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Dynamics of developmental fate decisions - Luís A. Nunes Amaral

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Advances in complex systems July 3-7, 2017

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Dynamics of developmental fate decisions - Luís A. Nunes Amaral

  1. 1. DYNAMICS OF DEVELOPMENTAL FATE DECISIONS Luís A. Nunes Amaral Northwestern University Biological Physics Seminar
  2. 2. Good to get some historical perspective 2
  3. 3. From ‘Lords of the fly’ 3 ‘… experimentalists trust and value most highly results that they can put to use productively in their own experimental work.’
  4. 4. A bit of history 4 Thomas Hunt Morgan (September 25, 1866 – December 4, 1945), an American evolutionary biologist, geneticist, embryologist, won the Nobel Prize in Physiology or Medicine in 1933 for discoveries elucidating the role that the chromosome plays in heredity. Morgan began to study the genetic characteristics of the fruit fly Drosophila melanogaster and demonstrated that genes are carried on chromosomes and are the mechanical basis of heredity. These discoveries formed the basis of the modern science of genetics. As a result of his work, Drosophila became a major model organism in contemporary genetics.
  5. 5. From ‘Lords of the fly’ 5 ‘Morgan’s discovery of the autocatalytic property of large-scale breeding – the breeder reactor – was a turning point in Drosophila’s natural history.’
  6. 6. The wonder of development
  7. 7. An egg is the organic vessel containing the zygote in which an animal embryo develops until it can survive on its own.
  8. 8. From the egg 8 Human fertilized egg 0.1 mm 0.5 mm 20 mm
  9. 9. To a multicellular organism 9 Spider mite: 0.4 mm Human: 1,700 mm Chicken: 200 mm Fruit fly: 3.5 mm
  10. 10. Formalizing development process
  11. 11. Waddington’s epigenetic landscape 11
  12. 12. Hematopoietic stem cells 12
  13. 13. Caenorhabditis elegans 13 https://www.youtube.com/watch?v=M2ApXHhYbaw
  14. 14. C. elegans 14
  15. 15. Mechanisms for development 15
  16. 16. Conservation of important genes 16
  17. 17. How do these mechanisms work?
  18. 18. Cell specialization 18
  19. 19. Cell communication 19
  20. 20. Inductive signaling 20
  21. 21. Signaling pathways are highly conserved 21
  22. 22. Morphogenic gradients 22
  23. 23. Patterning 23
  24. 24. Patterning in Drosophila 24
  25. 25. Drosophila melanogaster 25
  26. 26. Patterning of the complex eye
  27. 27. 27 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427658/ Studies of […] eye mutants have provided key insights into the areas of cell fate specification, lateral inhibition, signal transduction, transcription factor networks, planar cell polarity, cell proliferation and programmed cell death just to name a few.
  28. 28. The structure of the complex eye 28 Each complex eye comprises 475-490 ommatidia. The core of the adult ommatidium contains 8 photoreceptor neurons, 4 lens secreting cone cells and 2 primary pigment cells. Each ommatidium shares 6 secondary pigment cells, 3 tertiary pigment cells and 3 mechano-sensory bristle complexes with its surrounding neighbors. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427658/
  29. 29. Ommatidia formation 29 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427658/ All cells that are not incorporated into the pre- clusters undergo a single additional round of mitosis. This second mitotic wave is required to generate the remaining cells needed to produce mature ommatidia
  30. 30. Ommatidia formation 30
  31. 31. R8 recruitment 31 http://dev.biologists.org/content/137/14/2265.full
  32. 32. Recruitment of other photo-receptors 32
  33. 33. Recruitment of other photo-receptors 33 http://dev.biologists.org/content/137/14/2265.full Bistable expression of Yan and Pointed regulates the transit to differentiation Concentration Time Pointed (Activator) EGFR Signaling Yan Pointed Yan (Repressor) Multipotent State Differentiating State Genes Differentiation Yan Genes Differentiation Pnt 7 Remake plot
  34. 34. Recruitment of other photo-receptors 34 0.5 1.0 1.5 2.0 anConcentration(AU) Concentration Time 0.5 1.0 1.5 2 .0 0.5 1.0 1.5 2 .0 − 2 0 0.0 PntC PntConcentration(A.U.) PntConcentration(A.U.) D E Time (h) -10 0 10 2 0 3 0 40 5 0 Multipotent Cells R2/R5 Neurons tedConcentration(AU) R3/R4 Neur Concentration Pointed Time Multipotent Yan Differentiated Yan Pnt
