Enabling Technologies from the Biology Perspective
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1 Enabling Technologies from the Biology Perspective H. Steven Wiley January 22nd, 2002
2 What is a Systems Approach in the Context of Biological Organisms? Looking at cells as integrated systems and not as mere collections of parts Seeks to understand how changes in any given component will affect the behavior of the entire system Requires integration of information from molecular biology, biochemistry, physics and genetics to understand relationship between different sets of scientific data. Goal is a Higher order understanding of life processes. 7/26/201 2
3 Why should DOE care about Systems Biology? It offers a chance to actually solve problems. Conversion of biology from a descriptive to a quantitative science. This will enable bioengineering and rational design of biological systems. DOE is one of the only organizations that is capable of applying a systems approach to biology 7/26/201 3
4 What are the Computational Requirements of Systems Biology? In order to integrate the data and move biology to being a system science, we need to: - develop new theories and mathematics, - develop new algorithms, - implement on the largest available computer systems, - make full use of large, distributed databases, and - make the software and computer system usable by the biologist. 7/26/201 4
5 Biological Organisms are Dynamic and Recursive Virtual Biology Controlled culture conditions Input (Environment) Genome Cell State (Proteome) Gene expression Proteomics STATIC DYNAMIC Phenotypic analysis Cell Observatory Output (Response) 7/26/201 5
6 Systems Biology Requires Scientific Integration Theory Analysis Experiment To solve specific problems using a systems approach, you must couple theory with experiments and analysis in a recursive manner. This requires close coordination between program elements. 7/26/201 6
7 Understanding Complex Biological Systems Using a Top-Down:Bottom Down:Bottom-Up Hybrid Approach Bottom-Up defines the molecular components of the system This sets the physical constraints of system behavior This provides basis for generating mechanistic models Top-Down computational approach is used to attribute behavioral characteristics to system Encapsulate complexity in terms of modular structure Attribute behavioral outcome to molecular composition 7/26/201 7
8 Cell Migration Can Be Broken Down Into a Series of Steps... 7/26/201 8
9 Extend Model From High-level to Low Level Example: Wound Healing Response Heal Wound Detach Migrate Attach Proliferate Make Matrix??? Migrate Choose direction Release proteases Extend front Attach front Contract sides Retract rear It is very difficult to work the other way! Extend front Local Ca release Actin polymerize Cap ends Ruffle membrane Ruffle Activate rac Bind to membrane Dock vesicle Make fusion point Insert 7/26/201 9
10 Modular Perspective in Cell Biology Consider cells as composed of groups of interacting modules Modules are functionally independent Modules interact at defined (and limited) regulatory nodes Function of cells defined in terms of collection of modules 7/26/201 10
11 Systems Biology = Quantitative Biology System is defined as group of interacting parts Interactions are dependent on quantities Cellular processes are best described as rates Computer simulations require quantification 7/26/201 11
12 What, Where, Quantity, Quality? To successfully model a complex biological system, one must minimally know the following information: What parts are being made? (identity) Where are the proteins located in cell? (location) What are their levels? (quantity) How do they interact with their partners? (activity) As a function of covalent modification Contribution of steric restrictions Forward and reverse rate constants 7/26/201 12
13 What Is Necessary to Understand a Network? Nodes (Components) Connectivity (Interactions) Spatial Organization (Transport) Dynamic Range (Kinetics) Experimental Biology Modeling & Analysis 7/26/201 13
14 Growth Factor Pathways 7/26/201 14
15 How Can We Understand Signaling Networks? Hanahan & Weinberg, 2000 Hallmarks of Cancer 7/26/201 15
16 Structure of Cells 7/26/201 16
17 Intracellular Trafficking Pathways Recycling Tf-R Man-R Man-6-P-R Recycling endosomes 2 M T-R?? Sorting endosomes Golgi EGF-R EGF Synthesis EGFR Synthesis M6P-R Late endosomes ER Lysosomal Targeting 7/26/201 17
18 The EGFR System 1? 3 EGFR 2 erbb-2 ERK? Src Eps15 AP-2 Early Endosomes erbb-2 PLC Grb-2 4 Golgi Shc Cbl eps8 Late Endosomes Lysosomes?? 5 Annexin II? 6 7/26/201 18
19 How Do We go From Cartoon Model to Realistic Model? Model must be quantifiable Model must make very specific predictions Model must account for changes in components as well as input parameters Must be testable with experimental data Models and experiments need to be developed together 7/26/201 19
20 The EGFR System 1? 3 EGFR 2 erbb-2 ERK? Src Eps15 AP-2 Early Endosomes erbb-2 PLC Grb-2 4 Golgi Shc Cbl eps8 Late Endosomes Lysosomes?? 5 Annexin II? 6 7/26/201 20
21 Spatial Colocalization by Image Analysis For two molecules to interact, they must be located in the same space at the same time. This can be determined by image analysis Composite Receptor alone AND operation 7/26/201 21
