American Institute of Mathematical Sciences

Advanced
July  2021, 26(7): 3785-3821. doi: 10.3934/dcdsb.2020257

Evolutionary de Rham-Hodge method

Jiahui Chen 1, , Rundong Zhao 2, , Yiying Tong 2,, and  Guo-Wei Wei 3,,

1. 

Department of Mathematics, Michigan State University, MI 48824, USA
2. 

Department of Computer Science and Engineering, Michigan State University, MI 48824, USA
3. 

Department of Mathematics, Department of Biochemistry and Molecular Biology, Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA

* Corresponding author: Guo-Wei Wei

† Corresponding author: Yiying Tong

Received  March 2020 Revised  July 2020 Published  July 2021 Early access  August 2020

Fund Project: This work was supported in part by NSF Grants DMS1721024, DMS1761320, IIS1900473, NIH grants GM126189 and GM129004, Bristol-Myers Squibb and Pfizer. GWW thanks Vidit Nanda for useful discussion

The de Rham-Hodge theory is a landmark of the 20$ ^\text{th} $ Century's mathematics and has had a great impact on mathematics, physics, computer science, and engineering. This work introduces an evolutionary de Rham-Hodge method to provide a unified paradigm for the multiscale geometric and topological analysis of evolving manifolds constructed from a filtration, which induces a family of evolutionary de Rham complexes. While the present method can be easily applied to close manifolds, the emphasis is given to more challenging compact manifolds with 2-manifold boundaries, which require appropriate analysis and treatment of boundary conditions on differential forms to maintain proper topological properties. Three sets of unique evolutionary Hodge Laplacians are proposed to generate three sets of topology-preserving singular spectra, for which the multiplicities of zero eigenvalues correspond to exactly the persistent Betti numbers of dimensions 0, 1 and 2. Additionally, three sets of non-zero eigenvalues further reveal both topological persistence and geometric progression during the manifold evolution. Extensive numerical experiments are carried out via the discrete exterior calculus to demonstrate the potential of the proposed paradigm for data representation and shape analysis of both point cloud data and density maps. To demonstrate the utility of the proposed method, the application is considered to the protein B-factor predictions of a few challenging cases for which existing biophysical models break down.

Keywords: Topological persistence, geometric progression, evolutionary spectra, multiscale differential geometry, multiscale data representation, shape analysis, discrete exterior calculus and manifold evolution.
Mathematics Subject Classification: Primary:53Z10, 53Z50;Secondary:14F40.
Citation: Jiahui Chen, Rundong Zhao, Yiying Tong, Guo-Wei Wei. Evolutionary de Rham-Hodge method. Discrete and Continuous Dynamical Systems - B, 2021, 26 (7) : 3785-3821. doi: 10.3934/dcdsb.2020257
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show all references

References:
[1]

M. Akram and V. Michel, Regularisation of the Helmholtz decomposition and its application to geomagnetic field modelling, GEM-International Journal on Geomathematics, 1 (2010), 101-120.  doi: 10.1007/s13137-010-0001-y.

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P. Bendich, H. Edelsbrunner and M. Kerber, Computing robustness and persistence for images, IEEE Transactions on Visualization and Computer Graphics, 16 (2010), 1251-1260.  doi: 10.1109/TVCG.2010.139.

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Z. Cang, L. Mu and G.-W. Wei, Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening, PLOS Computational Biology, 14 (2018), e1005929. doi: 10.1371/journal.pcbi.1005929.

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Z. Cang, E. Munch and G.-W. Wei, Evolutionary homology on coupled dynamical systems with applications to protein flexibility analysis, Journal of Applied and Computational Topology, (2020), 1–27.

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Z. Cang and G.-W. Wei, TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions, PLOS Computational Biology, 13 (2017), 1005690. doi: 10.1371/journal.pcbi.1005690.

