The Role of Machine Learning in Modelling the Cell.

John Hawkins

ARC Centre for Complex Systems

University of Queensland

Australia

Overview of Talk

Overview of cell biology Modelling the cell Subcellular localisation signals Machine Learning in General Neural networks

Feed Forward versus Recurrent

Cell Biology – Quick and Dirty

Membrane bound Organelles

Nucleus DNA -> RNA ->

Protein Transport, e.g.

Mitochondria Peroxisome

Modification, e.g. Disulphide

Bond Formation Glycosylation

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 implemented by protein and RNA feedback onto the genes.

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

processes

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 }

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.

Biological Patterns

Motifs – General term for patterns Numerous Definitions & Visualisations

PROSITE Patterns – Regular Expression PROSITE Profiles – Probability Matrix LOGOs

Peroxisomal Localisation

Predominantly controlled by a C-terminal sequence called the PTS1 signal.

Roughly 12 residues long Known dependencies between

locations

Nuclear Export Some proteins move continuously between the

nucleus and cytoplasm of the cell. Either as:

Transporters Regulators

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.

Bias

Bias is generally unavoidable (Mitchell, 1980)

Three Sources of Bias Input Encoding Function Structure (Architecture) Parameter adjustment algorithm (learning)

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

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 )

RNNs in Bioinformatics

Bi-Directional RNN

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.

The End…

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