Theoretical and Computational Methods

Theoretical and Computational Methods

Working Group leads: Suvranu De; Vasilis Marmarelis

 

Goals and Objectives:

The goals of the Theoretical and Computational Working Group are to:

• Provide a clear understanding of state-of-the-art theoretical and computational methods for multiscale modeling of biological systems. Included in this are both hierarchical and concurrent scale coupling approaches as well as their hybrids.

• Focus on methods with strong mathematical foundations. Identify efficient and reliable techniques for multiscale error estimation, uncertainty quantification and stability analysis.

• Showcase clear examples of methods that have demonstrated strong potential for success in the solution of biological problems.

• Provide links to existing software related to multiscale modeling methods.

• Provide a fertile ground for discussion on current challenges and opportunities.

• Work with the other workgroups to identify problems and then posing them to the theoretical and computational modeling community.

• Organizing talks, symposia and conferences in the area of theoretical and computational multiscale modeling methods.

 

Participants:

  1. De Suvranu des@rpi.edu
  2. Marmarelis Vasilis marmarelis@hotmail.com
  3. Peng Grace penggr@mail.nih.gov
  4. Hwang Wonmuk hwm@yogi.tamu.edu
  5. Buehler Markus mbuehler@mit.edu
  6. Shephard Mark shephard@rpi.edu
  7. Picu Catalin cpicu@scorec.rpi.edu
  8. Kuhl Ellen ekuhl@stanford.edu
  9. David Daryn daryn.david@nih.gov
  10. Bassingthwaighte Jiim jbb2@uw.edu
  11. Thompson Elaine elaine.thompson@fda.hhs.gov
  12. Katzper Meyer lkatzper@msn.com
  13. McQueen Philip mcqueenp@mail.nih.gov
  14. Liu Delong liud2@mail.nih.gov
  15. Gregurick Susan susan.gregurick@nih.gov
  16. Shum Lillian ShumL@mail.nih.gov
  17. Dura-Bernal Salvador salvadordura@gmail.com
  18. Peng Grace grace.peng@nih.gov
  19. Sanger Terence tsanger @ usc.edu
  20. mazurchuk Richard richard.mazurchuk@yahoo.com
  21. Ashikaga Hiroshi hashika1@jhmi.edu
  22. Krauss Susan greencorsairlabs@yahoo.com
  23. Wang Xujing xujing.wang@nih.gov
  24. Pflieger Mark mep@sourcesignal.com
  25. Pepper John john.pepper@mail.nih.gov
  26. Neymotin Sam samn@neurosim.downstate.edu
  27. Srivastava Ranjan srivasta@engr.uconn.edu
  28. Radhakrishnan Ravi rradhak@seas.upenn.edu
  29. Cannon William william.cannon@pnnl.gov
  30. Fourkal Eugene eugene.fourkal@fccc.edu
  31. Candia Julián candia@umd.edu
  32. Swat Maciek mswat@indiana.edu
  33. Buehler Markus J. mbuehler@mit.edu
  34. Linderman Jennifer linderma@umich.edu
  35. Finley Stacey sdfinley@u.northwestern.edu
  36. Chu Liang-Hui chulianghui@gmail.com
  37. Zhang Le Le_Zhang@URMC.Rochester.edu
  38. Wang Jun kingjun@swu.edu.cn
  39. Sun Yuekai yuekai@stanford.edu
  40. Hormuth David david.a.hormuth@vanderbilt.edu
  41. Andasari Vivi vandasar@wakehealth.edu
  42. Peng Huiming hpeng@wakehealth.edu
  43. Stamatelos Spyros spyros@jhu.edu
  44. Lin Ching-Long ching-long-lin@uiowa.edu
  45. Kirschner Denise kirschne@umich.edu
  46. Anderson Warren warren.anderson@jefferson.edu
  47. Makadia Hirenkumar hirenkumar.makadia@jefferson.edu
  48. McDougal Robert robert.mcdougal@yale.edu
  49. Cook Daniel djcook@udel.edu
  50. Brown David dbrown@immunetrics.com
  51. Lazzi Gianluca lazzi@utah.edu
  52. Kuttippurathu Lakshmi Lakshmi.Kuttippurathu@jefferson.edu
  53. Ropella Glen gepr@tempusdictum.com
  54. Tartibi Mehrzad mtartibi@berkeley.edu
  55. Shams Hengameh hengameh@berkeley.edu
  56. Miga Michael michael.miga@vanderbilt.edu
  57. Verma Aalap aalapverma@gmail.com

