Supplement: miR-335 shapes a transcription

factor gradient

December 11, 2013

1

mmu-miR-335-5p vs Foxa2 (NM_010446) Mouse

Position in 3'UTR: 353-375

NM_010446 AAAAACTTTTGTGAGTGA-CTTGG 3'

|||:|| || ||||:

miR-335-5p UGUAA-AAAGCAAUAACGAGAACU 5'

Position in 3'UTR: 281-303

NM_010446 TTGATTTTTGTTGTTGTTCTCTA 3'

||||:|||:|||:||| |

miR-335-5p UGUAAAAAGCAAUAACGAGAACU 5'

mmu-miR-335-3p vs. Foxa2 (NM_010446) Mouse

Position in 3'UTR: 29-50

NM_010446 TGGTCACTGGGGACAA----GGGAAA 3'

||||| ||:| ||| |::|||

mmu-miR-335-3p -CCAGU--CCUC-GUUAUUACUUUUU 5'

Position in 3'UTR: 465-486

NM_010446 GGACCAGGAGAAAGGA-GAAAAA 3'

|||||| :| ||||||

mmu-miR-335-3p CCA-GUCCUCGUUAUUACUUUUU5'

Position in 3'UTR: 406-427

NM_010446 AGA-GGGTTGTACTGATGTTGAA 3'

:|| ||| ||||| :||

mmu-miR-335-3p CCAGUCCU-CGUUAUUACUUUUU 5'

Position in 3'UTR: 167-188

NM_010446 TGTCATTCTAAATAGGGAAGGG 5'

|||| ||||: |||:::

mmu-miR-335-3p CCAGUCCUCGUUAUUACUUUUU 3'

Position in 3'UTR: 382-403

NM_010446 AC-CATGTAGTTTTAACAGAAAA 3'

|| | ||| ||| :||||

mmu-miR-335-3p CCAGUCC-UCGUUAUUACUUUUU 5'

mmu-miR-335-3p vs. Sox17 (NM_011441) Mouse

Position in 3'UTR: 741-762

NM_011441 GAGTCAG-AGAAACTAATC-AAAA 3'

||||| || || |||| ||||

mmu-miR-335-3p C-CAGUCCUCGUU-AUUACUUUUU 5'

Position in 3'UTR: 132-153

NM_011441 TGCCACTTGAACAGT--TGAGGGG 3'

| | || ||:| |||::::

mmu-miR-335-3p CCA--GUCCUCGUUAUUACUUUUU 5'

Position in 3'UTR: 259-280

NM_011441 AAA---GTGTATTGATCTAGAGAAA 3'

| ||| |:|| ||:|||

mmu-miR-335-3p CCAGUCCUCGUUAUUA---CUUUUU 5'

mmu-miR-335-5p vs Sox17 (NM_011441) Mouse

Position in 3'UTR: 164-184

NM_011441 GAGATTTTTGTTTTAAATGC-CTTGA 3'

||||:||| ||| |||||

miR-335-5p UGUAAAAAGCAAUA---ACGAGAACU 5'

A

B

C

D

E

Figure S 2: Consensus Motifs (A-D) All predicted miR-335-p and miR-335-5p binding sites for Foxa2 and Sox17.Predictions were calculated using RNA22 allowing for 2 unpaired bases in seed sequence. (E) Target site consensus sequencesbased on previously described miR-335 target sites. To generate conserved miR335 target site consensus motifs we combineda number of available validated and predicted miR-335-3p and miR-335-5p target sequences From mouse and human 3’UTRsequences.

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Oct4 Foxa2 Sox17 CFP

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Figure S 4: Preprocessing, cell culture images. Zeiss image files were loaded into MATLAB using Bio-Formats [2].First row: Cell culture raw intensity images for all four measured channels (Oct-4, Foxa2, Sox17, CFP).Second row: Images after preprocessing. Images were filtered using the averaging MATLAB 2D digital filter function“fspecial”. Images were normalized using the “imadjust” function such that 1% of the data is saturated at low and highintensities.Third row: Cells were estimated using an implementation of the MSER algorithm [1]. Fluorescence intensities for eachestimated cell (seed) were calculated by averaging all pixel intensities. Raw image (four channels), preprocessed images andestimated cells are shown. Number of images used for the statistics is shown in Table S3.

