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1. #Perl grep regex file eec how to#
2. #Perl grep regex file eec install#
3. #Perl grep regex file eec code#
4. #Perl grep regex file eec simulator#
5. #Perl grep regex file eec mac#

While ChromoShake has been compiled on a Mac from source, the resulting build had a memory leak that prevented the system from running many iterations of a simulation ( see Note 3). ChromoShake can be run on systems with multiple CPUs or an Nvidia GPU ( see Note 2). if user name is lawrimor, C:\Users\lawrimor\chromoShake) or any directory lacking spaces in the directory path.

#Perl grep regex file eec install#

Install ChromoShake in your user root directory (i.e. This folder requires admin permission to alter files and the space in the “Program Files” directory can cause issues when trying to call ChromoShake from MATLAB. WARNING: Do not install ChromoShake in the default location on Windows System (C:\Program Files\\chromoShake_1.2.0\).

#Perl grep regex file eec code#

The ChromoShake Window’s installer is available at and the source code is available at. Lastly, we provide MATLAB scripts to convert other polymer simulations to ChromoShake’s file format to allow other simulations to utilize this visualization and analysis pipeline. We provide MATLAB image analysis scripts to introduce the user to automated image analysis using MATLAB. 2008) in conjunction with Python scripts converts ChromoShake simulation files to simulated fluorescence timelapses suitable for direct comparison to experimental timelapses.

#Perl grep regex file eec simulator#

The Microscope Simulator 2 program ( Quammen et al. RotoStep parses ChromoShake simulations, initially adding then simulating condensin complexes to the ChromoShake model by continually editing the ChromoShake simulations files using MATLAB. 2016) parses model configuration files and adds thermal noise to those models. The ChromoShake simulator ( Lawrimore et al. Each stage of the pipeline is further detailed in a flowchart ( Figure 2).

#Perl grep regex file eec how to#

The pipeline below instructs users on how to create, run, and visualize polymer simulations with condensin complexes ( Figure 1). An emergent view is that these structural proteins provide a kinetic advantage that exploits the natural fluctuations of DNA. The simulations provide critical intuition into processes in cellular environments that are not served by intuition in an inertia-dominated environment such as ours. These simulations highlight the dramatic increase in kinetics of retraction afforded by condensin. Here we describe the method to simulate translocation and loop extrusion. The dynamics of condensin stepping along single-tethered DNA result in extrusion of DNA loops. 1996)), we have shown that condensin can translocate along taut linear DNA and compact singly-tethered DNA chains ( Lawrimore et al. Using a computational model (Rotostep) to simulate hand-over-hand motion (e.g., microtubule-based kinesin motor ( Kull et al. (2017) demonstrated the ability of condensin to move in a processive fashion along DNA sheets under flow ( Fazio et al.

The heat repeat proteins are likely to be sites of DNA-binding within the condensin complex. Condensin is composed of five subunits, two coiled-coils SMC2 and 4, a kleisin (Brn1) and two Heat-repeat containing proteins (Ycs4 and Ycg1). Condensin and cohesin are two such complexes that act on chromosomes to facilitate their higher-order organization. The solution to dealing with an extremely viscous environment is to use energy-dependent machines to speed up DNA metabolic processes. The consequence of such a high effective viscosity is that the rate of entropic fluctuations on the chromosome will be excruciatingly slow.

This effective viscosity includes both molecular crowding and myriad short-lived interactions the chromosome encounters upon recoil to a random coil. They estimated the intracellular viscosity on the order of 141 poise or 14,100-fold higher than water. In that work, the recoil of a broken dicentric chromosome was visualized following DNA breakage in anaphase. 2009) made estimates on the nature of the cellular environment from the perspective of the chromosome. The use of small molecules to estimate viscosity inform us more about the interstitial water in the nucleoplasm than the viscosity affecting an entire chromosome. In contrast, simulating the physical properties of the cellular environment has proven more difficult. The use of bead-spring models to simulate the behavior of the chain have proven to be highly valuable. Understanding how biochemical reactions influence chromosome organization requires that we account for the large conformational fluctuations of the DNA itself. Loops spontaneously form as chromatin collides and wriggles about itself. Entropic penalties prevent chromosome intermixing, hence most of the interactions are intra-chromosomal loops.

Confining this long-chain polymer in the nucleus produces intra-chain interactions, otherwise known as loops. Thermal fluctuations dominate the spatial organization of chromosomes while active kinetic processes modulate this organization. Studying DNA from the perspective of a long-chain polymer has enabled tremendous strides in understanding genome organization.