All Issues

Volume 15, 2018

Volume 14, 2017

Volume 13, 2016

Volume 12, 2015

Volume 11, 2014

Volume 10, 2013

Volume 9, 2012

Volume 8, 2011

Volume 7, 2010

Volume 6, 2009

Volume 5, 2008

Volume 4, 2007

Volume 3, 2006

Volume 2, 2005

Volume 1, 2004

Mathematical Biosciences & Engineering

2015 , Volume 12 , Issue 5

Select all articles


Thermal detection of a prevascular tumor embedded in breast tissue
Ephraim Agyingi, Tamas Wiandt and Sophia A. Maggelakis
2015, 12(5): 907-915 doi: 10.3934/mbe.2015.12.907 +[Abstract](2915) +[PDF](1005.2KB)
This paper presents a mathematical model of heat transfer in a prevascular breast tumor. The model uses the steady state temperature of the breast at the skin surface to determine whether there is an underlying tumor and if so, verifies whether the tumor is growing or dormant. The model is governed by the Pennes equations and we present numerical simulations for versions of the model in two and three dimensions.
Parameters identification for a model of T cell homeostasis
Houssein Ayoub, Bedreddine Ainseba, Michel Langlais and Rodolphe Thiébaut
2015, 12(5): 917-936 doi: 10.3934/mbe.2015.12.917 +[Abstract](3044) +[PDF](1013.6KB)
In this study, we consider a model of T cell homeostasis based on the Smith-Martin model. This nonlinear model is structured by age and CD44 expression. First, we establish the mathematical well-posedness of the model system. Next, we prove the theoretical identifiability regarding the up-regulation of CD44, the proliferation time phase and the rate of entry into division, by using the experimental data. Finally, we compare two versions of the Smith-Martin model and we identify which model fits the experimental data best.
Uncertainty quantification in modeling HIV viral mechanics
H. T. Banks, Robert Baraldi, Karissa Cross, Kevin Flores, Christina McChesney, Laura Poag and Emma Thorpe
2015, 12(5): 937-964 doi: 10.3934/mbe.2015.12.937 +[Abstract](3318) +[PDF](639.5KB)
We consider an in-host model for HIV-1 infection dynamics developed and validated with patient data in earlier work [7]. We revisit the earlier model in light of progress over the last several years in understanding HIV-1 progression in humans. We then consider statistical models to describe the data and use these with residual plots in generalized least squares problems to develop accurate descriptions of the proper weights for the data. We use recent parameter subset selection techniques [5,6] to investigate the impact of estimated parameters on the corresponding selection scores. Bootstrapping and asymptotic theory are compared in the context of confidence intervals for the resulting parameter estimates.
Stochastic modelling of PTEN regulation in brain tumors: A model for glioblastoma multiforme
Margherita Carletti, Matteo Montani, Valentina Meschini, Marzia Bianchi and Lucia Radici
2015, 12(5): 965-981 doi: 10.3934/mbe.2015.12.965 +[Abstract](3218) +[PDF](721.6KB)
This work is the outcome of the partnership between the mathematical group of Department DISBEF and the biochemical group of Department DISB of the University of Urbino "Carlo Bo" in order to better understand some crucial aspects of brain cancer oncogenesis. Throughout our collaboration we discovered that biochemists are mainly attracted to the instantaneous behaviour of the whole cell, while mathematicians are mostly interested in the evolution along time of small and different parts of it. This collaboration has thus been very challenging. Starting from [23,24,25], we introduce a competitive stochastic model for post-transcriptional regulation of PTEN, including interactions with the miRNA and concurrent genes. Our model also covers protein formation and the backward mechanism going from the protein back to the miRNA. The numerical simulations show that the model reproduces the expected dynamics of normal glial cells. Moreover, the introduction of translational and transcriptional delays offers some interesting insights for the PTEN low expression as observed in brain tumor cells.
Multi-host transmission dynamics of schistosomiasis and its optimal control
Chunxiao Ding, Zhipeng Qiu and Huaiping Zhu
2015, 12(5): 983-1006 doi: 10.3934/mbe.2015.12.983 +[Abstract](4196) +[PDF](653.5KB)