5

Figure S 5: Immunostaining LOF

Immunostaining of 96hours differentiation shows increased levels of Foxa2 and Sox17 in sponge clone compared to thecontrol.

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PosteriorAnterior

normalized

expression

normalized

expression

normalized

expression

normalized

expression

PosteriorAnterior

Figure S 6: Control Embryo Images The estimated normalized Foxa2 (green) and Sox17 (red) gradients along theposterior-anterior axis derived from the mouse embryo images used for miR-335 loss of function validation. Shown are thegradients taken from 7 WT embryo images.

7

PosteriorAnterior

normalized

expression

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expression

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Figure S 7: Sponge 3P Embryo Images The estimated normalized Foxa2 (green) and Sox17 (red) gradients along theposterior-anterior axis derived from the mouse embryo images used for miR-335 loss of function validation. Shown are thegradients taken from 4 Sponge 3P embryo images.

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Figure S 8: Gradient Estimation To Estimate the protein gradient for Foxa2 and Sox17, normalized fluorescenceintensities along the posterior anterior axis were used. Intensity levels were calculated as the mean intensity along thex-axis. Intensities were then binned and the gradient calculated at the middle bin was used to estimate miRNA mediatedeffect on the gradient.

8

control sponge control sponge control sponge control sponge control sponge

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regular: single observations, thick: functional mean

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** ***

Figure S 9: Gradient Statistics

To estimate differences in gradients we compared sponge ansd control embryo images (Figs S6 - S7). All estimated gradientswere binned in 5 equidistant Anterior to Posterior sections. these binned intensity distributions were then pairwise comparedusing one-way ANOVA. We found significant differences between sponge and control embryos in the two Posterior bins.

9

References

[1] Felix Buggenthin, Carsten Marr, Michael Schwarzfischer, Philipp S. Hoppe, Oliver Hilsenbeck, TimmSchroeder, and Fabian J. Theis. An automatic method for robust and fast cell detection in brightfield images from high-throughput microscopy. BMC Bioinformatics, 14:297, 2013.

[2] Melissa Linkert, Curtis T Rueden, Chris Allan, Jean-Marie Burel, Will Moore, Andrew Patterson,Brian Loranger, Josh Moore, Carlos Neves, Donald Macdonald, Aleksandra Tarkowska, Caitlin Sticco,Emma Hill, Mike Rossner, Kevin W Eliceiri, and Jason R Swedlow. Metadata matters: access toimage data in the real world. J Cell Biol, 189(5):777–782, May 2010.

10

Supplement: miR-335 shapes a transcriptionfactor gradient

November 5, 2013

miranda PITA RNAhybridmiRNA Host Gene Foxa2 Sox17 Foxa2 Sox17 Foxa2 Sox17 correlation scoremmu-miR-677 Atp5b 0 0 1 1 0 0 0mmu-miR-677 Baz2a 0 0 1 1 0 0 0.09mmu-miR-328 Elmo3 0 0 1 1 0 0 -0.04mmu-miR-675 H19 0 0 1 1 0 0 -0.29mmu-miR-335 Mest 1 0 1 1 0 0 -0.52mmu-miR-466g Sfmbt2 1 1 1 1 0 1 -0.18

Table S 1: miRNA predictionSummary of all developmental expressed host genes and the intergenic miRNAs that were predicted to target Foxa2 andSox17 using miRecords. All intergeninc miRNAs and their host genes predicted using miRecords to target Foxa2 and Sox17.Shown are the results for the three target site prediction tools miranda, PITA and RNAhybrid. Zeros denote no interactionpredicted, ones denote a predicted interaction. The last column displays the correlation score, based on the expression datapublished by Sene et al [1].

