index.json

[{"authors":["admin"],"categories":null,"content":"I am interested in bridging the gap between mathematical/computational models and biological data. My research focuses primarily on mathematical modeling and control of poorly constrained biological networks, drug combination synergy, and multiscale models coupling intracellular and environmental dynamics. I have developed and applied approaches and models toward understanding cancer control, plant signaling, and yeast growth.\nI earned my PhD in Cancer Biology at Vanderbilt University under the guidance of Vito Quaranta, and am currently a postdoctoral scholar with Réka Albert in the Department of Physics at Pennsylvania State University.\n","date":1601510400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1605043254,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://djwooten.github.io/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"I am interested in bridging the gap between mathematical/computational models and biological data. My research focuses primarily on mathematical modeling and control of poorly constrained biological networks, drug combination synergy, and multiscale models coupling intracellular and environmental dynamics.","tags":null,"title":"","type":"authors"},{"authors":["","Réka Albert"],"categories":[],"content":"","date":1601510400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1605043254,"objectID":"34ed3b9ac86c681c3bb5fb9b52193d3c","permalink":"https://djwooten.github.io/publication/wooten-2020-synergy/","publishdate":"2020-11-10T21:20:53.649587Z","relpermalink":"/publication/wooten-2020-synergy/","section":"publication","summary":"Combinations of multiple pharmacological agents can achieve a substantial benefit over treatment with single agents alone. Combinations that achieve “more than the sum of their parts” are called synergistic. There have been many proposed frameworks to understand and quantify drug combination synergy with different assumptions and domains of applicability. We introduce here synergy, a Python library that (i) implements a broad array of popular synergy models, (ii) provides tools for evaluating confidence intervals and conducting power analysis, and (iii) provides standardized tools to analyze and visualize drug combinations and their synergies and antagonisms.","tags":["\"synergy\"","\"drug synergy\"","\"Loewe Additivity\"","\"Bliss Independence\"","\"MuSyC\"","\"python\"","\"combination index\"","\"braid\"","\"zip\"","\"schindler\""],"title":"synergy - A Python library for calculating, analyzing, and visualizing drug combination synergy","type":"publication"},{"authors":[],"categories":[],"content":"synergy A python package to calculate, analyze, and visualize drug combination synergy and antagonism. Currently supports multiple models of synergy, including MuSyC, Bliss, Loewe, Combination Index, ZIP, Zimmer, BRAID, Schindler, and HSA.\nInstallation Using PIP pip install synergy\nUsing conda not yet\nUsing git\ngit clone git@github.com:djwooten/synergy.git cd synergy pip install -e .  Example Usage Generate synthetic data to fit from synergy.combination import MuSyC from synergy.utils.dose_tools import grid  Initialize a model. I will use the MuSyC synergy model to generate data, but it could be done using Zimmer or BRAID as well.\nE0, E1, E2, E3 = 1, 0.7, 0.4, 0. h1, h2 = 2.3, 0.8 C1, C2 = 1e-2, 1e-1 alpha12, alpha21 = 3.2, 1.1 gamma12, gamma21 = 2.5, 0.8 truemodel = MuSyC(E0=E0, E1=E1, E2=E2, E3=E3, h1=h1, h2=h2, C1=C1, C2=C2,\t\\ alpha12=alpha12, alpha21=alpha21, gamma12=gamma12,\t\\ gamma21=gamma21)  Display the model\u0026rsquo;s parameters\nprint(truemodel)  MuSyC(E0=1.00, E1=0.70, E2=0.40, E3=0.00, h1=2.30, h2=0.80, C1=1.00e-02, C2=1.00e-01, oalpha12=3.20, oalpha21=1.10, beta=0.67, gamma12=2.50, gamma21=0.80)  Evaluate the model at doses d1=C1, d2=C2 (a combination of the EC50 of each drug)\nprint(truemodel.E(C1, C2))  0.3665489890285983  Generate a dose sampling grid to make \u0026ldquo;measurements\u0026rdquo; at. Drug 1 will be sampled at 8 doses, logarithmically spaced from C1/100 to C1*100. Drug 2 will be likewise sampled around C2. (8 doses of Drug 1) X (8 doses of Drug 2) = 64 total measurements.