Decarbonising Buildings: Making a net-zero built environment a reality
Professional Development Presented to ACS Student Group Oct 16, 2013
1. urp.ucsd.edu
From B Student to Associate Vice
Chancellor & Professor
Philip E. Bourne
pbourne@ucsd.edu
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2. Lesson 1 Change is Good
• My high school
teacher Mr. Wilson
said I would be a
failure at chemistry
• My PhD is in
chemistry
• The opportunity to
live in different
places shaped my
life
• Good friends are
forever
4. PhD – The Molecular Basis of
Cancer Treatment
Lesson 3 See things for what they are
• I thought I was at
the center of the
scientific universe
• I later discovered I
was actually in
deep space
5. Lesson 4: Follow Your Heart
• Your head will tell you stuff
• Your heart will tell you something different
• Follow your heart
Circa 1974
6. Postdoctoral Work – The
Molecular Basis of How the Body
Works
Lesson 5 Learn to live with regret
• Regrets: never
learnt another
language
12. Got Involved with the The Human Genome –
Was Only Possible by Applying Computers to
Problems in Biology
• Took at least 10 years
and ~$1Bn
• Biology’s equivalent of
landing on the moon
• We now have
thousands of genomes
• $50 genome is upon us
13. Came to UCSD to Apply Computers
to Big Biological Problems
• Possibly the best place in the
world to do computational
biology
14. Fell in Love with the Data Problem
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15. Number of released entries
Proteomics Data
Its Not Just About Numbers its About Complexity
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The Omics Revolution
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Year
15
Courtesy of the RCSB Protein Data Bank
17. 2005 - Started a New Journal to
Support My Field – Led to a Passion for
Open Access
18. Josh Sommer and Chordoma Disease
http://fora.tv/2010/04/23/Sage_Commons_Josh_Sommer_Chordoma_Foundation#fullprogram
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19. Josh Sommer – A Remarkable Young Man
Co-founder & Executive Director the Chordoma Foundation
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http://sagecongress.org/Presentations/Sommer.pdf
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Motivation
20. Chordoma
• A rare form of brain
cancer
• No known drugs
• Treatment – surgical
resection followed by
intense radiation
therapy
http://upload.wikimedia.org/wikipedia/commons/2/2b/Chordoma.JPG
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27. Lesson 7 – Go After the Big
Problems
1.
2.
3.
4.
5.
August 14, 2009
Can we improve how science
is disseminated and
comprehended?
What is the ancestry of the
protein structure universe and
what can we learn from it?
Are there alternative ways to
represent proteins from which
we can learn something new?
What really happens when we
take a drug?
Can we contribute to the
treatment of neglected
{tropical} diseases?
29. The Worst of Times
Source: http://www.pharmafocusasia.com/strategy/drug_discovery_india_force_to_reckon.htm
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30. Here is One Reason Why
• Tykerb – Breast cancer
• Gleevac – Leukemia, GI
cancers
• Nexavar – Kidney and liver
cancer
• Staurosporine – natural product
– alkaloid – uses many e.g.,
antifungal antihypertensive
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Collins and Workman 2006 Nature Chemical Biology 2 689-700
31. Bioinformatics – Reverse Engineering
Drug Discovery
Characterize ligand binding
site of primary target
(Geometric Potential)
Identify off-targets by ligand
binding site similarity
(Sequence order independent
profile-profile alignment)
Extract known drugs
or inhibitors of the
primary and/or off-targets
Search for similar
small molecules
…
Dock molecules to both
primary and off-targets
Statistics analysis
of docking score
correlations
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Xie and Bourne 2009
Bioinformatics 25(12) 305-312
32. The Problem with Tuberculosis
•
•
•
•
One third of global population infected
1.7 million deaths per year
95% of deaths in developing countries
Anti-TB drugs hardly changed in 40
years
• MDR-TB and XDR-TB pose a threat to
human health worldwide
• Development of novel, effective and
inexpensive drugs is an urgent priority
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33. Map 2 onto 1 – The TB-Drugome
http://funsite.sdsc.edu/drugome/TB/
Similarities between the binding sites of M.tb proteins (blue),
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and binding sites containing approved drugs (red).
34. From a Drug Repositioning Perspective
• Similarities between drug binding sites and
TB proteins are found for 61/268 drugs
• 41 of these drugs could potentially inhibit
more than one TB protein
chenodiol
testosterone
ritonavir
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conjugated
estrogens &
methotrexate
raloxifene
levothyroxine
alitretinoin
No. of potential TB targets
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37. What Would I Work On If Starting
Today?
• Neuroinformatics
• Translational research – interdisciplinary, lab
to market
• Science advocacy
• Anything big data
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Tuberculosis, which is caused by the bacterial pathogen Mycobacterium tuberculosis, is a leading cause of mortality among the infectious diseases. It has been estimated by the World Health Organization (WHO) that almost one-third of the world's population, around 2 billion people, is infected with the disease.
Every year, more than 8 million people develop an active form of the disease, which claims the lives of nearly 2 million. This translates to over 4,900 deaths per day, and more than 95% of these are in developing countries.
Despite the current global situation, antitubercular drugs have remained largely unchanged over the last four decades. The widespread use of these agents has provided a strong selective pressure for M.tuberculosis, thus encouraging the emergence of resistant strains.
Multidrug resistant (MDR) tuberculosis is defined as resistance to the first-line drugs isoniazid and rifampin. The effective treatment of MDR tuberculosis necessitates long-term use of second-line drug combinations, an unfortunate consequence of which is the emergence of further drug resistance.
Enter extensively drug resistant (XDR) tuberculosis - M.tuberculosis strains that are resistant to both isoniazid plus rifampin, as well as key second-line drugs. Since the only remaining drug classes exhibit such low potency and high toxicity, XDR tuberculosis is extremely difficult to treat.
The rise of XDR tuberculosis around the world imposes a great threat on human health, therefore reinforcing the development of new antitubercular agents as an urgent priority.
Very few Mtb proteins explored as drug targets
Multi-target therapy may be more effective than single-target therapy to treat infectious diseases
Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics.
GSMN-TB: Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/sbs.surrey.ac.uk/tb)
849 reactions, 739 metabolites, 726 genes
Can optimize the model for in vivo growth
Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth)