Resolving the Combinatorial Complexity of Smad Protein Complex Formation and Its Link to Gene Expression.

Upon stimulation of cells with transforming growth factor beta (TGF-beta), Smad proteins form trimeric complexes and activate a broad spectrum of target genes. It remains unresolved which of the possible Smad complexes are formed in cellular contexts and how these contribute to gene expression. By combining quantitative mass spectrometry with a computational selection strategy, we predict and provide experimental evidence for the three most relevant Smad complexes in the mouse hepatoma cell line Hepa1-6. Utilizing dynamic pathway modeling, we specify the contribution of each Smad complex to the expression of representative Smad target genes, and show that these contributions are conserved in human hepatoma cell lines and primary hepatocytes. We predict, based on gene expression data of patient samples, increased amounts of Smad2/3/4 proteins and Smad2 phosphorylation as hallmarks of hepatocellular carcinoma and experimentally verify this prediction. Our findings demonstrate that modeling approaches can disentangle the complexity of transcription factor complex formation and its impact on gene expression

Raw data for Fig.6B

<div><b>Contents:</b><br></div><div>These raw data files accompany Fig.6B of Sampattavanich et al., Cell Systems (2018). Refer to accompanying Git repository for scripts used for Fig.6B. </div><div><br></div><div><div><b>To use this data with our Git Repository:</b></div><div>Placed these in the /rawdata/dualsensors folder.</div></div><div><div><br></div><div><div><b>Experimental Setup:</b><br></div><div><p>MCF10A cells were serum-starved for 6 hours and later were stimulated with growth factors. After 4 hours, cells were exposed to various inhibitors.</p></div></div></div><div><br></div

Raw westernblot data for Fig.1C, S1G

<div><b>Contents:</b></div><div>These raw data files accompany Fig.1C and S1G of Sampattavanich et al., Cell Systems (2018). Refer to our Git repository for accompanying scripts. </div><div><br></div><div><b>To use this data with our Git Repository:</b></div><div>Placed these in the /rawdata/western folder.</div><div><br></div><div><b>Experimental Setup:</b></div><div><div>184A1 cells carrying the F3aN400-Venus reporter were serum deprived overnight before being stimulated with different growth factors, each at 100 ng/mL. Cell lysates were collected at 0, 15, 60, 180 and 480 minutes. Western</div><div>blot analysis was performed using antibodies against AKTS473, ERKT202/Y204, F3aN400S294, F3aN400S253 and beta-actin. </div></div

Timelapse videos of Fig.1D and 2A

<b>FoxO3a nuclear-cytoplasmic pulsing dynamics in 184A1 mammary epithelial cell line following growth-factor treatment with varying ERK/AKT activation loads. </b><div><br></div><div><i>Assay Description</i></div><div>The nuclear-cytoplasmic pulsing behavior of a FoxO3a fluorescent reporter (FoxO3aN400-Venus) expressed in the 184A1 mammary epithelial cell line was assessed at the single-cell level using live imaging of cells that were untreated or treated with one of six growth factors in the absence or presence of an AKT or MEK inhibitor.</div><div>Complete raw images can be found in the HMS OMERO link below.</div><div><b><br></b></div><div><b>Refer to platemap.pdf for the dosing and types of ligands, and inhibitors at each row,column combination.</b><br></div

Raw data for fPCA and pulsing analysis in Fig.1-6 and S1-6

<div><div><b>Contents:</b></div><div>These raw data files accompany the fPCA and pulsing analysis in Fig.1-6 and S1-6 in Sampattavanich et al., Cell Systems (2018). Refer to our Git repository for accompanying scripts.</div><div><b>To use this data with our Git Repository:</b><br></div><div><div>Placed these in the /rawdata/Workspace folder.</div></div></div

