Synthetic neuromorphic computing in living cells.
Luna RizikLoai DanialMouna HabibRon WeissRamez DanielPublished in: Nature communications (2022)
Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.
Keyphrases
- artificial intelligence
- living cells
- machine learning
- neural network
- small molecule
- deep learning
- fluorescent probe
- escherichia coli
- decision making
- big data
- induced apoptosis
- single molecule
- healthcare
- cell cycle arrest
- primary care
- working memory
- endoplasmic reticulum stress
- magnetic resonance
- genome wide
- copy number
- quality improvement
- protein protein
- signaling pathway
- magnetic resonance imaging
- cystic fibrosis
- gene expression
- multidrug resistant
- lymph node
- dna methylation
- genome wide identification
- pi k akt
- cerebral ischemia
- brain injury
- klebsiella pneumoniae
- gold nanoparticles
- early stage
- network analysis