2. Understanding the complex networks within a cell...
Parameters: ~10,000 genes
>100,000 proteins
Approach: measure model predict
From:

3. Understanding the complex networks within a cell...
Parameters: ~10,000 genes
>100,000 proteins
Approach: measure model predict
From:
Measure = quantify amounts of parameters versus time
Nearly possible
Major challenge

4. Understanding the complex networks of a cell...
Parameters: ~10,000 genes
>100,000 proteins
Approach: measure model predict
From:
Measure = quantify amounts of parameters versus time
Nearly possible
Major challenge
An instantaneous readout of these parameters will accelerate fundamental advances in biology and enable:
Drug discovery (cellular responses to drugs and environment)
Medical diagnostics (tumor identification and treatment)
Biowarfare detection (rapid pathogen identification)

5. (vary time, physiological state, disease, ...)
Where’s the bottleneck?
Detection
Quantity
1 2 3
Protein
...
Gene
Repeat
(vary time, physiological state, disease, ...)
Information
Cells
Extraction
DNA
and proteins *
Chemical
Processing
Label
and amplification

7. Today’s technology:
Genomics
~104 genes

8. Today’s technology: Genomics Proteomics ~104 genes ~10 proteins
Affymetrix
Sorger Lab, MIT
concentration
time (min)

9. Today’s technology: Genomics Proteomics
~104 genes
Proteomics
~10 proteins
Affymetrix
Sorger Lab, MIT
concentration
time (min)
Measurement time ~ few hours to a day!

11. Electronic detection of DNA hybridization
Glass

12. Electronic detection of DNA hybridization
Glass

13. Electronic detection of DNA hybridization
depletion
region
Silicon

14. Electronic detection of DNA hybridization
depletion
region
Silicon

15. Electronic detection of DNA hybridization
surface potential
active surface
*Fabricated by Emily Cooper at MIT’s MTL
DNA probe sequence Am
DNA probe sequence A
DNA target sequence cA
depletion
region
Silicon

16. Teaching: 6.151 Microfabrication Project Laboratory
Class of Spring 2002
Daniel J. Bedard, Antimony L. Gerhardt, Trisha Montalbo,
Peter R. Russo, Maxim Shusteff, Luke Theogarajan
Goal: Integrate microfluidics with microelectronics
Instructors: Martin Schmidt and Scott Manalis