# Presentation: 117252

## GTL Software Infrastructure: A Computer Science Perspective

- Rick Stevens
- Argonne National Laboratory
- University of Chicago

## Tool Building Will Be Critical for GTL Progress

- “If I had eight hours to chop down a tree,
- I’d spend six sharpening my axe.”
- -- Abraham Lincoln

## The Outline

- Database Tools and DB Frameworks
- Computational Toolkits Focused on Biological Systems
- Computational Mathematical Biology Topics
- Hierarchical Biological Systems Modeling Environment
- International Systems Biology Grid
- Workflow for Understanding Microbial Life
- Modeling Example
- Simulation Targets
- Technologies Needed for Biological CAD : Tomita’s E-cell List
- Top 10 Bioinformatics Goals: Richard Smith’s List
- Conclusions

## Database and DB Frameworks

- New Types of Data Support extend existing RDBMS [Oracle, db2]
- Sequences and Strings
- Trees and Clusters
- Networks and Pathways
- Deep Images
- 3D Models and Shapes
- Molecules and Coordinate Structures
- Hierarchical Models and Systems Descriptions
- Time Series and Sets
- Probabilities and Confidence Factors
- Visualizations
- Store, Retrieve, index, search, compare, transform, input/output, import/export, etc.

## Database and DB Frameworks [continued]

- Scalable performance [parallel, open source OS support]
- Robust Data Exchange
- Data Provenance and curation Tools
- Query Optimization including computing in the query
- Fast operator support for biological structures
- Regular expression, sequence similarity, tree comparisons, confidence factors, etc.
- Robust linkages to:
- Simulation [logical and numerical]
- Automated Deduction and Knowledge Structures [production systems]
- Interfaces to experimental systems [chips, detectors, microscopes, etc.]
- Workflow and planning
- Visualization and statistical analysis
- Distributed infrastructure

## Some Mathematical Biology Topics Relevant to GTL

- Population models, symbiosis and stability
- Discrete growth models
- Reaction kinetics
- Biological oscillators and switches
- Coupled oscillators
- Reaction-diffusion, chemotaxis and non-locality
- Oscillator generated wave phenomena and patterns
- Spatial pattern formation with population interactions
- Mechanical models for generating pattern and form in development
- Evolution and morphogenesis

## Mathematical Toolkits Focused on Biological Systems

- “A Mathematica for molecular, cellular and systems biology”
- Core data models and structures [see db]
- Optimized functions [see core libraries]
- Scripting environment [e.g. Python, PERL, ruby, etc.]
- Database accessors and built-in schemas
- Simulation interfaces
- Parallel and accelerated kernels
- Visualization interfaces [info-vis and sci-vis]
- Collaborative workflow and group use interfaces

## Some of the Core Library Functions O[1000]

- Algorithms for Restriction Maps and Map Assembly
- Planning cloning and clone libraries, building physical Genome Maps
- Sequence assembly, Multiple sequence assembly
- Data models and sequence analysis algorithms, multiple sequence alignment
- Probability and statistics for sequence alignment and patterns
- Gene prediction, Mutation analysis
- Trees and sequence comparisons, construction
- Phylogenetic tree construction and analysis
- Comparative Genomics
- Proteomics analysis
- Protein structure prediction and kinetics prediction
- Array analysis

##  Hierarchical Biological System Modeling Environment

- Genetic Sequences
- Molecular Machines
- Molecular Complexes and modules
- Networks + Pathways [metabolic, signaling, regulation]
- Structural components [ultrastructures]
- Cell Structure and Morphology
- Extracellular Environment
- Populations and Consortia etc.

## An International Systems Biology Grid

- A Data, Experiment and Simulation Grid Linking:
- People [biologists, computer scientists, mathematicians, etc.]
- Experimental systems [arrays, detectors, MS, MRI, EM, etc.]
- Databases [data centers, curators, analysis servers]
- Simulation Resources [supercomputers, visualization, desktops]
- Discovery Resources [search servers perhaps optimized]
- Education and Teaching Resources [classrooms, labs, etc.]
- Different than and more fine grain than current Grid Projects
- More laboratory integration [need small laboratory software interfaces]
- Most of the participants will be experimentalists [team workflow, visualization]
- More diversity of data sources and databases [integration, federation]
- More portals to simulation environments [ASP models]

## A Systems Biology Approach to Understanding Cellular Life Integrating:

- A Systems Biology Approach to Understanding Cellular Life Integrating:

- Theory
- Numerical methods
- Software development
- Data models and Visualization
- DB support
- Education

