Engineering Bacteria to Perform Mathematical Computations

Engineering Bacteria to Perform Mathematical Computations

Sub : Sci

Sec : Awareness in IT and Computers

Why in News

• The Saha Institute of Nuclear Physics in Kolkata achieved a significant breakthrough by engineering bacteria to solve mathematical problems. This advancement, led by synthetic biologist Dr. Sangram Bagh and his team, enables bacteria to behave like artificial neural networks, paving the way for future applications in biocomputing.

Bactoneurons as Single-Layered ANNs:

• The primary aim of the research is to engineer bacteria capable of performing abstract mathematical tasks, previously manageable only by humans or traditional computers.
• By introducing “genetic circuits” into bacteria, which are activated by chemical inducers, the researchers transformed these bacteria into biological computing units, or “bactoneurons.”
• When combined in solutions, these bactoneurons acted as a network, akin to artificial neural networks (ANNs), allowing bacteria to perform complex computations.
• Unlike multicellular organisms with specialized neurons, these engineered single-celled bacteria have shown sensitivity and responsiveness akin to intelligent organisms.
• This development challenges traditional definitions of intelligence, as bacteria without complex nervous systems can now tackle tasks such as identifying prime numbers or distinguishing vowels from consonants.
• The team used Escherichia coli as a model, inserting synthetic promoters and transcription factors to form genetic circuits that respond to specific chemical compounds.
• By combining four transcription factors and designing unique promoter sequences, they created diverse feedback and feed-forward mechanisms, allowing complex computations.
• Bacteria were trained to recognize binary input codes, where the presence or absence of specific chemicals represented binary values (1 or 0), akin to voltage states in traditional computing.
• Each engineered bacterial strain functions as a bactoneuron, a processing unit that performs designated tasks based on chemical inputs.
• By combining these bactoneurons, the researchers created bacterial networks capable of performing tasks like determining if a number is prime, or recognizing if a letter is a vowel.
• Each task’s outcome is visualized by fluorescent proteins, with green and red indicating different outputs.

Examples of Bacterial Computations:

• Bacterial computers were programmed to determine if numbers 0-9 were prime by converting these numbers into binary, then presenting the chemicals in a solution based on binary encoding.
• Bacteria could also assess if the square of a number could be expressed as the sum of three factorials, a significant computational feat.
• They solved optimization tasks like calculating the maximum number of sections resulting from a given number of straight cuts on a circular object.

Applications and Future Scope:

• Engineered bacterial computers could lead to advancements in early cancer detection, where bacteria identify molecular changes and signal the presence of cancerous cells.
• Programmable bacteria could revolutionize manufacturing processes by performing specific tasks at the cellular level, potentially reducing reliance on silicon-based traditional computers.
• Bacterial computers could be programmed to detect specific pollutants, signalling when they exceed safe thresholds.

What is an Artificial Neural Network?

• Artificial Neural Networks (ANNs) are computing systems inspired by biological neural networks in the brain, designed to simulate human cognitive functions like learning and problem-solving.
• ANN is inspired by the structure of the human brain, specifically its network of neurons.
• Neurons communicate through synapses, strengthening or weakening connections as new information is learned. Similarly, ANN nodes simulate neurons by adjusting connection strengths based on data input.
• ANNs learn by adjusting the strength of connections between nodes, much like how the brain strengthens connections between neurons when learning new information. This allows the ANN to recognize patterns and make decisions without being explicitly programmed to follow specific instructions.
• ANNs learn by adjusting the weights of connections during training through algorithms like backpropagation, which minimizes errors between predicted and actual outcomes.