Cellular Networks

1. Cellular Networks Use locks and keys toghether with R and F conjugation to build feed forward networks of cells

2. Changing connection strength Both connections of equal strength Connection between cell1 and cell3 is stronger

3. Graded response to input In liquid culture of 1/3 cellA 1/3 cellB, 1/3 cell2 expression of cell2’s output is P(cellA conjugating with cell2) Which in a well mixed culture is proportional to the concentrations of cellA and cell2 [[Pretend graph of output is here]] Cell 2 produces output when it receives key 2

4. Graded response to input In liquid culture of 1/3 cellA 1/3 cellB, 1/3 cell2 expression of cell2’s output is Cell2 out: P(CellA OR CellB conjugating with cell2) [[Pretend graph of output is here, higher output than just A alone]] Cell 2 produces output when it receives key 2

5. Inhibitory Signals • [[Name of the protein that turns off the cell pili]] to stop receiving input but still allow output • Digest/Degrade output plasmid • Conditional cell death • RNA based competition for key binding sites

6. What we have • Addressable communication • Hierarchical network architecture • Adjustable connection strengths • Graded aggregate response to input • Inhibitory signals All the components required for a feed forward neural network

7. Back Propagation Neural Network Input signals propagate forward increasing activity, both positive and negative Error signals propagate proportionally backwards returning activity to 0

8. General Node Design Receive input from many inputs, send output to many outputs, relay error from many outputs to many inputs

9. Bacterial Neural Networks • Massively Parallel • Probabilistic • Asynchronous • Continuous time • Can be tied into other pathways in cell or environmental conditions • Highly adaptive, can grow additional nodes • Complex behavior from simple, uniform node design with different lock/keys