\nd1, d2 = grid(C1/1e2, C1*1e2, C2/1e2, C2*1e2, 8, 8) print(d1.shape, d2.shape)  (64,) (64,)  Evaluate the model at those 64 dose combinations\nE = truemodel.E(d1, d2) print(E.shape)  (64,)  Add noise to get imperfect data\nimport numpy as np E_noisy = E * (1+0.1*(2*np.random.rand(len(E))-1)) print(E_noisy.shape)  (64,)  Fit synergy model to data Create a new synergy model to fit using the synthetic data. Here I use MuSyC, which is the same model we used to generate the synthetic data. bootstrap_iterations are used to get confidence intervals.\nmodel = MuSyC() model.fit(d1, d2, E_noisy, bootstrap_iterations=100) print(model)  MuSyC(E0=0.93, E1=0.68, E2=0.42, E3=0.00, h1=1.86, h2=1.12, C1=9.64e-03, C2=1.24e-01, alpha12=3.75, alpha21=1.08, beta=0.81, gamma12=2.01, gamma21=0.98)  This prints the best fit and lower and upper bound confidence intervals (defaults to 95%) for each parameter.\nprint(model.get_parameters())  {'E0': (0.93, [ 0.91223345 0.96329156]), 'E1': (0.68, [ 0.64643766 0.70749396]), 'E2': (0.42, [ 0.39022822 0.44990642]), 'E3': (0.00, [-0.02507603 0.02363363]), 'h1': (1.86, [ 1.26005438 2.73713318]), 'h2': (1.12, [ 0.93018994 1.43865508]), 'C1': (9.64e-03, [ 0.00760803 0.01384544]), 'C2': (1.24e-01, [ 0.10018859 0.15263104]), 'alpha12': (3.75, [ 2.85988609 4.6230902 ]), 'alpha21': (1.08, [ 0.73239517 1.79969918]), 'beta': (0.81, [ 0.69827786 0.95770258]), 'gamma12': (2.01, [ 1.44083572 2.76863031]), 'gamma21': (0.98, [ 0.56548907 1.83905139])}  Visualize from matplotlib import pyplot as plt from synergy.utils import plots fig = plt.figure(figsize=(12,6)) ax = fig.add_subplot(131) truemodel.plot_colormap(d1, d2, xlabel=\u0026quot;Drug1\u0026quot;, ylabel=\u0026quot;Drug2\u0026quot;, title=\u0026quot;True model\u0026quot;, ax=ax, vmin=0, vmax=1) ax = fig.add_subplot(132) plots.plot_colormap(d1, d2, E_noisy, ax=ax, title=\u0026quot;Noisy Data\u0026quot;, cmap=\u0026quot;viridis\u0026quot;, xlabel=\u0026quot;Drug1\u0026quot;, ylabel=\u0026quot;Drug2\u0026quot;, vmin=0, vmax=1) ax = fig.add_subplot(133) model.plot_colormap(d1, d2, xlabel=\u0026quot;Drug1\u0026quot;, ylabel=\u0026quot;Drug2\u0026quot;, title=\u0026quot;Fit model\u0026quot;, ax=ax, vmin=0, vmax=1) plt.tight_layout()   ","date":1587657607,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1587657607,"objectID":"0943c1f5a22e898aa3445f02c6e5aad1","permalink":"https://djwooten.github.io/software/synergy/","publishdate":"2020-04-23T12:00:07-04:00","relpermalink":"/software/synergy/","section":"software","summary":"Python package to calculate, analyze, and visualize drug combination synergy.","tags":[],"title":"synergy - Python Package","type":"software"},{"authors":["Christian T Meyer","","Carlos F Lopez","Vito Quaranta"],"categories":null,"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"4c67851870fd3ec07cfb0bbfcea1a562","permalink":"https://djwooten.github.io/publication/meyer-2020/","publishdate":"2020-03-29T15:05:49.035094Z","relpermalink":"/publication/meyer-2020/","section":"publication","summary":"Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models – fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.","tags":["drug synergy","Loewe Additivity","Bliss Independence"],"title":"Charting the Fragmented Landscape of Drug Synergy","type":"publication"},{"authors":[],"categories":[],"content":"","date":1573522585,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1573522585,"objectID":"d0ecd30e01a0b96b992b3ef95ed9bb89","permalink":"https://djwooten.github.io/project/data-driven/","publishdate":"2019-11-11T20:36:25-05:00","relpermalink":"/project/data-driven/","section":"project","summary":"Mechanistic biological models are powerful tools from which to derive predictions. Extensive work is typical to manually curate and build such models. How can we leverage big data to accelerate this process?","tags":[],"title":"Data Driven Mechanistic