Search results for: Bacteriophage λ

Authors: Simona Dostalova, Ana Maria Jimenez Jimenez, Marketa Vaculovicova, Vojtech Adam, Rene Kizek

Abstract:

Various nanomaterials can be used as a drug delivery vehicles in nanomedicine, called nanocarriers. They can either be organic or inorganic, synthetic or natural-based. Although synthetic nanocarriers are easier to produce, they can often be toxic for the organism and thus not suitable for use in treatment. From naturalbased nanocarriers, the most commonly used are protein cages or viral capsids. In this work, virus bacteriophage λ was used for delivery of different cytotoxic drugs (cisplatin, carboplatin, oxaliplatin and doxorubicin). Large quantities of phage λ were obtained from phage λ-producing strain of E. coli cultivated in medium with 0.2% maltose. After killing of E. coli with chloroform and its removal by centrifugation, the phage was concentrated by ultracentrifugation at 130 000×g and 4°C for 3 h. The encapsulation of the drugs was performed by infusion method and four different concentrations of the drugs were encapsulated (200; 100; 50; 25 μg·mL-1). Free drug molecules were removed by filtration. The encapsulation was verified using the absorbance for doxorubicin and atomic absorption spectrometry for platinum cytostatics. The amount of encapsulated drug linearly increased with the increasing concentration of applied drug with the determination coefficient R2=0.989 for doxorubicin; R2=0.967 for cisplatin; R2=0.989 for carboplatin and R2=0.996 for oxaliplatin. The overall encapsulation efficiency was calculated as 50% for doxorubicin; 8% for cisplatin; 6% for carboplatin and 10% for oxaliplatin.

Keywords: Bacteriophage λ, doxorubicin, platinum cytostatics, protein-based nanocarrier, viral capsid.

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Authors: Hai Xu, Yiwei Wang, Xi Bao, Bihua Deng, Pengcheng Li, Yu Lu

Abstract:

T7 phage could be used as a perfect vector for peptides expression and haptens presentation. T7-3GnRH recombinant phage was constructed by inserting three copies of Gonadotrophin Releasing Hormone (GnRH) gene into the multiple cloning site of T7 Select 415-1b phage genome. The positive T7-3GnRH phage was selected by using polymerase chain reaction amplification, and the p10B-3GnRH fusion protein was verified by SDS-PAGE and Western-blotting assay. T7-3GnRH vaccine was made and immunized with 10¹⁰ pfu in 0.2 ml per dose in mice. Blood samples were collected at an interval in weeks, and anti-GnRH antibody and testosterone concentrations were detected by ELISA and radioimmunoassay, respectively. The results show that T7-3GnRH phage particles confer a high immunogenicity to the GnRH-derived epitope. Moreover, the T7-3GnRH vaccine induced higher level of anti-GnRH antibody than ImproVac^®. However, the testosterone concentrations in both immunized groups were at a similar level, and the testis developments were significantly inhibited compared to controls. These findings demonstrated that the anti-GnRH antibody could neutralize the endogenous GnRH to down regulate testosterone level and limit testis development, highlighting the potential value of T7-3GnRH in the immunocastration vaccine research.

Keywords: Gonadotrophin releasing hormone, GnRH, immunocastration, T7 phage, phage vaccine.

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Authors: Werner Sandmann, Verena Wolf

Abstract:

Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a "higher-level paradigm" with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the model-s state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than non-simulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply non-simulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzyme-catalyzed substrate conversion and a part of the bacteriophage λ lysis/lysogeny pathway.

Keywords: Computational Modeling, Biological Networks, Stochastic Models, Markov Chains, Transition Class Models.

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