Minimal Mechanisms of Microtubule Length Regulation in Living Cells

The microtubule cytoskeleton is responsible for sustained, long-range intracellular transport of mRNAs, proteins, and organelles in neurons. Neuronal microtubules must be stable enough to ensure reliable transport, but they also undergo dynamic instability, as their plus and minus ends continuously switch between growth and shrinking. This process allows for continuous rebuilding of the cytoskeleton and for flexibility in injury settings. Motivated by in vivo experimental data on microtubule behavior in Drosophila neurons, we propose a mathematical model of dendritic microtubule dynamics, with a focus on understanding microtubule length, velocity, and state-duration distributions. We find that limitations on microtubule growth phases are needed for realistic dynamics, but the type of limiting mechanism leads to qualitatively different responses to plausible experimental perturbations. We therefore propose and investigate two minimally-complex length-limiting factors: limitation due to resource (tubulin) constraints and limitation due to catastrophe of large-length microtubules. We combine simulations of a detailed stochastic model with steady-state analysis of a mean-field ordinary differential equations model to map out qualitatively distinct parameter regimes. This provides a basis for predicting changes in microtubule dynamics, tubulin allocation, and the turnover rate of tubulin within microtubules in different experimental environments.

Ideal adaptive control in biological systems: an analysis of P -invariance and dynamical compensation properties

BACKGROUND

Dynamical compensation (DC) provides robustness to parameter fluctuations. As an example, DC enables control of the functional mass of endocrine or neuronal tissue essential for controlling blood glucose by insulin through a nonlinear feedback loop. Researchers have shown that DC is related to the structural unidentifiability and the P -invariance property. The P -invariance property is a sufficient and necessary condition for the DC property. DC has been seen in systems with at least three dimensions. In this article, we discuss DC and P -invariance from an adaptive control perspective. An adaptive controller automatically adjusts its parameters to optimise performance, maintain stability, and deal with uncertainties in a system.

RESULTS

We initiate our analysis by introducing a simplified two-dimensional dynamical model with DC, fostering experimentation and understanding of the system's behavior. We explore the system's behavior with time-varying input and disturbance signals, with a focus on illustrating the system's P -invariance properties in phase portraits and step-like response graphs.

CONCLUSIONS

We show that DC can be seen as a case of ideal adaptive control since the system is invariant to the compensated parameter.

What Influence Could the Acceptance of Visitors Cause on the Epidemic Dynamics of a Reinfectious Disease?: A Mathematical Model

The globalization in business and tourism becomes crucial more and more for the economical sustainability of local communities. In the presence of an epidemic outbreak, there must be such a decision on the policy by the host community as whether to accept visitors or not, the number of acceptable visitors, or the condition for acceptable visitors. Making use of an SIRI type of mathematical model, we consider the influence of visitors on the spread of a reinfectious disease in a community, especially assuming that a certain proportion of accepted visitors are immune. The reinfectivity of disease here means that the immunity gained by either vaccination or recovery is imperfect. With the mathematical results obtained by our analysis on the model for such an epidemic dynamics of resident and visitor populations, we find that the acceptance of visitors could have a significant influence on the disease's endemicity in the community, either suppressive or supportive.

An optimal control model for Covid-19 spread with impacts of vaccination and facemask

A non-linear system of differential equations was used to explain the spread of the COVID-19 virus and a SEIQR model was developed and tested to provide insights into the spread of the pandemic. This article, which is related to the aforementioned work as well as other work covering variations of SIR models, Hermite Wavelets Transform, and also the Generalized Compartmental COVID-19 model, we develop a mathematical control model and apply it to represent optimal vaccination strategy against COVID-19 using Pontryagin's Maximum Principle and also factoring in the effect of facemasks on the spread of the virus. As background work, we analyze the mathematical epidemiology model with the facemask effect on both reproduction number and stability, we also analyze the difference between confirmed COVID-19 cases of the Quarantine class and anonymous cases of the Infectious class that is expected to recover. We also apply control theory to mine insights for effective virus spread prevention strategies. Our models are validated using Matlab mathematical model validation tools. Statistical tests against data from Jordan are used to validate our work including the modeling of the relation between the facemask effect and COVID-19 spread. Furthermore, the relation between control measure ξ, cost, and Infected cases is also studied.

An epidemic dynamics model with limited isolation capacity

We consider a modified SIR model with a four-dimensional system of ordinary differential equations to consider the influence of a limited isolation capacity on the final epidemic size defined as the total number of individuals who experienced the disease at the end of an epidemic season. We derive the necessary and sufficient condition that the isolation reaches the capacity in a finite time on the way of the epidemic process, and show that the final epidemic size is monotonically decreasing in terms of the isolation capacity. We find further that the final epidemic size could have a discontinuous change at the critical value of isolation capacity below which the isolation reaches the capacity in a finite time. Our results imply that the breakdown of isolation with a limited capacity would cause a drastic increase of the epidemic size. Insufficient capacity of the isolation could lead to an unexpectedly severe epidemic situation, while such a severity would be avoidable with the sufficient isolation capacity.

Asymptotic behavior of an epidemic model with infinitely many variants

We investigate the long-time dynamics of a SIR epidemic model with infinitely many pathogen variants infecting a homogeneous host population. We show that the basic reproduction number [Formula: see text] of the pathogen can be defined in that case and corresponds to a threshold between the persistence ([Formula: see text]) and the extinction ([Formula: see text]) of the pathogen population. When [Formula: see text] and the maximal fitness is attained by at least one variant, we show that the systems reaches an endemic equilibrium state that can be explicitly determined from the initial data. When [Formula: see text] but none of the variants attain the maximal fitness, the situation is more intricate. We show that, in general, the pathogen is uniformly persistent and any family of variants that have a fitness which is uniformly lower than the optimal fitness, eventually gets extinct. We derive a condition under which the total pathogen population converges to a limit which can be computed explicitly. We also find counterexamples that show that, when our condition is not met, the total pathogen population may converge to an unexpected value, or the system can even reach an eternally transient behavior where the total pathogen population between several values. We illustrate our results with numerical simulations that emphasize the wide variety of possible dynamics.

