1,721,020 research outputs found
Distributed delays in a hybrid model of tumor-immune system interplay
No full textA tumor is kinetically characterized by the presence of multiple spatio-temporal scales in which its cells interplay with, for instance, endothelial cells or Immune system effectors, exchanging various chemical signals. By its nature, tumor growth is an ideal object of hybrid modeling where discrete sto- chastic processes model low-numbers entities, and mean-field equations model abundant chemical signals. Thus, we follow this approach to model tumor cells, effector cells and Interleukin-2, in order to capture the Immune surveillance effect. We here present a hybrid model with a generic delay kernel accounting that, due to many complex phenomena such as chemical transportation and cellular differentiation, the tumor-induced recruitment of effectors exhibits a lag period. This model is a Stochastic Hybrid Automata and its semantics is a Piecewise Deterministic Markov process where a two-dimensional stochastic process is interlinked to a multi-dimensional mean-field system. We instantiate the model with two well-known weak and strong delay kernels and perform simulations by using an algorithm to generate trajectories of this process. Via simulations and parametric sensitivity analysis techniques we (i) relate tumor mass growth with the two kernels, we (ii) measure the strength of the Immune surveillance in terms of probability distribution of the eradication times, and (iii) we prove, in the oscillatory regime, the existence of a stochastic bifurcation resulting in delay-induced tumor eradication
Optimal time-profiles of public health intervention to shape voluntary vaccination for childhood diseases
No full textIn order to seek the optimal time-profiles of public health systems (PHS) Intervention to favor vaccine propensity, we apply optimal control (OC) to a SIR model with voluntary vaccination and PHS intervention. We focus on short-term horizons, and on both continuous control strategies resulting from the forward-backward sweep deterministic algorithm, and piecewise-constant strategies (which are closer to the PHS way of working) investigated by the simulated annealing (SA) stochastic algorithm. For childhood diseases, where disease costs are much larger than vaccination costs, the OC solution sets at its maximum for most of the policy horizon, meaning that the PHS cannot further improve perceptions about the net benefit of immunization. Thus, the subsequent dynamics of vaccine uptake stems entirely from the declining perceived risk of infection (due to declining prevalence) which is communicated by direct contacts among parents, and unavoidably yields a future decline in vaccine uptake. We find that for relatively low communication costs, the piecewise control is close to the continuous control. For large communication costs the SA algorithm converges towards a non-monotone OC that can have oscillations
Exploring Consensus Robustness in Swarms with Disruptive Individuals
No full textAchieving consensus in collective systems is essential for coordinated behaviour, yet the presence of strongly opinionated minorities can disrupt opinion dynamics. In this paper, we investigate the robustness of consensus-reaching among stubborn individuals and contrarians, and we explore the effects of their interplay on consensus dynamics. We propose a methodology using formal technique of statistical model checking to quantify robustness under perturbations of the amount of disruptive individuals in the group. Unlike existing works that focus on robustness of a single group of disruptive individuals, our approach allows to investigate the robustness landscape for combinations of different disruptive agents. To this end, our approach can be used to guide the design and control of swarm robotics systems with a focus on resilience to disruptive agents
Optimal public health intervention in a behavioural vaccination model: the interplay between seasonality, behaviour and latency period
No full textHesitancy and refusal of vaccines preventing childhood diseases are spreading due to 'pseudo-rational' behaviours: parents overweigh real and imaginary side effects of vaccines. Nonetheless, the 'Public Health System' (PHS) may enact public campaigns to favour vaccine uptake. To determine the optimal time profiles for such campaigns, we apply the optimal control theory to an extension of the susceptible-infectious-removed (SIR)-based behavioural vaccination model by d'Onofrio et al. (2012, PLoS ONE, 7, e45653). The new model is of susceptible-exposed-infectious-removed (SEIR) type under seasonal fluctuations of the transmission rate. Our objective is to minimize the total costs of the disease: the disease burden, the vaccination costs and a less usual cost: the economic burden to enact the PHS campaigns. We apply the Pontryagin minimum principle and numerically explore the impact of seasonality, human behaviour and latency rate on the control and spread of the target disease. We focus on two noteworthy case studies: the low (resp. intermediate) relative perceived risk of vaccine side effects and relatively low (resp. very low) speed of imitation. One general result is that seasonality may produce a remarkable impact on PHS campaigns aimed at controlling, via an increase of the vaccination uptake, the spread of a target infectious disease. In particular, a higher amplitude of the seasonal variation produces a higher effort and this, in turn, beneficially impacts the induced vaccine uptake since the larger is the strength of seasonality, the longer the vaccine propensity remains large. However, such increased effort is not able to fully compensate the action of seasonality on the prevalence
The interplay of intrinsic and extrinsic bounded noises in biomolecular networks.
No full textAfter being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a biomolecular network. The influence of intrinsic and extrinsic noises on biomolecular networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: (i) the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, (ii) a model of enzymatic futile cycle and (iii) a genetic toggle switch. In (ii) and (iii) we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possible functional role of bounded noises
Bounded noise induced first-order phase transitions in a baseline non-spatial model of gene transcription
No full textIn this work we consider, from a statistical mechanics point of view, the effects of bounded stochastic perturbations of the protein decay rate for a bistable biomolecular network module. Namely, we consider the perturbations of the protein decay/binding rate constant (DBRC) in a circuit modeling the positive feedback of a transcription factor (TF) on its own synthesis. The DBRC models both the spontaneous degradation of the TF and its linking to other unknown biomolecular factors or drugs. We show that bounded perturbations of the DBRC preserve the positivity of the parameter value (and also its limited variation), and induce effects of interest. First, the noise amplitude induces a first-order phase transition. This is of interest since the system in study has neither spatial components nor it is composed by multiple interacting networks. In particular, we observe that the system passes from two to a unique stochastic attractor, and vice-versa. This behavior is different from noise-induced transitions (also termed phenomenological bifurcations), where a unique stochastic attractor changes its shape depending on the values of a parameter. Moreover, we observe irreversible jumps as a consequence of the above-mentioned phase transition. We show that the illustrated mechanism holds for general models with the same deterministic hysteresis bifurcation structure. Finally, we illustrate the possible implications of our findings to the intracellular pharmacodynamics of drugs delivered in continuous infusion. (C) 2017 Elsevier B.V. All rights reserved
Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic
No full textThe overall course of the COVID-19pandemic in Western countries has been characterized by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strengthening/lifting of social distancing measures, with the aim to balance the
protection of health and that of the society as a whole. After the arrival of vaccines,this multi-phasic character was further emphasized by the complicated deployment of vaccination campaigns and the onset of virus’ variants.To cope with this multi-phasic character,we propose a theoretical approach to the modeling of overall
pandemic courses, that we term multi-period/multi phasic, based on a specific definition of phase.This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus’ variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when
appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggests a simple, theory driven,approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning
(February2020) up to omicron onset, confirming thevalidity of the concept
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Fine-tuning anti-tumor immunotherapies via stochastic simulations
Full text linkAnti-tumor therapies aim at reducing to zero the number of tumor cells in a host within their end or, at least, aim at leaving the patient with a sufficiently small number of tumor cells so that the residual tumor can be eradicated by the immune system. Besides severe side-effects, a key problem of such therapies is finding a suitable scheduling of their administration to the patients. In this paper we study the effect of varying therapy-related parameters on the final outcome of the interplay between a tumor and the immune system
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