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  • 1.
    Erdem, Cemal
    et al.
    Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States.
    Birtwistle, Marc R.
    MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets2023In: Frontiers in Systems Biology, ISSN 2674-0702, Vol. 3, article id 1099413Article in journal (Refereed)
    Abstract [en]

    Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These interactions are used as a basis for new reactions in mechanistic models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models.

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  • 2.
    Erdem, Cemal
    et al.
    Computational and Quantitative Biology Lab, Koc University, Istanbul, Turkey.
    Bozkurt, Yasemin
    Erman, Burak
    Gül, Ahmet
    Demir, Alper
    Mathematical modeling of Behçet's disease: A dynamical systems approach2015In: Journal of biological systems, ISSN 0218-3390, Vol. 23, no 02, p. 231-257Article in journal (Refereed)
    Abstract [en]

    Behçet's Disease (BD) is a multi-systemic, auto-inflammatory disorder that is characterized by recurrent episodes of inflammatory manifestations affecting skin, mucosa, eyes, blood vessels, joints and several other organs. BD is classified as a multifactorial disease with an important contribution of genetics. Genetic studies suggest that there is a strong association of BD with a Class I major histocompatibility complex antigen, named HLA-B*51, along with several other weaker associations with genes encoding proteins involved in inflammation. However, pathogenic mechanisms associated with these genetic variations and their interactions with the environment have not been elucidated yet. In this paper, we present a mathematical model for BD based on a dynamical systems perspective that captures especially the relapsing nature of the disease. We propose a disease progression mechanism and construct a model, in the form of coupled ordinary differential equations (ODEs), which reveals the occurrence pattern of the disease in the population. According to our model, the disease has three distinct modes describing different phenotypes of people carrying HLA-B*51 tissue antigen, namely, the Healthy Carrier, the Potential Patient and the Active Patient. We herein present an exemplary mathematical model for BD, for the first time in the literature, that concisely captures the actions of many cell types together with genetic and environmental effects. The proposed model provides insight into this complex inflammatory disease which may lead to identification of new tools for its treatment and prevention.

  • 3.
    Erdem, Cemal
    et al.
    Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
    Gross, Sean M.
    Heiser, Laura M.
    Birtwistle, Marc R.
    MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms2023In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 3991Article in journal (Refereed)
    Abstract [en]

    Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2 , CLIC2 , FAM83D , ACSL5 , and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

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  • 4.
    Erdem, Cemal
    et al.
    Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America; University of Pittsburgh Drug Discovery Institute (UPDDI), University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
    Lee, Adrian V.
    Taylor, D. Lansing
    Lezon, Timothy R.
    Inhibition of RPS6K reveals context-dependent Akt activity in luminal breast cancer cells2021In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 17, no 6, article id e1009125Article in journal (Refereed)
    Abstract [en]

    Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contribute to the risk and advancement of many cancer types by activating cell survival cascades. Similarities between these pathways have thus far prevented the development of pharmacological interventions that specifically target either Ins or IGF1 signaling. To identify differences in early Ins and IGF1 signaling mechanisms, we developed a dual receptor (IGF1R & InsR) computational response model. The model suggested that ribosomal protein S6 kinase (RPS6K) plays a critical role in regulating MAPK and Akt activation levels in response to Ins and IGF1 stimulation. As predicted, perturbing RPS6K kinase activity led to an increased Akt activation with Ins stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating the emergence of compensation mechanisms without disrupting InsR signaling.

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  • 5.
    Erdem, Cemal
    et al.
    Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
    Mutsuddy, Arnab
    Bensman, Ethan M.
    Dodd, William B.
    Saint-Antoine, Michael M.
    Bouhaddou, Mehdi
    Blake, Robert C.
    Gross, Sean M.
    Heiser, Laura M.
    Feltus, F. Alex
    Birtwistle, Marc R.
    A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling2022In: Nature Communications, E-ISSN 2041-1723, Vol. 13, no 1, article id 3555Article in journal (Refereed)
    Abstract [en]

    Abstract Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

  • 6.
    Erdem, Cemal
    et al.
    Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania; University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
    Nagle, Alison M.
    Casa, Angelo J.
    Litzenburger, Beate C.
    Wang, Yu-fen
    Taylor, D. Lansing
    Lee, Adrian V.
    Lezon, Timothy R.
    Proteomic screening and lasso regression reveal differential signaling in insulin and insulin-like growth factor I (IGF1) pathways2016In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 15, no 9, p. 3045-3057Article in journal (Refereed)
    Abstract [en]

    Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.

