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.
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.