This chapter serves as an intuitive introduction to dynamical systems within the realm of biological systems, through visual representations of state space dynamics. Biological examples and experimental realizations are described to demonstrate how dynamical systems concepts are applicable in solving fundamental problems in cell biology. Differential equations are taken as typical of dynamical systems, and we explain topics such as nullcline and fixed points, linear stability analysis, and attractors, elucidating their significance using systems such as gene toggle switches. The introduction of limit cycles and the Poincaré–Bendixson theorem in two-dimensional systems is followed by examples such as the Brusselator and the repressilator system. Furthermore, we explore the basin structure in multi-attractor systems and provide detailed explanations using toggle switch systems to illustrate time-scale separation between variables and adiabatic elimination of variables. Several instances of co-dimension 1 bifurcations commonly observed in biological systems are presented, with a discussion of their biological significance in processes like cell differentiation. Finally, chaos theory is introduced.

Considers how to track cells and molecules. Introduces tracking algorithms and statistical tools to analyse the resultant data.

This chapter gives basic information about molecular communication. It introduces the concept and gives simple examples, explores the history of molecular communication, and discusses several examples to motivate the rest of the book.

For a book that attempts to explain how to understand visuals in life sciences, it seems prudent to first explain what we mean by “visual,” even if it may seem quite a common word.

In everyday conversation, “visual” is often used as an adjective and means “relating to seeing or sight,” as in “visual impression” or “visual effect.” In the context of this book, “visual” is used similarly as an adjective, but in addition, and more often, it is used as a noun. As a noun, it refers to the variety of images used in life science communication. For example, photographs are a type of visual commonly used in life science communication, and so are drawings.

The theory of evolution, as espoused by Charles Darwin in The Origin of Species in 1859, was difficult to accept for religious believers whose assumptions about the world were shattered by it, but Darwin’s The Descent of Man, published 12 years later, posed even greater challenges to people who did accept it, and those challenges continue today. It has often been noted that a disorienting consequence of the Enlightenment was to force people to recognize that humans were not created at the center of the universe in the image of God, but instead on a remote dust-speck of a planet, in the image of mold, rats, dogs, and chimps. For the entirety of recorded history, moral beliefs about humans had been based on the idea that people were in some fundamental sense apart from the rest of nature. Darwin disabused us of that notion once and for all. The scientific and social upheaval that has occurred since Darwin has been an extended process of coming to terms with a unification of humans and the rest of the natural world.

Who does not know the most basic fact from the science of genetics, that peas and people reproduce in a similar fashion?

It is taught in high schools. Gregor Mendel discovered the fundamental scientific way that organisms breed, and it works the same way in people as it does in peas. Everyone knows that. They may not remember the specifics, with dominant uppercase A and recessive lowercase a – but they know that humans and peas reproduce basically the same way, because they were taught it, and it’s true.

Now I am certainly not going to try and convince you otherwise. But have you ever actually seen peas reproduce? Thanks to the internet, you can readily see videos of plant breeding. The videos of humans breeding, of course, are posted on more restricted internet sites.

The genome is the totality of information that directs the making and the maintenance of you and every other living organism. Scattered among the familiar genes that code for the proteins of life are other genes. This is a book about the genes we call microRNA. It is 30 years since their discovery. They are gene regulators, every bit as vital as their more famous gene cousins. MicroRNAs fine-tune how much protein is made in our cells, each one coordinating the activity of hundreds of genes and bringing precision to the ‘noise’ of gene expression. Without them, life is virtually impossible. This introduction provides a personal account of what fascinated the author about these genes enough to make him redirect his research to microRNAs. The journey from studying pharmacology in the UK, to the USA where his interest in the brain disease epilepsy began, and later to Dublin, to work at the Royal College of Surgeons in Ireland. It lays out the contents and style of the book, which is part history of science, describing what we know and the experiments that underpin our understanding, and part memoir of the author’s own research, and the applications of microRNAs in medicine.

Chapter 1 focuses on terminology and basic concepts of the area, and places multivariate biomarker discovery in the context of biomarker studies and personalized medicine. For ease of reference, included are also short descriptions of some of the terms and concepts introduced and discussed in various parts of the book.

