National Institute for Mathematical and Biological Synthesis NIMBioS

Knoxville, TN, United States

National Institute for Mathematical and Biological Synthesis NIMBioS

Knoxville, TN, United States
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Kershenbaum A.,Haifa University | Kershenbaum A.,National Institute for Mathematical and Biological Synthesis NIMBioS | Blank L.,Haifa University | Sinai I.,Haifa University | And 4 more authors.
Oecologia | Year: 2014

When populations reside within a heterogeneous landscape, isolation by distance may not be a good predictor of genetic divergence if dispersal behaviour and therefore gene flow depend on landscape features. Commonly used approaches linking landscape features to gene flow include the least cost path (LCP), random walk (RW), and isolation by resistance (IBR) models. However, none of these models is likely to be the most appropriate for all species and in all environments. We compared the performance of LCP, RW and IBR models of dispersal with the aid of simulations conducted on artificially generated landscapes. We also applied each model to empirical data on the landscape genetics of the endangered fire salamander, Salamandra infraimmaculata, in northern Israel, where conservation planning requires an understanding of the dispersal corridors. Our simulations demonstrate that wide dispersal corridors of the low-cost environment facilitate dispersal in the IBR model, but inhibit dispersal in the RW model. In our empirical study, IBR explained the genetic divergence better than the LCP and RW models (partial Mantel correlation 0.413 for IBR, compared to 0.212 for LCP, and 0.340 for RW). Overall dispersal cost in salamanders was also well predicted by landscape feature slope steepness (76 %), and elevation (24 %). We conclude that fire salamander dispersal is well characterised by IBR predictions. Together with our simulation findings, these results indicate that wide dispersal corridors facilitate, rather than hinder, salamander dispersal. Comparison of genetic data to dispersal model outputs can be a useful technique in inferring dispersal behaviour from population genetic data. © 2014 Springer-Verlag Berlin Heidelberg.


Noecker C.,National institute for Mathematical and Biological synthesis NIMBioS | Noecker C.,University of Washington | Schaefer K.,National institute for Mathematical and Biological synthesis NIMBioS | Schaefer K.,University of Illinois at Chicago | And 7 more authors.
Viruses | Year: 2015

Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results suggest that, in order to appropriately model early HIV/SIV dynamics, additional factors must be considered in the model development. These may include variability in monkey susceptibility to infection, within-host competition between different viruses for target cells at the initial site of virus replication in the mucosa, innate immune response, and possibly the inclusion of several different tissue compartments. The sobering news is that while an increase in model complexity is needed to explain the available experimental data, testing and rejection of more complex models may require more quantitative data than is currently available. © 2015 by the authors; licensee MDPI, Basel, Switzerland.


Nikitina I.Y.,Central Tuberculosis Research Institute | Kondratuk N.A.,Central Tuberculosis Research Institute | Kosmiadi G.A.,Central Tuberculosis Research Institute | Amansahedov R.B.,Central Tuberculosis Research Institute | And 4 more authors.
PLoS ONE | Year: 2012

Background: Effector CD4 T cells represent a key component of the host's anti-tuberculosis immune defense. Successful differentiation and functioning of effector lymphocytes protects the host against severe M. tuberculosis (Mtb) infection. On the other hand, effector T cell differentiation depends on disease severity/activity, as T cell responses are driven by antigenic and inflammatory stimuli released during infection. Thus, tuberculosis (TB) progression and the degree of effector CD4 T cell differentiation are interrelated, but the relationships are complex and not well understood. We have analyzed an association between the degree of Mtb-specific CD4 T cell differentiation and severity/activity of pulmonary TB infection. Methodology/Principal Findings: The degree of CD4 T cell differentiation was assessed by measuring the percentages of highly differentiated CD27 low cells within a population of Mtb- specific CD4 T lymphocytes ("CD27 lowIFN-γ +" cells). The percentages of CD27 lowIFN-γ+ cells were low in healthy donors (median, 33.1%) and TB contacts (21.8%) but increased in TB patients (47.3%, p<0.0005). Within the group of patients, the percentages of CD27 lowIFN-γ + cells were uniformly high in the lungs (>76%), but varied in blood (12-92%). The major correlate for the accumulation of CD27 lowIFN-γ + cells in blood was lung destruction (r = 0.65, p = 2.7×10 -7). A cutoff of 47% of CD27 lowIFN-γ + cells discriminated patients with high and low degree of lung destruction (sensitivity 89%, specificity 74%); a decline in CD27 lowIFN-γ +cells following TB therapy correlated with repair and/or reduction of lung destruction (p<0.01). Conclusions: Highly differentiated CD27 low Mtb-specific (CD27 lowIFN-γ +) CD4 T cells accumulate in the lungs and circulate in the blood of patients with active pulmonary TB. Accumulation of CD27 lowIFN-γ + cells in the blood is associated with lung destruction. The findings indicate that there is no deficiency in CD4 T cell differentiation during TB; evaluation of CD27 lowIFN-γ + cells provides a valuable means to assess TB activity, lung destruction, and tissue repair following TB therapy. © 2012 Nikitina et al.


