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Stochastic modelling for quantitative description of heterogeneous system. Nat Rev Genet

School of Mathematics & Statistics and the Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE1 7RU, UK.
Nature Reviews Genetics (Impact Factor: 39.79). 02/2009; 10(2):122-33. DOI: 10.1038/nrg2509
Source: PubMed

ABSTRACT Two related developments are currently changing traditional approaches to computational systems biology modelling. First, stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the single-cell level. Second, sophisticated statistical methods and algorithms are being used to fit both deterministic and stochastic models to time course and other experimental data. Both frameworks are needed to adequately describe observed noise, variability and heterogeneity of biological systems over a range of scales of biological organization.

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    • "Calibrating the models, assessing their sensitivity to some assumptions (Saltelli et al. 2008; Augusiak, Van den Brink & Grimm 2014) and measuring data uncertainty are also challenging (Hartig et al. 2011). The analysis of complex models is the focus of intense research, not only in biology (Wilkinson 2009), but also in climatology (Edwards & Marsh 2005), industry (Lorenzo et al. 2011) and statistics (Kennedy & O'Hagan 2001), with an increasing number of software facilities to disseminate state-of-theart techniques (e.g. Jabot, Faure & Dumoulin 2013). "
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    ABSTRACT: 1. In a rapidly changing world, ecology has the potential to move from empirical and conceptual stages to application and management issues. It is now possible to make large-scale predictions up to continental or global scales, ranging from the future distribution of biological diversity to changes in ecosystem functioning and services. With these recent developments, ecology has a historical opportunity to become a major actor in the development of a sustainable human society. With this opportunity, however, also comes an important responsibility in developing appropriate predictive models, correctly interpreting their outcomes, and communicating their limitations. There is also a danger that predictions grow faster than our understanding of ecological systems, resulting in a gap between the scientists generating the predictions and stakeholders using them (conservation biologists, environmental managers, journalists, policymakers). 2. Here we use the context provided by the current surge of ecological predictions on the future of biodiversity to clarify what prediction means, and to pinpoint the challenges that should be addressed in order to improve predictive ecological models and the way they are understood and used. 3. Synthesis and applications. Ecologists face several challenges to ensure the healthy development of an operational predictive ecological science: (i) clarity on the distinction between explanatory and anticipatory predictions; (ii) developing new theories at the interface between explanatory and anticipatory predictions; (iii) open data to test and validate predictions; (iv) making predictions operational and (v) developing a genuine ethics of prediction.
    Journal of Applied Ecology 08/2015; DOI:10.1111/1365-2664.12482. · 4.75 Impact Factor
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    • "Calibrating the models, assessing their sensitivity to some assumptions (Saltelli et al. 2008; Augusiak, Van den Brink & Grimm 2014) and measuring data uncertainty are also challenging (Hartig et al. 2011). The analysis of complex models is the focus of intense research, not only in biology (Wilkinson 2009), but also in climatology (Edwards & Marsh 2005), industry (Lorenzo et al. 2011) and statistics (Kennedy & O'Hagan 2001), with an increasing number of software facilities to disseminate state-of-theart techniques (e.g. Jabot, Faure & Dumoulin 2013). "
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    • "This data is then used to inform computational models that are capable of examining quantitatively and qualitatively the behaviour of biological systems under a wide variety of conditions [8] [9]. The major advantage of this approach lies in the researcher's ability to model a multitude of complex biochemical events, many of which occur simultaneously [10]. This contrasts with the reductionist approach of studying biological systems by focusing on a small component operating in isolation. "
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    ABSTRACT: Systems biology and synthetic biology are emerging disciplines which are becoming increasingly utilised in several areas of bioscience. Toxicology is beginning to benefit from systems biology and we suggest in the future that is will also benefit from synthetic biology. Thus, a new era is on the horizon. This review illustrates how a suite of innovative techniques and tools can be applied to understanding complex health and toxicology issues. We review limitations confronted by the traditional computational approaches to toxicology and epidemiology research, using polycyclic aromatic hydrocarbons (PAHs) and their effects on adverse birth outcomes as an illustrative example. We introduce how systems toxicology (and their subdisciplines, genomic, proteomic, and metabolomic toxicology) will help to overcome such limitations. In particular, we discuss the advantages and disadvantages of mathematical frameworks that computationally represent biological systems. Finally, we discuss the nascent discipline of synthetic biology and highlight relevant toxicological centred applications of this technique, including improvements in personalised medicine. We conclude this review by presenting a number of opportunities and challenges that could shape the future of these rapidly evolving disciplines.
    01/2015; 2015:14. DOI:10.1155/2015/575403
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