Modelling drawbeads with finite elements and verification

Drawbeads are commonly used in deep drawing processes to control the flow of the blank during the forming operation. In finite element simulations of deep drawing the drawbead geometries are seldom included because of the small radii; because of these small radii a very large number of elements is required in 3-D simulations. To cope with this problem, a 2-D analysis of the drawbead has been performed and the calculated restraining force will be applied in the near future in 3-D simulations with an equivalent drawbead element. Modelling drawbeads by only applying an additional restraining force is not satisfactory. During the flow of the material through a drawbead, the strain distribution changes and the material usually becomes thinner. These effects must be incorporated in the equivalent drawbead element.\ud \ud For the modelling of the drawbead a 2-D plane strain finite element model was developed. Several simulations were carried out to investigate the behaviour of the drawbead. Various geometries were investigated, the friction coefficient was varied and also the frictionless case was taken into account.\ud \ud To verify the model an experimental set-up was built. An extensive set of drawbead geometries was used. The results are compared with the finite element calculations and the similarity is very satisfactory

Harnessing Intellectual Resources in a Collaborative Context to Create Value

The value of electronic collaboration has arisen as successful organisations recognize that they need to convert their intellectual resources into customized services. The shift from personal computing to interpersonal or collaborative computing has given rise to ways of working that may bring about better and more effective use of intellectual resources. Current efforts in managing knowledge have concentrated on producing; sharing and storing knowledge while business problems require the combined use of these intellectual resources to enable organisations to provide innovative and customized services. In this chapter the collaborative context is developed using a model for electronic collaboration through the use of which organisations may mobilse collaborative technologies and intellectual resources towards achieving joint effect.electronic collaboration;value creation;collaborative computing;knowledge management and intellectual resources

Modeling the binding of H-NS to DNA

Bacterial chromosomal DNA is organized within a structure called the nucleoid, which is distinctly different from the rest of the cytoplasm. Bacteria have a number of nucleoid-associated proteins that influence the organization of the nucleoid by bending, wrapping or bridging DNA. The Histone-like Nucleoid Structuring protein H-NS can bridge DNA by binding to two separate DNA duplexes, or shield the DNA by binding to distant sites on the same duplex, depending on external conditions. H-NS occurs in Gram-negative enterobacteria and silences genes involved in bacterial virulence and antibiotic resistance

Decoding dissociation of sequence-specific protein-DNA complexes with non-equilibrium simulations

Sequence-specific protein-DNA interactions are crucial in processes such as DNA organization, gene regulation and DNA replication. Obtaining detailed insights into the recognition mechanisms of protein-DNA complexes through experiments is hampered by a lack of resolution in both space and time. Here, we present a molecular simulation approach to quantify the sequence specificity of protein-DNA complexes, that yields results fast, and is generally applicable to any protein-DNA complex. The approach is based on molecular dynamics simulations in combination with a sophisticated steering potential and results in an estimate of the free energy difference of dissociation. We provide predictions of the nucleotide specific binding affinity of the minor groove binding Histone-like Nucleoid Structuring (H-NS) protein, that are in agreement with experimental data. Furthermore, our approach offers mechanistic insight into the process of dissociation. Applying our approach to the major groove binding ETS domain in complex with three different nucleotide sequences identified the high affinity consensus sequence, quantitatively in agreement with experiments. Our protocol facilitates quantitative prediction of protein-DNA complex stability, while also providing high resolution insights into recognition mechanisms. As such, our simulation approach has the potential to yield detailed and quantitative insights into biological processes involving sequence-specific protein-DNA interactions

Path Sampling Simulations Reveal How the Q61L Mutation Alters the Dynamics of KRas

[Image: see text] Flexibility is essential for many proteins to function, but can be difficult to characterize. Experiments lack resolution in space and time, while the time scales involved are prohibitively long for straightforward molecular dynamics simulations. In this work, we present a multiple state transition path sampling simulation study of a protein that has been notoriously difficult to characterize in its active state. The GTPase enzyme KRas is a signal transduction protein in pathways for cell differentiation, growth, and division. When active, KRas tightly binds guanosine triphosphate (GTP) in a rigid state. The protein–GTP complex can also visit more flexible states, in which it is not active. KRas mutations can affect the conversion between these rigid and flexible states, thus prolonging the activation of signal transduction pathways, which may result in tumor formation. In this work, we apply path sampling simulations to investigate the dynamic behavior of KRas-4B (wild type, WT) and the oncogenic mutant Q61L (Q61L). Our results show that KRas visits several conformational states, which are the same for WT and Q61L. The multiple state transition path sampling (MSTPS) method samples transitions between the different states in a single calculation. Tracking which transitions occur shows large differences between WT and Q61L. The MSTPS results further reveal that for Q61L, a route to a more flexible state is inaccessible, thus shifting the equilibrium to more rigid states. The methodology presented here enables a detailed characterization of protein flexibility on time scales not accessible with brute-force molecular dynamics simulations