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  1. Spectral Clustering

  • Data-Guided Control (DGC)

MATH-BIOINF-STAT-547: Mathematics of Data

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Hi-C: 1M Scale

Lieberman-Aiden, Erez, et al. "Comprehensive mapping of long-range interactions reveals folding principles of the human genome." science 326.5950 (2009): 289-293.

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Hi-C: 100KB Scale

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Chen J, Hero A, and Rajapakse I. "Spectral Identification of Topological Domains." Bioinformatics 32.14 (2016): 2151-2158.

Spectral Identification of Topological Domains

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Ng AY, Jordan MI, Weiss Y. "On spectral clustering: Analysis and an algorithm." In Advances in neural information processing systems (2002) (pp. 849-856).

Spectral Clustering

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Unnormalized Spectral Clustering

Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416. (Excellent Review!)

Page 399

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Normalized Spectral Clustering: Ng et al. (2002)

Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416. (Excellent Review!)

Page 399

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Normalized Spectral Clustering: Shi and Malik (2000)

Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416. (Excellent Review!)

Page 399

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Chen J, Hero A, and Rajapakse I. "Spectral Identification of Topological Domains." Bioinformatics 32.14 (2016): 2151-2158.

Spectral Identification of Topological Domains

Page 2154

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DATA-GUIDED CONTROL (DGC)

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Cellular Reprogramming

Takahashi, Kazutoshi, Koji Tanabe, Mari Ohnuki, Megumi Narita, Tomoko Ichisaka, Kiichiro Tomoda, and Shinya Yamanaka. "Induction of pluripotent stem cells from adult human fibroblasts by defined factors." cell 131, no. 5 (2007): 861-872.

Weintraub, Harold, et al. "Activation of muscle-specific genes in pigment, nerve, fat, liver, and fibroblast cell lines by forced expression of MyoD." Proceedings of the National Academy of Sciences 86.14 (1989): 5434-5438.

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4DN Data

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Desired Configuration

Initial Configuration

Given an Initial Condition, Desired Configuration, and the dynamics, are there Input(s) that steer the system towards the desired configuration?

Controllability: An Engineering Perspective

 

Discrete model

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Controllability: A genome perspective

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Algorithm for Cellular ReprogrammingControl Theory

  • Formally introduced in 1868 by James Clerk Maxwell
  • System: group of working parts that we wish to control
    • Cell/Genome
  • Controller: the ability to change the system
    • Transcription Factors (TFs)
  • Sensor: measurements obtained on the system
    • RNA-seq, Hi-C, etc…. (Data)

Output = New Cell State

System

Controller

Sensor

Output

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Data-Guided Control (DGC)

DATA (2015)

DGC (2017)

DARPA funded projects

Ronquist S, Patterson G, Muir LA, ….., Brockett R, Rajapakse I. "Algorithm for cellular reprogramming." Proceedings of the National Academy of Sciences. 2017 Nov 7;114(45):11832-7. Data-guided Control (DGC) Supporting Information

Chen H, Chen J, Muir LA, …., Smale S, Rajapakse I. "Functional Organization of the Human 4D Nucleome. " Proceedings of the National Academy of Sciences 112.26 (2015): 8002-8007. Supporting Information

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  •  

Our methods and data for the algorithm

TADs

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Various Domains of Attraction (DOA) as suggested by Waddington

Typically when a skin cell divides the daughter cells are also skin cells. However, over the last few decades laboratory studies have shown that by the insertion of certain transcription factors the cell division process can be steered so as produce different cell lines from skin cells. this can be formulated as a controllability problem and studied by standard control theoretic methods.

Reprogramming Cells

System

TFs

DOA