1

Consensus Sequence for miR-335-3p:>NM 010446 Foxa2GGACCAGGAGAAAGGAGAAAAA>NM 010446 Foxa2AGAGGGTTGTACTGATGTTGAA>NM 009238 Sox4GAGGCAGGAGAGGAGAGAGGGA>NM 009238 Sox4AGGGGAGCATTGGCATGGAGAA>NM 003107 SOX4TACAGGGGCAGTCAGTGGAGGG>NM 003107 SOX4GGGCCGGGGGGGGTAGGAGAGG>NM 011441 Sox17GAGTCAGAGAAACTAATCAAAA

Consensus Sequence for miR-335-5p:>utr|3MMUR058050 Foxa2TTTGATTTTTGTTGTTGTTCTCTA>utr|3MMUR058050 Foxa2AAAAACTTTTGTGAGTGACTTGGT>utr|3MMUR052152 Sox17CGAGATTTTTGTTTTAAATGCCTT>utr|3HSAR026431 SOX4CCTTGGTTTTGTTTTATTTTGCTT>utr|3HSAR022836 SOX17CTGGGTTTTTGTTGTTGCTGTTGT>utr|3HSAR023247 FOXA2TGATTTTTTTGTTGTTGTTGTTCT

Table S 2: Target SequencesBinding motifs were generated using WebLogo available at http://weblogo.berkeley.edu/ (Figure S ??). Sequences usedto generate consensus target motif are shown below.

0h 48h 76hB7 (Control clone) 20 22 22C10 (miR335 clone) 14 25 23D11 (miR335 clone) 18 32 23

Table S 3: Image analysisImages used for the time course differentiation analysis

2

Foxa2-UTR:5’- NNNCTCGAGGAAGATGGCTTTCAGGCCCTGCTAGCTC - 3’ (forward)5’- NNNGCGGCCGCATTCTAGCCAGAACACACATTTATAAGC - 3’ (reverse);

Foxa2 UTR mutated UTR:5’- NNNCTGCAGAGTTTGACGACTCAAGTTCTAATCTATTGCTGTTGTTGCAGAAAAGTCTGACTTTAAAAACAAACAAACAAACAAAAAACGCATCAGAGTCTGACGGTGTAAAACCATGTAGTTTTAACAG - 3 (forward)5’- NNNGCGGCCGCATTCTAGCCAGAACACACATTTATAAGC -3 (reverse);

Sox17-UTR:5’- NNNCTCGAGCGGTTGCCGACCCGACCTGAGGGCCAGAA -3 (forward)5’- NNNGCGGCCGCCACTAACAGTCACAACACAAACTTTATTTTG -3 (reverse)

Sox17 UTR mutated UTR:5’- NNNCTCGAGCGGTTGCCGACCCGACCTGAGGGCCAGAA -3 (forward)5’- GTGATTGTGGGGAGCAAGTCCCTCTTCGCATTTAAATCATATTTCTCGTGTAGCCCCTCAACTGTTCAAGTGGCAGAC - 3 (reverse)

miR-335-5p sponge primer:5’-TGCACGGGTCAGGAGCTGAATGAAAAAACTAAGCTTGATGGTCAGGAGCTCTGAATGAAAAACCGTGATACGGTCGGGAGTTCGGATGAAAAACAGACTGCA -3 (forward)5’-GTCTGTTTTTCATCCGAACTCCCGACCGTATCACGGTTTTTCATTCAGAGCTCCTGACCATCAAGCTTAGTTTTTTCATTCAGCTCCTGACCCGTGCATGCA -3 (reverse)

Table S 4: Primer sequences for cloning

Mest:5’- AGAACCGCAGAATCAACCTG -3’ (forward) and 5’- TTTGGAGAAGGAGAGGACGA -3’ (reverse);GAPDH:5’- GGCCAAGGTCATCCATGA -3 (forward) and 5’- TCAGTGTAGCCCAGGATG -3’ (reverse);

Table S 5: Primer sequences for RT–PCR

3

References[1] Kagnew Hailesellasse Sene, Christopher J Porter, Gareth Palidwor, Carolina Perez-Iratxeta, En-

rique M Muro, Pearl A Campbell, Michael A Rudnicki, and Miguel A Andrade-Navarro. Genefunction in early mouse embryonic stem cell differentiation. BMC Genomics, 8:85, 2007.