Experimental Design for Parameter Estimation of Gene Regulatory Networks

Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines

Raw data for fig.S1E

<div><div><b>Contents:</b></div><div>The two data files accompany Supplementary Fig.S1E of Sampattavanich et al., Cell Systems (2018). Complete raw images can be found in the HMS OMERO link below. Also refer to the accompanied video and platemap also in this collection.</div></div><div><i><br></i></div><div><div><b>To use this data with our Git Repository:</b></div><div>- Extract archived file to get 130722_SCdyn.csv</div><div>- Placed both .csv files in the /rawdata/Workspaces folder.</div><div><br></div></div><div><b>Experimental Setup:</b><br></div><div>The nuclear-cytoplasmic pulsing behavior of a FoxO3a fluorescent reporter (FoxO3aN400-Venus) expressed in the 184A1 mammary epithelial cell line was assessed at the single-cell level using live imaging of cells that were untreated or treated with one of six growth factors in the absence or presence of an AKT or MEK inhibitor.</div

Raw data for Fig 7

<div><b>Contents:</b><br></div>These data relate to Fig.7 of Sampattavanich et al., Cell Systems (2018). Refer to accompanying Git repository for scripts used for Fig.7. <div><br></div><div><div><b>To use this data with our Git Repository:</b></div><div>Placed these in the /rawdata/fixedcell folder.</div></div><div><div><br></div><div>Each cell line has three .MAT files for three proteins: FoxO3, ERK and AKT. Each single_Protein variable has the dimension of TimePoint x Row x Column. <div><br></div><div><i>Raw Index</i> indicates growth factor types, each at 100 ng/mL: (1)EGF (2)IGF1 (3)FGF1 (4)HRG (5)HGF (6)EPR (7)BTC (8)Non-stimulated control</div><div><br></div><div><i>Column Index</i> indicates drug pre-treatment condition: (1)DMSO control (2)AKTi(10uM) (3)MEKi(10uM) (4)AKTi(10uM)+MEKi(10uM)<br></div><div><i><br></i></div><div><i>Timepoint Index</i> varies between cell lines</div><div><div><i>184A1</i>: 08-19-2013 (result 08-22-2013) including 13-timepoints: 0, 5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 300, 480 minutes<br></div><div><div><i>MCF10A</i>: 11-26-2013 (result 11-29-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>SKBR3</i>: 10-22-2013 (result 10-25-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>BT-20</i>: 10-22-2013 (result 10-25-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>MCF7</i>: 11-05-2013 (result 11-09-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>T47D</i>: 11-26-2013 (result 11-29-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>Hs578T</i>: 11-05-2013 (result 11-08-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>MDA231</i>: 11-26-2013 (result 11-29-2013) including 8 time points:0,0:15,0:30,1,1:30,2,3,4HRs.<br></div><div><i>HCC1806</i>: 12-17-2013 (result 12-20-2013) including 8 time points: 0,0:15,0:30,1,1:30,2,3,4HRs.</div></div></div><div><br></div><div><p><b>Experimental Setup:</b><br></p><p>Cells were serum starved for 6 hours, and were then pre-treated with different inhibitor conditions for 1 hour. Cells were stimulated with different growth factor types, and fixed at various timepoints. Fixed cells were then stained with FoxO3, phospho-AKT, and phospho-ERK using standard immunofluorescent staining.</p></div></div></div

Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases

Extracellular growth factors signal to transcription factors via a limited number of cytoplasmic kinase cascades. It remains unclear how such cascades encode ligand identities and concentrations. In this paper, we use live-cell imaging and statistical modeling to study FOXO3, a transcription factor regulating diverse aspects of cellular physiology that is under combinatorial control. We show that FOXO3 nuclear-to-cytosolic translocation has two temporally distinct phases varying in magnitude with growth factor identity and cell type. These phases comprise synchronous translocation soon after ligand addition followed by an extended back-and-forth shuttling; this shuttling is pulsatile and does not have a characteristic frequency, unlike a simple oscillator. Early and late dynamics are differentially regulated by Akt and ERK and have low mutual information, potentially allowing the two phases to encode different information. In cancer cells in which ERK and Akt are dysregulated by oncogenic mutation, the diversity of states is lower