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Z. Cang and G.-W. Wei, Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction, International Journal for Numerical Methods in Biomedical Engineering, 34 (2018), e2914. doi: 10.1002/cnm.2914.

[13]

G. Carlsson, V. De Silva and D. Morozov, Zigzag persistent homology and real-valued functions, Proceedings of the Twenty-Fifth Annual Symposium on Computational Geometry, ACM, 2009, 247–256. doi: 10.1145/1542362.1542408.

[14]

G. Carlsson, T. Ishkhanov, V. De Silva and A. Zomorodian, On the local behavior of spaces of natural images, International Journal of Computer Vision, 76 (2008), 1-12.  doi: 10.1007/s11263-007-0056-x.

[15]

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Z. Chen, N. A. Baker and G.-W. Wei, Differential geometry based solvation model Ⅱ: Lagrangian formulation, Journal of Mathematical Biology, 63 (2011), 1139-1200.  doi: 10.1007/s00285-011-0402-z.

[17]

S. Chowdhury and F. Mémoli, Persistent path homology of directed networks, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, Philadephia, PA, 2018, 1152–1169. doi: 10.1137/1.9781611975031.75.

[18]

V. De Silva, R. Ghrist and A. Muhammad, Blind swarms for coverage in 2-D, Robotics: Science and Systems, 2005, 335–342. doi: 10.15607/RSS.2005.I.044.

[19]

M. Desbrun, E. Kanso and Y. Tong, Discrete differential forms for computational modeling, in Discrete Differential Geometry, Birkhäuser, Basel, 2008, 287–324. doi: 10.1007/978-3-7643-8621-4_16.

[20]

B. Di Fabio and C. Landi, A Mayer-Vietoris formula for persistent homology with an application to shape recognition in the presence of occlusions, Foundations of Computational Mathematics, 11 (2011), 499-527.  doi: 10.1007/s10208-011-9100-x.

[21]

Q. Du, C. Liu and X. Wang, A phase field approach in the numerical study of the elastic bending energy for vesicle membranes, Journal of Computational Physics, 198 (2004), 450-468.  doi: 10.1016/j.jcp.2004.01.029.

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H. Edelsbrunner and J. Harer, Computational Topology: An Introduction, American Mathematical Society, Providence, RI, 2010.

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N. Foster and D. Metaxas, Realistic animation of liquids, Graphical Models and Image Processing, 58 (1996), 471-483.  doi: 10.1006/gmip.1996.0039.

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K. O. Friedrichs, Differential forms on Riemannian manifolds, Communications on Pure and Applied Mathematics, 8 (1955), 551-590.  doi: 10.1002/cpa.3160080408.

[26]

P. Frosini and C. Landi, Size theory as a topological tool for computer vision, Pattern Recognition and Image Analysis, 9 (1999), 596-603. 

[27]

P. Frosini and C. Landi, Persistent Betti numbers for a noise tolerant shape-based approach to image retrieval, in Computer Analysis of Images and Patterns, Springer, Heidelberg, 2011, 294–301. doi: 10.1007/978-3-642-23672-3_36.

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M. Gameiro, Y. Hiraoka, S. Izumi, M. Kramar, K. Mischaikow and V. Nanda, A topological measurement of protein compressibility, Japan Journal of Industrial and Applied Mathematics, 32 (2015), 1-17.  doi: 10.1007/s13160-014-0153-5.

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H. Gao, M. K. Mandal, G. Guo and J. Wan, Singular point detection using discrete Hodge Helmholtz decomposition in fingerprint images, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, 2010, 1094–1097. doi: 10.1109/ICASSP.2010.5495348.

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J. Gomes and O. Faugeras, Using the vector distance functions to evolve manifolds of arbitrary codimension, International Conference on Scale-Space Theories in Computer Vision, Springer, 2001, 1–13.