4

Supplement: A dynamic model to study miRNA mediated impact onthe formation of Protein gradients0.1 Model 1: Endoderm differentiation, transient miR-335 expressionThe model consists of 1 external inputs, 4 dynamical variables indicated by square brackets and 8 reac-tions and was evaluated for 1 experimental conditions. In total 24 parameters are estimated from theexperimental data, yielding a value of the objective function −2 log(L) = 2746.2 for a total of 190 datapoints. The estimated parameter values are given in Section 0.3.

The model dynamics depends on the external inputs:

[kmg](t) = e− (β−t)2

2σ2 (1)

The rate equations corresponding to the reactions included in the model are given by:v1 = kf (2)v2 = [mRNA] · γf (3)

v3 = km · [kmg] +[complex] · γC

rm + 1(4)

v4 = [mRNA] · kon · [miR] (5)v5 = [complex] · koff (6)v6 = [complex] · γC (7)v7 = [mRNA] · kl (8)v8 = [Protein] · γP (9)

The ODE system determining the time evolution of the dynamical variables is given by:d[mRNA]/dt = +v1 − v2 − v4 + v5 (10)

d[complex]/dt = +v4 − v5 − v6 (11)d[miR]/dt = +v3 − v4 + v5 (12)

d[Protein]/dt = +v7 − v8 (13)

The ODE system was solved by a parallelized implementation of the CVODES algorithm [2]. It alsosupplies the parameter sensitivities utilized for parameter estimation.

The initial conditions for the ODE system are given by:[mRNA](0) = init mRNA (14)

[complex](0) = init complex (15)[miR](0) = init miR (16)

[Protein](0) = init Protein (17)

The ODE system is modified by the following parameter transformations:init Protein → 0 (18)init mRNA → 1 (19)

init miR → 1 (20)

0.1.1 Data: FACS sorted Foxa2++ Cells, qPCR, in-vitro differentiation imaging

Data used for parameter value estimation:

1

• Relative Foxa2 mRNA-levels (qPCR) were measured from from FACS sorted Foxa++ cells at t=0h,24h, 48h and 96h.

• Relative miR-335-levels (qPCR) were measured from from FACS sorted Foxa2++ cells at t=0h,24h, 48h and 96h.

• Foxa2 fusion protein fluorescence intensities were measured from in-vitro differentiated cells underendoderm conditions at t=0h, 48h and 96h.

The model outputs available in this data set are defined by:mRNA abs = scale fm1 · ([complex] + [mRNA]) (21)

miR abs = scale mir1 · ([complex] + [miR]) (22)Protein abs1 = [Protein] · scale P1 + 114 (23)

The error model that describes the measurement noise for each model output is given by:mRNA abs = sd mRNA abs (24)

miR abs = sd miR abs (25)Protein abs1 = sd Proteinabs (26)

The agreement of the model outputs and the experimental data, given in Table S 1, yields a value of theobjective function −2 log(L) = 2059.09 for 162 data points in this data set.

mRNA abs miR abs Protein abs1time [hour] conc. [au] conc. [au] conc [au]