MSM Meetings

2013 Meeting

Here is a link to the WG activities: http://www.imagwiki.nibib.nih.gov/mediawiki/index.php?title=File:MethodsWG.pdf


Discussion topics:

  1. Success stories: populate index of multiscale models http://www.imagwiki.nibib.nih.gov/mediawiki/index.php?title=New_Index_of_Predictive_Models
  2. How does one choose the representative volume element in biological systems?
  3. Uncertainty quantification: timely or too uncertain?
  4. Multi-variate Nested-loop Modeling

Presentations

  • Monday, June 24, 2013 at 4pm: Mechanics of the kinesin-based transport: From single-molecule to multi-motor behaviors, to cell division.
Dr. Wonmuk Hwang, Biomedical Engineering Department, Texas A&M
Abstract: The motor protein kinesin uses ATP (adenosinetriphosphate) as a fuel and walks along the microtubule track, carrying out critical tasks such as intracellular transport and cell division. I will explain atomic simulations and single-molecule experiments analyzing modular design in kinesin's mechanochemistry. Strategies for using these findings to study multi-motor behavior will be discussed, including cooperative transport and microtubule organization in mitotic spindle dynamics.

 

  • Friday May 11, 2012 at 1:00pm EDT
Synergistic Use of Data-based and Hypothesis-based Modeling of Biomedical Dynamic Systems, Vasilis Z. Marmarelis, Ph.D.
The inductive (data-based) and the deductive (hypothesis-based) approaches have played a complementary and mutually beneficial role in the history of science, whereby observations have led to the postulation of hypotheses that are subsequently tested by properly designed experiments. This forms an evolutionary process of hypothesis formulation and testing, leading to scientific advancement. In life sciences and medicine, the importance of discovering and quantifying the physiological mechanisms under normal and pathological conditions has given rise to mechanism-based modeling methods (e.g. compartmental or structural modeling) which rely on the current state of understanding of the system under study. However, the intrinsic complexity of physiological systems and the need for validation of the structural models present formidable challenges in the mechanism-based approach and motivate the complementary use of data-based modeling approaches (typically input-output or stimulus-response formulations). The latter seek to capture the essential functional characteristics of the physiological system in a manner consistent with the available data. Subsequent analysis of the obtained data-based models suggest hypothesis-based model forms that encapsulate the relevant physiological mechanisms and can be tested through properly designed experiments. In this process, the data-based model serves as “ground truth” for the validity of an equivalent hypothesis-based or mechanism-based model. Our experience over the last 30 years shows that this “virtuous cycle” of model development is enabled by the synergistic use of data-based and hypothesis-based approaches.
The study of functional and structural complexity in living systems requires reliable and robust modeling tools in a hierarchical context of multiple scales of time and space. Although mechanism-based models remain the ultimate objective of multi-scale modeling, data-based models can be helpful in pursuing this goal because of their applicability to arbitrary levels of systemic organization from molecular to cellular to multi-cellular to organ to multi-organ etc. This broad applicability depends on appropriate methods of modeling/analysis within the constraints imposed by experimental limitations. This talk seeks to stimulate our thinking on the synergistic use of data-based and hypothesis-based modeling methods in a practical context. It will summarize our findings to date and will present illustrative examples from neural and metabolic systems where this synergistic approach has yielded useful insights.
Archived Recording: https://webmeeting.nih.gov/p79925002/

 