A two-galectin network establishes mesenchymal condensation phenotype in limb development

Previous work showed that Gal-1A and Gal-8, two proteins belonging to the galactoside-binding galectin family, are the earliest determinants of the patterning of the skeletal elements of embryonic chicken limbs, and further, that their experimentally determined interactions in the embryonic limb bud can be interpreted via a reaction-diffusion-adhesion (2GL: two galectin plus ligands) model. Here, we use an ordinary differential equation-based approach to analyze the intrinsic switching modality of the 2GL network and characterize the network behavior independent of the diffusive and adhesive arms of the patterning mechanism. We identify two states: where the concentrations of both the galectins are respectively, negligible, and very high. This bistable switch-like system arises via a saddle-node bifurcation from a monostable state. For the case of mass-action production terms, we provide an explicit Lyapunov function for the system, which shows that it has no periodic solutions. Our model therefore predicts that the galectin network may exist in low expression and high expression states separated in space or time, without any intermediate states. We test these predictions in experiments performed with high density cultures of chick limb mesenchymal cells and observe that cells inside precartilage protocondensations express Gal-1A at a much higher rate than those outside, for which it was negligible. The Gal-1A and -8-based patterning network is therefore sufficient to partition the mesenchymal cell population into two discrete cell states with different developmental (chondrogenic vs. non-chondrogenic) fates. When incorporated into an adhesion and diffusion-enabled framework this system can generate a spatially patterned limb skeleton.

Investigating the Influence of Growth Arrest Mechanisms on Tumour Responses to Radiotherapy

Cancer is a heterogeneous disease and tumours of the same type can differ greatly at the genetic and phenotypic levels. Understanding how these differences impact sensitivity to treatment is an essential step towards patient-specific treatment design. In this paper, we investigate how two different mechanisms for growth control may affect tumour cell responses to fractionated radiotherapy (RT) by extending an existing ordinary differential equation model of tumour growth. In the absence of treatment, this model distinguishes between growth arrest due to nutrient insufficiency and competition for space and exhibits three growth regimes: nutrient limited, space limited (SL) and bistable (BS), where both mechanisms for growth arrest coexist. We study the effect of RT for tumours in each regime, finding that tumours in the SL regime typically respond best to RT, while tumours in the BS regime typically respond worst to RT. For tumours in each regime, we also identify the biological processes that may explain positive and negative treatment outcomes and the dosing regimen which maximises the reduction in tumour burden.

A survey on Lyapunov functions for epidemic compartmental models

In this survey, we propose an overview on Lyapunov functions for a variety of compartmental models in epidemiology. We exhibit the most widely employed functions, and provide a commentary on their use. Our aim is to provide a comprehensive starting point to readers who are attempting to prove global stability of systems of ODEs. The focus is on mathematical epidemiology, however some of the functions and strategies presented in this paper can be adapted to a wider variety of models, such as prey-predator or rumor spreading.

A symmetric version of the Euler equations by using Generalized Bernoulli Method

The aim of this article is to show a way to extend the usefulness of the Generalized Bernoulli Method (GBM) with the purpose to apply it for the case of variational problems with functionals that depend explicitly of all the variables. Moreover, after expressing the Euler equations in terms of this extension of GBM, we will see that the resulting equations acquire a symmetric form, which is not shared by the known Euler equations. We will see that this symmetry is useful because it allows us to recall these equations with ease. The presentation of three examples shows that by applying GBM, the Euler equations are obtained just as well as it does the known Euler formalism but with much less effort, which makes GBM ideal for practical applications. In fact, given a variational problem, GBM establishes the corresponding Euler equations by means of a systematic procedure, which is easy to recall, based in both elementary calculus and algebra without having to memorize the known formulas. Finally, in order to extend the practical applications of the proposed method, this work will employ GBM with the purpose to apply it for the case of solving isoperimetric problems.

Analyzing the effect of restrictions on the COVID-19 outbreak for some US states

The ongoing pandemic disease COVID‑19 has caused worldwide social and financial disruption. As many countries are engaged in designing vaccines, the harmful second and third waves of COVID‑19 have already appeared in many countries. To investigate changes in transmission rates and the effect of social distancing in the USA, we formulate a system of ordinary differential equations using data of confirmed cases and deaths in these states: California, Texas, Florida, Georgia, Illinois, Louisiana, Michigan, and Missouri. Our models and their parameter estimations show social distancing can reduce the transmission of COVID‑19 by 60% to 90%. Thus, obeying the movement restriction rules is crucial in reducing the magnitude of the outbreak waves. This study also estimates the percentage of people who were not social distancing ranges between 10% and 18% in these states. Our analysis shows the management restrictions taken by these states do not slow the disease progression enough to contain the outbreak.

Specification testing for ordinary differential equation models with fixed design and applications to COVID-19 epidemic models

Checking the models about the ongoing Coronavirus Disease 2019 (COVID-19) pandemic is an important issue. Some famous ordinary differential equation (ODE) models, such as the SIR and SEIR models have been used to describe and predict the epidemic trend. Still, in many cases, only part of the equations can be observed. A test is suggested to check possibly partially observed ODE models with a fixed design sampling scheme. The asymptotic properties of the test under the null, global and local alternative hypotheses are presented. Two new propositions about U-statistics with varying kernels based on independent but non-identical data are derived as essential tools. Some simulation studies are conducted to examine the performances of the test. Based on the available public data, it is found that the SEIR model, for modeling the data of COVID-19 infective cases in certain periods in Japan and Algeria, respectively, maybe not be appropriate by applying the proposed test.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.

