Postdoctoral Positions Available
The Human Metabolomics Project (HMP) http://www.metabolomics.ca is a
multi-year, multi-million dollar project (sponsored by Genome Canada), whose goal
is identifying, characterizing and quantifying the entire human metabolome: all
endogenous metabolites that can be found in the body at concentrations greater
than one micromolar.
We invite applications for a postdoctoral fellow position to work with us in this
exciting undertaking!
This position will be at the University of Alberta (Edmonton), and will involve very
close ties with the Alberta Ingenuity Centre for Machine Learning (AICML)
http://www.aicml.cs.ualberta.ca.
We are seeking applicants that have strong
mathematical and statistical skills, knowledge of machine learning and a background
in bioinformatics. (See webpage for a sampling of the bioinformatics challenges.)
The PDF will be encouraged to participate in the other bio- and medical-informatics
projects at the University of Alberta, the Cross Cancer Institute and the Institute for
Biocomplexity and Informatics http://www.ibi.ucalgary.ca at the University of
Calgary.
Appointments can start as soon as is practicable, but start dates are flexible.
All qualified candidates are encouraged to apply; however, Canadians and permanent
residents will be given priority.
Please send CV, a statement of research interests, and the names, e-mail addresses,
postal addresses and phone numbers of three references to:
Lori Querengesser
Dept of Computing Science
The University of Alberta
Edmonton, Alberta, Canada T6G 2E8
loriq@cs.ualberta.ca
780-492-5009 / 780-492-1071 (FAX)
The University of Alberta, and the Human Metabolome Project collaboratively
respect, appreciate, and encourage diversity.
Bioinformatics Challenges
Some 15 years ago, a group of bright and ambitious
researchers had this radical idea that it would be
possible to determine the entire 3.2B nucleotide human
genome. They pushed and pushed... and by late 2003,
it was a reality!
That was the genome -- the contents of our DNA
blueprints. Over the last few years, many researchers
have been exploring the human proteome, studying the
proteins that do the work in our cells. And there has
been tremendous progress here as well.
These proteins work on metabolites -- small molecules
(sugars, amino acids, bases) -- that are used, modified
and transformed within the cell, and then often
excreted, to appear in our bodily fluids (blood, urine,
CSF) Medical practice often involves testing these
bodily fluids for certain metabolites that are known to
relate to the person's disease state.
But biological and medical science knows only a
fragment of the relevant information: just a small
percentage of the human metabolome, and even less of
the disease to metabolite connections, and much less
of the quantitative facts.
We are now looking for a postdoc, to help us uncover
the
Complete Human Metabolome,
as part of a multi-year multi-million dollar project called
the Human Metabolome Project; see Job Posting.
You will work with a large collection of outstanding
researchers, including world-class biologists, chemists
and biochemists (working in NMR and MassSpec,
synthesis) and clinical researchers as well as a number
of bioinformaticians. We also have a programming staff
to further help translate your ideas into deployed code.
Below are some of the fascinating tasks associated with
this project:
1. Predict biotransformations
There are many known reactions that happen to
compounds within our bodies, including hydroxylation,
amidation, carboxylation and a host of other reactions
that transform one (drug or xenobiotic) compound to
some other chemical. While we typically know
necessary conditions for these transformations we do
not always have sufficient conditions, which means we
won't know whether this transformation will actually
happen, or not.
This subproject involves developing algorithms that can
predict whether a specific biotransformation will, or will
not, occur, based on properties of the specific
compound, and perhaps other information about the
person. We anticipate a large learning component here.
23. Predict chemical properties
The Human Metabolite Database will eventually list
several hundred properties of each of 1500-ish
metabolites -- Boiling point, Melting point, Solubility,
LogP, pKa, ... Many of these properties are known and
appear in the literature. But not all. It would be useful to
predict the properties in general, based on the chemical
structure and perhaps other properties of the
metabolite. We anticipate a large machine learning and
artificial intelligence component here.
3.
Predict
physiological
properties
physiological locations of metabolites
and
Recall that we only know a fraction of the human
metabolome and only a smaller fraction of their
important properties, including their locations and
typically concentrations. We would like to predict the
concentrations and locations of hundreds of lesserknown compounds, in blood, CSF, cells, organs and
other tissues, based on a classifier learned from several
hundred well-studied compounds. These predictions
will help experimentalists narrow down their search,
and reduce their efforts in looking for compounds in
specific tissues, organs or fluids. Once again we
anticipate a large learning component here.
4. Predicting pathways and the consequences of
mutations to pathways
Only a portion (1/4) of the metabolome has known
metabolic
enzymes
associated
with
specific
metabolites. We would like use what is known about the
existing metabolites and well-understood metabolic
pathways, to predict the pathways/enzymes associated
with these new or poorly characterized metabolites.
Similarly, the accumulation of certain metabolites in
blood, urine or CSF is often an indication of a
physiological problem or genetic mutation to one of the
enzymes involved in one or more pathways. We would
like to develop methods of solving the so-called clinical
"inverse" problem: Given a set of abnormal metabolites
or their abnormal concentrations, determine which
genes and which pathways have been disrupted (i.e.,
identify the genetic cause of these abnormalities).
Today, these problems are usually "solved" by hand
with physicians and chemists looking through tables
and charts, and scanning the literature. We believe
there are more efficient, electronic or computational
methods that could solve these problems which could
potentially lead to new disease or novel genetic
insights. Machine learning, graph analysis, pathway
analysis and metabolic simulation would all be required
in this work.
5. Text mining
When the human genome project began, there were a
number of available electronically datasets that
succinctly summarized many relevant information.
Unfortunately, that is not true with metabolites, today.
This often means professional researchers must spend
hours to days combing the literature by hand to find the
required information.
This subproject involves developing algorithms that can
scan text (PubMed abstracts, full articles, or even
textbooks) for relevant information. We anticipate a
large machine learning component here.