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Personalized medicine involves the prescription of specific therapeutics best suited for an individual based on their genetic or proteomic profile. This talk discusses current approaches in drug discovery/development, the role of genetics in drug metabolism, and lawful/ethical issues surrounding the deployment of new health technology. I highlight some bioinformatic roles in the drug discovery process, and discuss the use of semantic web technologies for data integration and knowledge discovery..
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Dumontier::BIOL1010:Towards Personalized Medicine
Towards Personalized
Medicine
Michel Dumontier, Ph.D.
Associate Professor of BioinformaticsDepartment of Biology, Institute of Biochemistry, School of Computer Science
Carleton University
Ottawa Institute for Systems BiologyOttawa-Carleton Institute for Biomedical Engineering
Nov 9, 2009
Dumontier::BIOL1010:Towards Personalized Medicine
Outline
• Personalized Medicine
• Drug Discovery
• Role of Bioinformatics
• Current Research
Dumontier::BIOL1010:Towards Personalized Medicine
“If it were not for the great variability among individuals, medicine might as well be a science and not an art”
Sir William Osler, 1892
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
SNPs – a major source of variation
• Single Nucleotide Polymorphisms (SNPs)– Single base change in DNA
AAGCCTA
AAGCTTA– SNPs arise as a consequence of mistakes
during normal DNA replication
• Average frequency 1/1000bp
Dumontier::BIOL1010:Towards Personalized Medicine
Human Variation• In human beings, 99.9 percent bases are same.• Remaining 0.1 percent makes a person unique.
– Different attributes / characteristics / traits • how a person looks, • diseases he or she develops.
• These variations can be:– Harmless (change in phenotype)– Harmful (diabetes, cancer, heart disease, Huntington's
disease, and hemophilia )– Latent (variations found in coding and regulatory
regions, are not harmful on their own, and the change in each gene only becomes apparent under certain conditions e.g. susceptibility to lung cancer)
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Personalized Medicine : BiDil
• Combination pill containing two medications for heart failure, cardiovascular disease, and/or diabetes.
• Clinical trials did not show overall benefit across entire population.
• Subgroup of patients showed best overall benefit– BiDil approved solely for use in African-descent patients.
Controversial!
Dumontier::BIOL1010:Towards Personalized Medicine
Personalized MedicineThe ability to offer • The Right Drug• To The Right Patient• For The Right Disease• At The Right Time• With The Right Dosage
Genetic and metabolic data will allow drugs to be tailored to patient subgroups
Dumontier::BIOL1010:Towards Personalized Medicine
Benefits of Personalized Medicine
• Better matching patients to drugs instead of “trial and error
• Customized pharmaceuticals may eliminate life-threatening adverse reactions
• Reduce costs of clinical trials by – Quickly identifying total failures– Favourable responses for particular backgrounds
• Improved efficacy of drugs
Dumontier::BIOL1010:Towards Personalized Medicine
PGx• Pharmacokinetics
– What the body does to the drug– dose, dosage regimen, delivery form – Drug fate: Absorption, distribution, metabolism, and elimination
of drugs (ADME)
• Pharmacodynamics– What the drug does to the body– Biochemical and physiological effects of drugs– mechanism of drug action– relationship between drug concentration and effect
• Pharmacokinetics and pharmacodynamics are essential to assess the drug efficacy.
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
PGx + genetics/genomics
• Pharmacogenetics– The effect of genetic variation on drug response.
• Pharmacogenomics– The application of genomics to the study of human
variability in drug response.