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T. Hazra, S. A. Ullah, S. Wang, E. Alexov and S. Zhao, A super-Gaussian Poisson–Boltzmann model for electrostatic free energy calculation: Smooth dielectric distribution for protein cavities and in both water and vacuum states, Journal of Mathematical Biology, 79 (2019), 631-672.  doi: 10.1007/s00285-019-01372-1.

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Figure 1.  Discrete de Rham cohomology; $ D_k $ is the combinatorial operators such that $ {D}_{k+1}{D}_{k} = 0 $; $ {S}_k $ is the discrete Hodge stars
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Figure 2.  Illustration of normal and tangential harmonic field extensions. Thick lines are the inputs and thin lines are the extended outputs. Left charts in both (a) and (b) show harmonic fields and their extensions while right charts give meticulous detail of interior parts. (a) Normal harmonic forms. A solid ball with a cavity extends inward to a solid ball without cavity. The outside surface is fixed. (b) Tangential harmonic forms. A torus extends to a solid ball
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Figure 3.  Persistence and progression on benzene
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Figure 4.  Snapshots of evolving manifold with the two-body system. a, b, c and d are snapshots from the beginning to the end. b and c show the transition of the Betti-0 number from 2 to 1
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Figure 5.  Eigenvalues and Betti numbers vs isovalue ($ c $) of the two-body system with $ \eta = 1.19 $ and $ \max(\rho)\approx 1.0 $. i shows the smallest eigenvalues of the $ T $ set. The drops at $ c = 0.6 $ correspond to snapshots in Figs. 4 b and c. ii and iii show the smallest eigenvalues of the $ C $ and $ N $ sets respectively
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Figure 6.  Snapshots of evolving manifolds with the four-body system. a is the initial point of four components; b and c show the transition of a ring formed and the persistent Betti-0 number changes from 4 to 1. g and h show the vanishing of the ring and the persistent Betti-1 number changes from 1 to 0.
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Figure 7.  Eigenvalues and Betti numbers vs isovalue ($ c $) of the four-body system with $ \eta = 1.19 $ and $ \max(\rho)\approx 1.2 $. i shows the smallest eigenvalues of the $ T $ set. At near $ c = 0.80 $, the persistent Betti-0 number changes from 4 to 1. ii shows the smallest eigenvalues of the $ C $ set. At around $ c = 1.02 $, the persistent Betti-1 number changes from 1 to 0. iii shows the smallest eigenvalues of the $ N $ set
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Figure 8.  Snapshots of evolving manifold with the eight-body system. a presents the initial state with eight components. b and c show the formation of 6 tunnels when the persistent Betti-0 number changes from 8 to 1 and the persistent Betti-1 number changes from 0 to 5. d and e illustrate that a cavity appears, so the persistent Betti-1 number drops to 0 and the persistent Betti-2 number increases to 1. f shows a solid volume without cavity. The gray planes cut manifolds to create cross-section views to illustrate the process of the formation of cavity as shown in b', c', d' and e'
