Pathways and networks
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Pathways and Networks. Joel R. Stiles, MD, PhD Overview; Algorithms, Software and Hardware Issues Ronald N. Germain, MD, PhD Modeling and Simulation in Immunology Timothy J. Kinsella, MD Systems Biology, Cancer Therapeutics, and Personalized Medicine. IMAG. Pathways and Networks.

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Pathways and networks

Pathways and Networks

  • Joel R. Stiles, MD, PhD

    • Overview; Algorithms, Software and Hardware Issues

  • Ronald N. Germain, MD, PhD

    • Modeling and Simulation in Immunology

  • Timothy J. Kinsella, MD

    • Systems Biology, Cancer Therapeutics, and Personalized Medicine

IMAG


Pathways and networks1

Pathways and Networks

Joel R. Stiles, MD, PhD

Director, National Resource for Biomedical Supercomputing, Pittsburgh Supercomputing Center

Department of Biological Sciences

Lane Center for Computational Biology

Carnegie Mellon University

IMAG


Pathways and networks2

Pathways and Networks

Scales:

  • Populations

  • Whole-Body

  • Cell-Tissue-Organ

  • Pathways and Networks

  • Atomic and Molecular

IMAG


Pathways and networks3

Pathways and Networks

Scales:

  • Populations

  • Whole-Body

  • Cell-Tissue-Organ

  • Pathways and Networks (not really a scale)

  • Atomic and Molecular

IMAG


Pathways and networks4

Pathways and Networks

Scales:

  • Populations

  • Whole-Body

  • Cell-Tissue-Organ

  • Pathways and Networks

  • Atomic and Molecular

can take inputs from here

IMAG


Pathways and networks5

Pathways and Networks

Scales:

  • Populations

  • Whole-Body

  • Cell-Tissue-Organ

  • Pathways and Networks

  • Atomic and Molecular

methods and insights apply at all levels

IMAG


Pathways and networks6

Pathways and Networks

Multiscale Methods and Approaches

So it might have been:

  • Populations

  • Whole-Body

  • Cell-Tissue-Organ

  • Subcellular-to-Cellular

  • Atomic and Molecular

Network analysis and discovery

Many others…

IMAG


Pathways and networks7

Pathways and Networks

So it might have been:

  • Populations

  • Whole-Body

  • Cell-Tissue-Organ

  • Subcellular-to-Cellular

  • Atomic and Molecular

Examples of Application Areas

Immunology

DNA damage and repair

Many others…

IMAG


Pathways and networks

Recent emphasis on pathways and networks has been:

  • Driven by developments in high-throughput biotechnology, resulting in a data-rich environment

  • Coincident with rise of Systems Biology and a vision of personalized medicine (e.g., Lee Hood’s P4 Medicine:

    • Personalized

    • Participatory

    • Predictive

    • Preventive

IMAG


Pathways and networks

Recent emphasis on pathways and networks has been:

  • Driven by developments in high-throughput biotechnology, resulting in a data-rich environment

  • Coincident with rise of Systems Biology and a vision of personalized medicine (e.g., Lee Hood’s P4 Medicine:

    • Personalized

    • Participatory

    • Predictive

    • Preventive

These will be achieved in the (relative) short term.

IMAG


Pathways and networks

Recent emphasis on pathways and networks has been:

  • Driven by developments in high-throughput biotechnology, resulting in a data-rich environment

  • Coincident with rise of Systems Biology and a vision of personalized medicine (e.g., Lee Hood’s P4 Medicine:

    • Personalized

    • Participatory

    • Predictive

    • Preventive

These are long-term goals and are critically dependent on longitudinal studies and modeling and simulation.


Pathways and networks

(http://web.mit.edu/8.592/www/lectures/lec19/BioNets.gif)

IMAG


Pathways and networks

Partial “Metabolome”

  • And we need more:

    • “Glycome”

    • “Lipidome”

    • “Kinome”

IMAG

(http://web.mit.edu/8.592/www/lectures/lec19/BiochemicalPaths.gif)


Pathways and networks

Impact and Acceptance:

  • Successes (what has worked):

    • Application of graph theory to analyze the topologies of genetic and molecular networks, defining organizing principles governing their dynamics

IMAG


Pathways and networks

Impact and Acceptance:

  • Successes (what has worked):

    • Cell Cycle

Jill C. Sible and John J. Tyson

IMAG


Pathways and networks

Impact and Acceptance:

  • Successes (what has worked):

    • Synthetic Biology

      • Modified Signaling Cascades

      • Engineered Oscillating Networks

        • E.g., the “Repressilator” (Elowitz & Leibler, Nature, 2000)

