INVESTIGADORES
ASURMENDI Sebastian
artículos
Título:
Modeling tobacco mosaic virus proliferation in protoplasts.
Autor/es:
JOSE C. MERCHUK; ASTERIO SÁNCHEZ-MIRÓN; SEBASTIAN ASURMENDI; MORDECHAI SHACHAM
Revista:
International Journal of Biology and Biomedical Engineering
Editorial:
North Atlantic University Union (NAUN)
Referencias:
Año: 2016 vol. 10 p. 202 - 210
ISSN:
1998-4510
Resumen:
Abstract?The tobacco mosaic virus (TMV) is one of the most studied viruses. It is frequently used as a model in the research of virus-host interactions. The interest in understanding the mechanism of its proliferation stems basically from the field of agriculture, due to the detrimental effect this virus has on several crops. In addition to this direct application, virus-mediated protein expression systems, which are well established for the synthesis of foreign proteins in animal cell cultures, are now being applied to plants and plant cells by means of plant viral vectors. The use of transformed roots for the propagation of viral vectors has also been proposed. This work presents a mechanistic model describing the transient process of TMV multiplication in a protoplast (a wall-deprived cell). It aims to be a mathematical tool able to simulate the transient behavior of the main molecular pools taking part in the process, which will be useful for exploring, understanding and predicting the dynamics of a hostvirus system. The variables considered are the pools of the main molecules taking part in the viral replication process. The basic balance equations for the cellular pools are presented and a satisfactory fit of the model to the experimental data is shown. The presented model is a necessary step toward the formulation of a basic mechanistic model for the systemic propagation of the virus in a plant tissue. It may be extended in many directions as to the optimization of a system for the production of a foreign protein, to the simulation of manipulation of the virus-cell interaction by external factors, to the mechanism of gene silencing or to the prediction of co-infection dynamics.