Cellular Networks

Cell 1 Cell 2

Cell 3 Cell 4

Cell 5 Cell 6

R

F

R

Cell 1 Cell 2

Cell 3 Cell 4

Cell 5 Cell 6

R

F

R

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

Changing connection strength

Cell 1 Cell 2

Cell 3

33% 33%

33%

Cell 1 Cell 2

Cell 3

50% 27%

33%

Both connections of equal strength Connection between cell1 and cell3 is stronger

Graded response to input

Cell 2 produces output when it receives key 2

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

Output product

Cell 2

Cell A Cell B

Key 2

[[Pretend graph of output is here]]

Graded response to input

Cell 2 produces output when it receives key 2

Output product

Cell 2

Cell A Cell B

Key 2 Key2In 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]]

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

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

Back Propagation Neural Network

Input1 Input2 Input3

Node1 Node2 Node3

Node4 Node5 Node6

Error1 Error2 Error3

Input1 Input2 Input3

Node1 Node2 Node3

Node4 Node5 Node6

Error1 Error2 Error3

Input signals propagate forward increasing activity, both positive and negative

Error signals propagate proportionally backwards returning activity to 0

General Node Design

Outputter[send the down stream sig to first out]

LockN_out1(keyN+R)---------

[send the error signal upstream]LockN_1_error_pos(keyN_error_pos)LockN_1_error_neg(keyN_error_neg)

--------[change the output of this signal]

LockN_1_error_pos(?gent?)LockN_1_error_neg(?ccd?)

Summation[Sum the inputs]

LockN(keyN_out1)...

LockN(keyN_outK)---------------

[propgate positve error to all input]LockN_error_pos(key_input_1_neg)

…LockN_erro_pos(key_input_M_neg)

----------------[propagate neg error to all input]

LockN_error_neg(key_input_1_neg)...

LockN_error_neg(key_input_M_neg)

KeyN_out KeyN_out

NodeN

KeyN+R KeyN+J

Outputter[send the down stream sig to Wth output]

LockN_outM(KeyN+J)---------

[send the error signal upstream]LockN_M_error_pos(keyN_error_pos)LockN_M_error_neg(keyN_error_neg)

---------[change the output of this signal]

LockN_M_error_pos(?gent?)LockN_M_error_neg(?ccd?)

KeyN_error_[pos||neg]KeyN_error_[pos||neg]

keyN_M_error_[pos||neg]Key_N_1_error[pos||neg]

Receive input from many inputs, send output to many outputs, relay error from many outputs to many inputs

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