Wednesday, August 31, 2011 3-4pm EST

Presenter: Yusheng Feng
TITLE: Multiscale Modeling and Real-Time Control for Nanoparticle-mediated Laser Surgery for Prostate Cancer
Abstract: Advances in computational science and engineering have shown unprecedented power and potential to assist cancer biologists, oncologists, radiologists, and surgeons by providing cutting-edge computational tools for scientific research and clinical applications such as multiscale modeling and image-guided control in real-time. In the past few years, we have demonstrated, in collaboration with M.D. Anderson Cancer Center, that MR temperature imaging (MRTI) guided laser therapy can be modulated by predictive real-time control for treating prostate cancer, which was tested on in vivo K-9 model. In this talk, I will address the main idea and general computational infrastructure, and discuss how this predictive computational system can be used for investigating cellular and tissue response to thermal therapies, as well as applications in treatment planning and surgical control for prostate caner. In particular, I will discuss a computational approach for estimating patient-specific tissue properties with nanoparticle inclusion using inverse analysis.
About Speaker: Prof. Yusheng Feng received his Ph.D. in Computational Mechanics from the University of Texas at Austin after he earned two Master’s degrees in Mechanical Engineering and Applied Mathematics from University of Oklahoma. He taught Mathematics at Concordia University and was a Research Associate at the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin before he joined UT San Antonio as an Associate Professor in 2007. His research work focuses on mathematical modeling and computer simulation for cancer research. In particular, he works on image-guided laser therapy for prostate cancer, treatment planning and real time model-based predictive control, as well as nano-particle mediated thermal therapy simulation. Dr. Feng is a recipient of NIH/NCI K25 career award for his work on integrative modeling of image-guided cancer treatment simulation. He is also PI and Co-PI for two NSF grants that made possible to establish SiViRT computation center and Advanced Visualization Lab (opened in May 2011).

Wednesday April 20, 2011 4-5pm ET

Presenter: Markus J. Buehler
TITLE: Tu(r)ning weakness to strength: Multiscale modeling of hierarchical protein materials
ABSTRACT - Biology efficiently creates hierarchical structures, where initiated at nano scales, are exhibited in macro or physiological multifunctional materials to provide a variety of functional properties that include: structural support, force generation, catalytic properties, or energy conversion. This is exemplified in a broad range of biological materials such as hair, skin, bone, spider silk or cells. For instance, despite its simple building blocks spider silk is one of the strongest, most extensible and toughest biological materials known, exceeding the properties of many engineered materials including steel.

 

Current Projects:

1. Jacobian-free multiscale method [RaDe11]

2. Multiscale modeling of hydroxyapatite nanocrystals [ZaDe11]

3. Multiscale modeling of bionanoporous materials [ZaDe10]

4. Finite element framework for scaffolded DNA origami structure, dynamics, and mechanics [CaKi11]

5. Finite element framework for protein dynamics and mechanics [Ba08, KiAl11, SeBa10]

6. Coarse grained molecular dynamics for biofilaments including: collagen, amyloid, microtubule, and alpha-helical filaments [Hw10,Hw07,LaHw09a, LaHwa09b,PaHw06,RaHw08,RaHw07]

7. Validation and Uncertainty Quantification of Multiscale Models. Developing methods of statistical calibration and validation based on Bayesian inference to account for uncertainty in parameters and quantities of interests calculated in multiscale models. [OdPrRo10, BaOdPr09, OdPrBaCh09, ChPruDhBaOd10, ChOdPr08]

8. Mathematical Framework for the Adaptive Modeling of Biopolymers [Kurt Anderson]

 

Current State of the Art:

1.[OdMoGh10] Oden, J.T.; Moser, R.; and Ghattas, O. “Quantification of uncertianty in computer predictions”, SIAM News, v. 43, no. 9-10, 2010.

2. [A review of global local multiscale methods]http://www.imagwiki.nibib.nih.gov/mediawiki/images/5/5f/Global-local_review_De.pdf

Challenges and Opportunities:

1. [Oden] Estimation and Control of Errors generated in multiscale models remains a central, open challenge in multiscale modeling. Multiscale modeling, by definition, is the transition from one scale to another. As one goes from fine scale to coarser scales, information is lost. This is a fundamental defect in most multiscale methodologies. To regain lost information, one must carefully define specific quantities of interest and estimate and control the error in these quantities in the transition from one scale to the other.

2. [Oden] Validation – MS modeling. The question of validation and the quantification of multiscale models is a highly complex and essentially open issue. Identifying parameters that control models of behavior at different scales is a major challenge in multiscale modeling.

