Quantitative biology: from molecular to cellular systems

It is true, as editor Michael E. Wall points out in the preface of his book, that ‘Quantitative methods are bringing about a revolution in modern molecular and cellular biology’ and that ‘Groundbreaking technical advances are fueling a rapid expansion in our ability to observe, as seen in multidisciplinary studies that integrate theory, computation, experimental assays, and the control of microenvironments.' In fact, quantitative biology has attracted an increasing number of researchers in recent years who are interested in studying various aspects of life using modern quantitative methodologies, not only on the molecular and cellular levels, but also on the organismal and ecosystem levels. A search in the PubMed data base using the keyword ‘quantitative biology’ revealed a significant increase in publications in recent years. For example, there was a surge in publications from 572 in the year 2000 to over 4000 in the year 2014. There are numerous conferences and symposia held on this topic, and there are now also universities which offer interdisciplinary programmes in quantitative biology on both the undergraduate and graduate levels. This indicates that quantitative biology, as a scientific field of inquiry, is here to stay and can be expected to provide new insights into life forms and their dynamic interactions with the environment on a scale never imagined before.

Wall assembled a group of 22 scientists from Australia, Canada, Scotland, and the USA who contributed a total of 13 self-contained chapters about diverse topics in quantitative biology. Although the authors of the individual chapters focus primarily on molecular and single-cell systems, an improved knowledge about the organization and functioning of these systems can be expected to influence our understanding of the dynamics of biological behaviour on higher organizational levels, such as multi-cell systems, populations, communities, and entire ecosystems.

The first chapter is entitled ‘Free Energies, Landscapes, and Fitness in Evolution Dynamics’. The author, Robert H. Austin, discusses the response of single cells (here: the bacterium Escherichia coli) to the stress caused by antibiotics. He views the complex landscape of stress as a ‘free energy landscape’ and emphasizes that the landscape theory allows researchers to visualize the movement of a biological entity in a multidimensional space. Austin's main interest is to describe quantitatively how the construction of a ‘fitness landscape’ allows bacterial cells to accelerate the evolution dynamics by rapidly developing antibiotic resistance. The acceleration of bacterial resistance to antibiotics is of great interest to biomedical researchers because it has become a pressing issue of concern in medicine and public health in recent decades [8, 9, 12].

The following chapter is about biological system design principles. Here, Michael A. Savageau points out that evolution has been viewed as a ‘haphazard process in which many different designs are generated by chance’. He uses the term system design to discuss issues associated with larger-scale system organization, which are, as he puts it, ‘variously referred to as topology, architecture, or structure’. He mentioned that systems seldom represent themselves as well-defined or closed, and that it is thus up to the researcher to make (good) choices. For example, an investigator could decide to determine the numerical ratio of internal interactions to external interactions. The most important thing in this type of research is ‘to preserve the integrity of critical functional groupings’. In other words, Savageau believes that it is not enough to

sample a large number of variables, such as one from each of the major classes of molecules in a cell, if these do not constitute an integrated system. Nor, is it sufficient to establish exhaustively one type of connected network, such as a transcription factor network, when other connections that are necessary to make a functional system are missing.

He emphasizes the value of mathematically controlled comparisons as such approaches combine various aspects from a number of existing methods. Savageau introduces the reader to three specific system design principles: the feed-forward control, two-component signal transduction, and coupled expression in gene circuits.

The authors of Chapter 3 discuss cellular-memory and stochasticity (or chance) of biochemical systems. They emphasize that memory operates at different time scales. For example, chemotaxis in bacteria can change rapidly (e.g. in seconds), while changes in the regulatory systems for sugar catabolism in organisms can take much longer (e.g. from minutes to hours). The authors explain two types of stochasticity that cause variation in cells: intrinsic stochasticity and extrinsic stochasticity. While the intrinsic type stems from the various biochemical reactions within a system, extrinsic stochasticity is generated through interactions with other stochastic systems in cells and/or the extracellular environment. They describe quantitatively events such as transcriptional and translational bursts in cells, and the characteristics of bistability of a biological system (i.e. the tendency towards one of two stable steady states). They point out that while bistable systems can have memory, stochasticity undermines memory (bistability). The fourth chapter is about information-processing. Here, Ilya Nemenman points out that ‘All living systems have evolved to perform certain tasks in specific contexts. There are [however] a lot fewer tasks than there are different biological solutions that nature has created.' The author's main focus in this chapter is to describe phenomena that are most common (i.e. have universal functions) among organisms, such as observation, signal transduction, regulation, sensing, and adaptation. Nemenman addresses issues associated with the quantifying of biological information-processing as well as the strategies for improving the performance of information-processing.

