Many biological systems can be explored by computer simulation: an imitation of the behavior of the system over time, done by running a computer program. Computer simulations are also termed in-silico experiments, paraphrasing the terms in-vitro and in-vivo. Computer simulation in biology can be used to replace or complement some tedious and costly lab experiments. It enables conducting numerous “experiments” under various conditions, at a scale that is infeasible experimentally.

The term image processing refers to the manipulation or analysis of digital images. While manipulation alters the image in some way to make it more meaningful or informative, analysis merely extracts information from the image without changing its pixels. This chapter describes some very basic notions in image processing, namely segmentation, morphological operators such as erosion and dilation and noise reduction, and labeling. Image processing is really a whole field of expertise, involving rather sophisticated mathematical methods. This chapter aims to familiarize the reader with the field, setting the ground for further exploration of this fascinating topic.

Images are used extensively nowadays. With the digital revolution, we easily generate, send, and observe images, all at a very low cost. Images are also used extensively in biological research and in the medical clinic. Biological images are studied mainly in basic science research, mostly at the cellular and molecular level. Medical images are used for clinical purposes, and focus on the tissue and organ level. Both kinds of images are often complicated and heterogeneous, and analyzing them requires sophisticated computational techniques. The goal of these techniques is to extract meaningful knowledge about the image content. For example, the automated identification of objects in the image (such as cells, intracellular components, or a cancer tumor), tracking cells, organelles, or cancer cells in consecutive video frames, or phenotype identification by object properties (size, light intensity, shape, etc.). Images also require large volumes of storage, raising the need for efficient compression algorithms, in order to store them.

In this part of the book, we will introduce the basics of the programming language Python (chapter 1), and learn how to use it efficiently, taking into consideration a program’s running time and memory allocation requirements (chapter 2). Later on, Python programs will accompany every topic presented throughout this book.

In many contexts, we are required to handle a large collection of objects in a way that supports inserting a new object, finding if an object is present, and possibly deleting an object. These operations typically appear in a series of arbitrary length. We want all these operations to be done as efficiently as possible. Consider a search engine (and its underlying infrastructure) like Google or Bing. One makes a query (e.g., “who is the King of Asteria”) and gets a response in about 945,000 results (0.44 seconds). One of the basic techniques behind such efficient implementations of search is called hashing.

In this chapter, we study another common string-related problem – pattern matching. Suppose we want to find a given sequence motif, or pattern, in a genome or protein, where the pattern is not unique. In other words, the pattern has more than a single possible matching sequence. To that end, we will introduce the fundamental notion of regular expressions, and their use to solve this problem. In addition, we will discuss the closely related notion of finite state machines (FSM), another basic concept in computer science.

When individuals sign on to a DNA ancestry test, they understand that the company will undertake an analysis of certain segments of their genome, called ancestry information markers (AIMs). These segments can, under proper analysis, reveal their genetic descent from certain regions of the world.

Over a period of 20 years, family genetic genealogy, through the purchase of consumer ancestry testing kits, has been one of the fastest growing family activities of this generation. Citing data from the International Society of Genetic Genealogy, the Washington Post reported in 2017 that eight million people worldwide were involved with recreational genomics. It is estimated that by 2019 about 25 million people had signed up for a DNA ancestry test offered by one of the dozens of companies that have entered this marketplace. The kits are sent to a person’s home with return packaging that includes a reservoir for depositing saliva or swabs for sampling cheek cells. The MIT Technology Review predicted that by 2021 there would be 100 million consumers of ancestry DNA services.

Most human genetic diversity is found within populations rather than between populations. Scientists have reported that any two individuals within a particular population are as different genetically as any two people selected from any two populations in the world. Given this finding, how can science use a small percentage of genetic diversity between populations as markers of ancestral origins?