Pathways and Networks

1. 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

2. 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

3. Pathways and Networks Scales: • Populations • Whole-Body • Cell-Tissue-Organ • Pathways and Networks • Atomic and Molecular IMAG

4. Pathways and Networks Scales: • Populations • Whole-Body • Cell-Tissue-Organ • Pathways and Networks (not really a scale) • Atomic and Molecular IMAG

5. Pathways and Networks Scales: • Populations • Whole-Body • Cell-Tissue-Organ • Pathways and Networks • Atomic and Molecular can take inputs from here IMAG

6. Pathways and Networks Scales: • Populations • Whole-Body • Cell-Tissue-Organ • Pathways and Networks • Atomic and Molecular methods and insights apply at all levels IMAG

7. 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

8. 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

9. 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

10. 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

11. 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.

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

13. Partial “Metabolome” • And we need more: • “Glycome” • “Lipidome” • “Kinome” • … IMAG (http://web.mit.edu/8.592/www/lectures/lec19/BiochemicalPaths.gif)

14. 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

15. Impact and Acceptance: • Successes (what has worked): • Cell Cycle Jill C. Sible and John J. Tyson IMAG

16. 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

17. 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

18. 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

19. 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

20. 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?

21. 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

22. 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

23. 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.

24. 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

25. 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.

26. 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