[Suggestions for chikungunya control based on a sensitivity analysis of a mathematical model]. / Sugerencias a partir del análisis de sensibilidad de un modelo matemático de transmisión de chikungunya.

Chikungunya fever seriously affects peoples' health and causes chronic joint pain and even disability. Chikungunya is transmitted by the bite of Aedes aegypti and Aedes albopictus. Outbreaks have been reported in throughout the world, including Latin America. Mathematical modeling studies of these outbreaks have calculated the values of various​ epidemiological parameters. Based on them, a mathematical model was prepared to simulate a chikungunya outbreak in a local population, which was transmitted from an neighboring infected population. A sensitivity and uncertainty analysis revealed that the mosquito-to-human and human-to-mosquito transmission rates are the variables with the highest correlation with the number infected people, which were greatest at 60 days after the first case in the neighboring population. Therefore, it is recommended to take this into consideration when planning policies to control such variables as isolation of infected people, distribution of mosquito netting and repellents, fumigation, among others.

Sugerencias a partir del análisis de sensibilidad de un modelo matemático de transmisión de chikungunya / Suggestions for chikungunya control based on a sensitivity analysis of a mathematical model

La fiebre chikungunya afecta seriamente la salud de las personas, causando dolores articulares crónicos e incluso discapacidad. Es transmitida por picadura de los mosquitos Aedes aegypti y Aedes albopictus. Se han reportado brotes en diversas partes del mundo, incluyendo Latinoamérica. Estudios de modelamiento matemático de dichos brotes han calculado valores de parámetros epidemiológicos. En base a ellos, se elaboró un modelo matemático para simular un brote epidémico de chikungunya en una población local, transmitido desde una población vecina infectada. Mediante análisis de sensibilidad e incertidumbre se obtuvo que la tasa de transmisión mosquito-humano y humano-mosquito son las variables con mayor correlación con la cantidad de infectados, la cual se pierde significativamente a partir de los 60 días del primer caso en la población vecina. Se recomienda tomar esto en cuenta al planificar medidas de control sobre dichas variables como aislamiento de infectados, repartición de mosquiteros y repelentes, fumigación, entre otras...

Chikungunya fever seriously affects peoplesÆ health and causes chronic joint pain and even disability. Chikungunya is transmitted by the bite of Aedes aegypti and Aedes albopictus. Outbreaks have been reported in throughout the world, including Latin America. Mathematical modeling studies of these outbreaks have calculated the values of various epidemiological parameters. Based on them, a mathematical model was prepared to simulate a chikungunya outbreak in a local population, which was transmitted from an neighboring infected population. A sensitivity and uncertainty analysis revealed that the mosquito-to-human and human-to-mosquito transmission rates are the variables with the highest correlation with the number infected people, which were greatest at 60 days after the first case in the neighboring population. Therefore, it is recommended to take this into consideration when planning policies to control such variables as isolation of infected people, distribution of mosquito netting and repellents, fumigation, among others...

Identifying DNA splice sites using hypernetworks with artificial molecular evolution.

Identifying DNA splice sites is a main task of gene hunting. We introduce the hyper-network architecture as a novel method for finding DNA splice sites. The hypernetwork architecture is a biologically inspired information processing system composed of networks of molecules forming cells, and a number of cells forming a tissue or organism. Its learning is based on molecular evolution. DNA examples taken from GenBank were translated into binary strings and fed into a hypernetwork for training. We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation. The hypernetwork generalization performance was comparable to well known classification algorithms. With the best hypernetwork obtained, including local information and heuristic rules, we built a system (HyperExon) to obtain splice site candidates. The HyperExon system outperformed leading splice recognition systems in the list of sequences tested.

Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model.

Infection with Mycobacterium tuberculosis is a major world health problem. An estimated 2 billion people are presently infected and the disease causes approximately 3 million deaths per year. After bacteria are inhaled into the lung, a complex immune response is triggered leading to the formation of multicellular structures termed granulomas. It is believed that the collection of host granulomas either contain bacteria resulting in a latent infection or are unable to do so, leading to active disease. Thus, understanding granuloma formation and function is essential for improving both diagnosis and treatment of tuberculosis. Granuloma formation is a complex spatio-temporal system involving interactions of bacteria, specific immune cells, including macrophages, CD4+ and CD8+ T cells, as well as immune effectors such as chemokine and cytokines. To study this complex dynamical system we have developed an agent-based model of granuloma formation in the lung. This model combines continuous representations of chemokines with discrete agent representations of macrophages and T cells in a cellular automata-like environment. Our results indicate that key host elements involved in granuloma formation are chemokine diffusion, prevention of macrophage overcrowding within the granuloma, arrival time, location and number of T cells within the granuloma, and an overall host ability to activate macrophages. Interestingly, a key bacterial factor is its intracellular growth rate, whereby slow growth actually facilitates survival.

The effect of molecular inhibition on evolutionary learning: studies in the hypernetwork architecture.

The hypernetwork architecture is a biologically inspired learning model based on abstract molecules and molecular interactions that exhibits functional and organizational correlation with biological systems. Hypernetwork organisms were trained, by molecular evolution, to solve N-input parity tasks. We found that learning improves when molecules exhibit inhibitory sites, allowing molecular inhibition and opening the possibility of forming negative feedback regulatory pathways. Optimal learning is achieved when at least 20% of the molecules in each cell have inhibitory sites. Intra-cellular as well as inter-cellular molecular inhibitions play an important role in the information processing of hypernetwork organisms, by maintaining a balance of the molecular cascade reactions. Similar mechanisms inside neurons are considered important for memory.