• Pharmacogenetics and pharmacogenomics are expected to play an important role in the development of better medicines for populations and targeted therapies with improved benefit/risk ratios for individuals
Dumontier::BIOL1010:Towards Personalized Medicine
Cytochrome P450 EnzymesIn bacteria, fungi, insects, plants, fish, mammalsCatalyze monooxygenation reaction:
RH + 2H+ +O2 + NADPH ROH + H2O + NADP+
Act on:– Endogenous substrates (cholesterol, steroids,
fatty acids)– Exogenous (drugs, food additives,
environmental toxins)Involved in
– Production of steroids– Metabolism of fatty acids, prostaglandins,
leukotrienes, retinoids– Activation or inactivation of therapeutic agents– Enzyme activation/inhibition resulting in drug-
drug interactions, adverse events
Dumontier::BIOL1010:Towards Personalized Medicine
CYP enzymes are involved in the metabolism of clinically important drugs
CYP Enzyme Examples of substrates
1A1 Caffeine, Testosterone, R-Warfarin
1A2 Acetaminophen, Caffeine, Phenacetin, R-Warfarin
2A6 17-Estradiol, Testosterone
2B6 Cyclophosphamide, Erythromycin, Testosterone
2C-family Acetaminophen, Tolbutamide (2C9); Hexobarbital, S- Warfarin (2C9,19); Phenytoin, Testosterone, R- Warfarin, Zidovudine (2C8,9,19);
2E1 Acetaminophen, Caffeine, Chlorzoxazone, Halothane
2D6 Acetaminophen, Codeine, Debrisoquine
3A4 Acetaminophen, Caffeine, Carbamazepine, Codeine, Cortisol, Erythromycin, Cyclophosphamide, S- and R-Warfarin, Phenytoin, Testosterone, Halothane, Zidovudine
S. Rendic Drug Metab Rev 34: 83-448, 2002
Dumontier::BIOL1010:Towards Personalized MedicineS. Rendic Drug Metab Rev 34: 83-448, 2002
Red indicates enzymes important in drug metabolism
Factors Influencing Activity and Level of CYP Enzymes
Nutrition 1A1;1A2; 1B1, 2A6, 2B6, 2C8,9,19; 2D6, 3A4,5
Smoking 1A1;1A2, 2E1
Alcohol 2E1
Drugs 1A1,1A2; 2A6; 2B6; 2C; 2D6; 3A3, 3A4,5
Environment 1A1,1A2; 2A6; 1B; 2E1; 3A3, 3A4,5
Genetic Polymorphism
2A6; 2C9,19; 2D6;
Dumontier::BIOL1010:Towards Personalized Medicine
Drug-Metabolizing Enzymes
Pharmacogenomics: Translating Functional Genomics into Rational Therapeutics. Evans and Relling Science 1999
Most DME have clinically relevant polymorphismsThose with changes in drug effects are separated from pie.
Phase I: modification of functional groups Phase II: conjugation with endogenous substitutents
Dumontier::BIOL1010:Towards Personalized MedicineWeinshilboum, R. N Engl J Med 2003;348:529-537
Nortriptyline (anti-depressant) Pharmacogenetics
Dumontier::BIOL1010:Towards Personalized MedicineWeinshilboum, R. N Engl J Med 2003;348:529-537
Use of probe drugs to determine metabolic activity due to CYP2D6 variants
Antihypertensive debrisoquin decreases blood pressure
Dumontier::BIOL1010:Towards Personalized Medicine
CYP3A4
• Abundant in liver and intestines and accounts for nearly 50% of CYP450 enzymes.
• Activity can vary markedly among members of a population
– Constitutive variability is ~5-fold but can increase to 400-fold through induction and inhibition
• Activity affected by other drugs:
– Grapefruit juice is an inhibitor
Felodipine is a calcium channel blocker (calcium antagonist), a drug used to control hypertension (high blood pressure)
5mg tablet with juice
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Wilson. PXR, CAR, and drug metabolism. Nat Rev Drug Disc 2002
CYP3A4 mediated Drug-Drug Interaction
PXR: pregnane X receptor; RXR: retinoid X receptor
• Protect against xenobiotics• Diverse drugs activate through heterodimer complex• Cause drug-drug interactions
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Codeine Metabolism
Gasche Y et al. Codeine intoxication associated with ultrarapid CYP2D6 metabolism. NEJM 2004
• 80% codeine normally converted to glucuronide, eliminated by kidney.
• 5-10% codeine is metabolized into morphine by CYP2D6
• inhibition of CYP3A4 or rapid metabolic variants of CYP2D6 during renal failure would show toxicity– 7% of caucasians have a
nonfunctional CYP2D6 variant
– <2% are CYP2D6 ultrarapid metabolizers which may suffer from opioid intoxication
Dumontier::BIOL1010:Towards Personalized Medicine
Known side effects
Unavoidable Avoidable
Medicationerrors
Product qualitydefects
Preventableadverseevents
Injuryor death
Remaininguncertainties
• Unexpected side effects• Unstudied uses• Unstudied populations
drug-drug interactions are mostly unavoidable
Dumontier::BIOL1010:Towards Personalized Medicine
LIPITOR:Known Side Effects
• Lipitor blocks the production of cholesterol in the body.