      • But in this area the preponderance of failures may be of considerably more interest than the successes…

    • Immunology (Germain)

    • Cancer (Kinsella)

IMAG


Pathways and networks

Impact, Acceptance, and Challenges:

  • What hasn’t worked (as well as we would like):

    • Intelligent Drug Design

      • Challenge: Computational Expense

        • Higher organisms/more complex systems

        • Long timescale molecular dynamics and quantum mechanics

        • Specialized hardware likely to be increasingly important (e.g., ASICs for Molecular and Brownian Dynamics)

IMAG


Pathways and networks

Impact, Acceptance, and Challenges:

  • What hasn’t worked (as well as we would like):

    • Cross-fertilization with some other fields, e.g., Computational Neuroscience

      • “Why Are Computational Neuroscience and Systems Biology So Separate?” Erik De Schutter, PLOS Comp. Bio. 2008

      • “Data-poor” area about to explode with massive data on actual synaptic connectivity of neural microcircuitry (the “Connectome”)

IMAG


Pathways and networks

Impact, Acceptance, and Challenges:

  • What hasn’t worked (as well as we would like):

    • Challenges related to the Connectome: data acquisition and data scale (presently terabytes, soon petabytes)

In-plane zoom series, Clay Reid, Harvard Center for Brain Science


Pathways and networks

Impact, Acceptance, and Challenges:

  • What hasn’t worked (as well as we would like):

    • Mapping of networks and pathways into spatially realistic models

    • Efficient exploration of stochastic methods in spatially realistic models

    • Challenges:

      • Unexpected outcomes in Synthetic Biology?

      • Software design and interoperability issues for building spatially realistic models

      • Software design and theoretical issues for stochastic simulations in spatially realistic models – how and when to use?


Pathways and networks

Computational Microphysiology Software Pipeline

Create or Edit Geometry

Various Software Packages

e.g., FormZ, XVoxTrace, NWGrid, Mesquite, LaGrit, VTK, OpenDX, PSC VB

DReAMM

Design, Render & Animate MCell Models

MCell

General Monte Carlo Simulator of Microcellular Physiology

*

Annotate

Geometry

Generate

Mesh(es)

*

Generate

Mesh(es)

Annotate

Mesh

*

Specify Non-spatial Model Parameters

Simulate Model

Visualize & Analyze Results


Pathways and networks

Computational Microphysiology Software Pipeline

Various Software Packages

e.g., Blender, FormZ, XVoxTrace, NWGrid, Mesquite, LaGrit, Cubit, NETGEN, VTK, ITK, PSC_DX, PSC Volume Browser

DReAMM

Design, Render & Animate MCell Models

MCell

General Monte Carlo Simulator of Microcellular Physiology

Create or Edit Geometry

*

Annotate

Geometry

Generate

Mesh(es)

*

Generate

Mesh(es)

Annotate

Mesh

*

Specify Non-spatial Model Parameters

Simulate Model

Other Simulation Software:

Virtual Cell, ECell, Gepasi/Copasi,

Physiome, Berkeley Madonna,

BioNetGen, Smoldyn, ChemCell, more…

Visualize & Analyze Results


Pathways and networks

When is it necessary to use spatially realistic models and stochastic simulations?

Intuition says when the copy number of molecules is small, leading to significant (Poisson) noise and possible effects on dynamics.

But that is only part of the answer.

Rephrase the question:

Under what conditions will spatially realistic stochastic simulations deviate from mass action simulations?

Answer: Whenever the expected mass action reaction time is short relative to the mixing time in space.


Pathways and networks

When is it necessary to use spatially realistic models and stochastic simulations?

  • What determines the expected mass action reaction time?

    • Rate constant k

    • Concentration of reactant(s) – implicitly assumes molecules are always randomized throughout space


Pathways and networks

When is it necessary to use spatially realistic models and stochastic simulations?

  • What determines the expected mass action reaction time?

    • Rate constant k

    • Concentration of reactant(s) – implicitly assumes molecules are always randomized throughout space

  • What determines mixing time?

    • Intermolecular distance (local “concentration”)

    • Molecular mobility (diffusion coefficients)

    • Cellular conditions such as:

      • Buffered diffusion

      • Subcellular compartmentalization, etc.


Pathways and networks

Major Challenges:

  • Software Development – much more costly than hardware development but receives far less support

  • Training/Professional Development/People Support

  • Experimental data acquisition to validate detailed physiological models

  • Hardware (specialized?) Development

  • Impact of all of the above on new drug development


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