Computational Models for Cell Reprogramming

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1 Computational Models for Cell Reprogramming Computational Models for Cell Reprogramming 1

2 Pluripotent Cells EuroStemCell web page Computational Models for Cell Reprogramming 2

3 Early Embryonic Development Niakan et al. Nature Protocols (213) Computational Models for Cell Reprogramming 3

4 Embryonic Stem Cells Computational Models for Cell Reprogramming 4

5 Differentiation Computational Models for Cell Reprogramming 5

6 Induced Pluripotent Stem (ips) cells Computational Models for Cell Reprogramming 6

7 Reprogramming Computational Models for Cell Reprogramming 7

8 Roads to Inducing Pluripotency Computational Models for Cell Reprogramming 8

9 Stem Cells Gene Regulatory Networks Topology Experimental: Boyer et al. Cell (25), Loh et al. Nature Gen (26) Computational: Chickarmane et al. PloS (26, 28) Computational Models for Cell Reprogramming 9

10 Shea-Akers Formalism - Multiple TF Depending on presence or absence of TF and/or RNAp the operator can be in various states denoted s. Each state has a statistical weight: Z(s) =number of ways the state can be realized e EnergyOfState k b T For a state where i TF are bound the statistical weight for any of the possible s states is given by Z(s) = e G(s) k b T i [T i] [T i ]-concentration of bound TF, e G(s) k b T - parameter for binding affinity, related to loss of free energy at binding. Z(s)is normalized such that the weight of the state with nothing bound is 1 i.e. Z = 1 Computational Models for Cell Reprogramming 1

11 The bound fraction is: P T = α Z(bound) Z There are 5 possible states of the system thus partition sum is: Z = 1 + p k p + A k A + A p k Ap + R k R all k are dissociation constants. P T = α p k p + A p k Ap 1 + p k p + A k A + A p k Ap + R k R Computational Models for Cell Reprogramming 11

12 The Core Gene Regulatory Network for ES cells LIF BMP4 Computational Models for Cell Reprogramming 12

13 LIF BMP4 Deterministic approach. Shea-Ackers equation d[n] dt d[os] dt d[fgf ] dt d[g] dt = k [OS](c + c 1 [N] 2 + k [OS] + c 2 LIF ) (1 + (k [OS](c 1 [N] 2 + k [OS] + c 2 LIF + c 3 [FGF ] 2 )) + c 4 [OS][G] 2 ) γ[n], = α + = = (e + e 1 [OS]) (1 + e 1 [OS] + e 2 [G] 2 γ[os], (1) ) (a + a 1 [OS]) γ[fgf ], (1 + a 1 [OS] + a 2 I 3 ) (b + b 1 [G] 2 + b 3 [OS]) (1 + b 1 [G] 2 + b 2 [N] 2 + b 3 [OS]) γ[g], Computational Models for Cell Reprogramming 13

14 LIF BMP4 Stochastic Simulation Results -Time Series 15 OCT4 SOX2 NANOG LIF+BMP4 15 OCT4 SOX2 NANOG 2i Concentration Time x Time x 1 4 Computational Models for Cell Reprogramming 14

15 Stochastic Simulation Results - Distributions 1.75 Nanog Oct4 Sox2 LIF+BMP Nanog Oct4 Sox2 2i/3i Density Concentration Concentration Computational Models for Cell Reprogramming 15

16 Transcription Factors Heterogeneity and Ground State Experimental Data Wray et al. Biochemical Transactions (21). Computational Models for Cell Reprogramming 16

17 Stochastic Simulation Results - Differentiation Occurs LIF BMP4 15 LIF+BMP4 2.5 x 15 2 Concentration 1 5 G Residence Time OCT4 SOX2 NANOG x Time LIF Computational Models for Cell Reprogramming 17

18 LIF BMP4 Reprogramming - Time Series 15 Concentration 1 5 G OCT4 SOX2 NANOG Time x 1 4 Computational Models for Cell Reprogramming 18

19 Reprogramming - Oct4 interval Computational Models for Cell Reprogramming 19

20 Reprogramming-Efficiency Results Experimental Data Model Simulation 3 ipsc Efficiency OCT4 over expression ipsc Efficiency OCT4 over expression Computational Models for Cell Reprogramming 2

