Bioinformatics
Jean-Christophe (JC) Nebel
Bioinformatics & Genomic Signal Processing Group
j.nebel@kingston.ac.uk
Kingston University London
What is bioinformatics?
Bioinformatics: word was coined in 1978
Bio-:
life
Informatics: information systems & computer science
Analysis of molecular biology data using techniques from
information systems, computer science, artificial
intelligence, statistics and mathematics
~computational biology (USA)
Molecular biology data?
DNA, RNA, genes, proteins…
Aim of bioinformatics
“To improve the quality of life” by understanding how it works
Health
• disease prevention:
• detect people at risk
• change of lifestyle, diet…
e.g. risk of cardiovascular diseases – exercise…
• study virus evolution
e.g. bird flu virus
• treatment:
• quantitative evaluation of disease spread
• rational drug design
e.g. first efficient drug against HIV (Norvir 1996)
• gene therapy
e.g. “bubble” kids with no immune system
• animal model
e.g. zebra fish is the new mouse
Other applications
Forensic (DNA fingerprints)
• criminal suspects (UK: database of 3M people)
• paternity tests
1/25 child is NOT from the expected father!
• identification of victims (Titanic, earthquakes…)
• prevent illegal trade (drugs, ivory…)
Paleoanthropology & archaeology
• Human evolution
e.g. where is the first American from?
Food industry
• GMOs (Genetically Modified Organisms)
Famine buster or Frankenfood?
Bringing a new drug to the market [1]
In silico
In vitro
In vivo
0. (Computational) ‘rational’ drug design
1. Biochemical assays ($400 per compound)
Drug target in solution with compound
2. Cell-based assays ($4,000 per compound)
Culture of living cells in solution with compound
3. Animal testing ($10,000-1,000,000 per compound)
~6 years, only 1/1000 compounds are tested in a clinical trial
4. Human clinical trials
• Phase I: side effects on (20-50) healthy volunteers
Human
30% of drugs are rejected (unsafe)
Clinical
• Phase II: positive effect on (20-300) patients
Trials
66% of drugs are rejected (poor efficacy)
• Phase III: positive effect on variety of (300–3,000) patients
75% of drugs are rejected
~8 years, fewer than 6% of compounds get approval
$300 million to $1.7 billion and up to 20 years, only 1/10 projects succeeds
[1] D Young, Computational drug design, N.J: John Wiley & Sons, 2009
Course structure
5 lectures (2x45’):
45’:
45’:
Biological background and problem
e.g.: what is DNA? + how to detect genes?
Informatics solution (databases, algorithms…)
-> handouts provided
-> lecture notes available after lecture
5 workshops (1h30’):
Use of bioinformatics tools