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Advancing systems medicine and therapeutics through biosimulation

Francis Levi

Francis Levi

U776, INSERM, hôpital Paul Brousse, 14-16 Avenue Paul Vaillant Couturier, Villejuif, France

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,
Erik Mosekilde

Erik Mosekilde

Department of Physics, Technical University of Denmark, Fysikvej 309, Lyngby, Denmark

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and
David A. Rand

David A. Rand

Warwick Systems Biology Centre and Mathematics Institute, University of Warwick, Coventry House, Coventry, UK

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Published:https://doi.org/10.1098/rsfs.2010.0019

    This Interface Focus issue presents 15 different facets of the research conducted by the participants of the BioSim Network (www.biosim-network.net). BioSim was a network of excellence under the Life Sciences, Genomics and Biotechnology for Health Thematic Priority Area of the 6th European Framework Programme. The immediate goal of the network was to develop modelling and simulation approaches that can help the pharmaceutical industry achieve a more rational drug development process. In a broader perspective, the network aimed at contributing in an essential manner to the development of an integrated and quantitative understanding of biological processes. It included a total of 40 partners, including 26 academic research groups from various universities, nine SMEs and Novo Nordisk, with many of the partners having strong collaborations with the pharmaceutical industry. The network established a platform for very fruitful interdisciplinary collaboration with biological groups providing insights into genetics, biochemistry, cellular biology, pharmacology, endocrinology, neurology and physiology; clinicians and hospital groups bringing experience in treatments of depression, various forms of tremor, and cancer clinical interventions and theoreticians providing expertise in bioinformatics, systems biology (SB), network theory, statistical physics, nonlinear dynamics and complex systems theory, as well as pharmacokinetics and clinical trial planning.

    The network has produced many outputs including a number of special and focus issues such as an issue of the Journal of Biological Physics on ‘Biosimulation’ (vol. 32, nos. 3–4, 2006), an issue on complexity in neurology and psychiatry in the Journal of Biological Physics (vol. 34, nos. 3–4, 2008) and an issue on biomedical applications of SB and biological physics of the Philosophical Transactions of the Royal Society A. This Interface Focus issue represents some of the outcomes of this interaction with a particular focus on fundamental issues concerning basic biological and biomedical science not too far from the clinic.

    We start with a highly stimulating discussion by Denis Noble about genetics and computational biology [1]. In an analogy with differentiation and integration in mathematics, Noble discusses strong and weak forms of what he calls the ‘differential view’ of genetics (i.e. the gene-centric view that relegates the organism itself to the role of disposable carrier of its gene) and the related implicit assumption that no differences in the phenotype that are not caused by a genetic difference can be inherited. He contrasts this with an ‘integral’ view in which networks of molecular interactions integrate the influences of many genes on each phenotype so that the effect of a modification in DNA depends on the context in which it occurs. He argues that this addresses not only the problem of the existence of inheritance beyond DNA, but that we also need it to address the fact that many interventions, at the level of the genome, such as knockouts, are effectively buffered by the organism. The discussion is used to clarify the issue around the debate in SB about bottom-up, middle-out and top-down modelling. It supports his advocacy of middle-out, which he interprets as one in which you start calculating at the level at which you have the relevant data.

    The paper by Swat et al. [2] starts with another stimulating discussion of the bottom-up and middle-out approaches to SB. It proposes that combined application of SB and physiology-based pharmacokinetic–pharmacodynamics (PKPD) may help SB reach its goal of addressing whole-body function and empower PKPD eventually to produce more effective molecular network-based treatment methods. In this context, it discusses the utility of databases and model repositories for biological systems and the need for easy exchange of models between modelling platforms. It discusses a surprising result of an analysis of robustness in yeast glycolysis. Finally, it describes a new SB driven PKPD platform that has been developed by the authors.

    Three of our papers deal with issues concerning normal and malignant cell proliferation, interaction between the circadian clock and the cell cycle, and chronotherapeutics. Modelling of the cell cycle has been a highly successful thread within SB particularly with the work of John Tyson & Bela Novak. An important development in this area was the publication in 2009 of a paper by Gérard & Goldbeter [3] modelling the cyclin/Cdk network driving the mammalian cell cycle. This is a highly complex model with 39 state variables and 164 parameters. In the paper included in this collection, they present a simplified skeleton version of this model with only six variables and 24 parameters. This model captures the key aspects of the temporal self-organization. This is a very useful addition to the literature because it extracts the essentials of this very complex system in a way that makes the nature of the dynamics very transparent. Moreover, it will be very useful in studies where the cell cycle is a component of interest but where one does not want to use a detailed full model. Since it has many less parameters, bifurcation analysis, etc., will be much more practicable.