• May reduce risk of hardening of the arteries, which can lead to heart attacks, stroke, and peripheral vascular disease
Dumontier::BIOL1010:Towards Personalized Medicine
Known side effects
Unavoidable Avoidable
Medicationerrors
Product qualitydefects
Preventableadverseevents
Injuryor death
Remaininguncertainties
• Unexpected side effects• Unstudied uses• Unstudied populations
Medication errors are a significant source of adverse events
• ADR is one of the leading causes of hospitalization and death • 6.7% of hospitalized patients have serious ADRs• 0.3% of hospitalized patients have fatal ADRs
Dumontier::BIOL1010:Towards Personalized Medicine
Drug Recalls
191
226
248
352
354
254
60 53 34 88 72 156
83 88248
316
176
72
0
200
400
1995 1996 1997 1998 1999 2000 2001 2002 2003
Fiscal year
Nu
mb
er
Prescription Over-the-counter
FDA: Center for Drug Evaluation and Research 2003 - Report to the Nation
Many factors contribute to drug recalls
Dumontier::BIOL1010:Towards Personalized Medicine
Treatment for Acute Pain
increased risk of heart attack and stroke(after 18 months)
VIOXX: Unknown Side Effects
Dumontier::BIOL1010:Towards Personalized Medicine
Diagnostics
AmpliChip CYP450: Range of drug metabolism phenotypes is observed for individuals based upon the cytochrome P-450 genes
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
But wait a minute…
Dumontier::BIOL1010:Towards Personalized Medicine
There is still lots to figure out…
• Science still early. Limited data in public domain.• Many variations not clinically significant• Expensive to test for genotype (currently)
• Ethical issues in testing individual genotype• Unclear how to deliver information to the
practitioner
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Things to Consider
• Does my doctor know enough about genomic medicine to be advising me? – Are there genetic counselors available?
• Will the test only be for this condition?– What if I am susceptible to another disease?
• Who will know about this? – Doctors… insurance companies?
• How exactly will the results be kept secure and in confidence?
Dumontier::BIOL1010:Towards Personalized Medicine
How much will this cost?
• More drugs may succeed in clinical trials due to positive outcome for smaller subset– Will pharma attempt to recoup costs with a pricier drug?
• Will public health cover the costs of genetic testing?– Reduce overall health cost due to fewer ADRs– Should we determine clinically validated genes or
should we sequence the genome?
• How will insurance premiums be affected?
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Personalied Medicine:What’s your take?
Dumontier::BIOL1010:Towards Personalized Medicine
Outline
• Personalized Medicine
• Drug Discovery
• Role of Bioinformatics
• Current Research
Dumontier::BIOL1010:Towards Personalized Medicine
What is a drug?• A drug Compound of definite composition and
having a pharmacological effect
• Natural products – plant extracts – animal fluids (e.g., snake venoms)– isolated products (biological)– chimeric / recombinant products (biological)
• Synthetic chemicals– derived from medicinal chemistry– derived from combinatorial chemistry
Dumontier::BIOL1010:Towards Personalized Medicine
Drug Development Life Cycle
Years
0 2 4 6 8 10 12 14 16
Discovery
Preclinical Testing(Lab and Animal Testing)
Phase I(20-30 Healthy Volunteers used to check for safety and dosage)
Phase II(100-300 Patient Volunteers used to check for efficacy and side effects)
Phase III(1000-5000 Patient Volunteers used to monitor reactions to long-term drug use)
FDA Review & Approval
Post-Marketing Testing
Dumontier::BIOL1010:Towards Personalized Medicine
Drug Discovery
• Discovery: – finding a lead compound (KD < 1μM)
– target, assay, chemical screening, hit identification, mechanism of action, lead identification, lead optimization
– in vivo proof of concept in animals and demonstration of therapeutic value
• Development: – Evaluate its effectiveness– begins when the decision is made to put a
molecule into clinical trials
Dumontier::BIOL1010:Towards Personalized Medicine
Barriers that a drug must overcome to reach intended target
– chemically stable in stomach (pH 1)– not digested by gastrointestinal enzymes– absorbed into the bloodstream
• pass through series of cell membranes
– not bind tightly to other substances– survive xenobiotic detoxification by liver enzymes– avoid excretion by kidneys– brain: cross blood-brain barrier (blocks polar
substances)– intracellular receptor: pass through cell membrane
Dumontier::BIOL1010:Towards Personalized Medicine
R&D Spending and New Medicines
PhRMA Annual Report 2005-2006
• 38 new medicines in 2004 – Cancer– Infectious diseases– Parkinson’s therapy– Radiation
contamination– Pain alleviation from
made from a synthetic form of a sea-snail venom.