In this paper we formulate a dynamical model to study the transmission dynamics of schistosomiasis in humans and snails. We also incorporate bovines in the model to study their impact on transmission and controlling the spread of Schistosoma japonicum in humans in China. The dynamics of the model is rigorously analyzed by using the theory of dynamical systems. The theoretical results show that the disease free equilibrium is globally asymptotically stable if $\mathcal R_0<1$, and if $\mathcal R_0>1$ the system has only one positive equilibrium. The local stability of the unique positive equilibrium is investigated and sufficient conditions are also provided for the global stability of the positive equilibrium. The optimal control theory are further applied to the model to study the corresponding optimal control problem. Both analytical and numerical results suggest that: (a) the infected bovines play an important role in the spread of schistosomiasis among humans, and killing the infected bovines will be useful to prevent transmission of schistosomiasis among humans; (b) optimal control strategy performs better than the constant controls in reducing the prevalence of the infected human and the cost for implementing optimal control is much less than that for constant controls; and (c) improving the treatment rate of infected humans, the killing rate of the infected bovines and the fishing rate of snails in the early stage of spread of schistosomiasis are very helpful to contain the prevalence of infected human case as well as minimize the total cost.
Order reduction for an RNA virus evolution model
Andrei Korobeinikov, Aleksei Archibasov and Vladimir Sobolev
2015, 12(5): 1007-1016 doi: 10.3934/mbe.2015.12.1007 +[Abstract](3278) +[PDF](993.4KB)
A mathematical or computational model in evolutionary biology should necessary combine several comparatively fast processes, which actually drive natural selection and evolution, with a very slow process of evolution. As a result, several very different time scales are simultaneously present in the model; this makes its analytical study an extremely difficult task. However, the significant difference of the time scales implies the existence of a possibility of the model order reduction through a process of time separation. In this paper we conduct the procedure of model order reduction for a reasonably simple model of RNA virus evolution reducing the original system of three integro-partial derivative equations to a single equation. Computations confirm that there is a good fit between the results for the original and reduced models.
Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering
Gerasimos G. Rigatos, Efthymia G. Rigatou and Jean Daniel Djida
2015, 12(5): 1017-1035 doi: 10.3934/mbe.2015.12.1017 +[Abstract](3443) +[PDF](446.8KB)
A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of $\chi^2$ change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies)
Analysis of a cancer dormancy model and control of immuno-therapy
Ben Sheller and Domenico D'Alessandro
2015, 12(5): 1037-1053 doi: 10.3934/mbe.2015.12.1037 +[Abstract](3004) +[PDF](2010.9KB)
The goal of this paper is to analyze a model of cancer-immune system interactions from [16], and to show how the introduction of control in this model can dramatically improve the hypothetical patient response and in effect prevent the cancer from growing. We examine all the equilibrium points of the model and classify them according to their stability properties. We identify an equilibrium point corresponding to a survivable amount of cancer cells which are exactly balanced by the immune response. This situation corresponds to cancer dormancy. By using Lyapunov stability theory we estimate the region of attraction of this equilibrium and propose two control laws which are able to stabilize the system effectively, improving the results of [16]. Ultimately, the analysis presented in this paper reveals that a slower, continuous introduction of antibodies over a short time scale, as opposed to mere inoculation, may lead to more efficient and safer treatments.
Ebola outbreak in West Africa: real-time estimation and multiple-wave prediction
Xiang-Sheng Wang and Luoyi Zhong
2015, 12(5): 1055-1063 doi: 10.3934/mbe.2015.12.1055 +[Abstract](3374) +[PDF](343.8KB)
Based on the reported data until 18 March 2015 and numerical fitting via a simple formula of cumulative case number, we provide real-time estimation on basic reproduction number, inflection point, peak time and final outbreak size of ongoing Ebola outbreak in West Africa. From our simulation, we conclude that the first wave has passed its inflection point and predict that a second epidemic wave may appear in the near future.
Heteroclinic bifurcation for a general predator-prey model with Allee effect and state feedback impulsive control strategy
Qizhen Xiao and Binxiang Dai
2015, 12(5): 1065-1081 doi: 10.3934/mbe.2015.12.1065 +[Abstract](3694) +[PDF](447.0KB)
In this paper, we analyze a general predator-prey model with state feedback impulsive harvesting strategies in which the prey species displays a strong Allee effect. We firstly show the existence of order-$1$ heteroclinic cycle and order-$1$ positive periodic solutions by using the geometric theory of differential equations for the unperturbed system. Based on the theory of rotated vector fields, the order-$1$ positive periodic solutions and heteroclinic bifurcation are studied for the perturbed system. Finally, some numerical simulations are provided to illustrate our main results. All the results indicate that the harvesting rate should be maintained at a reasonable range to keep the sustainable development of ecological systems.