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Figure 9.  Eigenvalues and Betti numbers vs isovalue ($ c $) of the eight-body system with $ \eta = 1.53 $ and $ \max(\rho)\approx1.1 $. i shows the Fiedler values of the $ T $ set and persistent Betti-0 numbers. ii shows the Fiedler values of the $ C $ set and persistent Betti-1 numbers. iii illustrates the comparison of $ \lambda_{l,1}^C $ and persistent $ \beta_2 $
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Figure 10.  Manifold evolution of benzene with $ \eta = 0.45\times r_{\rm vdw} $. a through h are snapshots from the start to the end. a and b show the transition of the persistent Betti-0 number from 12 to 6. c and d show the formation of a ring; The Betti-0 number changes from 6 to 1 and remains at one to the end, whereas the Betti-1 number changes from zero to one. d, e, f and g illustrate the deformation of the hexagonal tunnel to a round tunnel. From g to h, the ring disappears and the Betti-1 number changes from 1 back to 0
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Figure 11.  Eigenvalues and Betti numbers vs isovalue ($ c $) of the benzene system with $ \eta = 0.45 $ and $ \max(\rho)\approx1.1 $. i shows the smallest eigenvalues of the $ T $ set. The drops at $ c = 0.12 $ correspond to snapshots in Figs. 10 a and b. The drops at $ c = 0.22 $ correspond to snapshots in Figs. 10 c and d. ii shows the smallest eigenvalue of the $ N $ set. The drops at $ c = 0.9 $ correspond to snapshots in Figs. 10 g and h. iii shows the smallest eigenvalues of the $ C $ set
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Figure 12.  Illustration of fullurene ($ \text{C}_{60} $) manifold evolution with $ \eta = 0.5\times r_{\rm vdw} $. a presents sixty components around carbon atom positions. a and b show that the components connect if they share a pentagonal hole and persistent $ \beta_0 $ changes from 60 to 12 and persistent $ \beta_1 $ changes from 0 to 12. c shows the hexagonal holes are formed, resulting in the change of persistent $ \beta_0 $ to 1 and persistent $ \beta_1 $ to 31. (There are 32 rings, but only 31 are independent in terms of homology.) c and d show that the 12 pentagonal rings disappear and the persistent Betti-1 number drops from 31 to 19. d and e show that the 20 hexagonal rings disappear and a cavity forms inside, so that persistent $ \beta_1 $ drops to $ 0 $ and persistent $ \beta_2 $ increases to 1. The vertical plan cuts the manifolds that gives an illustration of cavity in d' and e'
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Figure 13.  Eigenvalues and Betti numbers vs isovalue ($ c $) of the fullurene ($ \text{C}_{60} $) system with $ \eta = 0.5\times r_{\rm vdw} $ and $ \max(\rho)\approx1.3 $. i gives the Fiedler values of the $ T $ set and persistent $ \beta_0 $. ii presents the comparison of $ \lambda^C_{l,1} $ and persistent $ \beta_1 $. iii shows the Fiedler values of the $ N $ set and persistent $ \beta_2 $