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  • 7. Gross, Sean M.
    et al.
    Dane, Mark A.
    Smith, Rebecca L.
    Devlin, Kaylyn L.
    McLean, Ian C.
    Derrick, Daniel S.
    Mills, Caitlin E.
    Subramanian, Kartik
    London, Alexandra B.
    Torre, Denis
    Evangelista, John Erol
    Clarke, Daniel J. B.
    Xie, Zhuorui
    Erdem, Cemal
    Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
    Lyons, Nicholas
    Natoli, Ted
    Pessa, Sarah
    Lu, Xiaodong
    Mullahoo, James
    Li, Jonathan
    Adam, Miriam
    Wassie, Brook
    Liu, Moqing
    Kilburn, David F.
    Liby, Tiera A.
    Bucher, Elmar
    Sanchez-Aguila, Crystal
    Daily, Kenneth
    Omberg, Larsson
    Wang, Yunguan
    Jacobson, Connor
    Yapp, Clarence
    Chung, Mirra
    Vidovic, Dusica
    Lu, Yiling
    Schurer, Stephan
    Lee, Albert
    Pillai, Ajay
    Subramanian, Aravind
    Papanastasiou, Malvina
    Fraenkel, Ernest
    Feiler, Heidi S.
    Mills, Gordon B.
    Jaffe, Jake D.
    Ma’ayan, Avi
    Birtwistle, Marc R.
    Sorger, Peter K.
    Korkola, James E.
    Gray, Joe W.
    Heiser, Laura M.
    A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses2022In: Communications Biology, E-ISSN 2399-3642, Vol. 5, no 1, article id 1066Article in journal (Refereed)
    Abstract [en]

    The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

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  • 8. Mutsuddy, Arnab
    et al.
    Erdem, Cemal
    Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
    Huggins, Jonah R
    Salim, Misha
    Cook, Daniel
    Hobbs, Nicole
    Feltus, F Alex
    Birtwistle, Marc R
    Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format2023In: Bioinformatics Advances, ISSN 2635-0041, Vol. 3, no 1, article id vbad039Article in journal (Refereed)
    Abstract [en]

    Summary: Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks.

    Availability and implementation: Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).

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  • 9. Zadeh, Cameron O.
    et al.
    Huggins, Jonah R.
    Sarmah, Deepraj
    Westbury, Baylee C.
    Interiano, William R.
    Jordan, Micah C.
    Phillips, S. Ashley
    Dodd, William B.
    Meredith, Wesley O.
    Harold, Nicholas J.
    Erdem, Cemal
    Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States.
    Birtwistle, Marc R.
    Mesowestern blot: Simultaneous analysis of hundreds of submicroliter lysates2022In: ACS Omega, E-ISSN 2470-1343, Vol. 7, no 33, p. 28912-28923Article in journal (Refereed)
    Abstract [en]

    Western blotting is a widely used technique for molecular-weight-resolved analysis of proteins and their posttranslational modifications, but high-throughput implementations of the standard slab gel arrangement are scarce. The previously developed Microwestern requires a piezoelectric pipetting instrument, which is not available for many labs. Here, we report the Mesowestern blot, which uses a 3D-printable gel casting mold to enable high-throughput Western blotting without piezoelectric pipetting and is compatible with the standard sample preparation and small (∼1 μL) sample sizes. The main tradeoffs are reduced molecular weight resolution and higher sample-to-sample CV, making it suitable for qualitative screening applications. The casted polyacrylamide gel contains 336, ∼0.5 μL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution profiles. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder and cell lysate (RIPA buffer) demonstrate that the protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semidry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is at least 32-fold, and at least ∼500 attomols of β-actin can be detected (∼29 ng of total protein from mammalian cell lysates: ∼100–300 cells). Because the gel mold is 3D-printable, users with access to additive manufacturing cores have significant design freedom for custom layouts. We expect that the technique could be easily adopted by any typical cell and molecular biology laboratory already performing Western blots.

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