0 1.36207 45.1219 78.0360 1 18.4586 5.25010 0.60037 NaN 133.990 NaN NaN 83.8890 NaN NaN 79.7270 NaN NaN 227.980 NaN NaN 22.5870 NaN NaN 344.130 NaN NaN 201.910 NaN NaN 120.260 NaN NaN 219.480 NaN NaN 17.8380 NaN NaN 5.06170 NaN NaN 228.20 NaN NaN 20.5740 NaN NaN 61.6270 NaN NaN 7.99660 NaN NaN 171.020 NaN NaN 89.1790 NaN NaN 13.9270 NaN NaN 239.370 NaN NaN 102.180 NaN NaN 121.790 NaN NaN 14.6340 NaN NaN 1.77290 NaN NaN 0.914850 NaN NaN 43.9130 NaN NaN 95.0190 NaN NaN 272.140 NaN NaN 199.80 NaN NaN 92.4910 NaN NaN 147.990 NaN NaN 224.580 NaN NaN 51.680 NaN NaN 83.0410 NaN NaN 200.61

2

0 NaN NaN 208.280 NaN NaN 101.590 NaN NaN 111.670 NaN NaN 129.75

24.000000 311.89 29.245 NaN24.000000 223.52 39.626 NaN24.000000 265.99 74.853 NaN48.000000 205.82 3.48683 449.548.000000 209.51 1 234.6348.000000 296.14 NaN 429.9348.000000 NaN NaN 499.5748.000000 NaN NaN 247.3748.000000 NaN NaN 142.748.000000 NaN NaN 177.2148.000000 NaN NaN 380.5248.000000 NaN NaN 110.8948.000000 NaN NaN 379.2448.000000 NaN NaN 233.1648.000000 NaN NaN 325.748.000000 NaN NaN 457.1148.000000 NaN NaN 235.848.000000 NaN NaN 0.459848.000000 NaN NaN 635.8948.000000 NaN NaN 456.5148.000000 NaN NaN 217.9348.000000 NaN NaN 354.7448.000000 NaN NaN 231.7748.000000 NaN NaN 138.9548.000000 NaN NaN 134.9748.000000 NaN NaN 338.8648.000000 NaN NaN 281.4148.000000 NaN NaN 371.2348.000000 NaN NaN 32.85348.000000 NaN NaN 577.4348.000000 NaN NaN 2.374348.000000 NaN NaN 49.40248.000000 NaN NaN 451.248.000000 NaN NaN 213.7948.000000 NaN NaN 363.3548.000000 NaN NaN 536.9848.000000 NaN NaN 468.0848.000000 NaN NaN 298.4548.000000 NaN NaN 425.1248.000000 NaN NaN 450.0448.000000 NaN NaN 367.2648.000000 NaN NaN 336.2548.000000 NaN NaN 651.1848.000000 NaN NaN 406.2548.000000 NaN NaN 391.6496.000000 173.41 3.32428 562.9596.000000 106.06 12.994 629.7196.000000 146.04 NaN 704.5396.000000 NaN NaN 801.0696.000000 NaN NaN 648.0696.000000 NaN NaN 514.0496.000000 NaN NaN 718.6396.000000 NaN NaN 190.1996.000000 NaN NaN 627.6596.000000 NaN NaN 623.8196.000000 NaN NaN 522.4896.000000 NaN NaN 639.5696.000000 NaN NaN 0.2266796.000000 NaN NaN 12.95796.000000 NaN NaN 795.6596.000000 NaN NaN 0.22186

3

96.000000 NaN NaN 903.8996.000000 NaN NaN 742.996.000000 NaN NaN 695.5596.000000 NaN NaN 792.8396.000000 NaN NaN 667.3496.000000 NaN NaN 61796.000000 NaN NaN 514.3796.000000 NaN NaN 859.9396.000000 NaN NaN 790.1496.000000 NaN NaN 738.696.000000 NaN NaN 865.4696.000000 NaN NaN 754.6796.000000 NaN NaN 108696.000000 NaN NaN 657.0596.000000 NaN NaN 562.8296.000000 NaN NaN 630.1496.000000 NaN NaN 611.9296.000000 NaN NaN 634.7396.000000 NaN NaN 611.9296.000000 NaN NaN 593.3996.000000 NaN NaN 651.8896.000000 NaN NaN 537.9996.000000 NaN NaN 669.5796.000000 NaN NaN 615.0596.000000 NaN NaN 818.0896.000000 NaN NaN 910.7796.000000 NaN NaN 860.1596.000000 NaN NaN 439.8196.000000 NaN NaN 661.2796.000000 NaN NaN 720.9896.000000 NaN NaN 950.8196.000000 NaN NaN 785.2996.000000 NaN NaN 723.8496.000000 NaN NaN 745.0796.000000 NaN NaN 768.5696.000000 NaN NaN 853.1996.000000 NaN NaN 610.6896.000000 NaN NaN 402.3696.000000 NaN NaN 670.4696.000000 NaN NaN 498.996.000000 NaN NaN 583.4296.000000 NaN NaN 493.3296.000000 NaN NaN 659.65

