Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Associative Learning in Single Cells QuickTime™ and a TIFF (Uncomp resse d) de com press or are nee ded to s ee this picture. Chrisantha Fernando & Jon Rowe1 Systems Biology Centre, 1School of Computer Science, University of Birmingham, Edgbaston, UK Introduction Associative learning in biology is not confined to neurons. For example the Paramecium caudatum (a single celled organism common in pond water) can be classically conditioned (Hennessey, 1979). A Potential Medical Application A Bacterial Gene Circuit Design More Realistic Models.. As a proof of concept, we developed designs to construct a 3-gene circuit on a bacterial plasmid. This should will bacteria to learn to associate two stimuli. reveal similar problems to when the Hebb rule is applied to neurons, e.g. saturation of output activity. Also we must deal with the persistence of memory across bacterial generations. Crucial features for a functional circuit are as follows… Reducing the bystander effect in chemotherapy 1. P must bind linearly without saturation to the promoters of Wi genes, for Wi to be produced proportional to the product [P]x[Ui]. QuickTime™ and a TIFF (U ncompressed) decompressor are needed to see this picture. 3. The gain in Wj when Ui +Uj are paired must be greater than the gain in Wj with 2Uj, if Wj > Wi initially. We show that gene circuits can carry out associative (Hebbian) learning in individual cells. One application is in gene therapy, where a gene learns under what conditions to express itself, to suit the patient it is in. Gene learning may be supervised or unsupervised. The engram is inherited epigenetically, e.g. as a transcription factor or phosphorylated kinase concentration. 2. For rapid learning, the promoters must be strong, thus some leakiness is likely, so [Wi] will be non-zero, even when Ui = 0. The circuit consists of transcription factor proteins. U1 and U2 represent inputs (repressors), P represents the output (activator), and W1 and W2 represent the weights (activators). An inducer acting on Ui to lift its repression, allows synthesis of P at rate UiWi. This is implemented by a simple cooperative AND-gate promoter (Buchler, 2003) P also positively feeds back, allowing Wi to be increased in proportion to WiP. W1 and W2 store the memory trace by decaying slowly. What is Hebbian Learning? Conclusion Hebb (1949) proposed a neural mechanism to explain classical conditioning. Variants of Hebbian learning are responsible for LTP, auto-associative memory, and self-organized map formation in cortex. Using a model similar to that of Elowitz and Leibler (2000) we see that the gain in response to U2 (red) over one paired UCS + CS trial is reduced. There is some response to U2 even before pairing of CS + UCS. Promoters are twice as strong as in E&L, repression and depression is complete, TFs bind with Hill coefficient n = 2. Synaptic strength is increased in proportion to the correlated firing of the post- and the pre-synaptic neuron. Information is encoded as interaction strengths between cells forming a neural network. Task A Task B1 Modelling suggests it will be possible (using synthetic biology techniques) to construct a gene circuit capable of associative learning within a bacterial plasmid. If Hebbian learning can be ported to the intra-cellular realm, then the scope of application for existing algorithms from neural networks and pattern classification is extended, following which, many potential medical applications suggest themselves. References & Acknowledgements The principle can be extended to any dynamical system of the form… xi dv = - v + w$u dt xw dw = u v dt Several ODE models of the above circuit were produced, the simplest is shown above. W1 starts high, so U1 is the unconditioned stimulus (derepressed form) After pairing of U1 and U2, the circuit responds to U2 alone, whereas before pairing there was no response to U2 presented alone. The circuit learns to associate U1 and U2. Task A. P output for various degrees of correlation between two Poisson spike train inputs encoded as [U1] and [U2], against U2 decay rate. Task B1 P output when two spikes are input, spike U2 at time t = 20, and spike U1 at t = Scale.i , i = {0, 40}. Task B2. As above Scale = 32, for varying P Kd. Hennessey, T. M., W. B. Rucker, and C. G. McDiarmid. "Classical Conditioning in Paramecia." Animal Learning & Behavior (1979) 7:417-23. Donald Olding Hebb, “The Organization of Behaviour” ,(1949) Nicolas E. Buchler, Ulrich Gerland, and Terence Hwa. “On Schemes of Combinatorial Transcription Logic” PNAS (2003), 100(9):5136-5141 Elowitz, M.B, and Leibler, S. “A synthetic oscillatory network of transcriptional regulators.” Nature (2000), 403:335-8. Task B2 Thanks to Lewis Bingle, Anthony Liekens, Dov Stekel, and the ESIGNET 6th Framework Grant for Cell Signaling Network Research.