Biology subjects

Swaminathan, A.

Publications and source records attributed to Swaminathan, A..

4 recordsLinked to original sources

bioRxiv2018

Widespread cytoplasmic polyadenylation programs asymmetry in the germline and early embryo

BACKGROUNDThe program of embryonic development is launched by selective activation of a silent maternal transcriptome. In Caenorhabditis elegans, nuclei of the adult germline are responsible for the synthesis of at least two distinct mRNA populations; those required for housekeeping functions, and those that program the oocyte-to-embryo transition. We mapped this separation by changes to the length-distribution of poly(A)-tails that depend on GLD-2 mediated cytoplasmic polyadenylation and its regulators genome-wide.\n\nRESULTSMore than 1000 targets of cytoplasmic polyadenylation were identified by differential polyadenylation. Amongst mRNA with the greatest dependence on GLD-2 were those encoding RNA binding proteins with known roles in spatiotemporal patterning such as mex-5 and pos-1. In General, the 3 UTR of GLD-2 targets were longer, contained cytosine-patches, and were enriched for non-standard polyadenylation-motifs. To identify the deadenylase that initiated transcript silencing, we depleted the known deadenylases in the gld-2(0) mutant background. Only the loss of CCF-1 suppressed the short-tailed phenotype of GLD-2 targets suggesting that in addition to its general role in RNA turnover, this is the major deadenylase for regulatory silencing of maternal mRNA. Analysis of poly(A)-tail length-change in the embryo lacking specific RNA-binding proteins revealed new candidates for asymmetric expression in the first embryonic divisions.\n\nCONCLUSIONThe concerted action of RNA binding proteins exquisitely regulates GLD-2 activity in space and time. We present our data as interactive web resources for a model where GLD-2 mediated cytoplasmic polyadenylation regulates target mRNA at each stage of worm germline and early embryonic development.

developmental biology↗

bioRxiv2018

Single day construction of multi-gene circuits with 3G assembly

The ability to rapidly design, build, and test prototypes is of key importance to every engineering discipline. DNA assembly often serves as a rate limiting step of the prototyping cycle for synthetic biology. Recently developed DNA assembly methods such as isothermal assembly and type IIS restriction enzyme systems take different approaches to accelerate DNA construction. We introduce a hybrid method, Golden Gate-Gibson (3G), that takes advantage of modular part libraries introduced by type IIS restriction enzyme systems and isothermal assembly s ability to build large DNA constructs in single pot reactions. Our method is highly efficient and rapid, facilitating construction of entire multi-gene circuits in a single day. Additionally, 3G allows generation of variant libraries enabling efficient screening of different possible circuit constructions. We characterize the efficiency and accuracy of 3G assembly for various construct sizes, and demonstrate 3G by characterizing variants of an inducible cell-lysis circuit.

bioengineering↗

bioRxiv2017

Quantitative Modeling of Integrase Dynamics Using a Novel Python Toolbox for Parameter Inference in Synthetic Biology

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, partioning models, and Bayesian inference - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML) or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the packages simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/biocircuits/bioscrape) along with more detailed documentation and examples.

synthetic biology↗

bioRxiv2017

Population regulation in microbial consortia using dual feedback control

An ongoing area of study in synthetic biology has been the design and construction of synthetic circuits that maintain homeostasis at the population level. Here, we are interested in designing a synthetic control circuit that regulates the total cell population and the relative ratio between cell strains in a culture containing two different cell strains. We have developed a dual feedback control strategy that uses two separate control loops to achieve the two functions respectively. By combining both of these control loops, we have created a population regulation circuit where both the total population size and relative cell type ratio can be set by reference signals. The dynamics of the regulation circuit show robustness and adaptation to perturbations in cell growth rate and changes in cell numbers. The control architecture is general and could apply to any organism for which synthetic biology tools for quorum sensing, comparison between outputs, and growth control are available.

synthetic biology↗