UNIVERSITY COLLEGE LONDON
Job Description and Person Specification
PhD Title:
Computational modeling of molecular nexopathies
Department:
Medical Physics and Bioengineering
Centre:
Centre for Medical Image Computing, Translational Imaging Group
Supervisors:
Prof. Sebastien Ourselin (primary), Dr. Jason Warren (secondary)
Duration:
4 years (1 year MRes, 3 year PhD)
Stipend value:
£18,000 per year tax free
Closing date for applications: July 4th, 2014
Project Outline:
Diffusive spread of pathogenic proteins and large-scale brain network disintegration
have emerged as core themes in our understanding of neurodegenerative disease.
This has been driven by recent rapid progress in molecular pathology, connectivitybased neuroimaging techniques and detailed macro-phenotyping of a range of
neurodegenerative diseases. To date, however, these themes have not been
satisfactorily linked together, due largely to a lack of comprehensive models for protein
– network interactions. Such a linkage would have considerable utility for
understanding disease pathophysiology and evaluating candidate biomarkers and
therapies, potentially offering a novel solution to traditional problems of nosology and
syndromic heterogeneity across the diverse spectrum of neurodegenerative disease.
Broadly relevant models have been proposed in allied complex systems (for example,
morphogenesis: Turing 1952, Phil Trans Roy Soc Lond B, 237, 37-72) and a
framework for understanding neurodegenerative diseases as coherent conjunctions of
protein and network properties or ‘molecular nexopathies’ has been proposed (Warren
et al, TINS 2013). This framework remains to be empirically tested.
This PhD project will address this key issue under the rubric of four key hypotheses:
1. Profiles of large-scale brain network disintegration (including highly focal and
asymmetric signatures) arise from specific interactions between pathogenic protein
[neurodegenerative proteinopathies] and intrinsic network morphological
characteristics
2. Particular pathogenic proteins selectively target clustered (shorter-range) or
distributed (longer range) network connections, corresponding to the small world
organisation of brain networks
3. Asymmetry of network damage reflects both i) intrinsic network polarities due e.g. to
protein expression patterns and ii) pathogenic protein intracellular cascades with net
transcellular (e.g., synaptic) final common effects corresponding primarily to loss of
function [‘inhibitory’] vs toxic gain of function [‘excitatory’] – the former tending to
produce graded (strongly asymmetric) damage across the targeted network, the latter
tending to produce more uniform (relatively symmetric) damage across the network
4. The characteristics in (2) and (3) interact initially in local neural circuits but these
interactions scale to large-scale network effects that can be detected using
macroscopic imaging techniques
The PhD programme will have computational network modelling using graph
theoretical techniques as its core methodology and will be structured in four key
phases each broadly addressed to the overarching hypothesis (H1):
1. Creation of artificial neural networks to model local neuronal circuits incorporating the
key properties in (H2) and (H3)
2. Mapping of spatial and temporal profiles of these local networks (circuits) under the
interaction of (H2) and (H3)
3. Determination of scaling rules for network profiles in (II) to large-scale brain networks
4. Comparison of the scaled profiles in (III) with empirically observed cross-sectional and
longitudinal neuroimaging datasets, thereby testing (H4). Genetic dementia cohorts will
represent crucial test cases.
This PhD project will be directly informed by empirical findings in real world
neurodegenerative disease cohorts. It will have clear translational potential to a range of
common and currently immensely disabling diseases. At the same time, it will exploit state of
the art graph theoretical and computational modelling resources within the emerging fields of
neural network biology and human brain connectomics.
The successful PhD candidate will likely have a primary background in computational biology,
but with demonstrated strong interest in applying computational modelling techniques to the
understanding and /or measurement of human neurological disease. They will also hold a first
or upper second undergraduate degree.
To apply for this post please e-mail applications@medphys.ucl.ac.uk with “Computational
modeling of molecular nexopathies” in the subject line. Please include:
1. Cover letter explaining why you feel suitable for this post and background.
2. CV
3. Names and contact details of 2 academic or work referees.
Please note that due to the nature of the funding only those who qualify for UK or EU fees
status can apply.