Dumontier::BIOL1010:Towards Personalized Medicine
An Analysis
• National Institute for Health Care Management– Changing Patterns of Pharmaceutical Innovation,
May 2002
• Quality of pharmaceutical innovation varies widely. – Breakthrough treatments for life threatening
diseases
TO– Minor modifications of drugs that have been on the
market for some time.
Dumontier::BIOL1010:Towards Personalized Medicine
Most drugs approved are only slightly modified
NME35%
Other11%
IMD54%
Dumontier::BIOL1010:Towards Personalized Medicine
Less innovative than you think
Dumontier::BIOL1010:Towards Personalized Medicine
Cost of developing drugs
• Global Alliance for Tuberculosis Drug Development– www.tballiance.org – "The Economics of TB Drug Development"
• Costs to discover and develop a new anti-TB drug range from $115 million to $240 million.– $40 million to $125 million for discovery– $76 million to $115 million for preclinical
development through Phase III trials
Dumontier::BIOL1010:Towards Personalized Medicine
Profits as a Percentage of Assets, 2002Top 7 of Fortune 500 Industries
7.2%
7.5%
8.2%
9.3%
9.5%
10.7%
14.0%
0% 2% 4% 6% 8% 10% 12% 14% 16%
Consumer Food Products
Apparel
Publishing, Printing
Food Services
Medical Products & Equipment
Household Products
Pharmaceuticals
Source: Fortune Magazine, April 14, 2003
Dumontier::BIOL1010:Towards Personalized Medicine
• Drug development has been and still is costly, risky, and lengthy
• R&D costs have increased, but the industry remains one of the most profitable
• Pharmaceutical innovation is targeted towards protecting interests
• The payoffs for improvements in the process are significant
The Drug Business
Dumontier::BIOL1010:Towards Personalized Medicine
What disease would you try to treat with a drug?
Dumontier::BIOL1010:Towards Personalized Medicine
Outline
• Personalized Medicine
• Drug Discovery
• Bioinformatics
• Current Research
Dumontier::BIOL1010:Towards Personalized Medicine
What is Bioinformatics?“Using computers to solve problems in biology”
• Bioinformatics is a scientific discipline that encompasses all aspects of biological information acquisition, processing, storage, distribution, analysis and interpretation.
• Bioinformatics combines the tools of Biology, Chemistry, Mathematics, Statistics and Computer Science to understand and model biological processes.
Dumontier::BIOL1010:Towards Personalized Medicine
Bioinformatics For Knowledge Discovery
Experimental Data– Sequence – Structure – Function– Expression– Regulation – Interactions – Pathways– Disease– Genetics– Taxonomy– Small molecules– Kinetics– Dynamics
model
validate
knowledge
simulate
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Bioinformatics & Drug Discovery
• Knowledge Discovery
• Identification of potential drug targets– Genomics– Proteomics
• Cheminformatics– Drug target modeling– Drug optimization
• Simulating drug effects on pathways
• Estimating toxicological effects
Dumontier::BIOL1010:Towards Personalized Medicine
Quick Survey of Bioinformatics Applications in Drug Discovery
• Tissue profiling
• Drug Screening
Dumontier::BIOL1010:Towards Personalized Medicine
Gene Expression• A gene is expressed
when it is transcribed from DNA to RNA and then possibly translated into a protein
• By measuring the products of transcription,we can assay gene expression
Dumontier::BIOL1010:Towards Personalized Medicine
• Differentiation: All cells in a body have the same genome. Expression is what differentiates one tissue from another, e.g. brain from liver.
• Physiology: Cells do their business (dividing, sending signals, digesting, etc.) largely via changes in expression
• Response to stimuli: Environmental changes (like drugs or disease) often cause changes in expression
• Disease markers and drug targets: changes in expression associated with disease can be diagnostic markers and/or suggest novel pharmaceutical approaches.
Importance of Gene Expression
Dumontier::BIOL1010:Towards Personalized Medicine
Microarrays Can Be Used To Determine Relative Gene Expression
Dumontier::BIOL1010:Towards Personalized Medicine
Distinct types of B-
cell lymphoma identified by gene
expression profiling
Dumontier::BIOL1010:Towards Personalized Medicine
Drug Screening
• If we have a target, how do we find some compounds that might bind to it?
