# Presentation: 332746

## Modeling and Simulation of Pathways, Cells and Communities 

- Overall Goal:
- Physically based “complete” model of a Cell
- Strong coupling to experimental data
- Genome + Environment  Phenotype
- Analysis, Interpretation, Prediction and Design
- Engineering level simulation system to support “design”
- High Level Scientific Drivers from DOE Mission Areas
- Environmental Remediation,
- Carbon Sequestration,
- Alternative Feedstocks
- Fundamental Science

## Science and Engineering Scenarios: Model Drivers

- Engineering *Radiodurans* to clean up aromatic hydrocarbons in a radiation intensive environment
- Microbial community communication [quorum sensing]
- Inter-cellular communication in bacterial communities
- Spore-to-Spore in Bacilli [end-to-end life cycle modeling]
- Understanding Cyanobacteria/Diatoms Roles for carbon sequestration
- Automatically generating Bergey’s manual entry from an environmentally collected genome sample
- Need to model both Prokaryotic and Eukaryotic systems
*In silico* strain collection

## Data Interfacing Between Modeling and Experiment

- Cellular Observatory  Computational Virtual Cell
- Tools [PSE] for integrating experimental data needed for simulation
- New tools for statistical design of experiments
- Connect simulations to observables
- Tools for “image data” to input to models [compartmental models]
- Database query and database design in support of modeling
- Databases to capture modeling output
- Tools for analysis of “data” from simulations
- Formalizing a scientific article
- Knowledge representation and engineering [ontologies]
- Uncertainty, metadata, new paradigm requires many scenarios

## Designing and Building Physically based Hierarchical models of cells and communities

- Models able to span full microbial lifecycle “Spore-to-Spore”
- Collection of “Community” codes – interfaces for component coupling
- Multiscale [space, time, complexity] coupling between length/time scales
- Molecules – complexes - pathways – functional subsystems – cells - communities
- Heterogeneous models.. Variety of materials and “subgrid” models
- More complex collections of components and materials than existing models of climate or engineering systems
- Effective abstractions are needs for multiple modeling hierarchies
- Structural models both internal and external
- Codes need to be scalable from desktops to the largest machines

## Hardware and Software Infrastructure needed

- Data synthesis requires Grids [linking both experiment and simulation]
- Cell modeling PSE coupled with data resources from experiment “GTL Grid”
- Full up cell models will require tightly coupled parallel architectures
- Smaller component models will run on workstations, full up cell simulations will require largest scale systems: Petaflops scale systems
- Very wide ranging needs for computing systems and infrastructure
- Distributing computing infrastructure and data resources close to both experimental groups and modeling groups
- No single architecture is appropriate, however there maybe a role for special purpose hardware
- Mixed modes discrete and continuum methods.. with multiscale
- Challenges for new algorithms and software tools
- High-resolution graphics and visualization challenge.. Info viz challenges

## End of viewgraphs presented

## Breakout Session

- Metabolic and regulatory pathway reconstruction and simulations, modeling cells and cell communities.
- What are the biological questions that we want to help answer with modeling..
- Engineering level Design Capability for the Cell
- Algorithms for parsing [independence]
- Building bridges between computer science.. Leverage existing models and new math approaches..
- Problems solving environment to support modeling
- Tie to DOE mission.. Bioremediation, feedstocks, carbon sequestration, environment

## Building engineering model tied to constraints
Focusing on modeling
Physically based input and output experimental data
Make sure that we have the experimental community and access to data..
Leverage high-throughput data..
Understanding the Logic of Life...
Genotype Phenotype relations
Analysis, interpretation, prediction and design
Input/output . . . Environment + genes = phenotype

- Focusing on modeling
- Physically based input and output experimental data
- Make sure that we have the experimental community and access to data..
- Leverage high-throughput data..
- Understanding the Logic of Life...
- Genotype Phenotype relations
- Analysis, interpretation, prediction and design
- Input/output . . . Environment + genes = phenotype

## Completion.. Cell cycle.. Signaling..
Reconstruction .. Operon and Metabolism.. Straightforward
Signaling, 
Accounting for the entire genome
Systematic analysis... of knockouts... 
Representation in a systemic fashion.. 
Genome scale assessment of kinetic properties..
Metabolomic data
General use software that is platform independent
Modeling philosophy that is relevant to biology

- Reconstruction .. Operon and Metabolism.. Straightforward
- Signaling,
- Accounting for the entire genome
- Systematic analysis... of knockouts...
- Representation in a systemic fashion..
- Genome scale assessment of kinetic properties..
- Metabolomic data
- General use software that is platform independent
- Modeling philosophy that is relevant to biology

## Need for models that marry regulatory and metabolitic
Metabolitic reconstruction
Control mechanisms
Cyanobacteria for sequestration
P. marinus, Anabena
Dynamical models of synechocytis
Synchronous culture ... analysis within cell cycle..
Reconstruction..
Epigenetic regulation
Genetic regulation

- Metabolitic reconstruction
- Control mechanisms
- Cyanobacteria for sequestration
- P. marinus, Anabena
- Dynamical models of synechocytis
- Synchronous culture ... analysis within cell cycle..
- Reconstruction..
- Epigenetic regulation
- Genetic regulation

## Methods
Experimental support
Need data from experimental setup 
Large-scale computations.. Nonlinear algebraic sets stiff ODEs integrations
Bifurcation analysis of reduced dynamical models
Models ... prediction...
Diagnostic [integrates experimental data] and prediction of observables.. And consistency.. Reconstruction problem..
Consistent representation of the information that will lead to the models

- Experimental support
- Need data from experimental setup
- Large-scale computations.. Nonlinear algebraic sets stiff ODEs integrations
- Bifurcation analysis of reduced dynamical models
- Models ... prediction...
- Diagnostic [integrates experimental data] and prediction of observables.. And consistency.. Reconstruction problem..
- Consistent representation of the information that will lead to the models

## Representation of data..
Deep structure and annotation of the data..
Fully annotated model .. Precisely specifying what is meant
Supports work on abstraction...
Extensible modeling languages..
Design of Experiments for detecting network structure
Physical models.. vs Bayesian models difficulties..
Cellular Observatory and Models

- Deep structure and annotation of the data..
- Fully annotated model .. Precisely specifying what is meant
- Supports work on abstraction...
- Extensible modeling languages..
- Design of Experiments for detecting network structure
- Physical models.. vs Bayesian models difficulties..
- Cellular Observatory and Models

## PSE the bridges sim and experiment..
Coupling the spatial and temporal scales in the models
Technologies...
Imaging, kinetics data, genomics, proteomics

- Coupling the spatial and temporal scales in the models
- Technologies...
- Imaging, kinetics data, genomics, proteomics

## Converting data to forms useful for modeling

- Formalizing a scientific article
- Knowledge representation and engineering [ontologies]
- Uncertainty, metadata, new paradigm requires many scenarios
- New tools for Statistical design of experiments
- “image data” to input to models [compartmental models]
- Database query and database design in support of modeling
- Tools for analysis of “data” from simulations

## Physically based hierarchical models 

- Community codes
- Coupling between length/time scales
- Heterogeneous models.. Variety of materials
- Effective abstractions
- Structural models both internal and external

## Hardware and Software Infrastructure needed

- Data synthesis requires Grids [experiment and simulation]
- Full up cell models will require tightly coupled parallel architectures
- Very wide ranging needs for computing systems and infrastructure
- [nonboolean regulation.. Stochastic modeling of metabolism.. Bad idea]
- Mixed modes discrete and continuum methods..with multiscale
- High-resolution graphics and visualization challenge.. Info viz challenges
- Simulation steering...