Biology subjects

Bonnerjee, D.

Publications and source records attributed to Bonnerjee, D..

3 recordsLinked to original sources

bioRxiv2021

A Synthetic Genetic Reversible Feynman Gate in a Single E.coli Cell and its Application in Bacterial to Mammalian Cell Information Transfer

Reversible computing is a nonconventional form of computing where the inputs and outputs are mapped in a unique one-to-one fashion. Reversible logic gates in single living cells have not been demonstrated. Here, we created a synthetic genetic reversible Feynman gate in a single E.coli cell. The inputs were extracellular chemicals, IPTG and aTc and the outputs were two fluorescence proteins EGFP and E2-Crimson. We developed a simple mathematical model and simulation to capture the essential features of the genetic Feynman gate and experimentally demonstrated that the behavior of the circuit was ultrasensitive and predictive. We showed an application by creating an intercellular Feynman gate, where input information from bacteria was computed and transferred to HeLa cells through shRNAs delivery and the output signals were observed as silencing of native AKT1 and CTNNB1 genes in HeLa cells. Given that one-to-one input-output mapping, such reversible genetic systems might have applications in diagnostics and sensing, where compositions of multiple input chemicals could be estimated from the outputs.

synthetic biology↗

bioRxiv2021

A single layer artificial neural network type architecture with molecular engineered bacteria for complex conventional and reversible computing

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created molecular-devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such molecular devices, we established a set of rules by corelating truth tables, mathematical equations of ANN, and molecular-device design, which unlike molecular computing, does not require circuit diagram and the equation directly correlates the design of the molecular-device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in new platform for biomolecular computing, reversible computing and in transforming living cells as ANN-enabled hardware.

synthetic biology↗

bioRxiv2021

Engineered Bacteria Computationally Solve Chemically Generated 2X2 Maze Problems

Maze generating and solving are challenging problems in mathematics and computing. Here we generated simple 2X2 maze problems applying four chemicals and created a set of engineered bacteria, which in a mixed population worked as a computational solver for any such problem. The input-output matrices of a mathematical maze were mapped through a truth table, where the 1 and 0 logic values of four chemical inputs determined the sixteen different 2X2 maze problems on a chemical space. Our engineered bacteria, which consisted of six different genetic logic circuits and distributed among six cell populations processed the chemical information and solved the problems by expressing or not expressing four different fluorescent proteins according to the input-output matrices. The three available solutions were visualized by glowing bacteria and for the thirteen cases, where there was no solution no bacteria glowed. This work may have significance in cell based computing and synthetic biology.

synthetic biology↗