6.092/HST.480 Bioinformatics & Proteomics: An Engineering-Based Problem Solving Approach
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1 6.092/HST.480 Bioinformatics & Proteomics: An Engineering-Based Problem Solving Approach Gil Alterovitz 1, Manolis Kellis 2, Marco Ramoni 1 1 Harvard/MIT Science and Technology (HST) 2 Electrical Engineering & Computer Science, MIT
2 Today Introduction- Gil Alterovitz Motivation: Why Bioinformatics? Course Introduction Introduction to Modern Biology: Part I Bioinformatics from Industry s Perspective: Mathworks- Rob Henson Bioinformatics in Industry Matlab Bioinformatics Toolbox Clustering and Related Technologies (DeRisi s Microarray Paper)
3 Motivation: Why Bioinformatics?
4 Why Engineering and Computer Science? Robotics/Automation: For lab automation, hypothesis testing and generation nature Network Theory: Modeling Protein Interaction Science A Map of the Interactome Network of the Metazoan C. elegans Visualization/Image Processing: Protein Expression 3-D Heat Map ARTIFICIAL INTELLIGENCE IN MEDICINE New Mass Spectrometry Technologies: With Clinical Applications Annals of Surgical Oncology Surfaced-Enhanced Laser Desorption/Ionization Time-of-Flight (SELDI-TOF) Differentiation of Serum Protein Profiles of BRCA-1 and Sporadic Breast Cancer Stephen Becker, MD, Lisa H. Cazares, Patrice Watson, PhD, Henry Lynch, MD, O John Semmes, PhD, Richard R. Drake, PhD Division and Christine Laronga, of Health MD
5 Signal Processing in Biology THE CELL CYCLE Cell with chromosomes in the nucleus 0.8 Phase shift Inhibitor-cyclin complex Cell division G1 Concentration Cyclin (No inhibitor present) Free cyclin DNA synthesis 0 Mitosis M CDK Cyclin S Chromosome duplication Figure by MIT OCW Chromosome separation G2 Model: Cell with duplicated chromosomes Figure by MIT OCW Gardner, T. S., Dolnik, M. & Collins, J. J. A theory for controlling cell cycle dynamics using a reversibly binding inhibitor. Proc Natl Acad Sci 95, (1998).
6 Course Introduction
7 Signal Processing in Bioinformatics Genes Expression Levels Coherence Gain Phase Shift ARP7 CHS2 arp7: Component of global transcriptional activator complex (Cytoskeleton) chs2: Chitin synthase II (Cell wall biogenesis) r = Figure by MIT OCW Coherence Function Transfer Function Phase Butte, A. J., Bao, L., Reis, B. Y., Watkins, T. W. & Kohane, I. S. Comparing the similarity of time-series gene expression using signal processing metrics. J Biomed Inform 34, (2001).
8 Instructors Gil Alterovitz HST Medical Engineering Medical Physics-Electrical Engineering and Computer Science, Graduate Student/Whitaker Fellow. Proteomics & Computational Biology, Introductory Material Robert Berwick Professor, Electrical Engineering and Computer Science, MIT Language/Sequence Analysis Rob Henson Director of Bioinformatics Group, Mathworks (Matlab). Mathematics and Signal Processing, Industrial Experience Manolis Kellis Assistant Professor, Electrical Engineering and Computer Science,, MIT Sequence Analysis Nanguneri Nirmala Functional Genomics Group, Novartis Institutes for BioMedical Research Expression Analysis, Industrial Experience Marco F. Ramoni Assistant Professor of Pediatrics and Medicine, Harvard Medical School Expression Analysis, Bayesian Networks Paola Sebastiani Associate Professor, Department of Biostatistics, Boston University ity Statistical Methodologies and Bioinformatics
