms for learning and prospering in a complex World" - in this book (based on earlier papers), Valiant recasts evolution as a type of machine learning. This is very interesting from a mathematical point of view, although it might not connect directly enough to biologically relevant questions (see also some of the discussion in Kaznatcheev (2013) and Kaznatcheev (2019)) and misses the computational power of frequency-dependent selection (see Kaznatcheev (2020)). But I would recommend reading this book.

Notes

1. In general, I consider these sort of models as heuristics and I am skeptical of their usefulleness beyond rhetorical devices/examples.
2. I think that literature reviews would be interesting computational modeling in specific subfields. In fact some colleagues and I have wanted to do this for the smaller area of evolutionary game theory (This answer can serve as a starting point for some algorithmic considerations of EGT).

Answer 2

Have you looked into "Fundamental of Molecular Evolution" by Dan Graur & Li.

Another suggestion in line of population genetics and different evo. theories would be - Evolutionary Genetics: Concepts and Case studies (Multi-author book. Editor Fox & Wolf)