Ngonghala C.N.,Harvard University | Ngonghala C.N.,National Institute for Mathematical and Biological Synthesis NIMBioS | Teboh-Ewungkem M.I.,Lehigh University | Ngwa G.A.,University of Buea
Journal of Mathematical Biology | Year: 2015

We derive and study a deterministic compartmental model for malaria transmission with varying human and mosquito populations. Our model considers disease-related deaths, asymptomatic immune humans who are also infectious, as well as mosquito demography, reproduction and feeding habits. Analysis of the model reveals the existence of a backward bifurcation and persistent limit cycles whose period and size is determined by two threshold parameters: the vectorial basic reproduction number $$\fancyscript{R}_{m}$$Rm, and the disease basic reproduction number $$\fancyscript{R}_0$$R0, whose size can be reduced by reducing $$\fancyscript{R}_{m}$$Rm. We conclude that malaria dynamics are indeed oscillatory when the methodology of explicitly incorporating the mosquito’s demography, feeding and reproductive patterns is considered in modeling the mosquito population dynamics. A sensitivity analysis reveals important control parameters that can affect the magnitudes of $$\fancyscript{R}_{m}$$Rm and $$\fancyscript{R}_0$$R0, threshold quantities to be taken into consideration when designing control strategies. Both $$\fancyscript{R}_{m}$$Rm and the intrinsic period of oscillation are shown to be highly sensitive to the mosquito’s birth constant $$\lambda _{m}$$λm and the mosquito’s feeding success probability $$p_{w}$$pw. Control of $$\lambda _{m}$$λm can be achieved by spraying, eliminating breeding sites or moving them away from human habitats, while $$p_{w}$$pw can be controlled via the use of mosquito repellant and insecticide-treated bed-nets. The disease threshold parameter $$\fancyscript{R}_0$$R0 is shown to be highly sensitive to $$p_{w}$$pw, and the intrinsic period of oscillation is also sensitive to the rate at which reproducing mosquitoes return to breeding sites. A global sensitivity and uncertainty analysis reveals that the ability of the mosquito to reproduce and uncertainties in the estimations of the rates at which exposed humans become infectious and infectious humans recover from malaria are critical in generating uncertainties in the disease classes. © 2014, Springer-Verlag Berlin Heidelberg.


Hoban S.,Morton Arboretum | Hoban S.,National Institute for Mathematical and Biological Synthesis NIMBioS | Kelley J.L.,Washington State University | Lotterhos K.E.,Northeastern University | And 7 more authors.
American Naturalist | Year: 2016

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genomescale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outliermethods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scanmethods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change. © 2016 by The University of Chicago.


Asih T.S.N.,Gadjah Mada University | Asih T.S.N.,State University of Semarang | Asih T.S.N.,National Institute for Mathematical and Biological Synthesis NIMBioS | Lenhart S.,University of Tennessee at Knoxville | And 6 more authors.
Bulletin of Mathematical Biology | Year: 2016

The development of cervical cells from normal cells infected by human papillomavirus into invasive cancer cells can be modeled using population dynamics of the cells and free virus. The cell populations are separated into four compartments: susceptible cells, infected cells, precancerous cells and cancer cells. The model system of differential equations also has a free virus compartment in the system, which infect normal cells. We analyze the local stability of the equilibrium points of the model and investigate the parameters, which play an important role in the progression toward invasive cancer. By simulation, we investigate the boundary between initial conditions of solutions, which tend to stable equilibrium point, representing controlled infection, and those which tend to unbounded growth of the cancer cell population. Parameters affected by drug treatment are varied, and their effect on the risk of cancer progression is explored. © 2015, Society for Mathematical Biology.


