As in physics and chemistry long ago, molecular life sciences are undergoing a foundational revision from empirical to mathematical. This trend has been prompted by insufficiency of the reductionist approach to provide quantitative explanations and predictions for the properties of molecular regulatory systems, whose observed behaviours are typically emergent phenomena governed by interactions between multiple components. A now classical example of this situation is the study of biological oscillations, such as circadian rhythms [1–3] and the cell cycle [4–6], where the most significant properties of oscillation (period, amplitude, robustness, etc.) are non-trivially related in general to the details of the underlying network.
Quantitative modelling paired with computational techniques can cut through much of this complexity by providing precise predictions for the behaviour of biological networks. However, quantitative models traditionally have been difficult for experimental biologists to construct and simulate. Consequently, scientists who would normally classify themselves as physicists, engineers or mathematicians increasingly became de facto biologists by meeting this need for quantitative modelling, although physical scientists often lack the broader context appreciated by traditional life scientists. The end result was that early quantitative biology was an uneasy hybrid of biological sciences and mathematical sciences, with interdisciplinary expertise often arising only at the postgraduate level. George Oster popularized the following summary from Aharon Katzir-Katchalsky, ‘Biologists can be divided into two classes: experimentalists who observe things that cannot be explained, and theoreticians who explain things that cannot be observed’ .
After decades of development, the situation has been changed dramatically. Alongside incredible advances in genetics, biochemistry and molecular biology, recent developments in interdisciplinary education and dual theoretical–experimental laboratory environments are producing a new generation of scientists trained from an early stage to competently address the overwhelming torrent of data now being generated by modern technologies. A deep understanding of the molecular basis of life is truly becoming a reality.
To observe and investigate this exciting trend, in this Theme Issue, we include a mixture of review and original research articles from a range of pioneering and young researchers in the field of computational cell biology. Given the fast-growing nature of quantitative biology, it would be impossible to give complete coverage of all the topics. Instead, we hope that this theme issue reflects the increasing importance of modelling in cell biology studies.
Starting with the very early days of computational cell biology, a topic that has been under continuous and focused study is how a cell receives, transmits and responds to various signalling clues, and then changes its fate accordingly. Not only is this topic of central importance to basic biology, but it is also a pillar of biomedical research. Tyson & Novak  provide a broad review on the progression of our understanding of cellular information processing over the past 50 years, with emphasis on cell growth, division and death. To complement this work, Yao  reviews a more narrowly focused history on experimental and modelling studies on the core Rb-E2F network regulating the transition between quiescence and proliferation states. Gérard & Goldbeter  continue the theme set by Yao by studying a model that addresses how the balance between quiescence and proliferation is regulated by various factors. Wang et al. (senior author Ao)  constructed and analysed an endogenous molecular–cellular network for normal liver and hepatocellular carcinoma, with careful attention to how feedback loops in the network can influence the progression of cancer. Finally, Wang et al. (senior author Xing)  developed a rigorous theoretical framework to describe cell phenotypic transition dynamics, and applied it to the mammalian cell reprogramming process.
Mechanochemical phenomena, spontaneous self-organization of biomolecules, and enzyme kinetics can loosely be classified as topics in biophysics, which historically has a very strong record of quantitative modelling approaches. In this issue, we provide three separate discussions of biophysics relevant to the molecular origin of cellular behaviour. Civelekoglu-Scholey & Cimini  detail a historic overview on how quantitative modelling helped inform our understanding of the mechanochemical aspects of mitosis. Shi et al.  develop and analyse a spatially resolved computational model to explore the role that lamin-B plays in mitotic spindle morphogenesis and mitotic matrix assembly. Hochendoner et al.  extended a biological queueing theory, originally used to understand the dynamics of gene networks with proteins targeted to a common protease ClpXP, to model the stochastic dynamics of a single ClpXP-like enzyme with multiple substrate binding sites. It is also worth mentioning that Wang et al. (senior author Xing)  borrowed ideas and techniques used in molecular biophysics studies.
This issue concludes with an article discussing biological rhythms, or oscillations. DeWoskin et al.  perform a study to show how various interactions and components in circadian clocks are fine-tuned to generate desired behaviour of across cell clusters and organisms.
We thank support from US National Science Foundation (award no. 1330180 to W.M. and DMS-0969417 to J.X.). C.H. is supported by DOI grant no. D12AP00005.
One contribution of 10 to a Theme Issue ‘Computational cell biology: from the past to the future’.
- © 2014 The Author(s) Published by the Royal Society. All rights reserved.