A dynamical model of combination therapy applied to glioma

Glioma is a human brain tumor that is very difficult to treat at an advanced stage. Studies of glioma biomarkers have shown that some markers are released into the bloodstream, so data from these markers indicate a decrease in the concentration of blood glucose and serum glucose in patients with glioma; these suggest an association between glucose and glioma. This decrease mechanism in glucose concentration can be described by the coupled ordinary differential equations of the early-stage glioma growth and interactions between glioma cells, immune cells, and glucose concentration. In this paper, we propose developing a new mathematical model to explain how glioma cells evolve and survive combination therapy between chemotherapy and oncolytic virotherapy, as an alternative to glioma treatment. In this study, three therapies were applied for analysis, that is, (1) chemotherapy, (2) virotherapy, and (3) a combination of chemotherapy and virotherapy. Virotherapy uses specialist viruses that only attack tumor cells. Based on the simulation results of the therapy carried out, we conclude that combination therapy can reduce the glioma cells significantly compared to the other two therapies. The simulation results of this combination therapy can be an alternative to glioma therapy.

The impact of reactive case detection on malaria transmission in Zanzibar in the presence of human mobility

Malaria persists at low levels on Zanzibar despite the use of vector control and case management. We use a metapopulation model to investigate the role of human mobility in malaria persistence on Zanzibar, and the impact of reactive case detection. The model was parameterized using survey data on malaria prevalence, reactive case detection, and travel history. We find that in the absence of imported cases from mainland Tanzania, malaria would likely cease to persist on Zanzibar. We also investigate potential intervention scenarios that may lead to elimination, especially through changes to reactive case detection. While we find that some additional cases are removed by reactive case detection, a large proportion of cases are missed due to many infections having a low parasite density that go undetected by rapid diagnostic tests, a low rate of those infected with malaria seeking treatment, and a low rate of follow up at the household level of malaria cases detected at health facilities. While improvements in reactive case detection would lead to a reduction in malaria prevalence, none of the intervention scenarios tested here were sufficient to reach elimination. Imported cases need to be treated to have a substantial impact on prevalence.

A new framework for polynomial approximation to differential equations

In this paper, we discuss a framework for the polynomial approximation to the solution of initial value problems for differential equations. The framework is based on an expansion of the vector field along an orthonormal basis, and relies on perturbation results for the considered problem. Initially devised for the approximation of ordinary differential equations, it is here further extended and, moreover, generalized to cope with constant delay differential equations. Relevant classes of Runge-Kutta methods can be derived within this framework.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Male mating choices: The drive behind menopause?

When we examine the life history of humans against our closest primate relatives, the other great apes, there is notably a greater longevity in humans which includes a distinctive postmenopausal life stage, leading to the question, "How did human females evolve to have old-age infertility?" In their paper "Mate choice and the origin of menopause" (Morton et al., 2013), Morton et al. developed an agent-based model (ABM) to investigate the novel hypothesis that ancestral male mating choices, particularly forgoing mating with older females, was the driving force behind the evolution of menopause. From their model, they concluded that indeed male preference for young female mates could have driven females to lose fertility at older ages through deleterious mutations, leading to menopause. In this work, we revisit their male-mate-choice hypothesis by formulating an analogous mathematical model using a system of ordinary differential equations (ODEs). We first show that our ODE model recreates the qualitative behaviour and hence conclusions of key scenarios in Morton et al. (2013). However, since our ODE system is less computationally demanding than their ABM, we also conduct a broader sensitivity analysis over a range of parameters and differing initial conditions to analyse the dependence on their conclusions to underlying assumptions. Our results challenge those of Morton et al. as we find that even the slightest deviation from an exclusive mating preference for younger females would counteract the evolution of menopause. Consequently, we propose that their male-mate-choice hypothesis is incomplete and needs further explanation of how a male strategy to exclusively mate with young females could have arisen in our common ancestors and remained evolutionary stable for long enough to drive the evolution of old-age female infertility.

Data-driven prediction of COVID-19 cases in Germany for decision making

Background

The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.

Methods

We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.

Results

The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.

Conclusions

We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01579-9).

A homeostasis criterion for limit cycle systems based on infinitesimal shape response curves

Homeostasis occurs in a control system when a quantity remains approximately constant as a parameter, representing an external perturbation, varies over some range. Golubitsky and Stewart (J Math Biol 74(1-2):387-407, 2017) developed a notion of infinitesimal homeostasis for equilibrium systems using singularity theory. Rhythmic physiological systems (breathing, locomotion, feeding) maintain homeostasis through control of large-amplitude limit cycles rather than equilibrium points. Here we take an initial step to study (infinitesimal) homeostasis for limit-cycle systems in terms of the average of a quantity taken around the limit cycle. We apply the "infinitesimal shape response curve" (iSRC) introduced by Wang et al. (SIAM J Appl Dyn Syst 82(7):1-43, 2021) to study infinitesimal homeostasis for limit-cycle systems in terms of the mean value of a quantity of interest, averaged around the limit cycle. Using the iSRC, which captures the linearized shape displacement of an oscillator upon a static perturbation, we provide a formula for the derivative of the averaged quantity with respect to the control parameter. Our expression allows one to identify homeostasis points for limit cycle systems in the averaging sense. We demonstrate in the Hodgkin-Huxley model and in a metabolic regulatory network model that the iSRC-based method provides an accurate representation of the sensitivity of averaged quantities.

