RESUMEN
Hi-C is a widely applied chromosome conformation capture (3C)-based technique, which has produced a large number of genomic contact maps with high sequencing depths for a wide range of cell types, enabling comprehensive analyses of the relationships between biological functionalities (e.g. gene regulation and expression) and the three-dimensional genome structure. Comparative analyses play significant roles in Hi-C data studies, which are designed to make comparisons between Hi-C contact maps, thus evaluating the consistency of replicate Hi-C experiments (i.e. reproducibility measurement) and detecting statistically differential interacting regions with biological significance (i.e. differential chromatin interaction detection). However, due to the complex and hierarchical nature of Hi-C contact maps, it remains challenging to conduct systematic and reliable comparative analyses of Hi-C data. Here, we proposed sslHiC, a contrastive self-supervised representation learning framework, for precisely modeling the multi-level features of chromosome conformation and automatically producing informative feature embeddings for genomic loci and their interactions to facilitate comparative analyses of Hi-C contact maps. Comprehensive computational experiments on both simulated and real datasets demonstrated that our method consistently outperformed the state-of-the-art baseline methods in providing reliable measurements of reproducibility and detecting differential interactions with biological meanings.
Asunto(s)
Cromatina , Cromosomas , Reproducibilidad de los Resultados , Cromatina/genética , Cromosomas/genética , Genómica/métodos , Aprendizaje Automático SupervisadoRESUMEN
While deterministic metapopulation models for the spread of epidemics between populations have been well-studied in the literature, variability in disease transmission rates and interaction rates between individual agents or populations suggests the need to consider stochastic fluctuations in model parameters in order to more fully represent realistic epidemics. In the present paper, we have extended a stochastic SIS epidemic model - which introduces stochastic perturbations in the form of white noise to the force of infection (the rate of disease transmission from classes of infected to susceptible populations) - to spatial networks, thereby obtaining a stochastic epidemic metapopulation model. We solved the stochastic model numerically and found that white noise terms do not drastically change the overall long-term dynamics of the system (for sufficiently small variance of the noise) relative to the dynamics of a corresponding deterministic system. The primary difference between the stochastic and deterministic metapopulation models is that for large time, solutions tend to quasi-stationary distributions in the stochastic setting, rather than to constant steady states in the deterministic setting. We then considered different approaches to controlling the spread of a stochastic SIS epidemic over spatial networks, comparing results for a spectrum of controls utilizing local to global information about the state of the epidemic. Variation in white noise was shown to be able to counteract the treatment rate (treated curing rate) of the epidemic, requiring greater treatment rates on the part of the control and suggesting that in real-life epidemics one should be mindful of such random variations in order for a treatment to be effective. Additionally, we point out some problems using white noise perturbations as a model, but show that a truncated noise process gives qualitatively comparable behaviors without these issues.
Asunto(s)
Enfermedades Transmisibles/epidemiología , Enfermedades Transmisibles/transmisión , Simulación por Computador , Modelos Biológicos , Humanos , Procesos EstocásticosRESUMEN
We extend two-species models of individual aggregation or clustering to two-dimensional spatial domains, allowing for more realistic movement of the populations compared with one spatial dimension. We assume that the domain is bounded and that there is no flux into or out of the domain. The motion of the species is along fitness gradients which allow the species to seek out a resource. In the case of competition, species which exploit the resource alone will disperse while avoiding one another. In the case where one of the species is a predator or generalist predator which exploits the other species, that species will tend to move toward the prey species, while the prey will tend to avoid the predator. We focus on three primary types of interspecies interactions: competition, generalist predator-prey, and predator-prey. We discuss the existence and stability of uniform steady states. While transient behaviors including clustering and colony formation occur, our stability results and numerical evidence lead us to believe that the long-time behavior of these models is dominated by spatially homogeneous steady states when the spatial domain is convex. Motivated by this, we investigate heterogeneous resources and hazards and demonstrate how the advective dispersal of species in these environments leads to asymptotic steady states that retain spatial aggregation or clustering in regions of resource abundance and away from hazards or regions or resource scarcity.