Applied Mathematics seminar | Yang Cao, Hybrid stochastic modeling of the budding yeast cell cycle control mechanismExport this event to calendar

Thursday, November 9, 2017 3:30 PM EST

MC 5501

Speaker

Yang Cao
Computer Science, Virginia Tech

Title

Hybrid stochastic modeling of the budding yeast cell cycle control mechanism

Abstract

The budding yeast cell cycle is regulated by complex and multi-scale control mechanisms, and is subject to inherent noise, resulted from low copy numbers of species in a cell. Noise in cellular systems is often modeled and simulated with Gillespie's stochastic simulation algorithm (SSA). However, the low efficiency of SSA limits its application to large practical biochemical networks, which often present multi-scale features in two aspects: species with different scales of abundances and reactions with different scales of firing frequencies. To improve the efficiency of stochastic simulations, Haseltine and Rawlings (HR) proposed a hybrid algorithm, which combines ordinary differential equations (ODEs) for traditional deterministic models and SSA for stochastic models. In this talk, we will present a comprehensive hybrid model that represents a gene-protein regulatory network of the budding yeast cell cycle control mechanism, respectively, by Gillespie’s stochastic simulation algorithm (SSA) and ordinary differential equations (ODEs). Simulation results of our model are compared with published experimental measurement on the budding yeast cell cycle, which demonstrates that our hybrid model well represents many critical characteristics of the budding yeast cell cycle, and reproduces phenotypes of more than 100 mutant cases. The proposed scheme is considerably faster in both modeling and simulation than the equivalent stochastic simulation. Meanwhile, the accuracy of the HR hybrid method is studied based on a linear chain reaction system. Our analysis shows that the hybrid method is valid for a much greater region in system parameter space than those for the slow scale SSA (ssSSA) and the stochastic quasi steady state assumption (SQSSA) methods.

S M T W T F S
28
29
30
31
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
1
2
  1. 2024 (39)
    1. May (1)
    2. April (3)
    3. March (7)
    4. February (14)
    5. January (14)
  2. 2023 (96)
    1. December (6)
    2. November (11)
    3. October (7)
    4. September (8)
    5. August (12)
    6. July (5)
    7. June (6)
    8. May (5)
    9. April (14)
    10. March (7)
    11. February (8)
    12. January (7)
  3. 2022 (106)
  4. 2021 (44)
  5. 2020 (32)
  6. 2019 (86)
  7. 2018 (70)
  8. 2017 (72)
  9. 2016 (76)
  10. 2015 (77)
  11. 2014 (67)
  12. 2013 (49)
  13. 2012 (19)
  14. 2011 (4)
  15. 2009 (5)
  16. 2008 (8)