Modelling and analysis of dynamics of viral infection of cells and of interferon resistance

AbstractInterferons are active biomolecules, which help fight viral infections by spreading from infected to uninfected cells and activate effector molecules, which confer resistance from the virus on cells. We propose a new model of dynamics of viral infection, including endocytosis, cell death, production of interferon and development of resistance. The novel element is a specific biologically justified mechanism of interferon action, which results in dynamics different from other infection models. The model reflects conditions prevailing in liquid cultures (ideal mixing), and the absence of cells or virus influx from outside. The basic model is a nonlinear system of five ordinary differential equations. For this variant, it is possible to characterise global behaviour, using a conservation law. Analytic results are supplemented by computational studies. The second variant of the model includes age-of-infection structure of infected cells, which is described by a transport-type partial differential equation for infected cells. The conclusions are: (i) If virus mortality is included, the virus becomes eventually extinct and subpopulations of uninfected and resistant cells are established. (ii) If virus mortality is not included, the dynamics may lead to extinction of uninfected cells. (iii) Switching off the interferon defense results in a decrease of the sum total of uninfected and resistant cells. (iv) Infection-age structure of infected cells may result in stabilisation or destabilisation of the system, depending on detailed assumptions. Our work seems to constitute the first comprehensive mathematical analysis of the cell-virus-interferon system based on biologically plausible hypotheses

Global dynamics of two-compartment models for cell production systems with regulatory mechanisms

We present a global stability analysis of two-compartment models of a hierarchical cell production system with a nonlinear regulatory feedback loop. The models describe cell differentiation processes with the stem cell division rate or the self-renewal fraction regulated by the number of mature cells. The two-compartment systems constitute a basic version of the multicompartment models proposed recently by Marciniak-Czochra and collaborators [25] to investigate the dynamics of the hematopoietic system. Using global stability analysis, we compare different regulatory mechanisms. For both models, we show that there exists a unique positive equilibrium that is globally asymptotically stable if and only if the respective reproduction numbers exceed one. The proof is based on constructing Lyapunov functions, which are appropriate to handle the specific nonlinearities of the model. Additionally, we propose a new model to test biological hypothesis on the regulation of the fraction of differentiating cells. We show that such regulatory mechanism is incapable of maintaining homeostasis and leads to unbounded cell growth. Potential biological implications are discussed

Stability analysis of multi-compartment models for cell production systems

We study two-and three-compartment models of a hierarchical cell production system with cell division regulated by the level of mature cells. We investigate the structure of equilibria with respect to parameters as well as local stability properties for the equilibria. To interpret the results we adapt the concept of reproduction numbers, which is well known in ecology, to stem cell population dynamics. In the two-compartment model, the positive equilibrium is stable wherever it exists. In the three-compartment model, we find that the intermediate stage of differentiation is responsible for the emergence of an instability region in the parameter plane. Moreover, we prove that this region shrinks as the mortality rate for mature cells increases and discuss this result

A model for stem cell population dynamics with regulated maturation delay

We develop a structured population model for the maturation process of stem cells in the form of a state-dependent delay differential equation. Moreover, results on existence, uniqueness and positivity of solutions as well as conditions of existence for equilibria and representations of these are established. We give biological interpretations for the conditions of existence of equilibria

Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy

We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes

Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy

Funding: German Research Foundation DFG (SFB 873; subproject B08) (T.S. and A.M.-C); Heidelberg Graduate School (T.L.).We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes.PostprintPeer reviewe

On the dynamics of a non-local parabolic equation arising from the Gierer-Meinhardt system

This is an author-created, un-copyedited version of an article accepted for publication in Nonlinearity. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://iopscience.iop.org/article/10.1088/1361-6544/aa64b2/metaThe purpose of the current paper is to contribute to the comprehension of the dynamics of the shadow system of an activator-inhibitor system known as a Gierer-Meinhardt model. Shadow systems are intended to work as an intermediate step between single equations and reaction-diffusion systems. In the case where the inhibitor's response to the activator's growth is rather weak, then the shadow system of the Gierer-Meinhardt model is reduced to a single though non-local equation whose dynamics will be investigated. We mainly focus on the derivation of blow-up results for this non-local equation which can be seen as instability patterns of the shadow system. In particular, a {\it diffusion driven instability (DDI)}, or {\it Turing instability}, in the neighbourhood of a constant stationary solution, which it is destabilised via diffusion-driven blow-up, is obtained. The latter actually indicates the formation of some unstable patterns, whilst some stability results of global-in-time solutions towards non-constant steady states guarantee the occurrence of some stable patterns