## A GTL Focus on Prokaryotic life [bacteria]

- ~4,000 known species of prokaryotic life
- It is estimated that we have identified only 1%-5% of extant species  80,000-400,000 species
- Can we attack the culturability problem via simulation?
- More diversity than Eukaryotic life forms
- More diverse metabolisms, more diverse environments
- A Human contains 1012 cells and 1013 bacterial cells
- Estimated that protoctista 250,000 species are extant [10,000 known today, 40,000 from paleontology]

## Modeling a Prokaryotic Cell?

- Typical bacteria [*E. coli*]
- 1000nm x 300nm x 300nm volume
- ~4000 genes and gene products
- 1⁄4 genes  protein synthesis
- 1⁄4 genes  glycolysis
- 1⁄4 genes  citric acid cycle
- rest genes involved in regulation, synthesis and degrading tasks
- Relatively few genes related to sensing and motility
- 1,000’s of small molecular species
- ~3 million total molecules to track

## Intracellular environment

- 100 nm3
- 450 proteins
- 30 ribosomes
- 340 tRNA molecules
- Several long mRNAs
- 30,000 small organic molecules
- 50,000 Ions
- Rest filled with water 70%

- From: David Goodsell, The Machinery of Life

## Cell Membranes and Cell Wall

- Cell wall
- Polysaccharides
- Porin pores
- Peptidoglycan
- crosslinked
- Periplasmic space
- Small proteins
- Complex inner membrane
- < 50% lipids

- From: David Goodsell, The Machinery of Life

## Flagellum and Flagellar Motor

- Transmembrane proton powered rotating motor
- About 10 Flagella per cell
- 5-10 um long
- Built from the inside out
- Propels cell ~10-20 um/sec
- Medium is extremely viscous

- From: David Goodsell, The Machinery of Life

## DNA Replication via DNA Polymerase

- DNA replication about 800 new nucleotides per second
- In circular DNA both directions at once
- 50 minute to duplicate entire circle of 4,700,000 nucleotides
- With cell replication ~30 minutes
- Simultaneous duplication possible

- From: David Goodsell, The Machinery of Life

## Phototrophic Prokaryotic Cell 

- From: Lynn Margulis, The Five Kingdoms of Life

## Flagellum [prok] vs Undulipodium [euk] Complexity

- From: Lynn Margulis, The Five Kingdoms of Life

## Some Modeling and Simulation Targets for GTL

- Modeling activity of single genes
- Probabilistic models of prokaryotic genes and regulation
- Logical models of regulatory control in eukaryotic systems
- Gene regulation networks and genetic network inference in computational models and applications large-scale gene expression data
- Atomistic level simulation of biomolecules
- Diffusion phenomena in cytoplasm and extracellular environment
- Kinetic models of excitable membranes and synaptic interactions
- Stochastic simulation of cell signaling pathways
- Complex dynamics of cell cycle regulation
- Model simplification

## Technologies for Biological CAD: Tomita’s list

**Enzyme engineering**: to refine enzymes and to analyze kinetic parameters *in vitro*.
**Metabolic engineering**: to analyze flux rates *in vivo*
**Analytical chemistry**: to determine and analyze the quantity of metabolites efficiently.
**Genetic engineering**: to cut-and-paste genes on demand, for modifying metabolic pathways.
**Simulation science**: to efficiently and accurately simulate a large-number of reactions.
**Knowledge engineering**: to construct, edit and maintain large-metabolic knowledge bases.
**Mathematical engineering**: to estimate and tune unknown parameters.

## Bioinformatics Goals: Richard Smith’s top 10 Challenges

*Precise, predictive model of transcription initiation and termination*: ability to predict where and when transcription will occur in a genome
*Precise, predictive model of RNA splicing/alternative splicing*: ability to predict the splicing pattern of any primary transcript
*Precise, quantitative models of signal transduction pathways*:ability to predict cellular response to external stimuli
- Determining effective *protein:DNA*, *protein:RNA* and *protein:protein* recognition codes
- Accurate ab initio structure prediction
- Rational design of small molecule inhibitors of proteins

## Bioinformatics Goals: Richard Smith top 10 Challenges

*Mechanistic understanding of protein evolution*: understanding exactly how new protein functions evolve
*Mechanistic understanding of speciation*: molecular details of how speciation occurs
*Continued development of effective gene ontologies* – systematic ways to describe the functions of any gene or protein
- Education: development of appropriate bioinformatics curricula for secondary, undergraduate, and graduate education

## Conclusions

- Priorities for R+D software infrastructure investment
- Extensions to current database capabilities
- Kernel functions for trees and networks, uncertainty representation
- New types of toolkits that integrate bioinformatics algorithms
- “Mathematica” or “matlab” for computational molecular and cell biology
- Visual representation and analysis of biological systems
- New algorithms for core problems in systems biology
- Perhaps linked with new developments in hardware
- Scalable hierarchical modeling environments
- Multiple levels of biological detail and levels of abstraction
- International grid for systems biology