A review of selected topics in physics based modeling for tunnel field-effect transistors

The research field on tunnel-FETs (TFETs) has been rapidly developing in the last ten years, driven by the quest for a new electronic switch operating at a supply voltage well below 1 V and thus delivering substantial improvements in the energy efficiency of integrated circuits. This paper reviews several aspects related to physics based modeling in TFETs, and shows how the description of these transistors implies a remarkable innovation and poses new challenges compared to conventional MOSFETs. A hierarchy of numerical models exist for TFETs covering a wide range of predictive capabilities and computational complexities. We start by reviewing seminal contributions on direct and indirect band-to-band tunneling (BTBT) modeling in semiconductors, from which most TCAD models have been actually derived. Then we move to the features and limitations of TCAD models themselves and to the discussion of what we define non-self-consistent quantum models, where BTBT is computed with rigorous quantum-mechanical models starting from frozen potential profiles and closed-boundary Schr\uf6dinger equation problems. We will then address models that solve the open-boundary Schr\uf6dinger equation problem, based either on the non-equilibrium Green's function NEGF or on the quantum-transmitting-boundary formalism, and show how the computational burden of these models may vary in a wide range depending on the Hamiltonian employed in the calculations. A specific section is devoted to TFETs based on 2D crystals and van der Waals hetero-structures. The main goal of this paper is to provide the reader with an introduction to the most important physics based models for TFETs, and with a possible guidance to the wide and rapidly developing literature in this exciting research field

Direct Repeat 6 from Human Herpesvirus-6B Encodes a Nuclear Protein that Forms a Complex with the Viral DNA Processivity Factor p41

The SalI-L fragment from human herpesvirus 6A (HHV-6A) encodes a protein DR7 that has been reported to produce fibrosarcomas when injected into nude mice, to transform NIH3T3 cells, and to interact with and inhibit the function of p53. The homologous gene in HHV-6B is dr6. Since p53 is deregulated in both HHV-6A and -6B, we characterized the expression of dr6 mRNA and the localization of the translated protein during HHV-6B infection of HCT116 cells. Expression of mRNA from dr6 was inhibited by cycloheximide and partly by phosphonoacetic acid, a known characteristic of herpesvirus early/late genes. DR6 could be detected as a nuclear protein at 24 hpi and accumulated to high levels at 48 and 72 hpi. DR6 located in dots resembling viral replication compartments. Furthermore, a novel interaction between DR6 and the viral DNA processivity factor, p41, could be detected by confocal microscopy and by co-immunoprecipitation analysis. In contrast, DR6 and p53 were found at distinct subcellular locations. Together, our data imply a novel function of DR6 during HHV-6B replication

EC-BLAST: a tool to automatically search and compare enzyme reactions.

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering

Origin of Co-Expression Patterns in E.coli and S.cerevisiae Emerging from Reverse Engineering Algorithms

BACKGROUND: The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of "physical" networks of interactions among genes or gene products. RESULTS: This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E. coli and S. cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor-binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E. coli. CONCLUSION: The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E. coli to S. cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies

Computational Bacterial Genome-Wide Analysis of Phylogenetic Profiles Reveals Potential Virulence Genes of Streptococcus agalactiae

The phylogenetic profile of a gene is a reflection of its evolutionary history and can be defined as the differential presence or absence of a gene in a set of reference genomes. It has been employed to facilitate the prediction of gene functions. However, the hypothesis that the application of this concept can also facilitate the discovery of bacterial virulence factors has not been fully examined. In this paper, we test this hypothesis and report a computational pipeline designed to identify previously unknown bacterial virulence genes using group B streptococcus (GBS) as an example. Phylogenetic profiles of all GBS genes across 467 bacterial reference genomes were determined by candidate-against-all BLAST searches,which were then used to identify candidate virulence genes by machine learning models. Evaluation experiments with known GBS virulence genes suggested good functional and model consistency in cross-validation analyses (areas under ROC curve, 0.80 and 0.98 respectively). Inspection of the top-10 genes in each of the 15 virulence functional groups revealed at least 15 (of 119) homologous genes implicated in virulence in other human pathogens but previously unrecognized as potential virulence genes in GBS. Among these highly-ranked genes, many encode hypothetical proteins with possible roles in GBS virulence. Thus, our approach has led to the identification of a set of genes potentially affecting the virulence potential of GBS, which are potential candidates for further in vitro and in vivo investigations. This computational pipeline can also be extended to in silico analysis of virulence determinants of other bacterial pathogens

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning

Enhanced protein fold recognition through a novel data integration approach

<p>Abstract</p> <p>Background</p> <p>Protein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived from local sequence alignments. This raises the issue of finding the most efficient method for combining these different informative data sources and exploring their relative significance for protein fold classification. Kernel methods have been extensively used for biological data analysis. They can incorporate separate fold discriminatory features into kernel matrices which encode the similarity between samples in their respective data sources.</p> <p>Results</p> <p>In this paper we consider the problem of integrating multiple data sources using a kernel-based approach. We propose a novel information-theoretic approach based on a Kullback-Leibler (KL) divergence between the output kernel matrix and the input kernel matrix so as to integrate heterogeneous data sources. One of the most appealing properties of this approach is that it can easily cope with multi-class classification and multi-task learning by an appropriate choice of the output kernel matrix. Based on the position of the output and input kernel matrices in the KL-divergence objective, there are two formulations which we respectively refer to as <it>MKLdiv-dc </it>and <it>MKLdiv-conv</it>. We propose to efficiently solve MKLdiv-dc by a difference of convex (DC) programming method and MKLdiv-conv by a projected gradient descent algorithm. The effectiveness of the proposed approaches is evaluated on a benchmark dataset for protein fold recognition and a yeast protein function prediction problem.</p> <p>Conclusion</p> <p>Our proposed methods MKLdiv-dc and MKLdiv-conv are able to achieve state-of-the-art performance on the SCOP PDB-40D benchmark dataset for protein fold prediction and provide useful insights into the relative significance of informative data sources. In particular, MKLdiv-dc further improves the fold discrimination accuracy to 75.19% which is a more than 5% improvement over competitive Bayesian probabilistic and SVM margin-based kernel learning methods. Furthermore, we report a competitive performance on the yeast protein function prediction problem.</p