A data-driven model of the role of energy in sepsis

Exposure to pathogens elicits a complex immune response involving multiple interdependent pathways. This response may mitigate detrimental effects and restore health but, if imbalanced, can lead to negative outcomes including sepsis. This complexity and need for balance pose a challenge for clinicians and have attracted attention from modelers seeking to apply computational tools to guide therapeutic approaches. In this work, we address a shortcoming of such past efforts by incorporating the dynamics of energy production and consumption into a computational model of the acute immune response. With this addition, we performed fits of model dynamics to data obtained from non-human primates exposed to Escherichia coli. Our analysis identifies parameters that may be crucial in determining survival outcomes and also highlights energy-related factors that modulate the immune response across baseline and altered glucose conditions.

Kernel Ordinary Differential Equations

Ordinary differential equation (ODE) is widely used in modeling biological and physical processes in science. In this article, we propose a new reproducing kernel-based approach for estimation and inference of ODE given noisy observations. We do not assume the functional forms in ODE to be known, or restrict them to be linear or additive, and we allow pairwise interactions. We perform sparse estimation to select individual functionals, and construct confidence intervals for the estimated signal trajectories. We establish the estimation optimality and selection consistency of kernel ODE under both the low-dimensional and high-dimensional settings, where the number of unknown functionals can be smaller or larger than the sample size. Our proposal builds upon the smoothing spline analysis of variance (SS-ANOVA) framework, but tackles several important problems that are not yet fully addressed, and thus extends the scope of existing SS-ANOVA as well. We demonstrate the efficacy of our method through numerous ODE examples.

Mathematical Modeling in Circadian Rhythmicity

Circadian clocks are autonomous systems able to oscillate in a self-sustained manner in the absence of external cues, although such Zeitgebers are typically present. At the cellular level, the molecular clockwork consists of a complex network of interlocked feedback loops. This chapter discusses self-sustained circadian oscillators in the context of nonlinear dynamics theory. We suggest basic steps that can help in constructing a mathematical model and introduce how self-sustained generations can be modeled using ordinary differential equations. Moreover, we discuss how coupled oscillators synchronize among themselves or entrain to periodic signals. The development of mathematical models over the last years has helped to understand such complex network systems and to highlight the basic building blocks in which oscillating systems are built upon. We argue that, through theoretical predictions, the use of simple models can guide experimental research and is thus suitable to model biological systems qualitatively.

MITODYN: An Open Source Software for Quantitative Modeling of Mitochondrial and Cellular Energy Metabolic Flux Dynamics in Health and Disease

Mitochondrial respiratory chain (RC) transforms the reductive power of NADH or FADH2 oxidation into a proton gradient between the matrix and cytosolic sides of the inner mitochondrial membrane, that ATP synthase uses to generate ATP. This process constitutes a bridge between carbohydrates' central metabolism and ATP-consuming cellular functions. Moreover, the RC is responsible for a large part of reactive oxygen species (ROS) generation that play signaling and oxidizing roles in cells. Mathematical methods and computational analysis are required to understand and predict the possible behavior of this metabolic system. Here we propose a software tool that helps to analyze individual steps of respiratory electron transport in their dynamics, thus deepening understanding of the mechanism of energy transformation and ROS generation in the RC. This software's core is a kinetic model of the RC represented by a system of ordinary differential equations (ODEs). This model enables the analysis of complex dynamic behavior of the RC, including multistationarity and oscillations. The proposed RC modeling method can be applied to study respiration and ROS generation in various organisms and naturally extended to explore carbohydrates' metabolism and linked metabolic processes.

Automatic Assembly and Calibration of Models of Enzymatic Reactions Based on Ordinary Differential Equations

Enzymatic reactions have been studied for more than a 100 years. Indeed, isolation of enzymes from biological materials is no longer the main source of enzymes today, as they are now largely produced using recombinant technology, or can even be synthesized from scratch. Studies of the three-dimensional structures of enzymes can provide answers to many questions, but the kinetics of enzymatic reactions is the only method that can lead to better understanding of their function. The complexity of high-throughput analysis of progress curves of data obtained can only be achieved through numerical solutions of a suitable system of ordinary differential equations. We have developed the freely available server ENZO: a web tool for derivation and evaluation of kinetic models of enzyme-catalyzed reactions ( http://enzo.cmm.ki.si/ ). ENZO can be used for simultaneous analysis of a series of progress curves obtained under many different conditions. In this chapter, we exemplify the principles and possibilities of this type of high-throughput analysis.

Pandemic fatigue impact on COVID-19 spread: A mathematical modelling answer to the Italian scenario

The COVID-19 outbreak has generated, in addition to the dramatic sanitary consequences, severe psychological repercussions for the populations affected by the pandemic. Simultaneously, these consequences can have related effects on the spread of the virus. Pandemic fatigue occurs when stress rises beyond a threshold, leading a person to feel demotivated to follow recommended behaviours to protect themselves and others. In the present paper, we introduce a new susceptible-infected-quarantined-recovered-dead (SIQRD) model in terms of a system of ordinary differential equations (ODE). The model considers the countermeasures taken by sanitary authorities and the effect of pandemic fatigue. The latter can be mitigated by fear of the disease's consequences modelled with the death rate in mind. The mathematical well-posedness of the model is proved. We show the numerical results to be consistent with the transmission dynamics data characterising the epidemic of the COVID-19 outbreak in Italy in 2020. We provide a measure of the possible pandemic fatigue impact. The model can be used to evaluate the public health interventions and prevent with specific actions the possible damages resulting from the social phenomenon of relaxation concerning the observance of the preventive rules imposed.

