![Biological Sequences
Many Important Biological Molecules are
Polymers.
Thus representable as a sequence of discrete
symbols.
Sequence M = [m1, m2, …, mn] where:
DNA mi { A, T, G, C }
RNA mi { A, U, G, C }
Protein mi { G, A, V, L, I, P, S, C, T, M, D,
E, H, K, R, N, Q, F, Y, W }](https://image.slidesharecdn.com/the-role-of-machine-learning-in-modelling-the-cell3526/85/The-role-of-machine-learning-in-modelling-the-cell-6-320.jpg)
The role of machine learning in modelling the cell
- 1. The Role of Machine Learning in Modelling the Cell. John Hawkins ARC Centre for Complex Systems University of Queensland Australia
- 2. Overview of Talk Overview of cell biology Modelling the cell Subcellular localisation signals Machine Learning in General Neural networks Feed Forward versus Recurrent
- 3. Cell Biology – Quick and Dirty Membrane bound Organelles Nucleus DNA -> RNA -> Protein Transport, e.g. Mitochondria Peroxisome Modification, e.g. Disulphide Bond Formation Glycosylation
- 4. Cell Feedback At a particular time point a set of genes will be expressed. These do not remain constant, instead the emerging picture is that There is some essential cycle of gene expression With a capacity to indulge in alternative pathways of expression under external stimulus. The pattern of expression is
- 5. Modelling the cell Ideally we would like to model the cell from the level of a 3D physical simulation. Currently this is infeasible So numerous approaches are taken to form abstractions Gene Regulatory Networks Differential equation models of particular pathways Machine learning models of particular
- 6. Biological Sequences Many Important Biological Molecules are Polymers. Thus representable as a sequence of discrete symbols. Sequence M = [m1, m2, …, mn] where: DNA mi { A, T, G, C } RNA mi { A, U, G, C } Protein mi { G, A, V, L, I, P, S, C, T, M, D, E, H, K, R, N, Q, F, Y, W }
- 7. Information Content How much information in a linear sequence? Two crucial elements to function Physical/chemical properties Molecular shape Each residue has well known properties Denaturation. (Anfinsen,1973). Sequence defines arrangement of chemical properties which in turn defines folding.
- 8. Biological Patterns Motifs – General term for patterns Numerous Definitions & Visualisations PROSITE Patterns – Regular Expression PROSITE Profiles – Probability Matrix LOGOs
- 9. Peroxisomal Localisation Predominantly controlled by a C- terminal sequence called the PTS1 signal. Roughly 12 residues long Known dependencies between locations
- 10. Nuclear Export Some proteins move continuously between the nucleus and cytoplasm of the cell. Either as: Transporters Regulators
- 11. Machine Learning Requires a set of examples, with Raw input, sequences data, and Known classes that the machine should predict In essence Function Approximation Start with a General parametrised function over the input data Adjust the parameters until the output of the function is a good approximation to the known classes of the examples.
- 12. Bias Bias is generally unavoidable (Mitchell, 1980) Three Sources of Bias Input Encoding Function Structure (Architecture) Parameter adjustment algorithm (learning)
- 13. Neural Networks Graphical Model consisting of layers of nodes connected by weights Feed forward neural networks Fixed input window Signal propagates in a single pass through the layers Recurrent Neural Networks Signal processed in parts Recurrent connections maintain a memory state Output generated after processing the last piece of the input signal
- 14. Simple Neural Networks F F N N O h = S (W1 ∙ I1 + W2 ∙ I2 + b) R N N O h = S (W1 ∙ I2 + W2 ∙ S (W1 ∙ I1 + b ) + b )
- 15. RNNs in Bioinformatics Bi-Directional RNN
- 16. Applications We have applied these techniques to Subcellular Localisation to Endoplasmic Reticulum Mitochondria Chloroplast Peroxisome http://pprowler.imb.uq.edu.au Working with whole genome data and wet lab biologists to use these tools for data mining.
- 17. The End… ?