Multiscale computational framework for simulation of cellular solids

Natural and man-made cellular solids have been used in a variety of engineering applications. Despite their widespread use, the behavior of such materials is not well understood because of their high heterogeneity and complex microstructure. A multiscale computational framework is formulated to study the behavior of structures and components made from cellular solids over a wide range of spatial and temporal scales. The framework consists of two levels of models, a continuum level and a microstructural level. The continuum-level models utilize finite element (FE) and mesh-free (MF) methods in conjunction with spatial domain decomposition and temporal multitime-stepping to capture different local- and global-scale behavior. At the microstructural level, the framework relies on a realistic representation of the foam microstructure and homogenization techniques to couple it with the continuum-level models. This study focuses on addressing the issues related to the formulation and implementation of this multiscale framework. These issues include formulating a variationally consistent coupling of FE and MF methods in space and time at the continuum level, and formulating an efficient micromechanical constitutive model for cellular solids to simulate a wide range of material behavior. Numerical characteristics of the computational framework are studied using several benchmark problems

Recursive Multi-Time-Step Coupling of Multiple Subdomains

The need for efficient computation methods for modeling of large-scale structures has become critically important over the past few years. Efficient means of analysis often involve coupling in space through domain decomposition and multi scale methods in time. The multi-time-step coupling method is a coupling method in time which allows for efficient analysis of large-scale problems for structural dynamics where a large structural model is decomposed into smaller subdomains that are solved independently and then coupled back together to obtain the global solution. For coupling of more than two subdomains that are solved at different timesteps, we employ recursive methods. Currently a constraint on this recursive coupling is that subdomains with the same time step must be coupled first before coupling with other subdomains of different time steps. In this research, we develop a computational algorithm to overcome this constraint and allow the user to specify general coupling orders for the different subdomains. Our efforts till now have been directed towards coding the recursive coupling of multi-subdomain models and we have verified that the equations that will allow us to overcome coupling constraint are correct. We are in the process of implementing these equations into our codes. Once in place, these sets of codes will allow users to conduct simulation of structural dynamics in a very efficient manner

EsATAC: an easy-to-use systematic pipeline for ATAC-seq data analysis

Summary ATAC-seq is rapidly emerging as one of the major experimental approaches to probe chromatin accessibility genome-wide. Here, we present ‘esATAC’, a highly integrated easy-to-use R/Bioconductor package, for systematic ATAC-seq data analysis. It covers essential steps for full analyzing procedure, including raw data processing, quality control and downstream statistical analysis such as peak calling, enrichment analysis and transcription factor footprinting. esATAC supports one command line execution for preset pipelines and provides flexible interfaces for building customized pipelines. Availability and implementation esATAC package is open source under the GPL-3.0 license. It is implemented in R and C++. Source code and binaries for Linux, MAC OS X and Windows are available through Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/esATAC.html). Supplementary information Supplementary data are available at Bioinformatics online. Document type: Articl

Functional importance of different patterns of correlation between adjacent cassette exons in human and mouse

<p>Abstract</p> <p>Background</p> <p>Alternative splicing expands transcriptome diversity and plays an important role in regulation of gene expression. Previous studies focus on the regulation of a single cassette exon, but recent experiments indicate that multiple cassette exons within a gene may interact with each other. This interaction can increase the potential to generate various transcripts and adds an extra layer of complexity to gene regulation. Several cases of exon interaction have been discovered. However, the extent to which the cassette exons coordinate with each other remains unknown.</p> <p>Results</p> <p>Based on EST data, we employed a metric of correlation coefficients to describe the interaction between two adjacent cassette exons and then categorized these exon pairs into three different groups by their interaction (correlation) patterns. Sequence analysis demonstrates that strongly-correlated groups are more conserved and contain a higher proportion of pairs with reading frame preservation in a combinatorial manner. Multiple genome comparison further indicates that different groups of correlated pairs have different evolutionary courses: (1) The vast majority of positively-correlated pairs are old, (2) most of the weakly-correlated pairs are relatively young, and (3) negatively-correlated pairs are a mixture of old and young events.</p> <p>Conclusion</p> <p>We performed a large-scale analysis of interactions between adjacent cassette exons. Compared with weakly-correlated pairs, the strongly-correlated pairs, including both the positively and negatively correlated ones, show more evidence that they are under delicate splicing control and tend to be functionally important. Additionally, the positively-correlated pairs bear strong resemblance to constitutive exons, which suggests that they may evolve from ancient constitutive exons, while negatively and weakly correlated pairs are more likely to contain newly emerging exons.</p

Multi-scale computational framework for simulation of cellular solids

Natural and man-made cellular solids have been used in a variety of engineering applications. Despite their widespread use, the behavior of such materials is not well understood because of their high heterogeneity and complex micro-structure. Conventionally, these materials have been studied with experimental testing to obtain rather simplistic models and empirical relationships describing their behavior. However, most existing methods are limited to the small deformation regime and almost none of the mechanics-based models are able to capture the behavior of cellular solids under moderate-to-high strain-rate loadings commonly encountered in practical applications such as impact absorption and blast mitigation. In this research, a multi-scale computational framework is formulated to study the behavior of structures and components made from cellular solids over a wide range of spatial and temporal scales. The framework consists of two levels of models, a continuum level and a micro-structural level. The continuum-level models utilize finite element and mesh-free methods in conjunction with spatial domain decomposition and temporal multi-time-stepping to capture different local- and global-scale behavior. At the micro-structural level, the framework relies on a realistic representation of the foam micro-structure and homogenization techniques to couple it with the continuum-level models. This research focuses on addressing different aspects of the formulation and implementation of this multi-scale framework. At the continuum-level, a variationally consistent coupling method is formulated for coupling subdomains that use different non-matching discretizations. It is shown that, while existing coupling methods in the literature are unable to pass patch tests in general, the variationally consistent method is able to pass arbitrary orders of patch tests exactly to within numerical precision for any numerical integration scheme that may be used for the subdomains. The method also shows good convergence for problems in general. Several numerical problems are solved using this method to compare its performance against existing coupling methods. For modeling of temporal multi-scale phenomena, two multi-time-step coupling methods are formulated. These methods allow one to use different time-steps to simulate different subdomains within a large structural model. A modified multi-time-step method for Newmark time integration schemes is formulated and shown to improve the computational efficiency of an existing consistent multi-time-step method. Another multi-time-step method is formulated for the Bathe time integration scheme and shown to work well for highly dynamic problems such as impact and wave propagation. At the micro-structural scale, a reduced-order model consisting of rotational and translational spring elements that simulate the behavior of individual ligaments of an open-cell foams is utilized. Using a computational homogenization approach, the micro-structural forces are up-scaled to the continuum-level to obtain macro-stress. A secant method is used to approximate the tangent from the micro-structural model for iterating the continuum-level solution to convergence. The performance of this micro-structural model is verified using the problem of crushing of a sample of open-cell foam

Using CRISPR-ERA Webserver for sgRNA Design

The CRISPR-Cas9 system is emerging as a powerful technology for gene editing (modifying the genome sequence) and gene regulation (without modifying the genome sequence). Designing sgRNAs for specific genes or regions of interest is indispensable to CRISPR-based applications. CRISPR-ERA (http://crispr-era.stanford.edu/) is one of the state-of-the-art designer webserver tools, which has been developed both for gene editing and gene regulation sgRNA design. This protocol discusses how to design sgRNA sequences and genome-wide sgRNA library using CRISPR-ERA

DIProT: A deep learning based interactive toolkit for efficient and effective Protein design

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design