Modeling codelivery of CD73 inhibitor and dendritic cell-based vaccines in cancer immunotherapy

Dendritic cells (DCs) are the dominant class of antigen-presenting cells in humans; therefore, a range of DC-based approaches have been established to promote an immune response against cancer cells. The efficacy of DC-based immunotherapeutic approaches is markedly affected by the immunosuppressive factors related to the tumor microenvironment, such as adenosine. In this paper, based on immunological theories and experimental data, a hybrid model is designed that offers some insights into the effects of DC-based immunotherapy combined with adenosine inhibition. The model combines an individual-based model for describing tumor-immune system interactions with a set of ordinary differential equations for adenosine modeling. Computational simulations of the proposed model clarify the conditions for the onset of a successful immune response against cancer cells. Global and local sensitivity analysis of the model highlights the importance of adenosine blockage for strengthening effector cells. The model is used to determine the most effective suppressive mechanism caused by adenosine, proper vaccination time, and the appropriate time interval between injections.

In silico simulation of the effect of hypoxia on MCF-7 cell cycle kinetics under fractionated radiotherapy

The treatment outcome of a given fractionated radiotherapy scheme is affected by oxygen tension and cell cycle kinetics of the tumor population. Numerous experimental studies have supported the variability of radiosensitivity with cell cycle phase. Oxygen modulates the radiosensitivity through hypoxia-inducible factor (HIF) stabilization and oxygen fixation hypothesis (OFH) mechanism. In this study, an existing mathematical model describing cell cycle kinetics was modified to include the oxygen-dependent G1/S transition rate and radiation inactivation rate. The radiation inactivation rate used was derived from the linear-quadratic (LQ) model with dependence on oxygen enhancement ratio (OER), while the oxygen-dependent correction for the G1/S phase transition was obtained from numerically solving the ODE system of cyclin D-HIF dynamics at different oxygen tensions. The corresponding cell cycle phase fractions of aerated MCF-7 tumor population, and the resulting growth curve obtained from numerically solving the developed mathematical model were found to be comparable to experimental data. Two breast radiotherapy fractionation schemes were investigated using the mathematical model. Results show that hypoxia causes the tumor to be more predominated by the tumor subpopulation in the G1 phase and decrease the fractional contribution of the more radioresistant tumor cells in the S phase. However, the advantage provided by hypoxia in terms of cell cycle phase distribution is largely offset by the radioresistance developed through OFH. The delayed proliferation caused by severe hypoxia slightly improves the radiotherapy efficacy compared to that with mild hypoxia for a high overall treatment duration as demonstrated in the 40-Gy fractionation scheme.

A new general integral transform for solving integral equations

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A new general integral transform which is covered all class of integral transform in the class of Laplace transform.

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We investigated the application of this new transform for solving ODE with constant and variable coefficient.

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This new transform can handle easily for fractional order integral equations and fractional order differential equations.

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We have discussed the advantage and disadvantage of other integral transformed which is defined during last 2 decades.

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We proved the related theorems for this new transform.

Introduction

Integral transforms are important to solve real problems. Appropriate choice of integral transforms helps to convert differential equations as well as integral equations into terms of an algebraic equation that can be solved easily.

During last two decades many integral transforms in the class of Laplace transform are introduced such as Sumudu, Elzaki, Natural, Aboodh, Pourreza, Mohand, G_transform, Sawi and Kamal transforms.

Objectives

In this paper, we introduce a general integral transform in the class of Laplace transform. We study the properties of this transform. Then we compare it with few exiting integral transforms in the Laplace family such as Laplace, Sumudu, Elzaki and G\_transforms, Pourreza, Aboodh and etc.

Methods

A new integral transform is introduced. Then some properties of this integral transform are discussed. This integral transform is used to solve this new transform is used for solving higher order initial value problems, integral equations and fractional order integral equation.

Results

It is proved that those new transforms in the class of Laplace transform which are introduced during last few decades are a special case of this general transform. It is shown that there is no advantage between theses transforms unless for special problems.

Conclusion

It has shown that this new integral transform covers those exiting transforms such as Laplace, Elzaki and Sumudu transforms for different value of p(s) and q(s). We used this new transform for solving ODE, integral equations and fractional integral equations. Also, we can introduce new integral transforms by using this new general integral transform.

Mathematical Model for Delayed Responses in Immune Checkpoint Blockades

We introduce a set of ordinary differential equations (ODEs) that qualitatively reproduce delayed responses observed in immune checkpoint blockade therapy (e.g. anti-CTLA-4 ipilimumab). This type of immunotherapy has been at the forefront of novel and promising cancer treatments over the past decade and was recognised by the 2018 Nobel Prize in Medicine. Our model describes the competition between effector T cells and non-effector T cells in a tumour. By calibrating a small subset of parameters that control immune checkpoint expression along with the patient's immune-system cancer readiness, our model is able to simulate either a complete absence of patient response to treatment, a quick anti-tumour T cell response (within days) or a delayed response (within months). Notably, the parameter space that generates a delayed response is thin and must be carefully calibrated, reflecting the observation that a small subset of patients experience such reactions to checkpoint blockade therapies. Finally, simulations predict that the anti-tumour T cell storm that breaks the delay is very short-lived compared to the length of time the cancer is able to stay suppressed. This suggests the tumour may subsist off an environment hostile to effector T cells; however, these cells are-at rare times-able to break through the tumour immunosuppressive defences to neutralise the tumour for a prolonged period. Our simulations aim to qualitatively describe the delayed response phenomenon without making precise fits to particular datasets, which are limited. It is our hope that our foundational model will stimulate further interest within the immunology modelling field.