Table MM 1: Experimental data for the experiment FACS sorted Foxa2++ Cells, in-vitro differentiationimaging

0.2 Model 2: Mesoderm differentiation, prolonged miR-335 expressionThe model consists of 0 external inputs, 4 dynamical variables indicated by square brackets and 8 reac-tions and was evaluated for 1 experimental conditions. In total 24 parameters are estimated from theexperimental data, yielding a value of the objective function −2 log(L) = 2746.2 for a total of 190 datapoints. The estimated parameter values are given in Section 0.3.

4

The rate equations corresponding to the reactions included in the model are given by:v1 = kfl (27)v2 = [mRNA] · γf (28)

v3 = kml +[complex] · γC

rm + 1(29)

v4 = [mRNA] · kon · [miR] (30)v5 = [complex] · koff (31)v6 = [complex] · γC (32)v7 = [mRNA] · kl (33)v8 = [Protein] · γP (34)

The ODE system determining the time evolution of the dynamical variables is given by:d[mRNA]/dt = +v1 − v2 − v4 + v5 (35)

d[complex]/dt = +v4 − v5 − v6 (36)d[miR]/dt = +v3 − v4 + v5 (37)

d[Protein]/dt = +v7 − v8 (38)

The ODE system was solved by a parallelized implementation of the CVODES algorithm [2]. It alsosupplies the parameter sensitivities utilized for parameter estimation.

The initial conditions for the ODE system are given by:[mRNA](0) = init mRNA (39)

[complex](0) = init complex (40)[miR](0) = init miR (41)

[Protein](0) = init Protein (42)

The ODE system is modified by the following parameter transformations:init Protein → 0 (43)init mRNA → 1 (44)

init miR → 1 (45)

5

0.2.1 Data: FACS sorted T++ cells, qPCR

Data used for parameter value estimation:

• Relative Foxa2 mRNA-levels (qPCR) were measured from from FACS sorted T++ cells at t=0h,24h, 48h and 96h.

• Relative miR-335-levels (qPCR) were measured from from FACS sorted T++ cells at t=0h, 24h,48h and 96h.

The model outputs available in this data set are defined by:mRNA abs = scale fm2 · ([complex] + [mRNA]) (46)

miR abs = scale mir2 · ([complex] + [miR]) (47)

The error model that describes the measurement noise for each model output is given by:mRNA abs = sd mRNA abs (48)

miR abs = sd miR abs (49)

The agreement of the model outputs and the experimental data, given in Table S 2, yields a value of theobjective function −2 log(L) = 771.766 for 24 data points in this data set.

mRNA abs miR abstime [hour] conc. [au] conc. [au]

0 7.67719 5.849810 9.09818 6.877680 1 3.17061

24.000000 113.787 246.48224.000000 170.97 384.56924.000000 123.447 139.55548.000000 240.749 613.51548.000000 143.485 918.67848.000000 228.588 343.50996.000000 234.27 1289.8496.000000 127.385 1373.8496.000000 235.344 689.912