• Combinatorial chemistry
• Computational screening
Dumontier::BIOL1010:Towards Personalized Medicine
Combinatorial Chemistry
• Parallel synthetic approach– Build on previous products– Generate diversity by adding R
groups– Recover most active compounds
• Solid phase synthesis– Wash away excess reagants &
other products– Can recover the main product
• Parallel testing
Dumontier::BIOL1010:Towards Personalized Medicine
combinatorial synthesis of non-peptide drugs
R
NH2
+OOH
NH2Bead
ONH
NH2Bead
R
ONH
NH2Bead
R
Cl
O
R+
ONH
NH
Bead
R
O
R
1)
2)
RXN 1
RXN 2
Dumontier::BIOL1010:Towards Personalized Medicine
Structure-Based Docking Methods
• Need 3D structure• Scan a virtual library of small molecules and “dock” them to a site of
interest on a protein structure• Predict binding energy• Filters thousands of compounds relatively quickly• Top hits can be used for more rigorous computational/experimental
characterization and optimization
Dumontier::BIOL1010:Towards Personalized Medicine
synthesisof compound
↓
manipulation of structure to get
better drug(greater efficacy, fewer side effects)Aspirin
Dumontier::BIOL1010:Towards Personalized Medicine
Quantitative Structure-Activity Relationship (QSAR)
• find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
• extract features (hydrophobicity, pK, van der Waals radii, hydrogen bonding energy, conformation)
• build mathematical relationship f(activity|features) • automatically assesses the contribution of each feature• can be used to make predictions on a new molecule
Dumontier::BIOL1010:Towards Personalized Medicine
3D QSAR for CYP3A4
Dumontier::BIOL1010:Towards Personalized Medicine
3D QSAR for CYP3A4 with known substrates
Dumontier::BIOL1010:Towards Personalized Medicine
How to discover a drug
Dumontier::BIOL1010:Towards Personalized Medicine
Cancer Therapy
• Matrix Metallo Proteinases (MMP)• Degrade proteins in the extracellular
matrix • Levels increased in areas surrounding
tumor• Makes it easier for cancer cells to
become metastatic
• MMP Inhibitors – Stop MMPs from working– Can block tumor growth
Dumontier::BIOL1010:Towards Personalized Medicine
Whittaker et al. Chem. Rev. 1999, 99, 2735-2776
“metallo” in MMP = zinc
→ catalytic domain contains 2 zinc atoms
MMP catalysis
Dumontier::BIOL1010:Towards Personalized Medicine
Peptidic hydroxamate inhibitors
• Specific for MMPs
• Better binding
• But poor oral bioavailability
(how much gets into the bloodstream)
Dumontier::BIOL1010:Towards Personalized Medicine
Drug Discovery by Agouron Pharmaceuticals
• Designed a new inhibitor
• Used structure of human MMPs bound to various inhibitors in silico
• Determined key residues, ligand substituents needed for binding Gelatinase A
Dumontier::BIOL1010:Towards Personalized Medicine
Structural bioinformatics to design nonpeptidic hydroxylates
oral bioavailabitybinding
anti-growth
anti-metastasis
repeat…
Dumontier::BIOL1010:Towards Personalized Medicine
Prinomastat• Good oral
bioavailability• Selective for specific
MMPs • Evidence showing
prevention of lung cancer metastasis in rat and mice models
• Clinical trials– cell lung cancer– prostate cancer
Dumontier::BIOL1010:Towards Personalized Medicine
Dumontier::BIOL1010:Towards Personalized Medicine
Outline
• Personalized Medicine?
• Drug Discovery
• Bioinformatics
• Current Research
Dumontier::BIOL1010:Towards Personalized Medicine
Goals
• Integrate pharmacogenomics knowledge• Improve our understanding of how genetic variations affect drug responses.• Deduce the biochemical mechanisms that underlie disease phenotypes• Predict side-effects from drug
interactions due to unexpected
cellular interactions
Dumontier::BIOL1010:Towards Personalized Medicine
How do we find this knowledge?
Dumontier::BIOL1010:Towards Personalized Medicine
PHARMGKB
+Role of genes, gene variants , drugs
+pharmacokinetics +pharmacodynamics
+ clinical outcomes.
+ Links to publications
- Natural language descriptions
- Variant details in publications
Dumontier::BIOL1010:Towards Personalized Medicine
Surface web:167 terabytes
Deep web:91,000 terabytes
545-to-one
Dumontier::BIOL1010:Towards Personalized Medicine
How do we integrate these resources?