A Data-Driven Mathematical Model of the Heroin and Fentanyl Epidemic in Tennessee

Opioid addiction represents a major national health issue spanning decades. In recent years, prescription opioid use disorder has increasingly led to heroin and fentanyl use, with subsequent increases in mortality rates due to overdose. In this paper, we present a mechanistic, epidemic model for prescription opioid addiction and illicit heroin or fentanyl addiction which aims to better understand and predict the dynamics between these two stages of opioid use disorder. Our model aims to be both parsimonious and robust: as a system of five differential equations it is appropriate for use in theory advancement and yet it remains powerful enough to capture state-level data from Tennessee for the period 2013-2018. A key finding from our data-driven analysis is that, in the face of changing policy around prescription opioids, heroin and fentanyl are now the driving force behind the Tennessee opioid epidemic. Model projections suggest that both addictions and overdoses related to heroin and fentanyl will continue to increase in the next few years (2020-2022), even as addiction to prescription drugs continues to fall. Finally, management strategy analysis suggests that in the changing face of the epidemic, the most successful approach will target availability of treatment with subsequent monitoring of stably recovered individuals to see that they do not relapse, coincident with direct efforts to decrease opioid overdose fatalities (e.g., further availability of Naloxone).

Mathematically modeling the effect of touch frequency on the environmental transmission of Clostridioides difficile in healthcare settings

Clostridioides difficile, formerly Clostridium difficile, is the leading cause of infectious diarrhea and one of the most common healthcare acquired infections in United States hospitals. C. difficile persists well in healthcare environments because it forms spores that can survive for long periods of time and can be transmitted to susceptible patients through contact with contaminated hands and fomites, objects or surfaces that can harbor infectious agents. Fomites can be classified as high-touch or low-touch based on the frequency they are contacted. The mathematical model in this study investigates the relative contribution of high-touch and low-touch fomites on new cases of C. difficile colonization among patients of a hospital ward. The dynamics of transmission are described by a system of ordinary differential equations representing four patient population classes and two pathogen environmental reservoirs. Parameters that have a significant effect on incidence, as determined by a global sensitivity analysis, are varied in stochastic simulations of the system to identify feasible strategies to prevent disease transmission. Results indicate that on average, under one-quarter of asymptomatically colonized patients are exposed to C. difficile via low-touch fomites. In comparison, over three-quarters of colonized patients are colonized through high-touch fomites, despite additional cleaning of high-touch fomites. Increased contacts with high-touch fomites increases the contribution of these fomites to the incidence of colonized individuals and decreasing the duration of a hospital visit reduces the amount of pathogen in the environment. Thus, enhanced efficacy of disinfection upon discharge and extra cleaning of high-touch fomites, reduced contact with high-touch fomites, and higher discharge rates, among other control measures, could lead to a decrease in the incidence of colonized individuals.

Speaking out: A mathematical model of language preservation

Languages evolve as an effect of communal competition while environmental and social dynamics characterize a language. There are thousands of languages spoken around the world but many of them are in danger of going extinct because of language competition and shifts. Modeling language preservation is important because the rise and fall of a language directly impacts the culture attached to it. We present a study of language competition and preservation between a bilingual population and two monolingual populations using a mathematical model involving nonlinear systems of differential equations. Building upon the ideas of previous models in the literature, the model utilizes population proportions with a simple structure that yields noteworthy behavior including a stable spiral with all three language groups preserved. We investigate how bilinguals and monolinguals can coexist as well as how they can affect one another.

Los idiomas evolucionan como efecto de la competencia comunitaria, mientras que las dinámicas ambientales y sociales caracterizan a un idioma. Se hablan miles de idiomas en todo el mundo, pero muchos de ellos están en peligro de extinción debido a la competencia lingüística y los cambios. Modelar la preservación del lenguaje es importante porque el auge y la caída de un idioma impacta directamente en la cultura que se le atribuye. Presentamos un estudio de la competencia y preservación del lenguaje entre una población bilingüe y dos poblaciones monolingües utilizando un modelo matemático que involucra sistemas no lineales de ecuaciones diferenciales. Sobre la base de las ideas de modelos anteriores en la literatura, el modelo utiliza proporciones de población con una estructura simple que produce un comportamiento notable que incluye una espiral estable con los tres grupos lingüísticos preservados. Investigamos cómo los bilingües y los monolingües pueden coexistir y cómo pueden afectarse unos a otros.

Probabilistic Model for Control of an Epidemic by Isolation and Quarantine

Assuming a homogeneous population, we apply the mass action law for rate of new infections and a second-order gamma distribution for removal probability to model spread of an epidemic. In numerical examinations of higher-order gamma distributions for removal probability, we discover an unexpected pattern in maximum fraction of population infected. We develop from first principles of probability an eighth-order system of ordinary differential equations to model effects of isolation and quarantine. We derive analytical expressions for reproduction numbers modeling isolation and quarantine when applied separately and together and verify them numerically. We quantify strength and speed required of these interventions to contain epidemics of varying severity and examine how their effectiveness depends on when they begin. We find that effectiveness is highly sensitive to small changes of intervention strength in a critical region. Finally, adding two more differential equations to capture natural population dynamics, we calculate endemic disease equilibria when affected by isolation and examine dynamics of coming to an equilibrium state.

Assessing the effects of diagnostic sensitivity on schistosomiasis dynamics

Schistosomiasis is a parasite infection that affects millions of people around the world. It is endemic in 13 different states in Brazil and responsible for increasing morbidity in the population. One of its main characteristics is a heterogeneous distribution of worm burden in the human population, which makes the diagnosis difficult. We aimed to investigate how the sensitivity of the diagnostic method may contribute to successful control interventions against infections in a population. In order to do that, we present an ordinary differential equations model that considers three levels of worm burden in the human population, a snail population, and a miracidium reservoir. Through a steady-state analysis and its local stability, we show how this worm-burden heterogeneity can be responsible for the persistence of infection, especially due to reinfection in the highest level of worm burden. The analysis highlights sensitive diagnosis, besides treatment and sanitary improvements, as a key factor for schistosomiasis transmission control.

