Dynamics of Unperturbed and Noisy Generalized Boolean Networks

For years, we have been building models of gene regulatory networks, where recent advances in molecular biology shed some light on new structural and dynamical properties of such highly complex systems. In this work, we propose a novel timing of updates in Random and Scale-Free Boolean Networks, inspired by recent findings in molecular biology. This update sequence is neither fully synchronous nor asynchronous, but rather takes into account the sequence in which genes affect each other. We have used both Kauffman's original model and Aldana's extension, which takes into account the structural properties about known parts of actual GRNs, where the degree distribution is right-skewed and long-tailed. The computer simulations of the dynamics of the new model compare favorably to the original ones and show biologically plausible results both in terms of attractors number and length. We have complemented this study with a complete analysis of our systems' stability under transient perturbations, which is one of biological networks defining attribute. Results are encouraging, as our model shows comparable and usually even better behavior than preceding ones without loosing Boolean networks attractive simplicity.Comment: 29 pages, publishe

Non-systemic transmission of tick-borne diseases: a network approach

Tick-Borne diseases can be transmitted via non-systemic (NS) transmission. This occurs when tick gets the infection by co-feeding with infected ticks on the same host resulting in a direct pathogen transmission between the vectors, without infecting the host. This transmission is peculiar, as it does not require any systemic infection of the host. The NS transmission is the main efficient transmission for the persistence of the Tick-Borne Encephalitis virus in nature. By describing the heterogeneous ticks aggregation on hosts through a \hyphenation{dynamical} bipartite graphs representation, we are able to mathematically define the NS transmission and to depict the epidemiological conditions for the pathogen persistence. Despite the fact that the underlying network is largely fragmented, analytical and computational results show that the larger is the variability of the aggregation, and the easier is for the pathogen to persist in the population.Comment: 15 pages, 4 figures, to be published in Communications in Nonlinear Science and Numerical Simulatio

Is anti-cholinesterase therapy of Alzheimer's disease delaying progression?

During the last decade, a systematic effort to develop a pharmacological treatment for Alzheimer's disease (AD) resulted in three drugs being registered for the first time in the US and Europe. All three compounds are cholinesterase inhibitors (ChEI). The major therapeutic effect of ChEI on AD patients is to maintain cognitive function at a stable level during a 6-month to 1-year period of treatment, as compared to placebo. Additional drug effects are to slow down cognitive deterioration and improve behavioral and daily living activity. Recent studies show that in many patients the cognitive stabilization effect can be prolonged up to 24 months. This long-lasting effect suggests a mechanism of action other than symptomatic, and directly cholinergic. In vitro and in vivo studies have consistently demonstrated a link between cholinergic activation and amyloid precursor protein (APP) metabolism. Lesions of cholinergic nuclei cause a rapid increase in cortical APP and cholinergic synaptic function; the effect of such lesions can be reversed by ChEI treatment. A reduction in cholin-ergic neurotransmission, experimental or pathological, leads to amyloidogenic metabolism and contributes to the development of neuropatholo-gy and cognitive dysfunction. To explain the long-term effect of ChEI, for which evidence is available on an experimental as well as clinical level, a mechanism based on beta-amyloid metabolism is postulated. The question whether cholinergic stabilization implies simply slowing down progression of disability or also involves delay of disease progression is discusse

Interplay of network dynamics and ties heterogeneity on spreading dynamics

The structure of a network dramatically affects the spreading phenomena unfolding upon it. The contact distribution of the nodes has long been recognized as the key ingredient in influencing the outbreak events. However, limited knowledge is currently available on the role of the weight of the edges on the persistence of a pathogen. At the same time, recent works showed a strong influence of temporal network dynamics on disease spreading. In this work we provide an analytical understanding, corroborated by numerical simulations, about the conditions for infected stable state in weighted networks. In particular, we reveal the role of heterogeneity of edge weights and of the dynamic assignment of weights on the ties in the network in driving the spread of the epidemic. In this context we show that when weights are dynamically assigned to ties in the network an heterogeneous distribution is able to hamper the diffusion of the disease, contrary to what happens when weights are fixed in time.Comment: 10 pages, 10 figure

Modeling the effects of variable feeding patterns of larval ticks on the transmission of Borrelia lusitaniae and Borrelia afzelii

Spirochetes belonging to the Borrelia burgdoferi sensu lato (sl) group cause Lyme Borreliosis (LB), which is the most commonly reported vector-borne zoonosis in Europe. B. burgdorferi sl is maintained in nature in a complex cycle involving Ixodes ricinus ticks and several species of vertebrate hosts. The transmission dynamics of B. burgdorferi sl is complicated by the varying competence of animals for different genospecies of spirochetes that, in turn, vary in their capability of causing disease. In this study, a set of difference equations simplifying the complex interaction between vectors and their hosts (competent and not for Borrelia) is built to gain insights into conditions underlying the dominance of B. lusitaniae (transmitted by lizards to susceptible ticks) and the maintenance of B. afzelii (transmitted by wild rodents) observed in a study area in Tuscany, Italy. Findings, in agreement with field observations, highlight the existence of a threshold for the fraction of larvae feeding on rodents below which the persistence of B. afzelii is not possible. Furthermore, thresholds change as nonlinear functions of the expected number of nymph bites on mice, and the transmission and recovery probabilities. In conclusion, our model provided an insight into mechanisms underlying the relative frequency of different Borrelia genospecies, as observed in field studies.Comment: 14 pages, 3 figures, to be published in Theoretical Population Biolog

Complex and dynamic population structures: synthesis, open questions, and future directions

The population structure of an evolutionary algorithm influences the dissemination and mixing of advantageous alleles, and therefore affects search performance. Much recent attention has focused on the analysis of complex population structures, characterized by heterogeneous connectivity distributions, non-trivial clustering properties, and degree-degree correlations. Here, we synthesize the results of these recent studies, discuss their limitations, and highlight several open questions regarding (1) unsolved theoretical issues and (2) the practical utility of complex population structures for evolutionary search. In addition, we will discuss an alternative complex population structure that is known to significantly influence dynamical processes, but has yet to be explored for evolutionary optimization. We then shift our attention toward dynamic population structures, which have received markedly less attention than their static counterparts. We will discuss the strengths and limitations of extant techniques and present open theoretical and experimental questions and directions for future research. In particular, we will focus on the prospects of "active linking,” wherein edges are dynamically rewired according to the genotypic or phenotypic properties of individuals, or according to the success of prior inter-individual interaction

Towards a Vectorial Approach to Predict Beef Farm Performance

Abbona, F., Vanneschi, L., & Giacobini, M. (2022). Towards a Vectorial Approach to Predict Beef Farm Performance. Applied Sciences, 12(3), 1-16. [1137]. https://doi.org/10.3390/app12031137 ------------------------------------ Funding: This work was partially supported by FCT, Portugal, through funding of projects BINDER (PTDC/CCI-INF/29168/2017) and AICE (DSAIPA/DS/0113/2019).Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.publishersversionpublishe