Dumontier::BIOL1010:Towards Personalized Medicine
Data silos – not made for sharingData silos – not made for sharing
Dumontier::BIOL1010:Towards Personalized Medicine
The Semantic Web will expose The Semantic Web will expose data and link knowledgedata and link knowledge
Dumontier::BIOL1010:Towards Personalized Medicine
Bio2RDF is building the linked data web for biological data
Dumontier::BIOL1010:Towards Personalized Medicine
Bio2RDF provides the Bio2RDF provides the methodology to create methodology to create and glue these different and glue these different databases.databases.
Dumontier::BIOL1010:Towards Personalized Medicine
Resource Description Framework (RDF)
• Allows one to express propositions, and reason about them
• Uniform Resource Identifier (URI) are entity names
• i.e P05067
is a name for Amyloid precursor protein APP
Protein
is a
• A RDF statement consists of:– Subject: resource identified by a URI
– Predicate: resource identified by a URI
– Object: resource or literal
Dumontier::BIOL1010:Towards Personalized Medicine
Now Link Data!
APP
Protein
is a
Alzheimer’s
Disease
is a
is involved in
Dumontier::BIOL1010:Towards Personalized Medicine
something you can lookup or search for with rich
descriptions
Dumontier::BIOL1010:Towards Personalized Medicine
Ontology as
Strategy
Dumontier::BIOL1010:Towards Personalized Medicine
APP
Protein
Is a
Molecule
is a
is a
Semantic Knowledge Base
fact
ontology
Knowledge base
Dumontier::BIOL1010:Towards Personalized Medicine
Ontologies
• Shared conceptualization
• Tell computers what we believe
• Allows computer programs called reasoners to make inferences
Dumontier::BIOL1010:Towards Personalized Medicine
Semantic Query Answering
http://smart.dumontierlab.com
ISWC Semantic Web Challenge: CS Honors: Alex De Leon
Dumontier::BIOL1010:Towards Personalized Medicine
Build aBuild aknowledge baseknowledge basefrom a series of questionsfrom a series of questions
Dumontier::BIOL1010:Towards Personalized Medicine
Cell Simulation
Molecules Interactions (metabolic/signaling) Compartments
Cell Simulation
+ +
Dumontier::BIOL1010:Towards Personalized Medicine
3D Particle Simulation
GridCell - Dr. Warren Gross & Laurier Boulianne, PhD Candidate Engineering
Dumontier::BIOL1010:Towards Personalized Medicine
3D Tetris or Science at Work?
Dumontier::BIOL1010:Towards Personalized Medicine
Cellular Visions: The Inner Life of a Cell
Dumontier::BIOL1010:Towards Personalized Medicine
Learn more and Get involved!
BIOC 3008 [0.5 credit]: BioinformaticsBIOC 4008 [0.5 credit]: Metabolic Modeling and Simulation
BIOC 2400 [0.5 credit]: Independent Research IBIOC 3400 [0.5 credit]: Independent Research IIBIOC 4906 [1.0 credit]: Interdisciplinary Research
BIOL 4900 [1.0 credit]: Biology Directed Special Studies BIOL 4901 [0.5 credit]: Biology Directed Special StudiesBIOL 4908 [1.0 credit]: Biology Research ThesisBIOC 4907 [1.0 credit]: Biochemistry Essay and Research ProposalBIOC 4908 [1.0 credit]: Biochemistry Research ProjectCHEM 4908 [1.0 credit]: Chemistry Research Project and SeminarCMPS 4909 [1.0 credit]: Computational Science Research ThesisCOMP 4901 [0.5 credit]: Computer Science Directed StudiesCOMP 4905 [0.5 credit]: Computer Science Research ThesisSYSC 4907 [0.5 credit]: Engineering Project
Dumontier::BIOL1010:Towards Personalized Medicine
Learn more and Get involved!
Faculty• Biology - Jim Cheetham, Ashkan Golshani, Myron Smith,
Bill Willmore, Iain Lambert, Susan Aitken, John Vierula• Computer Science - Frank Dehne• Systems and Computer Engineering - Jim Green,
Gabriel Wainer
Programs• BSc – Bioinformatics (Honours), Computational
Biochemistry (Honours), Computational Biology (Honours)
• BCSc – stream in Bioinformatics• Master’s Specialization in Bioinformatics
Dumontier::BIOL1010:Towards Personalized Medicine