A model of host response to a multi-stage pathogen

We model the immune surveillance of a pathogen which passes through $n$ immunologically distinct stages. The biological parameters of this system induce a partial order on the stages, and this, in turn, determines which stages will be subject to immune regulation. This corresponds to the system's unique asymptotically stable fixed point.Comment: 22 pages, no figure

An algebraic and graph theoretical framework to study monomial dynamical systems over a finite field

An n dimensional monomial dynamical system over a finite field K is a nonlinear deterministic time discrete dynamical system with the property that each of the n component functions is a monic nonzero monomial function in n variables. In this paper we provide an algebraic and graph theoretic framework to study the dynamic properties of monomial dynamical systems over a finite field. Within this framework, characterization theorems for fixed point systems (systems in which all trajectories end in steady states) are proved. In particular, we present an algorithm of polynomial complexity to test whether a given monomial dynamical system over a finite field is a fixed point system. Furthermore, theorems that complement previous work are presented and alternative proofs to previous results are supplied.Comment: 26 pages, typos removed, improved and extended. Currently under revie

An algebraic and graph theoretic framework to study monomial dynamical systems over a finite field

A monomial dynamical system over a finite field K is a nonlinear deterministic time discrete dynamical system with the property that each of the n component functions is a monic nonzero monomial function in n variables. In this paper we provide an algebraic and graph theoretic framework to study the dynamic properties of monomial dynamical systems over a finite field. Within this framework, characterization theorems for fixed point systems (systems in which all trajectories end in steady states) are proved. In particular, we present an algorithm of polynomial complexity to test whether a given monomial dynamical system over a finite field is a fixed point system. Furthermore, theorems that complement previous work are presented and alternative proofs to previous results are supplied

The Evolution of Virulence in RNA Viruses underaCompetition-Colonization Trade-Off

RNA viruses exist in large intra-host populations which display great genotypic and phenotypic diversity. We analyze a model of viral competition between two viruses infecting a constantly replenished cell pool. We assume a trade-off between the ability of the virus to colonize new cells (cell killing rate or virulence) and its local competitiveness (replicative success within coinfected cells). We characterize the conditions that allow for viral spread by means of the basic reproductive number and show that a local coexistence equilibrium exists, which is asymptotically stable. At this equilibrium, the less virulent competitor has a reproductive advantage over the more virulent colonizer reflected by a larger equilibrium population size of the competitor. The equilibria at which one virus outcompetes the other one are unstable, i.e., a second virus is always able to permanently invade. We generalize the two-virus model to multiple viral strains, each displaying a different virulence. To account for the large phenotypic diversity in viral populations, we consider a continuous spectrum of virulences and present a continuum limit of this multiple viral strains model that describes the time evolution of an initial continuous distribution of virulence without mutations. We provide a proof of the existence of solutions of the model equations, analytically assess the properties of stationary solutions, and present numerical approximations of solutions for different initial distributions. Our simulations suggest that initial continuous distributions of virulence evolve toward a distribution that is extremely skewed in favor of competitors. At equilibrium, only the least virulent part of the population survives. The discrepancy of this finding in the continuum limit with the two-virus model is attributed to the skewed equilibrium subpopulation sizes and to the transition to a continuum. Consequently, in viral quasispecies with high virulence diversity, the model predicts collective virulence attenuation. This result may contribute to understanding virulence attenuation, which has been reported in several experimental studie

Air pollution modelling for birth cohorts: a time-space regression model

To investigate air pollution effects during pregnancy or in the first weeks of life, models are needed that capture both the spatial and temporal variability of air pollution exposures.; We developed a time-space exposure model for ambient NO2 concentrations in Bern, Switzerland. We used NO2 data from passive monitoring conducted between 1998 and 2009: 101 rural sites (24,499 biweekly measurements) and 45 urban sites (4350 monthly measurements). We evaluated spatial predictors (land use; roads; traffic; population; annual NO2 from a dispersion model) and temporal predictors (meteorological conditions; NO2 from continuous monitoring station). Separate rural and urban models were developed by multivariable regression techniques. We performed ten-fold internal cross-validation, and an external validation using 57 NO2 passive measurements obtained at study participant's homes.; Traffic related explanatory variables and fixed site NO2 measurements were the most relevant predictors in both models. The coefficient of determination (R(2)) for the log transformed models were 0.63 (rural) and 0.54 (urban); cross-validation R(2)s were unchanged indicating robust coefficient estimates. External validation showed R(2)s of 0.54 (rural) and 0.67 (urban).; This approach is suitable for air pollution exposure prediction in epidemiologic research with time-vulnerable health effects such as those occurring during pregnancy or in the first weeks of life

Correlation properties of spontaneous motor activity in healthy infants: a new computer-assisted method to evaluate neurological maturation

Qualitative assessment of spontaneous motor activity in early infancy is widely used in clinical practice. It enables the description of maturational changes of motor behavior in both healthy infants and infants who are at risk for later neurological impairment. These assessments are, however, time-consuming and are dependent upon professional experience. Therefore, a simple physiological method that describes the complex behavior of spontaneous movements (SMs) in infants would be helpful. In this methodological study, we aimed to determine whether time series of motor acceleration measurements at 40-44weeks and 50-55weeks gestational age in healthy infants exhibit fractal-like properties and if this self-affinity of the acceleration signal is sensitive to maturation. Healthy motor state was ensured by General Movement assessment. We assessed statistical persistence in the acceleration time series by calculating the scaling exponent α via detrended fluctuation analysis of the time series. In hand trajectories of SMs in infants we found a mean α value of 1.198 (95% CI 1.167-1.230) at 40-44weeks. Alpha changed significantly (p=0.001) at 50-55weeks to a mean of 1.102 (1.055-1.149). Complementary multilevel regression analysis confirmed a decreasing trend of α with increasing age. Statistical persistence of fluctuation in hand trajectories of SMs is sensitive to neurological maturation and can be characterized by a simple parameter α in an automated and observer-independent fashion. Future studies including children at risk for neurological impairment should evaluate whether this method could be used as an early clinical screening tool for later neurological compromis