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Journal of The Royal Society Interface RSS feed -- current issue1742-5662September, 2017Journal of The Royal Society Interface1742-5689<![CDATA[The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170213?rss=1
The coefficient of determination R^{2} quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R^{2} for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R^{2} that we called for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.
]]>2017-09-13T00:09:17-07:00info:doi/10.1098/rsif.2017.0213hwp:master-id:royinterface;rsif.2017.02132017-09-13Life Sciences-Mathematics interface141342017021320170213<![CDATA[Collective memory in primate conflict implied by temporal scaling collapse]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170223?rss=1
In biological systems, prolonged conflict is costly, whereas contained conflict permits strategic innovation and refinement. Causes of variation in conflict size and duration are not well understood. We use a well-studied primate society model system to study how conflicts grow. We find conflict duration is a ‘first to fight’ growth process that scales superlinearly, with the number of possible pairwise interactions. This is in contrast with a ‘first to fail’ process that characterizes peaceful durations. Rescaling conflict distributions reveals a universal curve, showing that the typical time scale of correlated interactions exceeds nearly all individual fights. This temporal correlation implies collective memory across pairwise interactions beyond those assumed in standard models of contagion growth or iterated evolutionary games. By accounting for memory, we make quantitative predictions for interventions that mitigate or enhance the spread of conflict. Managing conflict involves balancing the efficient use of limited resources with an intervention strategy that allows for conflict while keeping it contained and controlled.
]]>2017-09-06T00:09:06-07:00info:doi/10.1098/rsif.2017.0223hwp:master-id:royinterface;rsif.2017.02232017-09-06Life Sciences-Physics interface141342017022320170223<![CDATA[A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170320?rss=1
When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.
]]>2017-09-20T00:09:17-07:00info:doi/10.1098/rsif.2017.0320hwp:master-id:royinterface;rsif.2017.03202017-09-20Life Sciences-Mathematics interface141342017032020170320<![CDATA[Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170340?rss=1
Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.
]]>2017-09-20T00:09:17-07:00info:doi/10.1098/rsif.2017.0340hwp:master-id:royinterface;rsif.2017.03402017-09-20Life Sciences-Mathematics interface141342017034020170340<![CDATA[Effect of aspirin on tumour cell colony formation and evolution]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170374?rss=1
Aspirin is known to reduce the risk of colorectal cancer (CRC) incidence, but the underlying mechanisms are not fully understood. In a previous study, we quantified the in vitro growth kinetics of different CRC tumour cell lines treated with varying doses of aspirin, measuring the rate of cell division and cell death. Here, we use these measured parameters to calculate the chances of successful clonal expansion and to determine the evolutionary potential of the tumour cell lines in the presence and absence of aspirin. The calculations indicate that aspirin increases the probability that a single tumour cell fails to clonally expand. Further, calculations suggest that aspirin increases the evolutionary potential of an expanding tumour cell colony. An aspirin-treated tumour cell population is predicted to result in the accumulation of more mutations (and is thus more virulent and more difficult to treat) than a cell population of the same size that grew without aspirin. This indicates a potential trade-off between delaying the onset of cancer and increasing its evolutionary potential through chemoprevention. Further work needs to investigate to what extent these findings apply to in vivo settings, and to what degree they contribute to the epidemiologically documented aspirin-mediated protection.
]]>2017-09-06T00:09:06-07:00info:doi/10.1098/rsif.2017.0374hwp:master-id:royinterface;rsif.2017.03742017-09-06Life Sciences-Mathematics interface141342017037420170374<![CDATA[Diamond thin films: giving biomedical applications a new shine]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170382?rss=1
Progress made in the last two decades in chemical vapour deposition technology has enabled the production of inexpensive, high-quality coatings made from diamond to become a scientific and commercial reality. Two properties of diamond make it a highly desirable candidate material for biomedical applications: first, it is bioinert, meaning that there is minimal immune response when diamond is implanted into the body, and second, its electrical conductivity can be altered in a controlled manner, from insulating to near-metallic. In vitro, diamond can be used as a substrate upon which a range of biological cells can be cultured. In vivo, diamond thin films have been proposed as coatings for implants and prostheses. Here, we review a large body of data regarding the use of diamond substrates for in vitro cell culture. We also detail more recent work exploring diamond-coated implants with the main targets being bone and neural tissue. We conclude that diamond emerges as one of the major new biomaterials of the twenty-first century that could shape the way medical treatment will be performed, especially when invasive procedures are required.
]]>2017-09-20T00:09:17-07:00info:doi/10.1098/rsif.2017.0382hwp:master-id:royinterface;rsif.2017.03822017-09-20Life Sciences-Physics interface141342017038220170382<![CDATA[Complex systems biology]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170391?rss=1
Complex systems theory is concerned with identifying and characterizing common design elements that are observed across diverse natural, technological and social complex systems. Systems biology, a more holistic approach to study molecules and cells in biology, has advanced rapidly in the past two decades. However, not much appreciation has been granted to the realization that the human cell is an exemplary complex system. Here, I outline general design principles identified in many complex systems, and then describe the human cell as a prototypical complex system. Considering concepts of complex systems theory in systems biology can illuminate our overall understanding of normal cell physiology and the alterations that lead to human disease.
]]>2017-09-20T00:09:17-07:00info:doi/10.1098/rsif.2017.0391hwp:master-id:royinterface;rsif.2017.03912017-09-20Review articles141342017039120170391<![CDATA[A light-dependent magnetoreception mechanism insensitive to light intensity and polarization]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170405?rss=1
Billions of migratory birds navigate thousands of kilometres every year aided by a magnetic compass sense, the biophysical mechanism of which is unclear. One leading hypothesis is that absorption of light by specialized photoreceptors in the retina produces short-lived chemical intermediates known as radical pairs whose chemistry is sensitive to tiny magnetic interactions. A potentially serious but largely ignored obstacle to this theory is how directional information derived from the Earth's magnetic field can be separated from the much stronger variations in the intensity and polarization of the incident light. Here we propose a simple solution in which these extraneous effects are cancelled by taking the ratio of the signals from two neighbouring populations of magnetoreceptors. Geometric and biological arguments are used to derive a set of conditions that make this possible. We argue that one likely location of the magnetoreceptor molecules would be in association with ordered opsin dimers in the membrane discs of the outer segments of double-cone photoreceptor cells.
]]>2017-09-06T00:09:06-07:00info:doi/10.1098/rsif.2017.0405hwp:master-id:royinterface;rsif.2017.04052017-09-06Life Sciences-Physics interface141342017040520170405<![CDATA[Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization]]>
http://rsif.royalsocietypublishing.org/cgi/content/short/14/134/20170520?rss=1
Maps of infectious disease—charting spatial variations in the force of infection, degree of endemicity and the burden on human health—provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method.
]]>2017-09-20T00:09:18-07:00info:doi/10.1098/rsif.2017.0520hwp:master-id:royinterface;rsif.2017.05202017-09-20Life Sciences-Mathematics interface141342017052020170520