A theoretical analysis of the prodrug delivery system for treating antibiotic-resistant bacteria.

Simulations were carried out to analyze a promising new antimicrobial treatment strategy for targeting antibiotic-resistant bacteria called the ß-lactamase-dependent prodrug delivery system. In this system, the antibacterial drugs are delivered as inactive precursors that only become activated after contact with an enzyme characteristic of many species of antibiotic-resistant bacteria (ß-lactamase enzyme). The addition of an activation step contributes an extra layer of complexity to the system that can lead to unexpected emergent behavior. In order to optimize for treatment success and minimize the risk of resistance development, there must be a clear understanding of the system dynamics taking place and how they impact on the overall response. It makes sense to use a systems biology approach to analyze this method because it can facilitate a better understanding of the complex emergent dynamics arising from diverse interactions in populations. This paper contains an initial theoretical examination of the dynamics of this system of activation and an assessment of its therapeutic potential from a theoretical standpoint using an agent-based modeling approach. It also contains a case study comparison with real-world results from an experimental study carried out on two prodrug candidate compounds in the literature.

Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach.

BACKGROUND: Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used. RESULTS: In this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix. CONCLUSION: The high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.

A computational model of antibiotic-resistance mechanisms in methicillin-resistant Staphylococcus aureus (MRSA).

An agent-based model of bacteria-antibiotic interactions has been developed that incorporates the antibiotic-resistance mechanisms of Methicillin-Resistant Staphylococcus aureus (MRSA). The model, called the Micro-Gen Bacterial Simulator, uses information about the cell biology of bacteria to produce global information about population growth in different environmental conditions. It facilitates a detailed systems-level investigation of the dynamics involved in bacteria-antibiotic interactions and a means to relate this information to traditional high-level properties such as the Minimum Inhibitory Concentration (MIC) of an antibiotic. The two main resistance strategies against beta-lactam antibiotics employed by MRSA were incorporated into the model: beta-lactamase enzymes, which hydrolytically cleave antibiotic molecules, and penicillin-binding proteins (PBP2a) with reduced binding affinities for antibiotics. Initial tests with three common antibiotics (penicillin, ampicillin and cephalothin) indicate that the model can be used to generate quantitatively accurate predictions of MICs for antibiotics against different strains of MRSA from basic cellular and biochemical information. Furthermore, by varying key parameters in the model, the relative impact of different kinetic parameters associated with the two resistance mechanisms to beta-lactam antibiotics on cell survival in the presence of antibiotics was investigated.