A process-based, stage-structured model of potato cyst nematode population dynamics: Effects of temperature and resistance

Potato cyst nematodes (PCN) are responsible for large losses in potato yields in many of the world's potato-growing regions. As soil temperatures increase due to climate change, there is potential for faster growth rates of PCN, allowing development of multiple generations in a growing season. We develop a process-based temperature-dependent model representing the life cycle of Globodera pallida, comprising juvenile, adult and cyst/diapause stages. To incorporate variability in the amount of time spent in each stage caused by genetic/environmental variation, the model is based on a mix of ordinary differential equations (ODEs) with sub-stages, and delay differential equations (DDEs). The effect of climate change is incorporated through the influence of soil temperature on the rate of development and survival in the hatching and juvenile stages. The level of the plant resistance to PCN is incorporated via the proportion of juveniles which become adults. After comparing the model with field data we run simulations to explore the effects of temperature and resistance on PCN populations. We find that with higher temperatures and longer growing seasons multiple generations of PCN can develop within a season, provided any required diapause period is short. Despite this, we show that growing resistant potatoes is a very effective control strategy and planting potatoes with even moderate levels of resistance can counter the effects of climate change.

A Deterministic Compartmental Modeling Framework for Disease Transmission

Mathematical models for the spread of diseases help us understand the mechanisms on how diseases spread, evaluate the possible effects of interventions, predict outcomes of epidemics, and forecast the course of outbreaks. Compartmental models are widely used in synthetic biology since they can represent a biological system as an assembly of various parts or compartments with different functions. Here we present a framework for the analysis of a compartmental model for the transmission of diseases using ordinary differential equations. We apply this method on a study about the spread of tuberculosis.

A Model to Investigate the Impact of Farm Practice on Antimicrobial Resistance in UK Dairy Farms

The ecological and human health impact of antibiotic use and the related antimicrobial resistance (AMR) in animal husbandry is poorly understood. In many countries, there has been considerable pressure to reduce overall antibiotic use in agriculture or to cease or minimise use of human critical antibiotics. However, a more nuanced approach would consider the differential impact of use of different antibiotic classes; for example, it is not known whether reduced use of bacteriostatic or bacteriolytic classes of antibiotics would be of greater value. We have developed an ordinary differential equation model to investigate the effects of farm practice on the spread and persistence of AMR in the dairy slurry tank environment. We model the chemical fate of bacteriolytic and bacteriostatic antibiotics within the slurry and their effect on a population of bacteria, which are capable of resistance to both types of antibiotic. Through our analysis, we find that changing the rate at which a slurry tank is emptied may delay the proliferation of multidrug-resistant bacteria by up to five years depending on conditions. This finding has implications for farming practice and the policies that influence waste management practices. We also find that, within our model, the development of multidrug resistance is particularly sensitive to the use of bacteriolytic antibiotics, rather than bacteriostatic antibiotics, and this may be cause for controlling the usage of bacteriolytic antibiotics in agriculture.

Poacher-population dynamics when legal trade of naturally deceased organisms funds anti-poaching enforcement

Can a regulated, legal market for wildlife products protect species threatened by poaching? It is one of the most controversial ideas in biodiversity conservation. Perhaps the most convincing reason for legalizing wildlife trade is that trade revenue could fund the protection and conservation of poached species. In this paper, we examine the possible poacher-population dynamic consequences of legal trade funding conservation. The model consists of a manager scavenging carcasses for wildlife product, who then sells the product, and directs a portion of the revenue towards funding anti-poaching law enforcement. Through a global analysis of the model, we derive the critical proportion of product the manager must scavenge, and the critical proportion of trade revenue the manager must allocate towards increased enforcement, in order for legal trade to lead to abundant long-term wildlife populations. We illustrate how the model could inform management with parameter values derived from the African elephant literature, under a hypothetical scenario where a manager scavenges elephant carcasses to sell ivory. We find that there is a large region of parameter space where populations go extinct under legal trade unless a significant portion of trade revenue is directed towards protecting populations from poaching. The model is general and therefore can be used as a starting point for exploring the consequences of funding many conservation programs using wildlife trade revenue.

Introduction to In Silico Modeling to Study ROS Dynamics

This book chapter is drafted for biologists with experimental experiences in ROS biology but being newcomers in the field of modeling. We start with a general introduction about computational modeling in biology and an overview of software tools suitable for beginners. This chapter encompasses an introduction to computational models with special focus on simulation of ROS dynamics. A step-by-step tutorial follows providing guidance for all relevant model development processes. This course of action gives a comprehensible way to understand the benefits of computational models and to gain the necessary knowledge to build own small equation-based models. Small models can be created without any special programming expertise or in-depth technical and mathematical knowledge. Afterward in the final section, a short overview of pitfalls, challenges, and limitations is provided, combined with suggestions for further reading to improve and expand modeling skills of biologists.

Validation of a yellow fever vaccine model using data from primary vaccination in children and adults, re-vaccination and dose-response in adults and studies with immunocompromised individuals

BACKGROUND

An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions.

RESULTS

This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature.

CONCLUSIONS

Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF.

A quantitative framework for exploring exit strategies from the COVID-19 lockdown

Following the highly restrictive measures adopted by many countries for combating the current pandemic, the number of individuals infected by SARS-CoV-2 and the associated number of deaths steadily decreased. This fact, together with the impossibility of maintaining the lockdown indefinitely, raises the crucial question of whether it is possible to design an exit strategy based on quantitative analysis. Guided by rigorous mathematical results, we show that this is indeed possible: we present a robust numerical algorithm which can compute the cumulative number of deaths that will occur as a result of increasing the number of contacts by a given multiple, using as input only the most reliable of all data available during the lockdown, namely the cumulative number of deaths.