Global stability of a multi-group model with vaccination age, distributed delay and random perturbation
Jinhu Xu and Yicang Zhou
2015, 12(5): 1083-1106 doi: 10.3934/mbe.2015.12.1083 +[Abstract](3711) +[PDF](617.3KB)
A multi-group epidemic model with distributed delay and vaccination age has been formulated and studied. Mathematical analysis shows that the global dynamics of the model is determined by the basic reproduction number $\mathcal{R}_0$: the disease-free equilibrium is globally asymptotically stable if $\mathcal{R}_0\leq1$, and the endemic equilibrium is globally asymptotically stable if $\mathcal{R}_0>1$. Lyapunov functionals are constructed by the non-negative matrix theory and a novel grouping technique to establish the global stability. The stochastic perturbation of the model is studied and it is proved that the endemic equilibrium of the stochastic model is stochastically asymptotically stable in the large under certain conditions.
Cilium height difference between strokes is more effective in driving fluid transport in mucociliary clearance: A numerical study
Ling Xu and Yi Jiang
2015, 12(5): 1107-1126 doi: 10.3934/mbe.2015.12.1107 +[Abstract](2730) +[PDF](5989.7KB)
Mucociliary clearance is the first line of defense in our airway. The purpose of this study is to identify and study key factors in the cilia motion that influence the transport ability of the mucociliary system. Using a rod-propel-fluid model, we examine the effects of cilia density, beating frequency, metachronal wavelength, and the extending height of the beating cilia. We first verify that asymmetry in the cilia motion is key to developing transport in the mucus flow. Next, two types of asymmetries between the effective and recovery strokes of the cilia motion are considered, the cilium beating velocity difference and the cilium height difference. We show that the cilium height difference is more efficient in driving the transport, and the more bend the cilium during the recovery stroke is, the more effective the transport would be. It is found that the transport capacity of the mucociliary system increases with cilia density and cilia beating frequency, but saturates above by a threshold value in both density and frequency. The metachronal wave that results from the phase lag among cilia does not contribute much to the mucus transport, which is consistent with the experimental observation of Sleigh (1989). We also test the effect of mucus viscosity, whose value is found to be inversely proportional to the transport ability. While multiple parts have to interplay and coordinate to allow for most effective mucociliary clearance, our findings from a simple model move us closer to understanding the effects of the cilia motion on the efficiency of this clearance system.
Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen
Gregory Zitelli, Seddik M. Djouadi and Judy D. Day
2015, 12(5): 1127-1139 doi: 10.3934/mbe.2015.12.1127 +[Abstract](3980) +[PDF](415.0KB)
The inflammatory response aims to restore homeostasis by means of removing a biological stress, such as an invading bacterial pathogen. In cases of acute systemic inflammation, the possibility of collateral tissue damage arises, which leads to a necessary down-regulation of the response. A reduced ordinary differential equations (ODE) model of acute inflammation was presented and investigated in [10]. That system contains multiple positive and negative feedback loops and is a highly coupled and nonlinear ODE. The implementation of nonlinear model predictive control (NMPC) as a methodology for determining proper therapeutic intervention for in silico patients displaying complex inflammatory states was initially explored in [5]. Since direct measurements of the bacterial population and the magnitude of tissue damage/dysfunction are not readily available or biologically feasible, the need for robust state estimation was evident. In this present work, we present results on the nonlinear reachability of the underlying model, and then focus our attention on improving the predictability of the underlying model by coupling the NMPC with a particle filter. The results, though comparable to the initial exploratory study, show that robust state estimation of this highly nonlinear model can provide an alternative to prior updating strategies used when only partial access to the unmeasurable states of the system are available.

2018 Impact Factor: 1.313




Email Alert

[Back to Top]