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Figure 14.  Illustration of fullurene ($ \text{C}_{60} $) manifold evolution with $ \eta = 0.8\times r_{\rm vdw} $. a shows 12 initial solid pentagonal components. b and c show the formation and contraction process of the 20 rings. d is the snapshot right after the formation of the cavity. e shows the final stage as a solid ball of this example
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Figure 15.  Eigenvalues and Betti numbers vs isovalue ($ c $) of the fullurene ($ \text{C}_{60} $) system with $ \eta = 0.8\times r_{\rm vdw} $; $ \max{\rho}\approx2.5 $. i gives the Fiedler values of the $ T $ set and persistent $ \beta_0 $. ii presents the comparison of $ \lambda^C_{l,1} $ and persistent $ \beta_1 $. iii shows the Fiedler values of the $ N $ set and persistent $ \beta_2 $
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Figure 16.  Experimental and predicted B-factor values plotted per residue (PDB IDs: 1CLL, 2HQK and 1V70). EXP: experimental values; EDH: evolutionary de Rham-Hodge (10 isovalues) method predicted values; GNM: Gaussian network method predicted values
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Figure 17.  The structure of calmodulin (PDB ID: 1CLL) visualized in Visual Molecular Dynamics (VMD) [33] and colored by experimental B-factors (left), EDH (10 isovalues) predict B-factors (middle) and GNM predicted B-factors (right) with red representing the most flexible regions
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Figure 18.  Illustration of surfaces extracted with different isovalues for EMD-1776. The isovalues for a, b, c and d are 0.14, 0.10, 0.07 and 0.04, respectively. In a, $ \beta_0 $ is 12 and $ \beta_1 $ and $ \beta_2 $ are 0; In b, $ \beta_0 = 4 $, $ \beta_1 = 4 $ and $ \beta_2 = 0 $; In c, $ \beta_0 = 1 $, $ \beta_1 = 13 $ and $ \beta_2 = 0 $; In d, $ \beta_0 = 1 $, $ \beta_1 = 9 $ and $ \beta_2 = 0 $
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Figure 19.  Eigenvalues and Betti numbers vs filtration of the EMD-1776 density map. The filtration goes from 2.68 (the largest isovalue (0.28) subtract by 0.14) to 2.78 (the largest isovalue (0.28) subtract by 0.04). i gives the Fiedler values of the $ T $ set and persistent $ \beta_0 $. ii presents the comparison of $ \lambda^C_{l,1} $ and persistent $ \beta_1 $. iii shows the Fiedler values of the $ N $ set and persistent $ \beta_2 $
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Table 1.  Exterior (odd rows) vs. traditional (even rows) calculus in $ \mathbb{R}^3 $. $ f^0 $, $ {\bf{v}}^1 $, $ {\bf{v}}^2 $ and $ f^3 $ stand for $ 0 $-, $ 1 $-, $ 2 $- and $ 3 $-forms with their components stored in either a scalar field $ f $ or vector field $ {\bf{v}} $
order $ 0 $ order $ 1 $ order $ 2 $ order $ 3 $
form $ f^0 $ $ {\bf{v}}^1({\bf{a}}) $ $ {\bf{v}}^2({\bf{a}},{\bf{b}}) $ $ f^3({\bf{a}},{\bf{b}},{\bf{c}}) $
$ f $ $ {\bf{v}}\cdot {\bf{a}} $ $ {\bf{v}}\cdot({\bf{a}}\times{\bf{b}}) $ $ f[({\bf{a}}\times{\bf{b}})\cdot{\bf{c}}] $
$ d $ $ df^0 $ $ d {\bf{v}}^1 $ $ d {\bf{v}}^2 $ $ d f^3 $
$ (\nabla f)^1 $ $ (\nabla \times {\bf{v}})^2 $ $ (\nabla \cdot {\bf{v}})^3 $ $ 0 $
$ \star $ $ \star f^0 $ $ \star {\bf{v}}^1 $ $ \star {\bf{v}}^2 $ $ \star f^3 $
