1. 1 EE381V: Genomic Signal Processing Lecture #13

2. 2 The Course So Far Gene finding DNA Genome assembly Regulatory motif discovery Comparative genomics Gene expression analysis Cluster discovery Regulatory networks inference Emerging network properties Protein network analysis SEQUENCES INTERACTIONS Sequence alignment DONE

3. Next: Biomolecular Detection Systems 3 ABI Prism ® 310 Genetic AnalyzerAffymetrix GeneChip ® Roche LightCycler ® DNA Sequencing Gene Expression Profiling DNA Amplification DONE

4. Fundamentals of Detection Biological Assay Transducer/ Interface Detection Circuitry Detection Biochemical Uncertainties Fabrication and Process Variation Electronic Noise Quantization Noise Molecular Biology Biochemistry Fabrication/ Synthesis Processes Circuit Design Signal Processing Biological Sample Data ADC DNA Microarrays Photodiode Image SensorAnalog to Digital Conversion Biomolecular Detection Systems Bio-molecular detection systems, in general, have the following sub-blocks: 4 Example:

5. * There are different transduction methods for “counting” the binding events, e.g., fluorescence, electrochemical, chemi-luminescence … 5 Affinity-Based Biosensors Exploit the affinity of certain biomolecules for each other to capture and detect:

6. Parallel Affinity-Based Sensing Biological sample Capturing sites Planar solid surface Capturing probe (A) Capturing probe (B) 6 For simultaneous detection of multiple targets, use affinity-based sensors in parallel:

7. DNA Microarrays DNA Probe (A) DNA Probe (B) Target DNA (B) Target DNA (A) Fluorescent Tag 20-100µm 50-300µm A B 10,000 spots 7 DNA microarrays are massively parallel affinity-based biosensor arrays:

8. 8 Applications of DNA Microarrays Recall central dogma: DNA microarrays interrupt the information flow and measure gene expression levels frequently, the task is to measure relative changes in mRNA levels this gives information about the cell from which the mRNA is sampled (e.g., cancer studies) Other applications: single nucleotide polymorphism (SNP) detection simultaneous detection of multiple viruses, biohazard / water testing

9. Energy ΔEΔE ACGT TGCA ACGT TGCA Hydrogen bonds Sensing in DNA Microarrays 9 When complementary ssDNA molecules get close to each other, electrostatic interactions (in form of hybridization bonds) may create dsDNA Because of thermal energy, the binding is a reversible stochastic process Relative stability of the dsDNA structures depend on the sequences

10. Stability of dsDNA: Melting Temperature dsDNA 50% ssDNA 50% 10 The melting temperature (T m ) of a DNA fragment: the temperature at which 50% of the molecules form a stable double helix while the other 50% are separated into single strand molecules Melting temperature is a function of DNA length, sequence content, salt concentration, and DNA concentration For sequences shorter than 18 ntds, there is a simple (Wallace) heuristic:

11. There are many different methods to approximate melting temperature: Wallace method (DNA strands less than 18mers): %GC method: 1. Breslauer, K.J. et al. (1986) Proc. Natl. Acad. Sci. USA. 83: 3746-3750. 2. Rychlik, W. et al. (1990) Nucleic Acids Res. 18: 6409-6412. Nearest neighbor method [1]: ΔH is the sum of nearest neighbor enthalpy changes A is the initiation constant of -10.8 cal/K° mole for non-self complementary sequences, -12.4 cal/Ko mole for complementary sequences ΔS is the sum of nearest neighbor entropy changes R is the gas constant 1.987 cal/K°mole C is the concentration of DNA (generally fixed at 250 pM) [2] Computing Melting Temperature 11

12. Temperature Probability of ssDNA T1T1 T2T2 T3T3 T4T4 T5T5 DNA (1)DNA (2)DNA (3)DNA (4)DNA (5) Melting temperature is a function of DNA length, sequence content, salt concentration, and DNA concentration DNA Melting Curve Computing Melting Temperature 12

13. Energy ΔE1ΔE1 (Bond stability) 1 > (Bond stability) 2 ΔE2ΔE2 Not a perfect match ΔE 1 > ΔE 2 13 Depending on sequences, non-specific binding may also happen: Nonspecific Binding (Cross-hybridization) So, non-specific binding is not as stable as specific binding

14. 14 Probe (A) Probe (B)Probe (C) (A) (B) (C) Non-specific binding in microarrays Interference may lead to erroneous conclusions Useful signal is affected by the interfering molecules, false positives possible Non-specific binding (cross-hybridization) manifests as interference:

15. The kinetics of reactions depends on: 1.The frequency that the reactive species (e.g., molecules) get close to each other 2.If in close proximity, can thermal energy “facilitate” microscopic interactions Energy ΔE2ΔE2 ΔE1ΔE1 State 2 Molecules activated State 1 Molecules close State 0 Molecules bind ssDNA (1)ssDNA (2) ssDNA (1) ssDNA (2) dsDNA (1+2) 15 Road to a Model: Underlying Physics

16. Arrhenius equation represents the dependence of the rate constant of a reaction on temperature : In its original form the pre-exponential factor and the activation energy are considered to be temperature-independent. Energy ΔE2ΔE2 ΔE1ΔE1 16 Road to a Model: Underlying Physics

17. The reaction kinetics of two molecules has the following Markov model: Energy ΔE2ΔE2 ΔE1ΔE1 2 1 0 State 2 Molecules activated State 1 Molecules close State 0 Molecules bind 17 Markov Model

18. 2 1 0 State 2 Molecules activated State 1 Molecules close State 0 Molecules bind A more practical model is the two-state Markov model 1 0 State 1 Molecules close State 0 Molecules bind 18 Markov Model

19. A more practical model is the two-state Markov model 1 0 State 1 Molecules close State 0 Molecules bind 19 Markov Model Steady-state distribution:

20. The Number of Captured Targets: A Random Process Distribution of Captured Particles Captured Targets at Time (hours) Captured Analytes The number of captured molecules forms a continuous-time Markov process 20

21. Array Fabrication 2 Sample Preparation 1 Incubation 3 Detection 4 21 The steps involved in an experiment: Microarrays