3. [Hwang] "As the scale increases, the complexity of the system also increases. Thus meso- to macro-scale descriptions may be effective only for certain aspects of the system rather than realistically capturing experimental results as a whole. As multiscale modeling of biological systems is a new area, I believe the first step would be to link between atomistic and the next level of coarse graining. Through such a careful bottom-up approach,systematic ways of describing larger scale biological phenomena based on the molecular-level interactions may be possible."

4. [Anderson]I) Reintroduction of energy back into system to correctly account for added fidelity of finer grain models II) Correctly dealing with non-uniqueness of transitioning to finer-grain III) need to develop appropriate domain specific multirate temporal integration methods to better deal with local temporal scales in a multibody context.

Journal Articles:

[Ba08] Bathe, M. A Finite Element framework for computation of protein normal modes and mechanical response. Proteins: Structure, Function, and Bioinformatics, 70: 1595–1609 (2008).

[BaOdPr09]Bauman, Paul T.; Oden, J. T.; and Prudhomme, Serge. “Adaptive Multiscale Modeling of Polymeric Materials with Arlequin Coupling and Goals Algorithms,” Computer Methods in Applied Mechanics and Engineering, v. 198, issues 5-8, pp. 799-818, 2009.

http://web.mit.edu/mbuehler/www/research/publications.htm

T. Knowles, M.J. Buehler , “Nanomechanics of functional and pathological amyloid materials,” Nature Nanotechnology, accepted for publication
A. Gautieri, S. Vesentini , A. Redaelli, M.J. Buehler , “Hierarchical structure and nanomechanics of collagen microfibrils from the atomistic scale up,” Nano Letters, Vol. 11(2), pp. 757-766, 2011. PMID: 21207932
S. Keten, M.J. Buehler, “Nanostructure and molecular mechanics of dragline spider silk protein assemblies,” Journal of the Royal Society Interface, Vol. 7(53), pp. 1709-1721, 2010. PMID: 20519206
S. Keten, Z. Xu, B. Ihle, M.J. Buehler, “Nanoconfinement controls stiffness, strength and mechanical toughness of beta-sheet crystals in silk,” Nature Materials, Vol. 9, pp. 359-367, 2010. PMID: 20228820
A. Gautieri, S. Uzel, S. Vesentini, A. Redaelli, M.J. Buehler, “Molecular and mesoscale disease mechanisms of Osteogenesis Imperfecta,” Biophysical J., Vol. 97(3), pp. 857-865, 2009 PMCID: PMC2718154
A. Nova, S, Keten, N. Pugno , A. Redaelli , M.J. Buehler , “Molecular and nanostructural mechanisms of deformation, strength and toughness of spider silk fibrils,” Nano Letters , Vol. 10(7), pp. 2626-2634, 2010 PMID: 20518518
M.J. Buehler, Y. Yung, “Deformation and failure of protein materials in extreme conditions and disease,” Nature Materials, Vol. 8(3), pp. 175-188, 2009. PMID: 19229265
S. Keten, M.J. Buehler, “Geometric confinement governs the rupture strength of H-bond assemblies at a critical length scale,” Nano Letters, Vol. 8(2), pp. 743-748, 2008. PMID: 18269263
M.J. Buehler, “Nature designs tough collagen: Explaining the nanostructure of collagen fibrils,” Proc. Nat’l Academy of Sciences USA, Vol. 103 (33), pp. 12285-12290, 2006. PMCID: PMC1567872

[CaKi11] Castro, C.E., Kilchherr, F., Kim, D.N., Lin Shiao, E., Wauer, T., Wortmann, P., Bathe, M., and Dietz, H. A primer to scaffolded DNA origami. Nature Methods, 8:221-229 (2011)

[ChPruDhBaOd10] Chamoin, L.; Prudhomme, S.; Ben Dhia, H.; Bauman, P.T.; and Oden, J. T. “Ghost forces and spurious effects in atomic-to-continuum coupling methods by the Arlequin approach,” Int. J. Numerical Methods in Engineering, published online: http://www3.interscience.wiley.com/journal/123337582/abstract?CRETRY=1&SRETRY=0, March 2010.

[ChOdPr08]Chamoin, Ludovic; Oden, J. Tinsley; and Prudhomme, Serge. “A stochastic coupling method for atomic-to-continuum Monte-Carlo simulations”, Computer Methods in Applied Mechanics and Engineering, Special Issue. Edited by Nicholas Zabaras, v. 197, issues 43-44, pages 3530-3546, 2008.

