In Silico Determination of Binding Free Energy and Bonding Interactions between Monosubstituted Cefovecin and Pseudomonas Aeruginosa Lipase

7-([(2Z)- 2-(2-amino- 1,3-thiazol- 4-yl)- 2-methoxyiminoacetyl]amino)- 8-oxo- 3[(2S)-oxolan- 2-yl]- 5-thia-1-azabicyclo[4.2.0]oct-2-ene-2-carboxylic acid (cefovecin) is an antibiotic of the cephalosporin class . In silico binding free energy of six analogous structurally diverse cefovecin with Pseudomonas aeruginosa lipase were determined using Patchdock and Firedock softwares. The bonding interactions were also studied. The binding energy of cefovecin was -44.30 kcal/mol. The free binding energies of COOH, COCH3, CH3, NO2, CF3 and NH2 analogues were -44.30, -43.55, -39.49, -43.40, -30.25 and -44.18 Kcal/mol respectively. All the monosubstituted analogues showed lower negative values than the non substituted cefovecin. These lower negative values indicate that that the reactions are feasible. Their inhibition is lower compared to cefovecin against Pseudomonas aeruginosa lipase. The modes of bonding of six analogous structurally diverse cefovecin with Pseudomonas aeruginosa lipase were attributed to hydrogen bonding and steric interaction

Desain Primer Secara in Silico Untuk Amplifikasi Fragmen Gen Rpob Mycobacterium Tuberculosis Dengan Polymerase Chain Reaction (Pcr)

Amplifikasi DNA Mycobacterium tuberculosis dari gen rpoB dilakukan dengan metode polymerase chain reaction (PCR). Amplifikasi DNA dengan PCR diperlukan sepasang primer (forward dan reverse) untuk membatasi daerah yang ingin diamplifikasi. Penelitian ini bertujuan untuk mendesain sepasang primer agar dapat mengamplifikasi fragmen 0,5 kb gen rpoB M.tuberculosis. Desain primer dilakukan secara in silico dengan bantuan program clone manager suite 6 (University of Groningen). Template yang digunakan dalam mendesain primer adalah sekuen gen rpoB M. tuberculosis H37RV wild type, yang diperoleh dari database NCBI dengan kode genbank U12205.1. Penelitian ini telah berhasil memperoleh sekuen sepasang primer (forward dan reverse) dengan panjang masing-masing adalah 22 oligonukleotida. Primer ini dapat mengamplifikasi secara in silico fragmen 0,5 kb gen rpoB M. tuberculosis pada rentang daerah 990-1496 pb dengan panjang fragmen sebesar 507 pb

Predicting optimal hematocrit in silico

Optimal hematocrit $H_o$ maximizes oxygen transport. In healthy humans, the average hematocrit $H$ is in the range of 40-45$\%$, but it can significantly change in blood pathologies such as severe anemia (low $H$) and polycythemia (high $H$). Whether the hematocrit level in humans corresponds to the optimal one is a long standing physiological question. Here, using numerical simulations with the Lattice Boltzmann method and two mechanical models of the red blood cell (RBC) we predict the optimal hematocrit, and explore how altering the mechanical properties of RBCs affects $H_o$. We develop a simplified analytical theory that accounts for results obtained from numerical simulations and provides insight into the physical mechanisms determining $H_o$. Our numerical and analytical models can easily be modified to incorporate a wide range of mechanical properties of RBCs as well as other soft particles thereby providing means for the rational design of blood substitutes. Our work lays the foundations for systematic theoretical study of the optimal hematocrit and its link with pathological RBCs associated with various diseases (e.g. sickle cell anemia, diabetes mellitus, malaria, elliptocytosis)

In silico transitions to multicellularity

The emergence of multicellularity and developmental programs are among the major problems of evolutionary biology. Traditionally, research in this area has been based on the combination of data analysis and experimental work on one hand and theoretical approximations on the other. A third possibility is provided by computer simulation models, which allow to both simulate reality and explore alternative possibilities. These in silico models offer a powerful window to the possible and the actual by means of modeling how virtual cells and groups of cells can evolve complex interactions beyond a set of isolated entities. Here we present several examples of such models, each one illustrating the potential for artificial modeling of the transition to multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to multicellular life (Springer

Combined In Silico, In Vivo, and In Vitro Studies Shed Insights into the Acute Inflammatory Response in Middle-Aged Mice

We combined in silico, in vivo, and in vitro studies to gain insights into age-dependent changes in acute inflammation in response to bacterial endotoxin (LPS). Time-course cytokine, chemokine, and NO2-/NO3- data from "middle-aged" (6-8 months old) C57BL/6 mice were used to re-parameterize a mechanistic mathematical model of acute inflammation originally calibrated for "young" (2-3 months old) mice. These studies suggested that macrophages from middle-aged mice are more susceptible to cell death, as well as producing higher levels of pro-inflammatory cytokines, vs. macrophages from young mice. In support of the in silico-derived hypotheses, resident peritoneal cells from endotoxemic middle-aged mice exhibited reduced viability and produced elevated levels of TNF-α, IL-6, IL-10, and KC/CXCL1 as compared to cells from young mice. Our studies demonstrate the utility of a combined in silico, in vivo, and in vitro approach to the study of acute inflammation in shock states, and suggest hypotheses with regard to the changes in the cytokine milieu that accompany aging. © 2013 Namas et al

Kernel methods for in silico chemogenomics

Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to screen for side-effects. While classical in silico approaches focus on predicting interactions with a given specific target, new chemogenomics approaches adopt cross-target views. Building on recent developments in the use of kernel methods in bio- and chemoinformatics, we present a systematic framework to screen the chemical space of small molecules for interaction with the biological space of proteins. We show that this framework allows information sharing across the targets, resulting in a dramatic improvement of ligand prediction accuracy for three important classes of drug targets: enzymes, GPCR and ion channels

Uji In Silico Senyawa Emodin sebagai Ligan pada Reseptor Estrogen Alfa

Breast cancer is a disease with abnormal cell proliferation at breast tissue that can invade the surrounding tissue and spread to another organ. Based on WHO (2014) at 2012, there are 48,998 breast cancer cases on women in Indonesia with 19,730 death cases. In breast cancer, overexpression estrogen receptor alpha (ER-α) usually observed. Hence ER-α became the focus of prevention and therapy for breast cancer. In vitro study of emodin shows IC50 2.7 µM and Ki 0.77 µM on ER-α. In silico research using docking protocol and post docking analysis protocol shown that emodin was not an active ligand on ER-α. The outcome shown as ChemPLP score with average -75.292 and PLIF bitstring. Emodin binds with LEU346, LEU387, and ARG394 residue. Currently, the protocol that used in this research has yet identified marginal compound like emodin as an active ligand on ER-α

Recombination luminescence in N-type Czochralski silicon

Recombination luminescence in Czochralski silico

Simulating Brain Tumor Heterogeneity with a Multiscale Agent-Based Model: Linking Molecular Signatures, Phenotypes and Expansion Rate

We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells' phenotype, it now adds an explicit treatment of tumor cell adhesion related to the model's biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell 'search precisions'. The in silico results show that microscopic tumor heterogeneity can impact the tumor system's multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.Comment: 37 pages, 10 figure