Modeling","type":"project"},{"authors":[],"categories":[],"content":"","date":1573494132,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1573494132,"objectID":"29d4e61f5897fd4b9fc7f1b04ae4bca0","permalink":"https://djwooten.github.io/project/multiscale/","publishdate":"2019-11-11T13:42:12-04:00","relpermalink":"/project/multiscale/","section":"project","summary":"Cells shape their environment, and their environment in turn shapes them. What dynamics can emerge from these feedbacks?","tags":[],"title":"Multiscale Cell / Micro- environment Dynamics","type":"project"},{"authors":["","Sarah M Groves","Darren R Tyson","Qi Liu","Jing S Lim","Réka Albert","Carlos F Lopez","Julien Sage","Vito Quaranta"],"categories":null,"content":"","date":1572480000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1572480000,"objectID":"19e01eac8242b0ca1eaec83953c6a011","permalink":"https://djwooten.github.io/publication/wooten-506402/","publishdate":"2019-10-31T00:00:00Z","relpermalink":"/publication/wooten-506402/","section":"publication","summary":"Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.","tags":null,"title":"Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers","type":"publication"},{"authors":[],"categories":[],"content":"In response to treatment, cancer cells can react in complex, difficult to predict ways. Here we consider this through a lens of treatment-induced driving forces pushing cancer cells around an epigenetic landscape. This force may cause new phenotypes to emerge, old phenotypes to disapear, and/or the balance between pre-existing phenotypes to shift. The overall goal of this project is to identify treatments that can epigenetically reprogram drug-resistant cancer cells toward drug-sensitive states.\n Identify regulatory and epigenetic factors stabilizing drug-resistant or quiescent phenotypes Identify treatments that can target those key regulators to de-stabilize undesirable states Validate drug combinations in which one agent reprograms cells toward the sensitive state, and the other agent kills cells in sensitive state  To achieve these ends, we employ a mixture of bioinformatic analyses and mechanistic modeling.\n","date":1567532532,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567532532,"objectID":"8b6b5eece1f683b6cce7701797537e25","permalink":"https://djwooten.github.io/project/cancer-control/","publishdate":"2019-09-03T13:42:12-04:00","relpermalink":"/project/cancer-control/","section":"project","summary":"Through mechanistic modeling, identify molecular drivers of therapeutic resistance and reprogram cancer cells toward drug-sensitive attractors.","tags":[],"title":"Cancer Control","type":"project"},{"authors":[],"categories":[],"content":"Many diseases require combinations of multiple drugs for effective treatment. I am interested in identifying, quantifying, and understanding synergistic or antagonistic interactions between drugs in combinations.\nA web application for calculating MuSyC synergy parameters for combinations is now available! https://musyc.lolab.xyz/\n","date":1567526407,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567526407,"objectID":"a998ff19138dcb5c68a6bcb4304cc32c","permalink":"https://djwooten.github.io/project/synergy/","publishdate":"2019-09-03T12:00:07-04:00","relpermalink":"/project/synergy/","section":"project","summary":"How to identify, quantify, and understand synergistic interactions between drugs in combinations","tags":[],"title":"Drug Synergy","type":"project"},{"authors":["","Christian T Meyer","Vito Quaranta","Carlos F Lopez"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"73c2e8b369e09d00e2613c0e7a20f55b","permalink":"https://djwooten.github.io/publication/wooten-683433/","publishdate":"2019-09-02T23:54:29.240017Z","relpermalink":"/publication/wooten-683433/","section":"publication","summary":"Drug combination discovery depends on reliable synergy metrics; however, no consensus exists on the appropriate synergy model to prioritize lead candidates. The fragmented state of the field confounds analysis, reproducibility, and clinical translation of combinations. Here we present a mass-action based formalism to accurately measure the synergy of drug combinations. In this