Table MM 2: Experimental data for the experiment data T mir335

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name θmin θ θmax log non-log θ fitted1 β -5 -0.4448 +3 1 +3.59 · 10−01 12 σ -1 +1.0799 +3 1 +1.20 · 10+01 13 γC -5 -1.2519 +3 1 +5.60 · 10−02 14 γP -5 -1.3587 +3 1 +4.38 · 10−02 15 γf -5 -0.7836 +3 1 +1.65 · 10−01 16 init complex -5 -4.5437 +3 1 +2.86 · 10−05 17 koff -5 -4.9931 +3 1 +1.02 · 10−05 18 kon -5 -1.1736 +3 1 +6.71 · 10−02 19 kl -5 -1.2797 +3 1 +5.25 · 10−02 110 kf -5 +0.8349 +3 1 +6.84 · 10+00 111 kfl -5 +0.5243 +3 1 +3.34 · 10+00 112 km -1 -5.0000 +1 1 +1.00 · 10−05 113 kml -5 +1.4920 +3 1 +3.10 · 10+01 114 rm -5 +2.1296 +3 1 +1.35 · 10+02 115 scale P1 -5 +1.0609 +3 1 +1.15 · 10+01 116 scale fm1 -5 +0.4827 +3 1 +3.04 · 10+00 117 scale fm2 -5 +0.5326 +3 1 +3.41 · 10+00 118 scale mir1 -5 -0.4557 +3 1 +3.50 · 10−01 119 scale mir2 -5 -0.3817 +3 1 +4.15 · 10−01 120 sd Proteinabs -3 +2.2452 +3 1 +1.76 · 10+02 121 sd mRNA abs -5 +1.5000 +2 1 +3.16 · 10+01 122 sd miR abs -5 +1.5000 +2 1 +3.16 · 10+01 1

Table MM 3: Estimated parameter valuesθ indicates the estimated value of the parameters. θmin and θmax indicate the upper and lower bounds for the parameters.The log-column indicates if the value of a parameter was log-transformed. If log = 1 the non-log-column indicates the non-logarithmic value of the estimate. The fitted-column indicates if the parameter value was estimated (1), was temporarilyfixed (0) or if its value was fixed to a constant value (2).

0.3 Estimated model parametersThe model parameter were estimated by maximum likelihood estimation applying the MATLAB lsqnonlinalgorithm. In Table S 3 the estimated parameter values are given. Parameters highlighted in red colorindicate parameter values close to their bounds. The parameter name prefix init indicates the initial valueof a dynamic variable. The parameter name prefix scale indicates a scaling factor of the experimentaldata. The parameter name prefix sd indicates the magnitude of the measurement noise for a specificmeasurement.

0.4 Profile likelihood of model parametersIn order to evaluate the identifiability of the model parameters and to assess confidence intervals theprofile likelihood [3] was calculated. The mean calculation time of the profile likelihood per parameterwas 00:00:05.92 ± 00:00:01.68. An overview is displayed in Figure S 1.

0.5 Confidence intervals for the model parametersIn Table S 4, 95% confidence intervals for the estimated parameter values derived by the profile likelihood[3] are given.

0.6 Model predictionsTo assess confidence we perfomed an approach analogously to [1]. Model predictions were performed bymanually setting km to 0 (LOF) or 10 (GOF), respectively, and simulation of the model within parameterconfidence intervals. The corresponding trajectories for mRNA, miR and Protein are shown in Figure S 2.

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10

−1.6 −1.4 −1.2 −1log (γC)

−1.3588 −1.3587 −1.3586log

10(γP)

−0.7836 −0.7835 −0.7834log

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−4 −2 0log

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0.8347 0.8348 0.8349log

10(kf)

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10(kfl)

0.2 0.4 0.6 0.8 1 1.2log

10(km)

1.46 1.48 1.5 1.52

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log10

(kml)

−2 lo

g(P

L)

1 2 3log

10(rm)

0.8 1 1.2 1.4 1.6log

10(scale_P1)

−1.577 −1.5769 −1.5768log

10(scale_P3)

0 0.2 0.4 0.6log

10(scale_fm1)

0.3 0.4 0.5 0.6 0.7log

10(scale_fm2)

27502760277027802790

−2 lo

g(P

L)

−4 −2 0log

10(scale_mir1)

−0.42−0.4 −0.38−0.36−0.34log

10(scale_mir2)

