343,984 research outputs found
Population variability in animal health: Influence on dose-exposure-response relationships: Part II: Modelling and simulation
During the 2017 Biennial meeting, the American Academy of Veterinary Pharmacology and Therapeutics hosted a 1‐day session on the influence of population variability on dose‐exposure‐response relationships. In Part I, we highlighted some of the sources of population variability. Part II provides a summary of discussions on modelling and simulation tools that utilize existing pharmacokinetic data, can integrate drug physicochemical characteristics with species physiological characteristics and dosing information or that combine observed with predicted and in vitro information to explore and describe sources of variability that may influence the safe and effective use of veterinary pharmaceuticals
Self-adaptive exploration in evolutionary search
We address a primary question of computational as well as biological research
on evolution: How can an exploration strategy adapt in such a way as to exploit
the information gained about the problem at hand? We first introduce an
integrated formalism of evolutionary search which provides a unified view on
different specific approaches. On this basis we discuss the implications of
indirect modeling (via a ``genotype-phenotype mapping'') on the exploration
strategy. Notions such as modularity, pleiotropy and functional phenotypic
complex are discussed as implications. Then, rigorously reflecting the notion
of self-adaptability, we introduce a new definition that captures
self-adaptability of exploration: different genotypes that map to the same
phenotype may represent (also topologically) different exploration strategies;
self-adaptability requires a variation of exploration strategies along such a
``neutral space''. By this definition, the concept of neutrality becomes a
central concern of this paper. Finally, we present examples of these concepts:
For a specific grammar-type encoding, we observe a large variability of
exploration strategies for a fixed phenotype, and a self-adaptive drift towards
short representations with highly structured exploration strategy that matches
the ``problem's structure''.Comment: 24 pages, 5 figure
Examples of mathematical modeling tales from the crypt
Mathematical modeling is being increasingly recognized within the biomedical sciences as an important tool that can aid the understanding of biological systems. The heavily regulated cell renewal cycle in the colonic crypt provides a good example of how modeling can be used to find out key features of the system kinetics, and help to explain both the breakdown of homeostasis and the initiation of tumorigenesis. We use the cell population model by Johnston et al. (2007) Proc. Natl. Acad. Sci. USA 104, 4008-4013, to illustrate the power of mathematical modeling by considering two key questions about the cell population dynamics in the colonic crypt. We ask: how can a model describe both homeostasis and unregulated growth in tumorigenesis; and to which parameters in the system is the model most sensitive? In order to address these questions, we discuss what type of modeling approach is most appropriate in the crypt. We use the model to argue why tumorigenesis is observed to occur in stages with long lag phases between periods of rapid growth, and we identify the key parameters
Full Open Population Capture-Recapture Models with Individual Covariates
Traditional analyses of capture-recapture data are based on likelihood
functions that explicitly integrate out all missing data. We use a complete
data likelihood (CDL) to show how a wide range of capture-recapture models can
be easily fitted using readily available software JAGS/BUGS even when there are
individual-specific time-varying covariates. The models we describe extend
those that condition on first capture to include abundance parameters, or
parameters related to abundance, such as population size, birth rates or
lifetime. The use of a CDL means that any missing data, including uncertain
individual covariates, can be included in models without the need for
customized likelihood functions. This approach also facilitates modeling
processes of demographic interest rather than the complexities caused by
non-ignorable missing data. We illustrate using two examples, (i) open
population modeling in the presence of a censored time-varying individual
covariate in a full robust-design, and (ii) full open population multi-state
modeling in the presence of a partially observed categorical variable
Analysis of time-to-event for observational studies: Guidance to the use of intensity models
This paper provides guidance for researchers with some mathematical
background on the conduct of time-to-event analysis in observational studies
based on intensity (hazard) models. Discussions of basic concepts like time
axis, event definition and censoring are given. Hazard models are introduced,
with special emphasis on the Cox proportional hazards regression model. We
provide check lists that may be useful both when fitting the model and
assessing its goodness of fit and when interpreting the results. Special
attention is paid to how to avoid problems with immortal time bias by
introducing time-dependent covariates. We discuss prediction based on hazard
models and difficulties when attempting to draw proper causal conclusions from
such models. Finally, we present a series of examples where the methods and
check lists are exemplified. Computational details and implementation using the
freely available R software are documented in Supplementary Material. The paper
was prepared as part of the STRATOS initiative.Comment: 28 pages, 12 figures. For associated Supplementary material, see
http://publicifsv.sund.ku.dk/~pka/STRATOSTG8
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
Randomized experiments have been critical tools of decision making for
decades. However, subjects can show significant heterogeneity in response to
treatments in many important applications. Therefore it is not enough to simply
know which treatment is optimal for the entire population. What we need is a
model that correctly customize treatment assignment base on subject
characteristics. The problem of constructing such models from randomized
experiments data is known as Uplift Modeling in the literature. Many algorithms
have been proposed for uplift modeling and some have generated promising
results on various data sets. Yet little is known about the theoretical
properties of these algorithms. In this paper, we propose a new tree-based
ensemble algorithm for uplift modeling. Experiments show that our algorithm can
achieve competitive results on both synthetic and industry-provided data. In
addition, by properly tuning the "node size" parameter, our algorithm is proved
to be consistent under mild regularity conditions. This is the first consistent
algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin
Bridging Physics and Biology Teaching through Modeling
As the frontiers of biology become increasingly interdisciplinary, the
physics education community has engaged in ongoing efforts to make physics
classes more relevant to life sciences majors. These efforts are complicated by
the many apparent differences between these fields, including the types of
systems that each studies, the behavior of those systems, the kinds of
measurements that each makes, and the role of mathematics in each field.
Nonetheless, physics and biology are both sciences that rely on observations
and measurements to construct models of the natural world. In the present
theoretical article, we propose that efforts to bridge the teaching of these
two disciplines must emphasize shared scientific practices, particularly
scientific modeling. We define modeling using language common to both
disciplines and highlight how an understanding of the modeling process can help
reconcile apparent differences between the teaching of physics and biology. We
elaborate how models can be used for explanatory, predictive, and functional
purposes and present common models from each discipline demonstrating key
modeling principles. By framing interdisciplinary teaching in the context of
modeling, we aim to bridge physics and biology teaching and to equip students
with modeling competencies applicable across any scientific discipline.Comment: 10 pages, 2 figures, 3 table
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