    There is currently great interest in the coupling between the circadian clock and the cell cycle and the extent to which they are synchronized. This is of medical significance because experimental and clinical data show that circadian disruption accelerates malignant proliferation, and that DNA damage can reset the circadian clock. The paper by Altinok et al. [4] presents an automaton model that studies the interaction between the cell cycle and the circadian clock in populations of cells and investigates the outcome in terms of the proportions of cells in the four phases of the cell cycle brought about by the opposing forces for and against synchronization.

    The paper by Lévi et al. [5] addresses an issue of great potential clinical interest and is about chronotherapeutics, an area where Lévi has been one of the pioneers. Chronotherapeutics involves the administration of treatments according to circadian rhythms and such circadian timing of anti-cancer medications has been shown to significantly improve treatment tolerability and efficacy in experimental and clinical studies. Lévi introduces a non-invasive approach using temperature to accurately detect and monitor circadian rhythms in patients. Since there is considerable variation in the phasing and robustness of circadian rhythms in patients, having a robust circadian biomarker to enable personalized chronomodulated drug delivery is potentially very important.

    The largely empirical development of electric stimulation to treat neurological disorders has led to deep brain stimulation (DBS), a remarkably efficient, yet invasive treatment. In their review, Modolo et al. [6] illustrate how biophysical multi-scale spatio-temporal modelling is beginning to emerge as a driving force orienting the development of innovative brain stimulation methods that may move DBS forward and increase efficiency by addressing the need to understand the underlying physiological mechanisms and the complex relationship existing between brain processing and behaviour.

    Normal brain function requires an appropriate and well-coordinated action of related neuronal populations: mean neuronal discharge rate has to be appropriate while spiking or bursting at abnormally high frequencies, hypoactivity or silencing should be avoided. Moreover, interactions within and between neuronal populations are crucial for normal brain function and synchronization processes are significant for neuronal information processing. By contrast, excessive, pathologically strong neuronal synchronization severely impairs brain function. Thus, the question is raised of whether, given these constraints, it is possible to specifically counteract neuronal hypoactivity in a safe manner, i.e. without running the risk of inducing abnormal synchronization. The paper by Lysyansky et al. [7] addresses this and shows computationally that it is possible to stimulate predominately silent neuronal populations in a way that their mean firing rate increases without a net increase of their synchronization.

    Another paper in this general area concerns stimulation-dependent conduction block in myelinated nerve fibres. Each fibre is formed by an axon and Schwann cells or oligodendrocytes that sheath the axon by winding around it in tight myelin layers. Accumulation of extracellular potassium ions owing to repetitive firing leads to Schwann cell swelling and myelin restructuring and this impacts electric properties of the myelin. The paper by Brazhe et al. [8] models submyelin potassium accumulation and related changes in myelin resistance during prolonged high-frequency stimulation. The authors predict that potassium-mediated decrease in myelin resistance leads to a functional excitation block with various patterns of altered spike trains. Such stimulation-dependent conduction block is important in several aspects, such as high-frequency stimulation treatment of Parkinson's disease and dystonia in DBS.

    The paper by Benson et al. [9] is concerned with quantitative and predictive cardiac electrophysiology. Computational models of virtual cardiac tissues have proved to be an effective tool for reconstructing and dissecting cardiac propagation patterns, and for proposing hypotheses that can be tested experimentally. Reaction–diffusion computational models of cardiac electrophysiology require both dynamic excitation models that reconstruct the action potentials of myocytes, as well as datasets of cardiac geometry and architecture that determine how excitation spreads through the tissue. In this paper, Benson et al. [9] review an experimental pipeline for small mammalian hearts to construct and validate datasets for cardiac geometry and architecture.

    Hypertension is the subject of the paper by Jacobsen et al. [10]. Essential hypertension is characterized by an elevated peripheral resistance residing at the level of the microcirculation. The increased physical hindrance to blood flow appears to be structural in nature and structural changes found in small muscular arteries and arterioles in human essential hypertension as well as in experimental hypertension in animals, results from a process known as inward eutrophic remodelling. The authors develop a model of the vascular wall and address the question of whether inward eutrophic remodelling contribute to a reduced ability of the local circulation to respond to an increase in tissue demand following, for example, physical exercise.