22 Movement of EGFR through early endosomes as determined by image analysis 7/26/201 22
23 Colocalization of EGFR and HER2 QuickTime and a DV - NTSC decompressor are needed to see this picture. 7/26/201 23
24 QuickTime and a Cinepak decompressor are needed to see this picture. 7/26/201 24
25 Trafficking model for EGFR signaling c,x s,f c,f s,x coated pit EE vesicles smooth pit EE vesicles Shc Grb PLC- c,m s,m k r,s sorting endosome k r,x kr,d k l,d k l,x lysosome 20 nm of ligand is injected in 15 seconds (red: EGF, blue: TGF- ) Stochastic/Kinetic Simulations Dynamic Monte Carlo method (Probability weighted) A probabilistic approach as opposed to deterministic Extension of the Monte Carlo method to the time domain H Resat, HS Wiley, DA Dixon 7/26/201 25
26 Signal Pathways: Computer Simulation of Protein- Protein Complexes Ras acts as the GTP-activated molecular switch in the cell signaling pathway for gene expression, controlling cell proliferation and differentiation. Mutations found at Gln-61 in 30% of human tumors. EGFR P P GRB2 SOS RAS RAF P Issues for modeling the Ras:Raf signaling complex : 50,000 atoms, long-time simulations, solvent, lack of knowledge of the entire structure GDP GTP P MEK MAPK P P PKA ELK-1 P P TCF P CREB TP Straatsma, H Resat, JH Miller, T Soares, DA Dixon, PNAS, /26/201 26
27 Model for the Role of Ca 2+ in Stress-Response Pathways: NWGrid/NWPhys H Trease, JH Miller Plasma Membrane cytokine PIP2 PLCb G proteincoupled receptor Ga bg Pi3K SRC Rasactivation Complex GTP GDP IP 3 DAG PKC protease MAPK cascade Ca 2+ nucleus Smooth Endoplasmic Reticulum 7/26/201 27
28 Signaling Through Growth Factor Receptors Ligand (Extracellular molecule) Membrane receptor Intracellular signaling cascade Physiological response INPUT SENSOR ACTUATOR OUTPUT 7/26/201 28
29 Central Role of Computation and Simulation Binding & Transport C G 2 input G 1 G 4 Signaling P a Ligand release E3 p G 3 Computer simulations provide a conceptual framework for organizing system behavior 7/26/201 29
30 Cell State Dictates Cell Response DNA is information storage of cell Proteins are the program running the cell The running program dictates cellular response Thus, one cannot adequately model or predict cellular responses UNLESS the proteome of a given cell is first specified! 7/26/201 30
31 Proteomics What parts are being made? (identity) Develop technologies for global proteomics Develop technologies for selective proteomics What is their state of modification? (activity) Phosphorylation state Other covalent modifications What are their levels? (quantity) High-throughput quantification Rapid quantification 7/26/201 31
32 Capillary LC-FTICR 2-D display of peptides from a yeast soluble protein digest >160,000 isotopic distributions corresponding to >100,000 polypeptides detected 2,500 2,243 1,987 1,731 MW 1,475 1, minutes /26/201 32
33 Probability Network for Proteomics Genome AGTCCGTAACGGCTAGCTGATCTGACTAGTCTAGCGCGCTAGGAGAGAGTATATCCCCGCTTCTAATCGAC Protein Peptide MS/MS LC Prob Bioinform Total Prob 7/26/201 33
34 Software provides connection between visual displays and data 7/26/201 34
35 Expression analysis from MS Proteomic Data Expression levels between cell populations as measured by FTICR MS are correlated and quantitative allowing for expression and network analysis. WR Cannon H( x) N H( x, y) i 1 N i 1 p ( x)log p ( x) i p ( x, y)log i i Pacific pi ( x, y Northwest ) National Laboratory 7/26/201 35
36 Database Requirements for Proteomics 1. Build Knowledge Base Pooled Proteins Pooled Peptides >10 3 MS/MS Daughter Peaks MS/MS MS/MS MS/MS Hypothetical Hypothetical Hypothetical C Archive data 2. High-throughput Experiments FTICR-MS Pooled Proteins Pooled Peptides Proteomic State 7/26/201 36
37 Automated data mining of genome superfamily sequences Data integration and method validation Automatic Superfamily Finder Random hits Fragments Members Superfamily organizer Motif Domain Pattern Integrator CxxxCx[F,Y,W]C N C Operon Text Extracts and validates superfamily sequences based on PSI-BLAST (automation tool under development) HJ Sofia Organizes and visualizes superfamily sequences (Prototype built) Supports data mining 7/26/201 37
38 Computational Cell Environment Analysis Tools Starlight Similarity Box Psort Prosite Announcements Collaborators Data Sets Experimental FTICR CARS Computational Reference Links 7/26/201 38
39 New Model for Biological Analysis Information Synthesis Mathematical, Computational, Visualization 1. Enhance understanding of known systems Hypothesis Computation 2. Provide qualitative/ quantitative insights direct new experiments or design new systems Analytical Experimental, Robotic Computation 3. Provide quantitative results to: - Replace experiments that are too difficult, dangerous or expensive - Go into new temporal and spatial domains - Extend limited experimental data into new domains of parameter space 7/26/201 39
40 Computational Sciences Ties Systems Biology Together Computational Biology Kinetic Simulations Structural Biology Cellular Objects Genomic Data Proteomic Data High-Throughput Technologies Information Theory Micro Array Data Extensible BioNetwork Environment Antibody Probes Petri Nets Molecular Biology Cellular Observatory Cell Biology Hypothesis-driven Research 7/26/201 40
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