Synthetic neuromorphic computing in living cells

Rizik, Luna; Danial, Loai; Habib, Mouna; Weiss, Ron; Daniel, Ramez

doi:10.1038/s41467-022-33288-8

Cited by 34 publications

(39 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proposition 2. For any initial condition and constant positive inputs, and for all values of the positive parameters, the state of system (1)-( 3) (equivalently, of (10)-( 12)) converges to the affine variety (20). Then, for any initial state in this variety, the system state structurally converges to the (unique) equilibrium.…”

Section: Stability Analysismentioning

confidence: 99%

Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers

Samaniego,

Wallace,

Blanchini

et al. 2024

Preprint

View full text Add to dashboard Cite

The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks-capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification.

show abstract

Section: Stability Analysismentioning

confidence: 99%

Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers

Samaniego,

Wallace,

Blanchini

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Model-guided approaches have been developed to create complex genetically encoded circuits, allowing for the design and prediction of dynamic signal processing in engineered cell populations. − Numerous types of protein-based and RNA-based circuitry have been employed to construct these synthetic regulatory networks and compute Boolean logic in cells, such as transcription factors, − protein–protein interactions, − sequestration approaches, , RNA-based regulation (e.g., trans-acting RNA, toehold switches, STARs), , CRISPR interference systems, − and riboswitches. − This has generated a large set of genetic parts from which to choose when designing a circuit. However, applying the genetic circuit components used in previous works in a different microorganism, such as a probiotic bacterium, and integrating them with other genetic parts and sensors for the new host is often not straightforward and can require significant engineering to reacquire functionality.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

2023

View full text Add to dashboard Cite

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

show abstract

“…The non-trivial properties of biological systems are determined mostly by the way the elements of those systems combine to form hierarchal structures (Hopfield 1982, 1984, Ma et al 2014, Ma and Tang 2015, El-Gaby et al 2021. In tissues and organs like the heart, the cardiovascular tissue, the brain and spinal cord, the kidney, the functions of the systems depend less on the specialization of individual cells and more on the interaction among cells (Bhalla and Iyengar 1999, MacLellan et al 2012, Souza and do Amaral 2019, Artime and De Domenico 2022, Rizik et al 2022. Biological tissues and organs are therefore complex systems.…”

Section: Introductionmentioning

confidence: 99%

The effective enhancement of information in 3D small-world networks of biological neuronal cells

Gentile

2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

The cardiovascular system, the kidney, or the brain, are examples of complex systems - where the properties of the systems arise because of the layout of cells in those systems. One way to characterize systems is using networks, where elements and interactions of the systems are represented as nodes and links of a graph. Network’s topology can be, in turn, measured by the small-world coefficient. Small world networks feature increased clustering and shorter paths compared to random or periodic networks of the same size. This suggests that systems with small world attributes can also efficiently transport signals, nutrients, or information within their body. While several reports in literature have illustrated that real biological systems are small-world, yet little is known about how information varies as a function of the small-world-ness (sw) of three dimensional graphs. Here, we used a model of the brain to estimate quantitatively the information processed in 3D networks. In the model, nodes of the graph are neuronal units capable to receive, integrate and transmit signals to other neurons of the system in parallel. The information encoded in the signals was then extracted using the techniques of information theory. In simulations where the topology of networks of 400 nodes was varied over large intervals, we found that in the 0-9 sw range information scales linearly with the small world coefficient, with a five-fold increase. Results of the paper and review of the existing literature on model organisms suggest that a small-world architecture may offer an evolutionary advantage to biological systems.

show abstract

Synthetic neuromorphic computing in living cells

Cited by 34 publications

References 75 publications

Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers

Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

The effective enhancement of information in 3D small-world networks of biological neuronal cells

Contact Info

Product

Resources

About