21 Summary One Layer Model The proposed model explains observed heterogeneity of key regulators characterizes the mes cells under certain external stimuli conditions describes the occurrence of transitions from the mes cells to the differentiated state provides a framework for reprogramming from somatic cells shows reprogramming efficiency as a function of OCT4 over-expression and concentration of medium Chickarmane, V. Olariu, V. Peterson, C. (212) Probing the role of stochasticity in a model of the embryonic stem cell heterogeneous gene expression and reprogramming efficiency. BMC System Biology 13(6):98. Computational Models for Cell Reprogramming 21

22 Simplified Gene Regulatory Network Topology Young R.A. Cell(211) Costa et al. Nature(214) Koh et al. Cell Stem Cell(211) Computational Models for Cell Reprogramming 22

23 Fast Complex Formation [N free ] + [T free ] [N T ] K d = [N free] [T free ] [N T ] [N total ] = [N free ] + [N T ] [T total ] = [T free ] + [N T ] [N T ] = K d + [N total ] + [T total ] 2 ( ) 2 K d + [N total ] + [T total ] [N total] [T total] 2 Computational Models for Cell Reprogramming 23

24 Slow Gene Regulation [N total ] t [O total ] t [T total ] t [O total ] = N over + LIF + p N K O [N total ] 1 + [O total] K O = [O total ] ( [N T ] ) n K O over + LIF + p O O K 1 + [O NT ( total] [N T ] ) [O total ] n 1 + K O K NT [O total ] ( [N T ] = T over + p T K O 1 + [O total] K O 1 + ) n K NT ( [N T ] ) n K NT [T total ] Computational Models for Cell Reprogramming 24

25 Reprogramming Simulation Results 1 OCT4 NANOG TET1 Expression Level Over-expression Oct4 ON Oct4 OFF Nanog ON Nanog OFF Tet1 ON Computational Models for Cell Reprogramming 25 Tet1 OFF

26 pre-ips and Differentiation Simulation Results a b Oct4 Nanog Tet1 1 Oct4 Nanog Tet1 1 Expression Level Over-expression =.1 Over-expression =.13 Over-expression=.2 Expression Level c 1 Expression Level Oct4 ON LIF Oct4 OFF Oct4+ Nanog ON Oct4+ OFF Nanog Tet1 Oct4 Nanog LIF Withdrawal Oc4 ON Oct4 OFF Nanog ON Nanog OFF Tet1 ON Tet1 OFF Computational Models for Cell Reprogramming 26

27 Overview Pluripotency Model Multilayer Model DNA methylation model results a b Computational Models for Cell Reprogramming 27

28 Promoter CpG sites methylation and demethylation model dm dt = σ u m2 κ u 2 m + µ u β m du dt = κ u2 m σ u m 2 µ u + β m select two random CpG sites. If both are in m, then select another CpG site and set its state to m with a probability σ. If both are in u, select another CpG site and set its state to u with a probability κ. select a random CpG site and change it to u with a probability µ if the CpG site is in state m or set it to m with a probability β if the CpG site is in state u. Computational Models for Cell Reprogramming 28

29 MEF The Framework OCT4 NANOG MEF σ1 σ3 σ2 ES ips β 1 u h m μ1 Tet1 β 2 μ 2 OCT4 NANOG OCT4 NANOG Fraction of unmethylated CpG sites Tet1 Probability density ips MEF4 7 MEF ips MEF4 7 Probability density MEF ips MEF4 7 MEF NANOG N & T TET1 ES ES ips MEF4 7 &.5 1 Methylation.5 1 Methylation Methylation Methylation OCT4 Computational Models for Cell Reprogramming 29

30 Double Layer model results Oct4 Nanog Tet1 Expression Level Methylated Unmethylated Over-Expression Oct4 ON Oct4 OFF Nanog ON Nanog OFF Tet1 ON Tet1 OFF Computational Models for Cell Reprogramming 3

31 Conclusions multilayer model = transcriptional regulation + DNA methylation modifications recapitulates important experimental results elucidates the role of Tet1 in activating promoters by demethylation of CpG islands for promoters of Tet1 and Oct4. The deterministic model explains the qualitative ability to reprogram for different scenarios. The stochastic model allowed us to understand why reprogramming typically is a rare event. Olariu, V. Lvkvist, C. Sneppen, K. (216) Nanog, Oct4 and Tet1 interplay in establishing pluripotency. Scientific Reports Computational Models for Cell Reprogramming 31

32 THANK YOU! Computational Models for Cell Reprogramming 32