Single cell analysis: the new frontier in ‘Omics’ Daojing Wang 1 and Steven Bodovitz 2 1. Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 2. BioPerspectives, San Francisco, CA Corresponding author: Wang, D. (djwang@lbl.gov) Cellular heterogeneity arising from stochastic expression of genes, proteins, and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capabilities of ‘Omics’ technologies. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics, and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third- and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in Omics, and single cell Omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity. Single cell analysis: needs and applications Cellular heterogeneity Cellular heterogeneity within an isogenic cell population is a widespread event [1, 2]. Stochastic gene and protein expression at the single cell level has been clearly demonstrated in different systems using a variety of techniques [3-5]. Therefore, analyzing cell ensembles individually with high spatiotemporal resolutions will lead to a

Single cell analysis: the new frontier in 'Omics'

The π-Calculus: A theory of mobile processes by Davide Sangiorgi and David Walker Formal methods have formed the foundation of Computer Science since its inception. Although, initially these formal methods dealt with processes and systems on an individual basis, the paradigm has shifted with the dawn of the age of computer networks. When dealing with systems with interconnected, communicating, dependent, cooperative, and competitive components, the older outlook of analyzing and developing singular systems—and the tools that went with it—were hardly suitable. This led to the development of theories and tools that would support the new paradigm. On the tools end, the development has been widespread and satisfactory: programming languages, development frameworks, databases, and even end-user software products such as word processors, have gained network-awareness. However on the theoretical end, the development has been far less satisfactory. The major work was done by Robin Milner, Joachim Parrow, and David Walker who developed the formalism known as π-calculus in 1989. π-calculus is a process calculus that treats communication between its components as the basic form of computation. It has been quite successful as a foundation of several other calculi in the field and as Milner puts it, it has become common to express ideas about interactions and mobility in variants of the calculus. Introduction The current book serves as a comprehensive reference to π-calculus. Besides Milner's own book on the subject, this is the only other book-length publication on the topic. In many ways, it is much more comprehensive than Milner's: a much wider area of topics are dealt with and in more detail as well. Contents The book is split into seven part. The first part presents the basic theory of π-calculus. However, basic does not mean concise: every topic is discussed in great detail. The section on bisimulation is particularly intensive and provides several insights about the motivation for the theory. Part two discusses several variants of the original calculus. By varying several characteristics of the calculus, such as whether a process can communicate with more than processes at a time, we can obtain these variants. A number of interesting properties of the language are studied by the other when discussing these variants. As can be understood from the title, the calculus' contribution to analyzing mobile processes is a major topic, and it is dealt with extensively starting from part three. The basics are introduced by the way of a sophisticated typing system whose application in speciying complex processes is presented in part four. Part five looks at higher-order π-calculus in which composed systems are considered as first-class citizens. Part six is one of the best in the book and discusses the relation between π-calculus and lambda-calculus, which is an older and more basic calculus. Finally part seven shows how π-calculus can be employed in studying practical, modern software engineering concepts such as object-oriented programming. Impressions One of my disappointments with this book is in how often the reader is left perplexed with some of the theoretical developments, specially in part three. π-calculus is a complicated topic, even for the graduate student audience to which this book is directed, and the author would have done much better by reducing the number of topics and instead focusing on more lucid and detailed explanations. There are several experimental digressions throughout the book, which although interesting, take away from some of the momentum of sequential study. For example, topics such as comparison and encoding of one language to another could be easily moved to a separate section in order to make the content more suitable for self-study. Another issue is the little effort towards making the connection from the theoretical to the practical. The main reason why formal methods have not been adopted in mainstream software development pracitces is that often it is unclear to developers how formalisms can contribute towards the software engineering process. The book would have served its purpose much better if it had spent part of eah chapter discussing the practical application of that chapter's content. For example, congruence checking and bisimulation can be incredbily exciting topics for programmers to learn if they can see practical applications of such powerful techniques. Beyond the above criticism, the book is absolutely indispensible to students and researchers in the field of formal methods. Currently it serves as the primary reference for anyone who wishes to learn the various aspects of π-calculus in detail. Raheel Ahmad

The Ï-Calculus: A theory of mobile processes

https://www.inf.ed.ac.uk/publications/online/1231.pdf

Bio-PEPA: A framework for the modelling and analysis of biological systems

https://www.cell.com/immunity/pdf/S1074-7613(12)00509-2.pdf

The Transcription Factor STAT-1 Couples Macrophage Synthesis of 25-Hydroxycholesterol to the Interferon Antiviral Response

Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation. Despite its simple structure, no analytic solutions to the Chemical Master Equation are known for most systems. Moreover, stochastic simulations are computationally expensive, making systematic analysis and statistical inference a challenging task. Consequently, significant effort has been spent in recent decades on the development of efficient approximation and inference methods. This article gives an introduction to basic modelling concepts as well as an overview of state of the art methods. First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods. We discuss their various properties and review recent advances and remaining challenges for these methods. We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages. Finally, we discuss the problem of inference from experimental data in the Bayesian framework and review recent methods developed the literature. In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.