    The kidneys also play an important role in regulating the blood pressure both via the excretion of water and salts and through the production of hormones that, together with hormones from other organs, regulate the peripheral resistance of the vascular system. To protect its own function and secure a relatively constant blood flow the individual functional unit (nephron) of the kidney disposes of two different regulatory mechanisms. Together these mechanisms function as a high-pass filter for arterial pressure variations such that rapid oscillations are allowed to pass into the tubular system of the nephron while more lasting pressure variations are damped out. Both regulatory mechanisms tend to be unstable though and produce self-sustained oscillations of the nephron pressures and flows. Using a model that accounts for the main mechanisms behind these oscillations, the paper by Laugesen et al. [11] examines how the regulatory mechanisms react to an external periodic variation in arterial pressure with frequencies close to one of the internally generated cycles. This leads the authors to develop relatively detailed diagrams for the bifurcation structure for the forced system. In particular, the authors show how the system undergoes a period-doubling cascade along the edge of a resonance region and how this cascade is accompanied by a similar period-doubling process for the quasi-periodic dynamics that exists outside the range of resonance.

    Stimulation or inhibition of insulin secretion is coupled to changes in blood glucose via electrical activity in pancreatic β-cells: the β-cell electrical activity causes an increase in the submembrane [Ca2+] that, in turn, triggers exocytotic release of the insulin-containing secretory granules. While insulin secretion has traditionally been investigated using biochemical techniques, more recently high-resolution biophysical and optical techniques have been applied. The paper by Galvanovskis et al. [12] presents a new biophysical method to measure release of endogenous intragranular constituents that are co-released with insulin. The technique involves the expression of ionotropic membrane receptors in the β-cell plasma membrane. It enables measurements of exocytosis of individual vesicles with sub-millisecond resolution, allows fine details of the release process to be monitored, and raises the possibility that the fusion pore functions as a molecular sieve allowing differential release of low- and high-molecular-weight granule constituents.

    Two of our papers concern the suprachiasmatic nucleus (SCN). The SCN coordinates physiological and behavioural circadian rhythms and is composed of a heterogeneous network of coupled oscillating cells that entrain to daily light–dark cycles. Desynchronization experiments demonstrate a rich variety of locomotor patterns in the SCN. For example, rats exposed to certain light–dark cycles can express both a fast and a slow rhythm. These have previously been interpreted as the output of different SCN cell subpopulations. In the paper by Granada et al. [13] a completely different interpretation is presented. By combining signal analysis with the theory of coupled oscillators, they show that these desynchronized patterns naturally occur in a single periodically driven oscillator (or in a synchronized system of oscillators), and a framework for analysing such phenomena is presented. In particular, they predict a third spectral component that has since been found.

    In the second paper concerned with the SCN, Komin et al. [14] discuss the entrainment of this coupled neuronal system by the external light–dark forcing. They point out that recent experiments show a significant level of heterogeneity in the intrinsic periods of the oscillators in individual neurons and present a model that shows that the presence of some level of dispersion in the intrinsic periods can improve the response of the coupled neuronal system to the external light–dark forcing.

    The paper by Domijan & Rand [15] is about the design principles of regulatory networks of which circadian oscillators are an important example. They consider the design principles that enable such systems to cope with multiple apparently conflicting demands owing to their perturbation by environmental factors such as light and temperature. For example, on the one hand, circadian clocks must be sensitive to some environmental factors in order to be robustly entrained by them, while, on the other hand, they have to be able to buffer the effects of the substantial daily variations in such factors and also maintain robust well-entrained and correctly-phased oscillations in sustained longer term temperatures over the year that may differ by more than 15°C (temperature compensation). The authors present a new theory based on balance equations that are a consequence of the principal component approach to global sensitivity analysis. They argue that it follows from these balance equations that it is relatively easy for evolution to adjust a clock network so as meet these apparently conflicting demands. Moreover, the balance equations allow them to propose a different approach to temperature compensation where instead of considering a free-running clock, they study temperature buffering of the phases in a light-entrained clock. They claim that this is a more appropriate physiological setting than the free-running clock usually discussed in temperature compensation studies.

    Footnotes

    One contribution of 16 to a Theme Issue ‘Advancing systems medicine and therapeutics through biosimulation’.

    References