The present study reported a single step synthesis of silver nanoparticles using ampicillin (Amp-AgNps), a second-generation β lactam antibiotic to get nanoformulation having dual properties that of antibiotic and silver. The Amp-AgNps was characterized by UV-VIS spectroscopy, TEM, XRD, FTIR and TGA. FTIR and TGA results suggested that amine group of Ampicllin reduce the metalic silver into nano form. These results were further validated by computational molecular dynamics simulation. The antibacterial potential of Amp-AgNps was investigated against sensitive and drug resistant bacteria. MIC of Amp-AgNps against 6 different bacterial strains were in the range of 3–28 µg/ml which is much lower than the MIC of ampicillin (12–720 µg/ml) and chemically synthesized silver nanoparticles (280–640 µg/ml). The repeated exposure to drugs may lead to development of resistance mechanism in bacteria against that drug, so the efficacy of Amp-AgNps after repeated exposure to bacterial strains were also studied. The results indicate that bacterial strains do not show any resistance to these Amp-AgNps even after exposure up to 15 successive cycles. The biocompatibility of these Amp-AgNps was checked against cell lines by using Keratinocytes cell lines (HaCaT).

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Systems biology is an emerging interdisciplinary area of research that focuses on study of complex interactions in a biological system, such as gene regulatory networks. The discovery of gene regulatory networks leads to a wide range of applications, such as pathways related to a disease that can unveil in what way the disease acts and provide novel tentative drug targets. In addition, the development of biological models from discovered networks or pathways can help to predict the responses to disease and can be much useful for the novel drug development and treatments. The inference of regulatory networks from biological data is still in its infancy stage. This paper proposes a recurrent neural network (RNN) based gene regulatory network (GRN) model hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between the biological closeness and mathematical flexibility to model GRN. The RNN is able to capture complex, non-linear and dynamic relationship among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation even in noisy data. Hence, non-linear version of Kalman filter, i.e., generalized extended Kalman filter has been applied for weight update during network training. The developed model has been applied on DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We compared our results with other stateof-the-art techniques that show superiority of our model. Further, 5% Gaussian noise has been added in the dataset and result of the proposed model shows negligible effect of noise on the results.