1. Neural cell engineering for building and understanding 'brain like' neural microcircuits.

We are building synthetic genetic circuits for direct conversion of various types human cells to neural like cells. Our goal is to build 'brain like' neural microcircuits (brain on a chip) to i) understand how neural microcircuit in brain works and ii) build human designed functions with the neural microcircuits. 

2. Biological computing  and artificial intelligence (AI) with synthetically engineered bacteria.

We use molecular biology parts as the hardware to implement computational devices in living cells. Our lab is developing new rules and systems to build cell based 'computers', and 'AI'. Such cellular computers take their own decision and perform accordingly. Those systems have tremendous potential in bio-robotics, novel therapeutics, programmed materials and space technology.

Can we make bacteria as artificially intelligent (AI)?

Our recent work shows that may be possible. We have created the first artificial neural network (ANN) with generically engineered bacteria, where engineered bacteria work as artificial neuro-synapses and demonstrated complex computation. This work has significance in creating new technology platform for transforming engineered biological cells as ANN enabled hardware. (Sarkar et al, Chem Sci, 2021)     https://pubs.rsc.org/en/content/articlelanding/2021/sc/d1sc01505b (Front Cover Page Article) 

This work is highlighted in Nature India ( Tweaking Bacterial Cells to Make Artificial Neural Network)  https://www.nature.com/articles/d44151-021-00081-3

A double Feynman Gate with Cellular Artificial Neural Networks Wetware

Richard Feynman once suggested that biological cells could be a choice for reversible computing due to their very low energy budget compared to an electronic computer. We built an artificial neural network using genetically engineered E. coli, such that they could perform complex computations and form a double Feynman logic gate. Double Feynman gate is a form of reversible computing, which is different from conventional computing.There are five sets of genetically engineered bacteria that, in a test tube, work as a biocomputer and form artificial neural networks. The biocomputer takes three chemicals as input signals and produces two light-emitting proteins as output, such that bacteria start glowing. Thus, based on the chemical signal present in the environment, the population of bacteria would decide which protein needed to be expressed. These bacterial artificial neural networks decide and work as a double Feynman gate (Rajkamal Srivastava and Sangram Bagh* (2023) , A Logically Reversible Double Feynman Gate with Molecular Engineered Bacteria Arranged in an Artificial Neural Network-Type Architecture, ACS Synthetic Biology, 2023, 12, 1, 51–60 (Front Cover Page Article)  

Can engineered bacteria add and subtract numbers? We are currently expanding the applicability of the system such that  engineered bacteria can add and subtract numbers.

Can we build a biocomputer with engineered bacterial community to solve mathematical problems?

We synthetically engineered a set of bacteria, which work as a distributed biocomputer to solve a set of computational/mathematical maze problems. This is the first time we are showing engineered bacteria can solve abstract mathematical/computational problems. This work has significance in creating new technology for cellular cryptography and steganography (Sarkar, et al ACS Syn Bio, 2021). The work has been featured in MIT Technology Review. https://www.technologyreview.com/2021/11/09/1039107/e-coli-maze-solving-biocomputer/

 Complex electronic analogous genetic device in single bacterial cell to process higher order information

We develop strategies and methods to create electronic analogous complex synthetic genetic circuits within single bacterial cell to perform higher order information processing. Our recent work on creating 3-input-3-output integrated circuits has been published as a front cover page article in Bioconjugate Chemistry, 2020 https://pubs.acs.org/doi/abs/10.1021/acs.bioconjchem.9b00517 (Front Cover Page Article)

3.  Creating smart therapeutics and novel cellular sensors with synthetic genetic circuits.

Cancer gene therapy with bio-bots 

We are developing a cellular robotics platform for cancer gene therapy. We reprogram microbial cells  as 'robots' (biobots) to sense varieties of extracellular signals similar to a cancer microenvironment, to decode those signals, to co-ordinate with other cells and decide to invade cancer cells. We apply those 'bio-bots' for programmed delivery of therapeutic genes and RNAi into the  cancer cells.  See our recent publication (Srivastava R. et al, ACS Synthetic Biology,  2022, 11, 3, 1040–1048  https://pubs.acs.org/doi/abs/10.1021/acssynbio.1c00392. 

This work is highlighted in Nature India (Engineered Bacteria Can Silence Genes in Cancer Cells)    https://www.nature.com/articles/d44151-022-00032-6

 Novel cellular sensors for Space Synthetic Biology

How about a set of synthetic genetic systems inside appropriate organisms, specially designed for microgravity conditions for potential uses in space stations and during exo-planet journeys? 

Synthetic Biology has been proposed as a key component for regenerative and sustainable closed loop solution in long-term space habitation. Here we have developed the first microgravity biosensor using synthetic genetic systems inside appropriate organism. Our work is the first to integrate microgravity as a physical signal within cellular processes in a human designed way (Mukhopadhyay and Bagh, Biosensor and Bioelectronics, 2020).  Our previous publication was featured in Nature India, May,2016 (http://www.natureasia.com/en/nindia/article/10.1038/nindia.2016.72) and Times of india.