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…
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