2.1 2.2 2.3 2.4log

10(sd_foxPabs)

1.3 1.4 1.5log

10(sd_foxm_abs)

27502760277027802790

−2 lo

g(P

L)

1.485 1.49 1.495 1.5log

10(sd_miR_abs)

0 1 2log

10(σ)

−1.2797 −1.2796 −1.2795log

10(kfl)

−4 −2 0 2

27502760277027802790

95% (point−wise)

95% (simultaneous)

log10

(β)

−2 lo

g(P

L)

Figure MM 1: Overview of the profile likelihood of the model parametersThe solid lines indicate the profile likelihood. The dashed lines indicate the threshold to assess confidence intervals. Thegray asterisk indicate the optimal parameter values.

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name θ σ−ptw σ+

ptw σ−sim σ+

sim

1 β -0.445 -Inf +1.291 -Inf +1.6402 σ +1.080 +0.551 +1.489 +0.041 +2.0993 γC -1.252 -1.419 -1.113 -1.657 -1.0114 γP -1.359 -1.359 -1.359 -1.359 +Inf5 γf -0.784 -0.784 -0.783 -Inf +Inf6 init complex -4.544 -Inf -0.071 -Inf +Inf7 koff -4.993 -Inf -1.463 -Inf -0.9578 kon -1.174 -1.312 -1.092 -Inf +Inf9 kl -1.280 -1.280 -1.280 -Inf +Inf10 kf +0.835 +0.835 +0.835 -Inf +Inf11 kfl +0.524 +0.449 +0.565 +0.333 +0.64112 km +1.126 +0.714 +Inf +0.226 +Inf13 kml +1.492 +1.473 +1.509 +1.451 +1.53014 rm +2.130 +1.025 +Inf -Inf +Inf15 scale P1 +1.061 +0.788 +1.122 +0.686 +Inf16 scale fm1 +0.483 +0.347 +0.520 -0.079 +0.58817 scale fm2 +0.533 +0.454 +0.670 +0.329 +0.75018 scale mir1 -0.456 -1.038 -0.069 -Inf +Inf19 scale mir2 -0.382 -0.403 -0.364 -0.428 -0.34220 sd Protein abs +2.245 +2.196 +2.297 +2.106 +2.42221 sd mRNA abs +1.500 +1.435 +Inf +1.261 +Inf22 sd miR abs +1.500 +1.498 +Inf +1.485 +Inf

Table MM 4: Confidence intervals for the estimated parameter values derived by the profile likelihoodθ indicates the estimated optimal parameter value. σ−

ptw and σ+ptw indicate 95% point-wise confidence intervals. σ−

sim andσ+

sim indicate 95% simultaneous confidence intervals.

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Foxa2 Protein Foxa2 mRNA miR-335

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Figure MM 2: PLE trajectories Shown are the trajectories for estimated parameter below the 95% PLE threshold (fistrow). Second and Third row: All trajectories for estimated parameter below threshold for LOF simulations; miR expressionrate

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References[1] Julie Bachmann, Andreas Raue, Marcel Schilling, Martin E Bohm, Clemens Kreutz, Daniel Kaschek,

Hauke Busch, Norbert Gretz, Wolf D Lehmann, Jens Timmer, and Ursula Klingmuller. Division oflabor by dual feedback regulators controls jak2/stat5 signaling over broad ligand range. Mol SystBiol, 7:516, 2011.

[2] A.C. Hindmarsh, P.N. Brown, K.E. Grant, S.L. Lee, R. Serban, D.E. Shumaker, and C.S. Wood-ward. Sundials: Suite of nonlinear and differential/algebraic equation solvers. ACM Transactions onMathematical Software (TOMS), 31(3):363–396, 2005.

[3] A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling, U. Klingmuller, and J. Timmer. Struc-tural and practical identifiability analysis of partially observed dynamical models by exploiting theprofile likelihood. Bioinformatics, 25(15):1923–1929, Aug 2009.

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