Akcay E.,National Institute for Mathematical and Biological Synthesis NIMBioS | Roughgarden J.,Stanford University
Proceedings of the Royal Society B: Biological Sciences | Year: 2011

Most of the work in evolutionary game theory starts with a model of a social situation that gives rise to a particular payoff matrix and analyses how behaviour evolves through natural selection. Here, we invert this approach and ask, given a model of how individuals behave, how the payoff matrix will evolve through natural selection. In particular, we ask whether a prisoner's dilemma game is stable against invasions by mutant genotypes that alter the payoffs. To answer this question, we develop a two-tiered framework with goal-oriented dynamics at the behavioural time scale and a diploid population genetic model at the evolutionary time scale. Our results are two-fold: first, we show that the prisoner's dilemma is subject to invasions by mutants that provide incentives for cooperation to their partners, and that the resulting game is a coordination game similar to the hawk-dove game. Second, we find that for a large class of mutants and symmetric games, a stable genetic polymorphism will exist in the locus determining the payoff matrix, resulting in a complex pattern of behaviour al diversity in the population. Our results highlight the importance of considering the evolution of payoff matrices to understand the evolution of animal social systems. © 2010 The Royal Society.


Gonzalez-Forero M.,University of Tennessee at Knoxville | Gonzalez-Forero M.,National Institute for Mathematical and Biological Synthesis NIMBioS
Evolution | Year: 2014

Individuals can manipulate the behavior of social partners. However, manipulation may conflict with the fitness interests of the manipulated individuals. Manipulated individuals can then be favored to resist manipulation, possibly reducing or eliminating the manipulated behavior in the long run. I use a mathematical model to show that conflicts where manipulation and resistance coevolve can disappear as a result of the coevolutionary process. I find that while manipulated individuals are selected to resist, they can simultaneously be favored to express the manipulated behavior at higher efficiency (i.e., providing increasing fitness effects to recipients of the manipulated behavior). Efficiency can increase to a point at which selection for resistance disappears. This process yields an efficient social behavior that is induced by social partners, and over which the inducing and induced individuals are no longer in conflict. A necessary factor is costly inefficiency. I develop the model to address the evolution of advanced eusociality via maternal manipulation (AEMM). The model predicts AEMM to be particularly likely in taxa with ancestrally imperfect resistance to maternal manipulation. Costly inefficiency occurs if the cost of delayed dispersal is larger than the benefit of exploiting the maternal patch. I discuss broader implications of the process. © 2014 The Society for the Study of Evolution.


PubMed | Gadjah Mada University, National Institute for Mathematical and Biological Synthesis NIMBioS and University of Tennessee at Knoxville
Type: Journal Article | Journal: Bulletin of mathematical biology | Year: 2016

The development of cervical cells from normal cells infected by human papillomavirus into invasive cancer cells can be modeled using population dynamics of the cells and free virus. The cell populations are separated into four compartments: susceptible cells, infected cells, precancerous cells and cancer cells. The model system of differential equations also has a free virus compartment in the system, which infect normal cells. We analyze the local stability of the equilibrium points of the model and investigate the parameters, which play an important role in the progression toward invasive cancer. By simulation, we investigate the boundary between initial conditions of solutions, which tend to stable equilibrium point, representing controlled infection, and those which tend to unbounded growth of the cancer cell population. Parameters affected by drug treatment are varied, and their effect on the risk of cancer progression is explored.


PubMed | University of Tennessee at Knoxville and National institute for Mathematical and Biological synthesis NIMBioS
Type: Journal Article | Journal: Viruses | Year: 2015

Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1-2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the standard mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results suggest that, in order to appropriately model early HIV/SIV dynamics, additional factors must be considered in the model development. These may include variability in monkey susceptibility to infection, within-host competition between different viruses for target cells at the initial site of virus replication in the mucosa, innate immune response, and possibly the inclusion of several different tissue compartments. The sobering news is that while an increase in model complexity is needed to explain the available experimental data, testing and rejection of more complex models may require more quantitative data than is currently available.

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