9 Organization: Levels of Abstraction Part I: Sequence Part II: Expression Part III: Proteomics Part IV: Systems/Misc.
10 Part I / II Tue, January 4, 2005, 11:00am-11:45pm 11:45pm Review of Modern Biology- Gil Alterovitz Tue, January 4, 2005, 11:45am-12:30pm 12:30pm Introduction to Bioinformatics Laboratory / Bioinformatics in the e Computer Industry- Rob Henson / Gil Alterovitz Thurs, January 6, 2005, 11:00am-11:45pm 11:45pm Review of Modern Biology II- Gil Alterovitz Thurs, January 6, 2005, 11:45am-12:30pm 12:30pm Sequence Analysis: Motif and Regulation- Manolis Kellis Tue, January 11, 2005, 11:00am-11:45pm 11:45pm Sequence Analysis: Genes and Genome- Manolis Kellis Tue, January 11, 2005, 11:45am-12:30pm 12:30pm Sequence Analysis: Gene Evolution- Manolis Kellis and Robert Berwick Thurs, January 13, 2005, 11:00am-11:45pm 11:45pm Microarray Expression Data Analysis- Marco Ramoni Thurs, January 13, 2005, 11:45am-12:30pm 12:30pm Machine Learning: Bayesian Methodologies- Marco Ramoni
11 Part IV / III Tue, January 18, 2005, 11:00am-12:00pm 12:00pm Bioinformatics in the Biotech Industry- Nanguneri Nirmala Tue, January 18, 2005, 12:00am-12:30pm 12:30pm Control and Feedback in Systems- Gil Alterovitz Thurs, January 20, 2005, 11:00am-11:45pm 11:45pm Scale-free Networks I-I Paola Sebastiani Thurs, January 20, 2005, 11:45am-12:30pm 12:30pm Scale-free Networks II- Paola Sebastiani Tue, January 25, 2005, 11:00am-11:45pm 11:45pm Statistical Models and Stochastic Processes in Proteomics- Gil Alterovitz Tue, January 25, 2005, 11:45am-12:30pm 12:30pm Signal Processing for Proteomics Gil Alterovitz Thurs, January 27, 2005, 11:00am-12:00pm 12:00pm Biological Methods, Automation, Robotics- Gil Alterovitz Thurs, January 27, 2005, 12:00pm-12:30pm 12:30pm Project Discussion and Wrap-up up- Gil Alterovitz Parts III/IV switched to accommodate speakers.
12 Date: Winter 2005 Credits/Hours: Class Information Four weeks: TR, 11:00 am-12:30 pm Total hours: 12 scheduled + estimated 28 outside = 40 Weekly load: 3 scheduled + estimated 7 outside = 10 Units: 3 credits (1-0-2), U Audience: upper undergraduate/graduate. Student Prerequisites: or equivalent recommended or equivalent recommended or equivalent recommended Grades: P/D/F Time/Location: Lecture TR, 11a-12:30p 12:30p Lab (optional), 9a-~10:40a (cluster of dual-processor, dual LCD panel Windows-based machines with Matlab pre-installed).
13 Resources Book chapter: G. Alterovitz, E. Afkhami, and M. Ramoni, "Robotics, Automation, and Statistical Learning for Proteomics," in Focus on Robotics and Intelligent I Systems Research, vol. 1, F. Columbus, Ed. New York: Nova Science Publishers, Inc., 2005 (In press). Reference texts: A. V. Oppenheim, A. S. Willsky, and H. Nawab, Signals and Systems, 3rd ed. Englewood Cliffs, NJ: Prentice Hall, A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Processes.. New York, NY: McGraw-Hill, I. S. Kohane, A. T. Kho, and A. J. Butte, Microarrays for an Integrative Genomics.. Cambridge, MA: MIT Press, Other: 17 other papers/resources (Nature,( PNAS, Machine Learning, Bioinformatics, Physical Review E, etc.)
14 Academic Information Labs/Homework 3 Labs (homeworks) Final Project- Student selected based on one of the four areas. Grading Labs 40% Final Project 50% Participation 10%