  35. 35. How where these results obtained?
  36. 36. Null Mutants! Staining using antibodies
  37. 37. Summary of previous lecture 37
  38. 38. Summary of previous lecture 38
  39. 39. Summary of previous lecture 39 0.5 1.0 1.5 2.0 YanConcentration(AU) Concentration Time 0.5 1.0 1.5 2 .0 PntConcentration(A.U.) D Time (h) -10 0 10 2 0 3 0 40 5 0 Multipotent Cells R2/R5 Neurons R3/R4 Concentration Pointed Time Multipotent Yan Differentiated Yan Pnt
  40. 40. The human team 40 Sebastian Bernasek Chem. Engineer Neda Bagheri Electr. Engineer Rich Carthew Biologist Justin Cassidi Biologist Nicolas Pelaez Biologist Ilaria Rebay Biologist
  41. 41. Dynamics of cell fate determinants 41 Peláez et al., eLife 4, e08924 (2015)
  42. 42. The first challenge 42 Qi et al., Int Conf Signal Process Proc. 2013, 670-674 (2014)
  43. 43. Performance of segmentation algorithm 43 Qi et al., Int Conf Signal Process Proc. 2013, 670-674 (2014) Original image Gold standard Our segmentation Comparison
  44. 44. Our images are more challenging 44 Peláez et al., eLife 4, e08924 (2015)
  45. 45. Flies that express Histone-RFP and Yan-YFP 45 Peláez et al., eLife 4, e08924 (2015)
  46. 46. So, what did we find?
  47. 47. This is the expectation! 47 0.5 1.0 1.5 2.0 YanConcentration(AU) Concentration Time 0.5 1.0 1.5 2 .0 PntConcentration(A.U.) D Time (h) -10 0 10 2 0 3 0 40 5 0 Multipotent Cells R2/R5 Neurons R3/R4 Concentration Pointed Time Multipotent Yan Differentiated Yan Pnt
  48. 48. This is the observed outcome for Yan 48 Approximately exponential decay Peláez et al., eLife 4, e08924 (2015)
  49. 49. This is the observed outcome for Pnt 49 Unpublished (2017)
  50. 50. Let’s look at Yan in more detail
  51. 51. Process is insensitive to absolute Yan levels 51
  52. 52. Rates of decay of Yan levels 52
  53. 53. Variability across cells highest at ramping up 53
  54. 54. What is going on between Yan and Pnt?
  55. 55. Comparison of levels within same cells 55 Unpublished (2017)
  56. 56. We have not yet developed modeling approach, but feel we now have the data to attempt to validate models
  57. 57. Why this may be better Yan and Pnt are very powerful transcription factors. Probably not safe to have high levels of either for long! System is re-usable: Whether particular fate is chosen or not, levels of Yan and Pnt return to baseline.
  58. 58. Why are gene regulatory networks so large? 58 Unpublished (2017)
  59. 59. Because otherwise things breakdown… 59 miR-7 +miR-7 - Unpublished (2017)
  60. 60. But what if we slow things down? 60 Glucose restriction Unpublished (2017)
  61. 61. Lots of good things happen! 61 Unpublished (2017)
  62. 62. What if we slow things down by reducing number of ribosomes?
  63. 63. Lots of good things happen! 63 Unpublished (2017)
  64. 64. Even when you remove a big system! 64 Unpublished (2017)
  65. 65. Modeling approach 65 Unpublished (2017) D is activation level of gene in DNA R is number of mRNAs available for translations P is number level of proteins Assumptions: D takes continuous value between 0 and some maximum R and P are integer but large enough that can be seen as continuous Formation Degradation
  66. 66. Feedback control 66 Unpublished (2017)
  67. 67. Redundant feedback control 67 Unpublished (2017)
  68. 68. Challenges of analysis of results 68 Unpublished (2017) We are not modeling any particular system System is almost certainly nonlinear Change in metabolic rate will likely affect different parameters differently We cannot know values of any parameters Parameters are effective anyway
  69. 69. Addressing challenges 69 Unpublished (2017) Scan many possible parameter values (at least order an order of magnitude change for each parameter value) Consider linear and nonlinear version of equations Consider different possibilities for how change in metabolic rate will affect different parameters
  70. 70. Analysis of simulation results 70 Unpublished (2017)
  71. 71. Modeling approach 71 Unpublished (2017)
  72. 72. Redundancy allows for speed Gene regulation network redundancy enables organisms to increase metabolic rate while avoiding developmental mistakes. Shorter developmental times, increase fitness by reducing time organism is helpless. Provides driving force for increasing complexity.
  73. 73. PROMPTING IS CURRENTLY THANK YOU! NORTHWESTERN MEMORIAL

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