work, we clarify the relationship between the dominant drug synergy principles and show how biases emerge due to intrinsic assumptions which hinder their broad applicability. We further present a mapping of commonly used frameworks onto a unified synergy landscape, which identifies fundamental issues impacting the interpretation of synergy in discovery efforts. Specifically, we infer how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact the classification of synergy in large combination screens misleading discovery efforts. The proposed approach has potential to accelerate the translatability and reproducibility of drug-synergy studies, by bridging the gap between the curative potential of drug mixtures and the complexity in their study.","tags":null,"title":"A Consensus Framework Unifies Multi-Drug Synergy Metrics","type":"publication"},{"authors":["Christian T Meyer","","B Bishal Paudel","Joshua Bauer","Keisha N Hardeman","David Westover","Christine M Lovly","Leonard A Harris","Darren R Tyson","Vito Quaranta"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"a28ef4d0cdd2c796c750eda2825e48e6","permalink":"https://djwooten.github.io/publication/meyer-2019/","publishdate":"2019-09-02T23:54:29.238997Z","relpermalink":"/publication/meyer-2019/","section":"publication","summary":"Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both. Meyer et al. developed a framework for measuring drug combination synergy. The framework, termed MuSyC, distinguishes between two types of synergy. The first quantifies the change in the maximal effect with the combination (synergistic efficacy), and the second measures the change in a drug's potency due to the combination (synergistic potency). By decoupling these two synergies conflated in prior methods, MuSyC rationally guides discovery and translation of drug combinations for the improvement of therapeutic efficacy and reduction of off-target toxicities via dose reduction.","tags":["BRAF-mutant melanoma","drug synergy","high-throughput combination drug screens","non-small-cell lung cancer","systems pharmacology"],"title":"Quantifying Drug Combination Synergy along Potency and Efficacy Axes","type":"publication"},{"authors":["Akshitkumar M Mistry","","L Taylor Davis","Bret C Mobley","Vito Quaranta","Rebecca A Ihrie"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"75a0b1879423da233a9217b76dbb71f5","permalink":"https://djwooten.github.io/publication/mistry-2019/","publishdate":"2019-09-02T23:54:29.238661Z","relpermalink":"/publication/mistry-2019/","section":"publication","summary":"Whether patients with glioblastoma that contacts the ventricular-subventricular zone stem cell niche (VSVZ + GBM) have a distinct survival profile from VSVZ − GBM patients independent of other known predictors or molecular profiles is unclear. Using multivariate Cox analysis to adjust survival for widely-accepted predictors, hazard ratios (HRs) for overall (OS) and progression free (PFS) survival between VSVZ + GBM and VSVZ − GBM patients were calculated in 170 single-institution patients and 254 patients included in both The Cancer Genome (TCGA) and Imaging (TCIA) atlases. An adjusted, multivariable analysis revealed that VSVZ contact was independently associated with decreased survival in both datasets. TCGA molecular data analyses revealed that VSVZ contact by GBM was independent of mutational, DNA methylation, gene expression, and protein expression signatures in the bulk tumor. Therefore, while survival of GBM patients is independently stratified by VSVZ contact, with VSVZ + GBM patients displaying a poor prognosis, the VSVZ + GBMs do not possess a distinct molecular signature at the bulk sample level. Focused examination of the interplay between the VSVZ microenvironment and subsets of GBM cells proximal to this region is warranted.","tags":null,"title":"Ventricular-Subventricular Zone Contact by Glioblastoma is Not Associated with Molecular Signatures in Bulk Tumor Data","type":"publication"},{"authors":["","Vito