15 Miscellaneous Fill out background sheet and turn them in at the front.
16 Modern Biology in Two Lectures (Part I Today)
17 Genes to Proteins Transcription Translation DNA: Lifetime Plan mrna: Task List Protein: Machines 5 ATCTACAGATCAGCTACGACGCGACGAT TTAGCAGCAGCGACGCGACAGCAGCTAGTG ACGATAGCACATAGTTAGCACAGAGCAGAC ACAGACAGCACAGCGACAGCGACGACG-3 5 AUCUACAGAUCAGCUACGACGCGACGAU UUAGCAGCAGCGACGCGACAGCAGCUAGUG ACGAUAGCACAUAGUUAGCACAGAGCAGAC ACAGACAGCACAGCGACAGCGACGACG-3 MWTRFDSALPRSTPSTAKLVMPOILLLLEE EDTYESAQYKTWLMVCSDETTTE Protein mrna Ribosome Figure by MIT OCW Figure by MIT OCW DNA Sequencing Source: HPCGG Relative Expression Levels Identification Post translation modification Splicing variants Relative expression levels
18 Transcription DNA RNA Atom Color G=Guanine C=Cytosine A=Adenine, T=Thymine (DNA only) U=Uracil (RNA only). (DNA) T U (RNA) DNA, RNA= sequence of nucleotide bases Parity Bit Analogy Redundant information in second strand for error correction. Oxygen Nitrogen Carbon Phosphorus Sulfur Hydrogen Red Blue Green Magenta Yellow Grey DNA = deoxyribonucleic acid RNA = ribonucleic acid Figure by MIT OCW
19 Translation U SECOND BASE C A G U UUU UUC UUA UUG Phenylalanine Leucine UCU UCC UCA UCG Serine UAU UAC UAA UAG Tyrosine Stop codon Stop codon UGU UGC UGA UGG Cysteine Stop codon Tryptophan U C A G Amino Group (NH 2 ) Carboxyl Group (COOH) FIRST BASE C A CUU CUC CUA CUG AUU AUC AUA AUG Leucine Isoleucine Methionine start codon CCU CCC CCA CCG ACU ACC ACA ACG Proline Threonine CAU CAC CAA CAG AAU AAC AAA AAG Histidine Glutamine Asparagine Lysine CGU CGC CGA CGG AGU AGC AGA AGG Arginine Serine Arginine U C A G U C A G THIRD BASE G GUU GUC GUA GUG Valine GCU GCC GCA GCG Alanine GAU GAC GAA GAG Aspartic acid Glutamic acid GGU GGC GGA GGG Glycine U C A G cysteine R (Side Chain) Figure by MIT OCW RNA Protein Protein = Sequence of Amino Acids
20 Name Symbol Mass (-H 2 O) Side Chain Occurrence (%) Alanine A, Ala CH Arginine R, Arg HN=C(NH 2 )-NH-(CH 2 ) Asparagine N, Asn H 2 N-CO-CH Aspartic acid D, Asp HOOC-CH Cysteine C, Cys HS-CH Glutamine Q, Gln H 2 N-CO- (CH 2 ) Glutamic acid E, Glu HOOC-(CH 2 ) Glycine G, Gly H Histidine H, His N=CH-NH-CH=C-CH Isoleucine I, Ile CH 3 -CH 2 -CH(CH 3 ) Leucine L, Leu (CH 3 ) 2 -CH-CH Lysine K, Lys H 2 N-(CH 2 ) Methionine M, Met CH 3 -S-(CH 2 ) amino acids in total. Letters- compared to DNA/RNA
21 Genes Gene 1 Gene 2 Intergenic Sequence Communication analogy: start, message, stop. Source: Ehsan Afkhami
22 [Slide not shown] Rob Henson Rob Henson comes to us from Mathworks- creators of Matlab software. Rob studied Mathematics at Cambridge University. He spent 7 years in Japan working in the software industry before coming to the US. At Mathworks, he leads the bioinformatics group- which released the newest version of their bioinformatics toolbox a couple months ago. It is my great pleasure to introduce Rob- who will be talking about clustering technologies in bioinformatics and his perspective from industry. Thank you for coming today.
23 [Rob Henson s lecture (will be posted when available)] Outline Bioinformatics from Industry s Perspective: Mathworks- Rob Henson Bioinformatics in Industry Matlab Bioinformatics Toolbox Clustering and Related Technologies (DeRisi s Microarray Paper)
24 Modern Biology in Two Lectures (Part II Thurs) Splicing, Alternative Splicing, Post- Translational Modifications, and Bioinformatics Tools and Databases
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