Global Dynamics of a Breast Cancer Competition Model

In this paper, we present a system of five ordinary differential equations which consider population dynamics among cancer stem cells, tumor cells, and healthy cells. Additionally, we consider the effects of excess estrogen and the body's natural immune response on the aforementioned cell populations. Employing a variety of analytical methods, we study the global dynamics of the full system, along with various submodels. We find sufficient conditions on parameter values to ensure cancer persistence in the absence of immune cells, and cancer eradication when an immune response is included. We conclude with a discussion on the biological implications of the resulting global dynamics.

Why Males Compete Rather Than Care, with an Application to Supplying Collective Goods

The question of why males invest more into competition than offspring care is an age-old problem in evolutionary biology. On the one hand, paternal care could increase the fraction of offspring surviving to maturity. On the other hand, competition could increase the likelihood of more paternities and thus the relative number of offspring produced. While drivers of these behaviours are often intertwined with a wide range of other constraints, here we present a simple dynamic model to investigate the benefits of these two alternative fitness-enhancing pathways. Using this framework, we evaluate the sensitivity of equilibrium dynamics to changes in payoffs for male allocation to mating versus parenting. Even with strong effects of care on offspring survivorship, small competitive benefits can outweigh benefits from care. We consider an application of the model that includes men's competition for hunting reputations where big game supplies a benefit to all and find a frequency-dependent parameter region within which, depending on initial population proportions, either strategy may outperform the other. Results demonstrate that allocation to competition gives males greater fitness than offspring care for a range of circumstances that are dependent on life-history parameters and, for the large-game hunting application, frequency dependent. The greater the collective benefit, the more individuals can be selected to supply it.

Convergence rates of Gaussian ODE filters

A recently introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution x and its first q derivatives a priori as a Gauss-Markov process X , which is then iteratively conditioned on information about x ˙ . This article establishes worst-case local convergence rates of order q + 1 for a wide range of versions of this Gaussian ODE filter, as well as global convergence rates of order q in the case of q = 1 and an integrated Brownian motion prior, and analyses how inaccurate information on x ˙ coming from approximate evaluations of f affects these rates. Moreover, we show that, in the globally convergent case, the posterior credible intervals are well calibrated in the sense that they globally contract at the same rate as the truncation error. We illustrate these theoretical results by numerical experiments which might indicate their generalizability to q ∈ { 2 , 3 , … } .

Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

Implications of immune-mediated metastatic growth on metastatic dormancy, blow-up, early detection, and treatment

Metastatic seeding of distant organs can occur in the very early stages of primary tumor development. Once seeded, these micrometastases may enter a dormant phase that can last decades. Curiously, the surgical removal of the primary tumor can stimulate the accelerated growth of distant metastases, a phenomenon known as metastatic blow-up. Recent clinical evidence has shown that the immune response can have strong tumor promoting effects. In this work, we investigate if the pro-tumor effects of the immune response can have a significant contribution to metastatic dormancy and metastatic blow-up. We develop an ordinary differential equation model of the immune-mediated theory of metastasis. We include both anti- and pro-tumor immune effects, in addition to the experimentally observed phenomenon of tumor-induced immune cell phenotypic plasticity. Using geometric singular perturbation analysis, we derive a rather simple model that captures the main processes and, at the same time, can be fully analyzed. Literature-derived parameter estimates are obtained, and model robustness is demonstrated through a time dependent sensitivity analysis. We determine conditions under which the parameterized model can successfully explain both metastatic dormancy and blow-up. The results confirm the significant active role of the immune system in the metastatic process. Numerical simulations suggest a novel measure to predict the occurrence of future metastatic blow-up in addition to new potential avenues for treatment of clinically undetectable micrometastases.

On COVID-19 Modelling

This is an analysis of the COVID-19 pandemic by comparably simple mathematical and numerical methods. The final goal is to predict the peak of the epidemic outbreak per country with a reliable technique. The difference to other modelling approaches is to stay extremely close to the available data, using as few hypotheses and parameters as possible. For the convenience of readers, the basic notions of modelling epidemics are collected first, focusing on the standard SIR model. Proofs of various properties of the model are included. But such models are not directly compatible with available data. Therefore a special variation of a SIR model is presented that directly works with the data provided by the Johns Hopkins University. It allows to monitor the registered part of the pandemic, but is unable to deal with the hidden part. To reconstruct data for the unregistered Infected, a second model uses current experimental values of the infection fatality rate and a data-driven estimation of a specific form of the recovery rate. All other ingredients are data-driven as well. This model allows predictions of infection peaks. Various examples of predictions are provided for illustration. They show what countries have to face that are still expecting their infection peak. Running the model on earlier data shows how closely the predictions follow the transition from an uncontrolled outbreak to the mitigation situation by non-pharmaceutical interventions like contact restrictions.

Supplementary Information

The online version of this article (10.1365/s13291-020-00219-9) contains supplementary material, which is available to authorized users.

Mathematical modelling of the role of mucosal vaccine on the within-host dynamics of Chlamydia trachomatis

A mathematical model of the within-host replicative dynamics of C. trachomatis infection and its interactions with the immune system, in the presence of a mucosal vaccine, is presented. Our aim is to estimate the requisite efficacy of an efficacious mucosal vaccine that could promote a stable disease-free state in vivo. Sensitivity analysis was used to quantify how variability in the model parameters influence the value of the disease threshold R₀. This shows that the two most important factors to be considered for achieving a disease-free state state in vivo, based on their influence on R₀, are the efficacy of the Chlamydia vaccine, and the rate at which the humoral immune response protects healthy epithelial cells from infection. Numerical simulations of the model show that a vaccine with a minimum efficacy of 86% may be required for the in vivo control of Chlamydia burden. Such effective but imperfect Chlamydia vaccine could confer long-term protective immunity to genital Chlamydia infections. Conditions under which lower vaccine efficacies may suffice are also explored.