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: 36.98). 02/2009; 10(2):122-33. DOI: 10.1038/nrg2509
Source: PubMed


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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    • "According to this fundamental memory-free hypothesis of Markov model and the Equation (1) and (2), at time t, the cell might eventually fall into slots as displayed in Figure 2a. The mathematical equations have been solved according to methods displayed in (Wilkinson, 2009). "
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    ABSTRACT: Phenotype variations define heterogeneity of biological and molecular systems, which play a crucial role in several mechanisms. Heterogeneity has been demonstrated in tumor cells. Here, samples from blood of patients affected from colon tumor were analyzed and fished with a microfluidic assay based on galactose active moieties, and incubated, for culturing, in SCID mice. Following the experimental investigation, a model based on Markov theory was implemented and discussed to explain the equilibrium existing between phenotypes of subpopulations of cells sorted using the microfluidic assay. The model in combination with the experimental results had many implications for tumor heterogeneity. It displayed interconversion of phenotypes, as observed after experiments. The interconversion generates of metastatic cells and implies that targeting the CTCs will be not an efficient method to prevent tumor recurrence. Most importantly, understanding the transitions between cell phenotypes in cell population can boost the understanding of tumor generation and growth.
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    • "Other examples can be listed in metabolism and nutrient uptake, such as the lactosepathway switch in E.coli (Bhogale et al. 2014), or in connection to fate selection in viral infection, such as the Pyelonephritis-Associated Pili (PAP) epigenetic switch in E. Coli (Munsky et al. 2014). Variability is thus enhanced by networks that can produce multiple, mutually exclusive profiles of gene expression: this fact, in combination with other processes of randomly expressing genes and silencing others, is thought to have a selective advantage, as it allows organisms to display phenotypic variants also in uniform genetic and environmental conditions (Wilkinson 2009). These phenomena can be thought to belong to the class of " variability generators " introduced by Buiatti and Buiatti (2008) and described as exploration tools of the phase space that are essential for the adaptation to changing contexts. "
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    ABSTRACT: In biology, phenotypes' variability stems from stochastic gene expression as well as from ex-trinsic fluctuations that are largely based on the contingency of developmental paths and on ecosystemic changes. Both forms of randomness constructively contribute to biological robustness, as resilience, far away from conventional computable dynamics, where elaboration and transmission of information are robust when they resist to noise. We first survey how fluctuations may be inserted in biochemical equations as probabilistic terms, in conjunction to diffusion or path integrals, and treated by statistical approaches to physics. Further work allows to better grasp the role of biological " resonance " (interactions between different levels of organization) and plasticity, in a highly unconventional frame that seems more suitable for biological processes. In contrast to physical conservation properties, thus symmetries , symmetry breaking is particularly relevant in biology; it provides another key component of biological historicity and of randomness as a source of diversity and, thus, of onto-phylogenetic stability and organization as these are also based on variation and adaptativity.
    UCNC 2015, Auckland; 08/2015
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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. · 4.56 Impact Factor
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