8,192 research outputs found
Equation-free modeling of evolving diseases: Coarse-grained computations with individual-based models
We demonstrate how direct simulation of stochastic, individual-based models
can be combined with continuum numerical analysis techniques to study the
dynamics of evolving diseases. % Sidestepping the necessity of obtaining
explicit population-level models, the approach analyzes the (unavailable in
closed form) `coarse' macroscopic equations, estimating the necessary
quantities through appropriately initialized, short `bursts' of
individual-based dynamic simulation. % We illustrate this approach by analyzing
a stochastic and discrete model for the evolution of disease agents caused by
point mutations within individual hosts. % Building up from classical SIR and
SIRS models, our example uses a one-dimensional lattice for variant space, and
assumes a finite number of individuals. % Macroscopic computational tasks
enabled through this approach include stationary state computation, coarse
projective integration, parametric continuation and stability analysis.Comment: 16 pages, 8 figure
Flow hydrodynamics across open channel flows with riparian zones: implications for riverbank stability
Riverbank vegetation is of high importance both for preserving the form (morphology) and function (ecology) of natural river systems. Revegetation of riverbanks is commonly used as a means of stream rehabilitation and management of bank instability and erosion. In this experimental study, the effect of different riverbank vegetation densities on flow hydrodynamics across the channel, including the riparian zone, are reported and discussed. The configuration of vegetation elements follows either linear or staggered arrangements as vegetation density is progressively increased, within a representative range of vegetation densities found in nature. Hydrodynamic measurements including mean streamwise velocity and turbulent intensity flow profiles are recorded via acoustic Doppler velocimetry (ADV)—both at the main channel and within the riverbank. These results show that for the main channel and the toe of riverbank, turbulence intensity for the low densities (λ ≈ 0 to 0.12 m−1) can increase up to 40% compared the case of high densities (λ = 0.94 to 1.9 m−1). Further analysis of these data allowed the estimation of bed-shear stresses, demonstrating 86% and 71% increase at the main channel and near the toe region, for increasing densities (λ = 0 to 1.9 m−1). Quantifying these hydrodynamic effects is important for assessing the contribution of physically representative ranges of riparian vegetation densities on hydrogeomorphologic feedback
Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin
One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution
Towards developmental modelling of tree root systems
Knowledge of belowground structures and processes is essential for understanding and predicting ecosystem functioning, and consequently in the development of adaptive strategies to safeguard production from trees and woody plants into the future. In the past, research has mainly been concentrated on growth models for the prediction of agronomic or forest production. Newly emerging scientific challenges, e.g. climate change and sustainable development, call for new integrated predictive methods where root systems development will become a key element for understanding global biological systems. The types of input data available from the various branches of woody root research, including biomass allocation, architecture, biomechanics, water and nutrient supply, are discussed with a view to the possibility of incorporating them into a more generic developmental model. We discuss here the main focus of root system modelling to date, including a description of simple allometric biomass models, and biomechanical stress models, and then build in complexity through static growth models towards architecture models. The next progressive and logical step in developing an inclusive developmental model that integrates these modelling approaches is discussed.Knowledge of belowground structures and processes is essential for understanding and predicting ecosystem functioning, and consequently in the development of adaptive strategies to safeguard production from trees and woody plants into the future. In the past, research has mainly been concentrated on growth models for the prediction of agronomic or forest production. Newly emerging scientific challenges, e.g. climate change and sustainable development, call for new integrated predictive methods where root systems development will become a key element for understanding global biological systems. The types of input data available from the various branches of woody root research, including biomass allocation, architecture, biomechanics, water and nutrient supply, are discussed with a view to the possibility of incorporating them into a more generic developmental model. We discuss here the main focus of root system modelling to date, including a description of simple allometric biomass models, and biomechanical stress models, and then build in complexity through static growth models towards architecture models. The next progressive and logical step in developing an inclusive developmental model that integrates these modelling approaches is discussed.Knowledge of belowground structures and processes is essential for understanding and predicting ecosystem functioning, and consequently in the development of adaptive strategies to safeguard production from trees and woody plants into the future. In the past, research has mainly been concentrated on growth models for the prediction of agronomic or forest production. Newly emerging scientific challenges, e.g. climate change and sustainable development, call for new integrated predictive methods where root systems development will become a key element for understanding global biological systems. The types of input data available from the various branches of woody root research, including biomass allocation, architecture, biomechanics, water and nutrient supply, are discussed with a view to the possibility of incorporating them into a more generic developmental model. We discuss here the main focus of root system modelling to date, including a description of simple allometric biomass models, and biomechanical stress models, and then build in complexity through static growth models towards architecture models. The next progressive and logical step in developing an inclusive developmental model that integrates these modelling approaches is discussed.Peer reviewe
Part 3: Systemic risk in ecology and engineering
The Federal Reserve Bank of New York released a report -- New Directions for Understanding Systemic Risk -- that presents key findings from a cross-disciplinary conference that it cosponsored in May 2006 with the National Academy of Sciences' Board on Mathematical Sciences and Their Applications. ; The pace of financial innovation over the past decade has increased the complexity and interconnectedness of the financial system. This development is important to central banks, such as the Federal Reserve, because of their traditional role in addressing systemic risks to the financial system. ; To encourage innovative thinking about systemic issues, the New York Fed partnered with the National Academy of Sciences to bring together more than 100 experts on systemic risk from 22 countries to compare cross-disciplinary perspectives on monitoring, addressing and preventing this type of risk. ; This report, released as part of the Bank's Economic Policy Review series, outlines some of the key points concerning systemic risk made by the various disciplines represented - including economic research, ecology, physics and engineering - as well as presentations on market-oriented models of financial crises, and systemic risk in the payments system and the interbank funds market. The report concludes with observations gathered from the sessions and a discussion of potential applications to policy. ; The three papers presented in this conference session highlighted the positive feedback effects that produce herdlike behavior in markets, and the subsequent discussion focused in part on means of encouraging heterogeneous investment strategies to counter such behavior. Participants in the session also discussed the types of models used to study systemic risk and commented on the challenges and trade-offs researchers face in developing their models.Financial risk management ; Financial markets ; Financial stability ; Financial crises
Predictability of evolutionary trajectories in fitness landscapes
Experimental studies on enzyme evolution show that only a small fraction of
all possible mutation trajectories are accessible to evolution. However, these
experiments deal with individual enzymes and explore a tiny part of the fitness
landscape. We report an exhaustive analysis of fitness landscapes constructed
with an off-lattice model of protein folding where fitness is equated with
robustness to misfolding. This model mimics the essential features of the
interactions between amino acids, is consistent with the key paradigms of
protein folding and reproduces the universal distribution of evolutionary rates
among orthologous proteins. We introduce mean path divergence as a quantitative
measure of the degree to which the starting and ending points determine the
path of evolution in fitness landscapes. Global measures of landscape roughness
are good predictors of path divergence in all studied landscapes: the mean path
divergence is greater in smooth landscapes than in rough ones. The
model-derived and experimental landscapes are significantly smoother than
random landscapes and resemble additive landscapes perturbed with moderate
amounts of noise; thus, these landscapes are substantially robust to mutation.
The model landscapes show a deficit of suboptimal peaks even compared with
noisy additive landscapes with similar overall roughness. We suggest that
smoothness and the substantial deficit of peaks in the fitness landscapes of
protein evolution are fundamental consequences of the physics of protein
folding.Comment: 14 pages, 7 figure
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