/pdf/approximation-and-inference-methods-for-stochastic-1fa9sdchnt.pdf

Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

Circadian clocks are gene regulatory networks whose role is to help the organisms to cope with variations in environmental conditions such as the day/night cycle. In this work, we explored the effects of molecular noise in single cells on the behaviour of the circadian clock in the plant model species Arabidopsis thaliana. The computational modelling language Bio-PEPA enabled us to give a stochastic interpretation of an existing deterministic model of the clock, and to easily compare the results obtained via stochastic simulation and via numerical solution of the deterministic model. First, the introduction of stochasticity in the model allowed us to estimate the unknown size of the system. Moreover, stochasticity improved the description of the available experimental data in several light conditions: noise-induced fluctuations yield a faster entrainment of the plant clock under certain photoperiods and are able to explain the experimentally observed dampening of the oscillations in plants under constant light conditions. The model predicts that the desynchronization between noisy oscillations in single cells contributes to the observed damped oscillations at the level of the cell population. Analysis of the phase, period and amplitude distributions under various light conditions demonstrated robust entrainment of the plant clock to light/dark cycles which closely matched the available experimental data.

/pdf/stochastic-properties-of-the-plant-circadian-clock-2qvev5sqj9.pdf

Stochastic properties of the plant circadian clock

https://homepages.inf.ed.ac.uk/jeh/Bio-PEPA/MeCBIC08_CG.pdf

Modelling Biological Compartments in Bio-PEPA

Circadian clocks are oscillatory genetic networks that help organisms adapt to the 24-hour day/night cycle. The clock of the green alga Ostreococcus tauri is the simplest plant clock discovered so far. Its many advantages as an experimental system facilitate the testing of computational predictions. We present a model of the Ostreococcus clock in the stochastic process algebra Bio-PEPA and exploit its mapping to di erent analysis techniques, such as ordinary di erential equations, stochastic simulation algorithms and model-checking. The small number of molecules reported for this system tests the limits of the continuous approximation underlying di erential equations. We investigate the di erence between continuous-deterministic and discrete-stochastic approaches. Stochastic simulation and model-checking allow us to formulate new hypotheses on the system behaviour, such as the presence of self-sustained oscillations in single cells under constant light conditions. We investigate how to model the timing of dawn and dusk in the context of model-checking, which we use to compute how the probability distributions of key biochemical species change over time. These show that the relative variation in expression level is smallest at the time of peak expression, making peak time an optimal experimental phase marker. Building on these analyses, we use approaches from evolutionary systems biology to investigate how changes in the rate of mRNA degradation impacts the phase of a key protein likely to a ect fitness. We explore how robust this circadian clock is towards such potential mutational changes in its underlying biochemistry. Our work shows that multiple approaches lead to a more complete understanding of the clock.

/pdf/complementary-approaches-to-understanding-the-plant-3vapwp6nt8.pdf

Complementary approaches to understanding the plant circadian clock

Bio-PEPA is a timed process algebra designedspecifically for the description of biological phenomena and theiranalysis through quantitative methods such asstochastic simulation and probabilistic model-checking.Two software tools are available for modelling with Bio-PEPA, theBio-PEPA Workbench and the Bio-PEPA Eclipse Plugin. The Bio-PEPAWorkbench is the research prototype tool which allows us to try outnew language features and new types of analysis through rapidprototyping. The Bio-PEPA Eclipse Plugin is a polished modellingenvironment which targets end-users who wish to do Bio-PEPA modellingsupported by a comprehensive integrated development environment. Bothmodelling tools allow the user to analyse their model both in thediscrete stochastic regime and in the sure continuous regime whilemaintaining only a single source in the Bio-PEPA language.

/pdf/the-bio-pepa-tool-suite-4ectrt2ubx.pdf

The Bio-PEPA Tool Suite

The aim of this work is twofold. First, we propose an high level textual modelling language, which is meant to be biologically intuitive and hence easily usable by life scientists in modelling intracellular systems. Secondly, we provide an automatic translation of the proposed language into Beta-binders, a bio-inspired process calculus, which allows life scientists to formally analyse and simulate their models. We use the Gp130 signalling pathway as a case study.

/pdf/an-automated-translation-from-a-narrative-language-for-57bw6bkr0u.pdf

Maria Luisa Guerriero

Papers

Stochastic properties of the plant circadian clock

Modelling Biological Compartments in Bio-PEPA

Complementary approaches to understanding the plant circadian clock

The Bio-PEPA Tool Suite

An automated translation from a narrative language for biological modelling into process algebra