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Exploratory Adaptation in Random Networks - Naama Brenner

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Exploratory Adaptation in Random Networks - Naama Brenner

  1. 1. Naama Brenner Dept of Chemical Engineering & Network Biology Research Lab Technion Exploratory Adaptation in Random Networks
  2. 2. Unforeseen challenges A novel, stressful situation Not previously encountered No available response ? Improvisation Reorganization Exploration Gene regulation and expression is also capable of “Cells, Embryos and Evolution” J. Gerhart & M. Kirschner
  3. 3. Regulatory evolution * Gene co-option * Gene recruitment Reorganization of regulatory modes -> Creation of novel phenotype B. Prud’homme et al. (2007) Developmental genes are highly conserved Their control elements are complex and divergent
  4. 4. his3GAL promoter * HIS3 gene recruited under the GAL regulation network … … Input: carbon source his3 Synthetic Gene Recruitment Stolovicki et al. (2006); Stern et al. 2007; David et al. 2010; Katzir et al. 2012
  5. 5. Gene expression tunes to challenge Stolovicki et al. (2006)
  6. 6. Non-repeatability of adaptation at the microscopic (gene) level Biological replicates -> A non-repeatable expression pattern; exploratory dynamics at the microscopic level? Stern et al. (2007) Same experiment, two time points
  7. 7. Unforeseen challenges A novel, stressful situation Not previously encountered No available response ? Exploration Reorganization Adaptation Learning Random network model of gene regulation That can adapt by exploratory dynamics
  8. 8. Random networks as models of gene regulation S. Kauffman “The origins of order” (1993) A. Wagner “The origins of evolutionary innovation” (2011) Boolean networks “N-K model” Fixed points of the dynamics As stable cell types Binary neural-network (spin-glass) models Mutations and fitness in evolving network populations Non-specific properties Fixed points, Modularity, Robustness…
  9. 9. 1. Properties of gene regulation that might support exploratory adaptation 2. An organizing principle to support convergence to new stable phenotypes 3. A theoretical model implementing this principle Random networks as models of gene regulation Non-specific properties within a single cell – exploratory adaptation Furusawa & Kaneko 2006, 2013
  10. 10. 1. Properties of gene regulation that might support exploratory adaptation - A large number of interacting degrees of freedom Many possible bindings for each TF Heterogeneous network of interactions Guelzim et al. (2002) Harbison et al. (2004)
  11. 11. 1. Properties of gene regulation that might support exploratory adaptation - Context-dependent binding of TFs A large space of combinations in two tested familiar environments Harbison et al. (2004)
  12. 12. 1. Properties of gene regulation that might support exploratory adaptation - Intrinsically Disordered Protein (IDP) domains: Protein that exists in a dynamic ensemble of conformations with no specific equilibrium structure. ~ 90% TFs have extended disordered regions ~40% of all proteins P53: tumor suppressor signaling protein cell-cycle progression, apoptosis induction, DNA repair, stress response Fuxreiter et al. (2008) Liu et al. (2006) Uversky & Dunker (2010) Conformation and function depends on context – cellular environment
  13. 13. 1. Properties of gene regulation that might support exploratory adaptation - Alternative Splicing of TFs Several possibilities Alternative structures Different interactions Common: ~2/3 of human genome Est. average 7 AS forms per gene Niklas et al. (2015) Pan et al. (2008)
  14. 14. 1. Properties of gene regulation that might support exploratory adaptation - Post Translational Modification Chromatin structure is affected by PTM of histone proteins TFs are regulated by e.g. phosphorylation (more than other proteins) And also in their ID domains - Degenerate mapping to phenotype: A phenotype can be realized by many different gene expression patterns Niklas et al. (2015)
  15. 15. 2. An organizing principle to support convergence to new stable phenotypes Drive Reduction: a primitive form of learning - Stress induces a random exploration in the space of possible configurations - As long as stress is high, keep exploring / searching - When a stable configuration is encountered, stress is reduced, exploration too Example in low-dimensional space: Bacterial chemotaxis
  16. 16. - A large number of interacting microscopic variables - A global, coarse-grained phenotype which is sensitive to external constraint - Unforeseen, arbitrary challenge induces a stress which drives a random search - Stabilization by drive reduction principle - within a short timescale (lifetime of the organism) and without selection 3. A theoretical model implementing this principle
  17. 17. Cellular network model  1 2, ,... Nx x x x r Large number of microscopic variables ( )x W x x  r r r& Nonlinear equation of motion Interactions and relaxation Sompolinsky et al. (1988) Random Gaussian matrix: uniform circular spectrum Transition to chaos at threshold interactions More complex networks – just starting to be explored
  18. 18. -50 0 50 0 200 400 600 800 x Time Macroscopic phenotype Cellular network model  1 2, ,... Nx x x x r Large number of microscopic variables y b x  r r ( )x W x x  r r r& Nonlinear equation of motion Interactions and relaxation Typically irregular dynamics -20 0 20 y 𝑦* y b x  r r * y y constraint Schreier et al., 2016 (arXiv)
  19. 19. “The curse of dimensionality: Random and independent changes in high-dimensional space Convergence not a-priori guaranteed Simplest attempt: W is a full random matrix with Gaussian elements -> no convergence observed in simulations Sparse random matrix -> no convergence Main Results: 1. Possible convergence to stable state satisfying the constraint 2. Convergence non-universal, depends on network properties 3. Complex and interesting, not yet understood, search dynamics
  20. 20. Summary - Exposing cells to unforeseen regulatory challenge reveals their ability to individually adapt in one or a few generations. - Global dynamics of the gene regulatory network produces multiple non-repeatable expression patterns. - A random network model of gene regulation, with a stress signal feeding back to the connection strengths, demonstrates the principle of exploratory adaptation. - Convergence is possible but non-universal. A broad distribution of outgoing connections facilitates it.
  21. 21. Conclusions & speculations - Cellular adaptation can occur by temporal exploration and stabilize by “drive reduction”. - This process can be viewed as a simple for of learning: modest learning task but no computation required. - Demonstrates an organizing principle that guides exploratory adaptation and selects from the vast number of gene expression patterns.
  22. 22. Acknowledgements Hallel Schreier, Technion Yoav Soen, Weizmann Institute Technion Network Biology Research Lab: Erez Braun, Shimon Marom, Omri Barak, Ron Meir, Noam Ziv Network Biology Research Lab

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