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Transcription Networks - Basic Concepts The cell continuously monitors its environment and calculates the amount of each protein that is needed. The rate of production is controlled by transcription networks. To represent environmental states, cells use special proteins called transcription factors as symbols. Transcription factors are molecules that can rapidly switch between active and inactive molecular states, with rates determined by environmental factors. Transcription factors bind to DNA and regulate read-rate of genes. Transcription factors regulate their target genes to mobilize the appropriate protein response in each case. flowchart LR genes --transcribed--> mRNA --translated--> proteins; proteins--modify-->environment; environment --what?--> genes E. coli: has an internal representation of about 300 degrees of freedom. Hence, it has 300 transcription factors. ...

Welcome to my website! Here, I post about stuff I like and the things I do. I { read, code, think, analyse, and solve } in all permutations. This website cum blog is an attempt to include writing in this set. Below is a short overview of the website. projects: where I post about my scientific and coding projects. publications: lists all the articles I have written and published somewhere. blog: where I will post articles and notes on various themes. gallery: has some cool pictures of me and my work. about me: an overview of my interests & qualifications. collaborate: has a list of projects for which collaborators are required. This website is still being actively developed and is broken at a few places. ...

Namespaces don’t work: ## Both the functions would fail to pickle, ## instead just have a blank function in a module. class Namespace: @staticmethod def test_static(): return "Finished" def test(): return "Finished" ## This might work exec(open("code/Trappy-Scopes/trackyscope/splitprocessor.py", "r").read()) Imports need to happen inside the function, also PATH list of each function is independent and needs to be redefined: def process_func(): import sys sys.path.appen("path-to-your-module") ## Calculations return ## Won't work import time def process_func(): time.sleep(2) return "Finished" ## Works def process_func(): import time time.sleep(2) return "Finished" cell1.client.get_versions(check=True) ...

Introduction We will set-up a cluster using a job-scheduler (SLURM) and use dask-distributed to manage jobs. One does not normally choose the job-scheduler, it is given to us (like your institute or HPC service would use a specific one). It is for this reason that dask comes in handy. It implements a trivial-parallelisation model on top of most common job-schedulers (SLURM, PBS, etc) to name a few. The interface it provides resembles python’s multiprocessing module. ...

Bacterial chemotaxis signal response: allows bacteria to navigate. Chemicals are either attractants or repellants. Common attractants: sugars and amino acids serene and aspartate, and repellents such as certain metal ions and amino acid leucine. bacteria can detect concentration gradients as small as a change of one molecule per cell volume across the cell length. They detect such gradients over background concentrations spanning five orders of magnitude. (no reference) Biased random walk: walk straight for 1sec and then tumbes in about 0.1 sec. Ecoli keeps track of the gradient and then adjusts the tumbling frequency accordingly. ...

Langevin Model: Theory and Units by Yatharth Bhasin (version 2) This article explains the basic set of equations governing the Langevin model simulation of an FCS process. Further, it includes a thorough discussion on the units conversions needed for the simulation. Implementation is also briefly discussed. The last section details a conversion case study. Langevin Equation The Langevin equation simulates Brownian motion of particles. The equation can be solved to simulate non-equilibrium systems. ...