# Presentation: 470908

## Enabling Technologies from the Biology Perspective

- H. Steven Wiley
- January 22nd, 2002

## 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.

## 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

## What are the Computational Requirements of Systems Biology?

- 	- 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.

**What are the Computational Requirements of Systems Biology?**

## Biological Organisms are Dynamic and Recursive

**Biological Organisms are Dynamic and Recursive**

## Systems Biology Requires Scientific Integration

**Systems Biology Requires Scientific Integration**

- 	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.

**Notes:**
The GTL program describes programmatic integration, but does not indicate how scientific integration will be achieved.
All “systems biology” programs acknowledge the need to physically integrate the multidisciplinary components.
DOE does not currently have an integrated computation-biology-instrumentation facility.

## Understanding Complex Biological Systems Using a Top-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

## Cell Migration Can Be Broken Down Into a Series of Steps...

## Extend Model From High-level to Low LevelExample: Wound Healing Response

## 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

## 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

## What, Where, Quantity, Quality?

**What, Where, Quantity, Quality?**

- 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

**To successfully model a complex biological system, one must minimally know the following information:**

## What Is Necessary to Understand a Network?

**Modeling & Analysis**

**Experimental Biology**

## Growth Factor Pathways

## How Can We Understand Signaling Networks?

**Hanahan & Weinberg, 2000**
_**“**__**Hallmarks of Cancer”**_

## Structure of Cells

## Intracellular Trafficking Pathways

## The EGFR System

**The EGFR System**

## How Do We go From Cartoon Model to Realistic Model?

**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

## The EGFR System

**The EGFR System**

## 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

## Movement of EGFR through early endosomes as determined by image analysis

## Colocalization of EGFR and HER2

**Colocalization of EGFR and HER2**

## Slide 24

## Trafficking model for EGFR signaling

**Trafficking model for EGFR signaling**

**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

## 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.

**Signal Pathways: Computer Simulation of Protein-Protein Complexes **

-  Issues for modeling the Ras:Raf signaling complex :
- 50,000 atoms, long-time simulations, solvent, lack of knowledge of the entire structure

- TP Straatsma, H Resat, JH Miller, T Soares, DA Dixon, PNAS, 2001

## Model for the Role of Ca2+ in Stress-Response Pathways: NWGrid/NWPhys

**Model for the Role of Ca****2+**** in Stress-Response Pathways: NWGrid/NWPhys**

- H Trease, JH Miller

**G protein-coupled receptor**

## Signaling Through Growth Factor Receptors

**Notes:**
What we do is to take what is known about the molecular biology of signaling networks and conceptualize them. By looking at the different parts of the pathways as concepts we can understand them in terms of controls and regulators.

## Central Role of Computation and Simulation

**Central Role of Computation and Simulation**

- Computer simulations provide a conceptual framework for organizing system behavior

## Cell State Dictates Cell Response 

**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!

## 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

## Slide 32

## Probability Network for Proteomics

- Genome

- Protein

- Peptide

- MS/MS

- LC

- Total Prob

- Bioinform

- Prob

- AGTCCGTAACGGCTAGCTGATCTGACTAGTCTAGCGCGCTAGGAGAGAGTATATCCCCGCTTCTAATCGAC

## Software provides connection between visual displays and data

**Software provides connection between visual displays and data**

## Expression analysis from MS Proteomic Data

**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

**Notes:**

Figure 1. The first plot is a scatter plot of mass classes (ie observations in the ms data that are grouped by mass ranges) between two cell populations.
The fact that a large majority of the points lie on the diagonal means that the levels are correlated. This plot is one that is generally associated with
DNA microarrays, not protein expression measurement. The most important point is that the points that fall off the diagonal are up or down regulated and the 
data can be made quantitative.

Figure 2. Graph consisting of proteins (nodes) that are related by the mutual information statistic. The relationships are symbolized by double-headed arrows (edges), which  represents a range of values of the mutual information statistic above a arbitrary cutoff value. The data used in the analysis were from FTICR MS analysis of Deinococcus radiodurans cells grown under 15 different experimental conditions, in which the FTICR MS results indicated the presence or absence of each protein. The information theory analysis used this two-state model (present/absent).

Although, the data set is small and the data itself only consists of information on whether the protein was observed or not (rather than expression levels), the results are intriguing. Five of the six proteins in the group on the right of the figure are all associated with biosynthesis: (a) the ribosomal protein L29; (b) methionyl-tRNA synthetase, (c) a signal recognition particle protein that binds RNA; (d) Guanosine monophosphate synthase, an enzyme involved in GTP biosynthesis; and (e) adenylosuccinate lyase, an enzyme involved in de novo purine synthesis. The six protein is of unknown function. The value of these networks is two-fold: first they provide a computational framework for discover-driven science; second, they capture knowledge from diverse experimental sources and allow the biologist to make more insightful hypotheses.

## Database Requirements for Proteomics

- Pooled
- Proteins

- Pooled
- Peptides

**Proteomic State**

- FTICR-MS

_**1. Build Knowledge Base**_

_**2. High-throughput Experiments**_

- >103

## Automated data mining of genome superfamily sequences – Data integration and method validation

*Automatic *
*Superfamily Finder*

*Superfamily *
*organizer*

*Text** *

*Motif*

*Pattern *
*Integrator*

*Extracts and validates superfamily sequences based on PSI-BLAST*
*(automation tool under development)*

*Organizes and visualizes*
*superfamily sequences*
*(Prototype built)*

- HJ Sofia

## Computational Cell Environment

**Computational Cell Environment**

**Announcements**

**Reference Links**

**Collaborators**

**Data Sets**
**  ****Experimental**
**  ****FTICR**
**  ****CARS**
**  ****Computational**

**Analysis Tools**
**  ****Starlight**
**  ****Similarity Box**
**  ****Psort**
**  ****Prosite**

## New Model for Biological Analysis

***New Model for Biological Analysis***

- Analytical
- Experimental,
- Robotic
**Computation**

- Information
- Synthesis
- Mathematical,
**Computational**, Visualization

- Hypothesis
**Computation**

**1. Enhance understanding of known systems**

**2. Provide qualitative/ quantitative insights ******** direct new experiments or design new systems**

**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**

## Computational Sciences Ties Systems Biology Together

**Computational Sciences Ties Systems Biology Together**