Quaranta"],"categories":null,"content":"","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"4f1a9ea579ac720d1d21f7e7106c2083","permalink":"https://djwooten.github.io/publication/wooten-2017/","publishdate":"2019-09-02T23:54:29.23977Z","relpermalink":"/publication/wooten-2017/","section":"publication","summary":"© 2017 A cell's phenotype is the observable actualization of complex interactions between its genome, epigenome, and local environment. While traditional views in cancer have held that cellular and tumor phenotypes are largely functions of genomic instability, increasing attention has recently been given to epigenetic and microenvironmental influences. Such non-genetic factors allow cancer cells to experience intrinsic diversity and plasticity, and at the tumor level can result in phenotypic heterogeneity and treatment evasion. In 2006, Takahashi and Yamanaka exploited the epigenome's plasticity by “reprogramming” differentiated cells into a pluripotent state by inducing expression of a cocktail of four transcription factors. Recent advances in cancer biology have shown not only that cellular reprogramming is possible for malignant cells, but it may provide a foundation for future therapies. Nevertheless, cell reprogramming experiments are frequently plagued by low efficiency, activation of aberrant transcriptional programs, instability, and often rely on expertise gathered from systems which may not translate directly to cancer. Here, we review a theoretical framework tracing back to Waddington's epigenetic landscape which may be used to derive quantitative and qualitative understanding of cellular reprogramming. Implications for tumor heterogeneity, evolution and adaptation are discussed in the context of designing new treatments to re-sensitize recalcitrant tumors. This article is part of a Special Issue entitled: Evolutionary principles — heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.","tags":["Attractors","Epigenetics","Gene regulatory network","Mathematical modeling","Reprogramming","Transcription factors"],"title":"Mathematical models of cell phenotype regulation and reprogramming: Make cancer cells sensitive again!","type":"publication"},{"authors":["Akshata R Udyavar","","Megan Hoeksema","Mukesh Bansal","Andrea Califano","Lourdes Estrada","Santiago Schnell","Jonathan M Irish","Pierre P Massion","Vito Quaranta"],"categories":null,"content":"","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"cba1a37d8c436e699c7c1ef77c2460a2","permalink":"https://djwooten.github.io/publication/udyavar-2017/","publishdate":"2019-09-02T23:54:29.239442Z","relpermalink":"/publication/udyavar-2017/","section":"publication","summary":"© 2016 American Association for Cancer Research. Small cell lung cancer (SCLC) is a devastating disease due to its propensity for early invasion and refractory relapse after initial treatment response. Although these aggressive traits have been associated with phenotypic heterogeneity, our understanding of this association remains incomplete. To fill this knowledge gap, we inferred a set of 33 transcription factors (TF) associated with gene signatures of the known neuroendocrine/ epithelial (NE) and non-neuroendocrine/mesenchymallike (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and was simulated using Boolean modeling. These simulations predicted that the network settles into attractors, or TF expression patterns, that correlate with NE or ML phenotypes, suggesting that TF network dynamics underlie the emergence of heterogeneous SCLC phenotypes. However, several cell lines and patient tumor specimens failed to correlate with either the NE or ML attractors. By flow cytometry, single cells within these cell lines simultaneously expressed surface markers of both NE and ML differentiation, confirming the existence of a \"hybrid\" phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting possible escape fromtreatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape may provide insights to clinical applications.","tags":null,"title":"Novel hybrid phenotype revealed in small cell lung cancer by a transcription factor network model that can explain tumor heterogeneity","type":"publication"}]