[Hw10] Hwang, W. ``Ch. 18: How to measure biomolecular forces: A ``Tug-of-war approach, in Computational Modeling in Biomechanics (S. De, M. R. K. Mofrad, and F. Guilak, eds.) (Springer, 2010).

[Hw07] Hwang, W., ``Calculation of conformation-dependent biomolecular forces, J Chem Phys. 127 175104 (2007)

[KiAl11] Kim, D.N., Altschuler, J., Strong, C., McGill, G., and Bathe, M. Conformational dynamics data bank: a database for conformational dynamics of proteins and supramolecular protein assemblies. Nucleic Acids Research, 39: D451-455 (2011)

[LaHw09a] Lakkaraju, S.K. and Hwang, W., ``Critical buckling length versus persistence length: What governs biofilament conformation? Phys Rev Lett. 102 118102 (2009)

[LaHw09b] Lakkaraju, S.K. and Hwang, W., ``Modulation of elasticity in functionally distinct domains of the tropomyosin coiled-coil. Cell Molec Bioeng. 2 57--65 (2009)

[OdPrBaCh09]Oden, J.T.; Prudhomme, S.; Bauman, P.; and Chamoin, Ludovic. “Estimation and Control of Modeling Error: A General Approach to Multiscale Modeling “, in Bridging the Scales in Science and Engineering, Edited by Jacob Fish, Oxford University Press, Section 4, Chapter 10, 2009.

[OdPrRo10] Oden, J.T.; Prudhomme, S.; Romkes, A.; and Bauman, P. “Multi-scale modeling of physical phenomena: Adaptive control of models,” SIAM Journal on Scientific Computing, v.28, no. 6, pp. 2359-2389, 2006.

[PaHw06] Park, J., Kahng, B., Kamm, R.D., and Hwang, W., ``Atomistic simulation approach to a continuum description of self-assembled beta-sheet filaments, Biophys J. 90 2510-2524 (2006).

[RaDe11] Rahul, and De, S., “Efficient preconditioning for Jacobian-free multiscale methods”, International journal for Numerical Methods in Engineering, in press.

[RaHw08] Ravikumar, K.M., and Hwang, W., ``Region-specific role of water in collagen unwinding and assembly, Proteins, 72 1320--1332 (2008).

[RaHw07] Ravikumar, K.M., Humphrey, J.D., and Hwang, W., ``Spontaneous unwinding of a labile domain in a collagen triple helix, J Mech Mater Struct. 2 999-1010 (2007)

[SeBa10] Sedeh, R., Bathe, M., and Bathe, K.J. The subspace iteration method in protein normal mode analysis.Journal of Computational Chemistry, 31: 66–74 (2010).

[ZaDe11] Zamiri, A., and De, S., “Mechanical properties of hydroxyapatite single crystals from nanoindentation data”, Journal of the Mechanical Behavior of Biomedical Materials, in press.

[ZaDe10] Zamiri, A., and De, S., “Modeling the mechanical response of tetragonal lysozyme crystals”, Langmuir, 26(6), 4251-4257, 2010.

Related websites:

1. http://cando.dna-origami.org

2. http://cdyn.org

3. http://biomed.tamu.edu/hwanglab

4. ICES multiscale modeling group website http://www.ices.utexas.edu/centers/mmg/

5. Buehler Lab

6. http://bmsr.usc.edu/

Softwares:

1. http://cando.dna-origami.org

2. http://bmsr.usc.edu/software/

Past Conferences:

Multiscale Methods and Validation in Medicine and Biology I: Biomechanics and Mechanobiology http://mmvmb.usacm.org/

Upcoming Conferences:

MS#17 (Computational Mechanics of Biomaterials) in the International Workshop on Computational Mechanics of Materials, September 24-26, 2012, http://iwcmm22.jhu.edu/

Track 5: Multiscale Modeling and Experiment in Biology and Medicine being organized as part of the ASME 2013 2nd Global Congress on Nanoengineering for Medicine and Biology, Feb 4-6, 2013,http://www.asmeconferences.org/NEMB2013/index.cfm

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