‘Quantitative In Vitro Biochemistry. One Molecule at a Time’ is the title of Chapter 5. Jeffrey A. Hanson and Haw Yang mention that in enzyme biochemistry the study of the molecular basis for enzyme function is so complex that it requires collaborative efforts of scientists from different disciplines, including biologists, chemists, physicists, and mathematicians. They describe the application and technical challenges of the single-molecule fluorescence spectroscopy technique for the study of enzyme conformational dynamics. Single-molecule imaging experiments have gained much popularity in recent years and are considered powerful techniques in the study of biological phenomena [7]. In the sixth chapter, Cy M. Jeffries and Jill Trewhella take a look at a method called the small-angle scattering (SAS) of X-rays (SAXS) or neutrons (SANS). The SAS method allows investigators to characterize structural parameters that are related to the size and shape of biological macromolecules (e.g. proteins). The authors explain that ‘as an incident X-ray or neutron plane wave passes through a macromolecule, it can pass through unaffected, can be absorbed, or can interact with individual atoms to generate secondary scattered waves’. Several specific subtopics are discussed in this chapter, including the presentation of SAS profiles, issues associated with (buffer/solvent) solutions (i.e. solvent subtraction and contrast), and molecular modeling. Research involving the SAS method has accelerated in recent years, which includes the characterization of molecular interactions [1] and the development of software for comparing atomistic models with data from SAS [11].

Chapter 7 is about the dynamics of subcellular signalling. More specifically, the authors describe how cells make decisions about whether or not, how, and when to respond to stimuli from the external environment, and how this information is processed by signalling networks and through signalling compartmentation. They discuss various techniques used for live-cell imaging and quantitative modelling of subcellular signalling dynamics, including fluorescence microscopy and 2D/3D computational model development, respectively. They emphasize that using different experimental designs (here: the integration of imaging and mathematical modelling approaches) can provide researchers with new insights into the complex mechanisms of cell signalling. In the following chapter, Philippe Cluzel adds to the discussion about cell dynamics by addressing issues related to single-cell behaviour. More precisely, he discusses the random (spontaneous) fluctuations and responses to external stimuli in single cells, and the relationship between these two phenomena. Cluzel describes a technique called fluorescence correlation spectroscopy (FCS), which, as he points out, was originally developed for the measurement of small traces of dyes in solution and later used to study living single cells. He describes in detail the use of FCS for the measurement of intracellular protein diffusion as well as diffusion of RNA in single bacteria, and the modelling of cell-to-cell variability.

The ninth chapter is entitled ‘Modeling Genetic Parts for Synthetic Biology’. The authors wrote in the introduction of this chapter that genetic parts are ‘defined by their sequence, which can be inserted into a specific location upstream, downstream, or within a gene to achieve controllable expression’. They focus on biophysical models to predict the functions of the following four genetic parts: promoters, transcriptional terminators, mRNA stability tags, and ribosome-binding sites. The authors review different quantitative model approaches and discuss their usefulness to predict the function of each of these genetic parts. Synthetic biology is an interdisciplinary field of study, which is not only well suited for computer-aided modelling and design, but is believed to significantly transform our understanding of living organisms [2,4].

Brian Munsky is the author of Chapter 10. He discusses phenomena of cellular variability in biological systems and illustrates computational analyses of these phenomena. He describes mesoscopic modelling of biomolecular reactions, the analysis of population statistics with finite-state-projection (FSP) approaches, and provides information about the FSP two-species software toolkit. Munsky presents examples of stochastic analysis tools, in particular the use of stochastic models for the analysis and identification of single-cell variability in gene regulatory systems.

The 11th chapter is, broadly speaking, about the role of phosphates in biology. Susan S. Taylor and Alexandr P. Kornev discuss the discovery of protein kinases as regulatory enzymes and then specifically focus on protein kinase A (PKA), which represents a group (a superfamily) of enzymes with activity dependent on cellular levels of cyclic adenosine monophosphate (cAMP). They mention that protein kinases are highly regulated by dynamic molecular switches, and that ‘Failure to properly turn off a protein kinase can have catastrophic consequences as evidenced by the many oncogenic kinases that lead to malignancies.' In this chapter, the authors review the evolution of the protein kinase superfamily as well as the structure, regulation, and localization of these enzymes within cells. They discuss the benefits of genome sequencing for elucidating features of protein kinases, and point to the necessity to develop novel techniques and apply hybrid methods in order to further advance our understanding of the dynamic features of these enzymes.