$ f^3 $ $ {\bf{v}}^2 $ $ {\bf{v}}^1 $ $ f^0 $
$ \delta $ $ \delta f^0 $ $ \delta {\bf{v}}^1 $ $ \delta {\bf{v}}^2 $ $ \delta f^3 $
$ 0 $ $ (- \nabla \cdot {\bf{v}})^0 $ $ (\nabla \times {\bf{v}})^1 $ $ (-\nabla f)^2 $
$ \wedge $ $ f^0\!\wedge\! g^0 $ $ f^0\!\wedge\!{\bf{v}}^1 $ $ f^0\!\wedge\!{\bf{v}}^2 $, $ {\bf{v}}^1\!\wedge\!{\bf{u}}^1 $ $ f^0\!\wedge\!g^3 $, $ {\bf{v}}^1\!\wedge\!{\bf{u}}^2 $
$ (fg)^0 $ $ (f{\bf{v}})^1 $ $ (f{\bf{v}})^2 $, $ ({\bf{v}}\!\times\!{\bf{u}})^2 $ $ (fg)^3 $, $ ({\bf{v}}\cdot{\bf{u}})^3 $
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order $ 0 $ order $ 1 $ order $ 2 $ order $ 3 $
form $ f^0 $ $ {\bf{v}}^1({\bf{a}}) $ $ {\bf{v}}^2({\bf{a}},{\bf{b}}) $ $ f^3({\bf{a}},{\bf{b}},{\bf{c}}) $
$ f $ $ {\bf{v}}\cdot {\bf{a}} $ $ {\bf{v}}\cdot({\bf{a}}\times{\bf{b}}) $ $ f[({\bf{a}}\times{\bf{b}})\cdot{\bf{c}}] $
$ d $ $ df^0 $ $ d {\bf{v}}^1 $ $ d {\bf{v}}^2 $ $ d f^3 $
$ (\nabla f)^1 $ $ (\nabla \times {\bf{v}})^2 $ $ (\nabla \cdot {\bf{v}})^3 $ $ 0 $
$ \star $ $ \star f^0 $ $ \star {\bf{v}}^1 $ $ \star {\bf{v}}^2 $ $ \star f^3 $
$ f^3 $ $ {\bf{v}}^2 $ $ {\bf{v}}^1 $ $ f^0 $
$ \delta $ $ \delta f^0 $ $ \delta {\bf{v}}^1 $ $ \delta {\bf{v}}^2 $ $ \delta f^3 $
$ 0 $ $ (- \nabla \cdot {\bf{v}})^0 $ $ (\nabla \times {\bf{v}})^1 $ $ (-\nabla f)^2 $
$ \wedge $ $ f^0\!\wedge\! g^0 $ $ f^0\!\wedge\!{\bf{v}}^1 $ $ f^0\!\wedge\!{\bf{v}}^2 $, $ {\bf{v}}^1\!\wedge\!{\bf{u}}^1 $ $ f^0\!\wedge\!g^3 $, $ {\bf{v}}^1\!\wedge\!{\bf{u}}^2 $
$ (fg)^0 $ $ (f{\bf{v}})^1 $ $ (f{\bf{v}})^2 $, $ ({\bf{v}}\!\times\!{\bf{u}})^2 $ $ (fg)^3 $, $ ({\bf{v}}\cdot{\bf{u}})^3 $
Table 2.  Boundary conditions of tangential and normal form
type $ f^0 $ $ {\bf{v}}^1 $ $ {\bf{v}}^2 $ $ f^3 $
tangential unrestricted $ {\bf{v}}\cdot {\bf{n}} = 0 $ $ {\bf{v}} \parallel {\bf{n}} $ $ f|_{\partial M} = 0 $
normal $ f|_{\partial M} = 0 $ $ {\bf{v}} \parallel {\bf{n}} $ $ {\bf{v}} \cdot {\bf{n}}=0 $ unrestricted
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type $ f^0 $ $ {\bf{v}}^1 $ $ {\bf{v}}^2 $ $ f^3 $
tangential unrestricted $ {\bf{v}}\cdot {\bf{n}} = 0 $ $ {\bf{v}} \parallel {\bf{n}} $ $ f|_{\partial M} = 0 $
normal $ f|_{\partial M} = 0 $ $ {\bf{v}} \parallel {\bf{n}} $ $ {\bf{v}} \cdot {\bf{n}}=0 $ unrestricted
Table 3.  Pearson correlation coefficients in B-factor predictions using GNM, mGNM and EDH for four proteins. Here, mGNM stands for multiscale GNM with two different kernels [70]. $ N_{C^\alpha} $ is the number of residues. In cases of EDH, three different isovalue sets are applied with 10, 20 and 40 points of equal spaces on the interval of [$ 0.1, 1.0 $]
PDB ID $ N_{C^\alpha} $ GNM[70] mGNM[70] EDH (10) EDH (20) EDH (40)
1CLL 292 0.261 0.763 0.789 0.797 0.850
1V70 105 0.162 0.750 0.754 0.772 0.858
2HQK 216 0.365 0.833 0.854 0.880 0.886
1WHI 122 0.270 0.484 0.640 0.711 0.794
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PDB ID $ N_{C^\alpha} $ GNM[70] mGNM[70] EDH (10) EDH (20) EDH (40)
1CLL 292 0.261 0.763 0.789 0.797 0.850
1V70 105 0.162 0.750 0.754 0.772 0.858
2HQK 216 0.365 0.833 0.854 0.880 0.886
1WHI 122 0.270 0.484 0.640 0.711 0.794
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