In this introductory chapter to a book on the biophysical foundations and computational modeling of electric and magnetic signals in the brain, we give a brief summary of measurement techniques and modeling approaches in computational neuroscience.

We begin by illustrating the interplay between questions of scientific interest and the use of data in seeking answers. Graphs provide a window through which meaning can often be extracted from data. Numeric summary statistics and probability distributions provide a form of quantitative scaffolding for models of random as well as nonrandom variation. Simple regression models foreshadow the issues that arise in the more complex models considered later in the book. Frequentist and Bayesian approaches to statistical inference are contrasted, the latter primarily using the Bayes Factor to complement the limited perspective that p-values offer. Akaike Information Criterion (AIC) and related "information" statistics provide a further perspective. Resampling methods, where the one available dataset is used to provide an empirical substitute for a theoretical distribution, are introduced. Remaining topics are of a more general nature. RStudio is one of several tools that can help in organizing and managing work. The checks provided by independent replication at another time and place are an indispensable complement to statistical analysis. Questions of data quality, of relevance to the questions asked, of the processes that generated the data, and of generalization, remain just as important for machine learning and other new analysis approaches as for more classical methods.

Chapter 1 explains the possible role and place of physicochemical mechanics

This chapter gives a minimalistic, combinatorial introduction to molecular biology, omitting the description of most biochemical processes and focusing on inputs and outputs, abstracted as mathematical objects.

What is No-Boundary Thinking (NBT)? Is it a philosophy term or a science term? Why do we need it? Since 2013, the NBT national network has had many discussions and today wants to have a book to include some of the NBT group members’ thoughts. Some may affect NBT, some may not. Still, we would like to put it all together.

The stone is still there in the garden. That’s what gets me. It’s not the house itself – houses decay slowly and can be preserved pretty easily, especially in Britain where even an eighteenth-century country house is not “old.” It’s not even the tree behind the house, alive when Charles Darwin still lived in his Down House, now propped up by guywires against inevitable collapse as a kind of totem of the great naturalist’s existence. If you leave the rear exit, the one that takes you to Darwin’s preserved greenhouse and the stunning flora on a pretty path lined in that particular English way of making the perfectly manicured seem somehow “natural,” you might glance to the left and see behind a small iron fence a one-foot-wide stone. A round mill stone or pottery wheel, it was, or appears to have been.

In this chapter we provide an overview of data modeling and describe the formulation of probabilistic models. We introduce random variables, their probability distributions, associated probability densities, examples of common densities, and the fundamental theorem of simulation to draw samples from discrete or continuous probability distributions. We then present the mathematical machinery required in describing and handling probabilistic models, including models with complex variable dependencies. In doing so, we introduce the concepts of joint, conditional, and marginal probability distributions, marginalization, and ancestral sampling.

Ever since living beings arose from non-living organic compounds on a primordial planet, more than 3.5 billion years ago, a multitude of organisms has unceasingly flourished by means of the reproduction of pre-existing organisms. Through reproduction, living beings generate other material systems that to some extent are of the same kind as themselves. The succession of generations through reproduction is an essential element of the continuity of life. Not surprisingly, the ability to reproduce is acknowledged as one of the most important properties to characterize living systems. But let’s step back and put reproduction in a wider context, the endurance of material systems.

The view of living systems as machines is based on the idea of a fixed sequence of cause and effect: from genotype to phenotype, from genes to proteins and to life functions. This idea became the Central Dogma: the genotype maps to the phenotype in a one-way causative fashion, making us prisoners of our genes.

“Just the facts, ma’am. Just the facts!” This famous directive by Sergeant Joe Friday – apparently never actually made in this form – is from the television series Dragnet. Unfortunately, while this may be adequate for detecting and solving crime, not so elsewhere. The idea that science is simply a matter of recording empirical experience is hopelessly inadequate and misleading. Science is about empirical experience, but it is about such experience as encountered and interpreted – and with effort and good fortune – as explained by us.