Chapter 12 is devoted to the stochastic simulation of the phage lambda gene regulatory circuitry. Phage lambda is a virus (a bacteriophage) that infects E. coli. It is a temperate bacteriophage (as opposed to virulent bacteriophages) that allows it to remain within host cells after infection and reproduce in synchrony along with the host for long periods (a state called lysogeny) or, dependent on environmental conditions, enter into a lytic cycle that results in the lysis of the host cell. Phage lambda has been extensively used as a tool in microbial genetics and molecular biology [3, 5, 6]. John W. Little and Adam P. Arkin, the authors of the 12th chapter, describe the mechanisms underlying the regulatory circuitry of phage lambda and point out that the different regulatory events that lead to lysis or lysogeny make this virus/host system an excellent subject for modelling. They describe various modelling approaches, including stochastic modelling to simulate the stability of the lysogenic state as well as the process of prophage induction.

The final chapter (Chapter 13) is about chemotaxis, a biological phenomenon (‘a rich biological behaviour’) in which an organism senses its chemical environment and directs its movement towards an attractant and/or away from a repellent [10]. Howard C. Berg, the author of this final chapter, leads the reader on a historical tour of chemotaxis systems (with a focus on bacterial chemotactic movement behaviours) and its dynamic mechanisms [e.g. flagellar propulsion, smooth swimming and erratic motion (‘runs and tumbles’, respectively), signalling events, and adaptation to stimuli]. He mentions that a great deal of research has been done to understand the hydrodynamics of the flagellar propulsion system and the chemotactic signal membrane-crossing events, and that computer simulations have helped illuminating many aspects of the chemotaxis systems. He made an interesting statement in the ‘Afterthought’ section of this chapter:

We look carefully at what the cell does and at the machinery that it has contrived to do the job, and then we try to figure out how that machinery functions. Physics helps us recognize the constraints that the cell has had to deal with, and modeling helps us appreciate relevant mechanisms and to find out what we do not understand.

In my opinion, the editor and contributors developed a fascinating and important book. Its major strength lies in the combination of (a) historical reviews about a number of biological phenomena; (b) the presentation of theories, and how viewpoints have changed or stayed the same over time; (c) the description of older methods, including their strengths and weaknesses; (d) the introduction to novel quantitative approaches and imaging techniques, and their integration and application to biological dynamics; and (e) well-defined thoughts about the direction of future research in quantitative biology. The book is part of the series ‘Mathematical and Computational Biology’, whose aim and scope is ‘to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine’. The reader of Quantitative biology: from molecular to cellular systems will recognize how well Wall's book fits as an entry into this series and how much it contributes to the scientific literature.

The editor mentions in the preface how the chapters are organized into sections: while the first four chapters describe fundamental concepts (perspectives on evolution dynamics, system design principles, chance and memory, and information-processing), the following six chapters (Chapters 5–10) deal with techniques that are transforming research in biological dynamics. These include, for example, the single-molecule imaging, SAS, fluorescence microscopy, 2D/3D computational models, FCS, synthetic biology, and FSP. The remaining Chapters 11–13 are devoted to specific biological systems (i.e. PKA, genetic switches of phage lambda, and chemotaxis behaviour). Although some readers may find the description of the grouping of chapters in the preface sufficient, I would have preferred to see it (again) in the table of contents. I believe it would have provided a clearer overview about the three main sections of subject matters discussed by the authors. I noticed that in this book the chapter numbers are written in high-lighted grey boxes on the right lower corner of each page. I have rarely seen this approach but think it is a good idea as it can help readers to quickly access individual chapters.

The figures and tables shown in this book are all of high quality; most are presented in black-and-white, but some are printed in colour on glossy paper. A random test of key-word entries in the 10-page index did not reveal any mistakes in regard to the page numbers referred to. I would have liked seeing a complete glossary in this book (note: a short subtopic glossary is provided by the authors of Chapter 11). The reason is because the description of terminology from different scientific disciplines, new concepts, and advanced techniques could have been helpful to readers, particularly to newcomers to the interdisciplinary field of quantitative biology.

In conclusion, I believe Wall's book will prove to be of exceptional value to researchers, teachers, and students who either want to be, or are already considering themselves, a member of the community of ‘quantitative biologists’. As I had mentioned at the beginning of my review, the field of quantitative biology is rapidly expanding. So, in light of this expansion, I believe the biggest contribution Wall makes with this book is that he puts many of the recent advancements in theory and practice in quantitative biology into perspective. One final comment I would like to make relates to a statement Wall makes in the preface. He wrote that he will be donating his royalties from the sale of this book to a medical foundation (the Cystic Fibrosis Foundation), a move that I think is admirable and worth mentioning here.