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1. A data-driven method for quantifying the impact of a genetic circuit on its host

Author

Hasnain, Aqib, Sinha, Subhrajit, Dorfan, Yuval, Borujeni, Amin Espah, Park, Yongjin, Maschhoff, Paul, Saxena, Uma, Urrutia, Joshua, Gaffney, Niall, Becker, Diveena, Siba, Atsede, Maheshri, Narendra, Gordon, Ben, Voigt, Chris, and Yeung, Enoch

Subjects
Mathematics - Dynamical Systems, Quantitative Biology - Quantitative Methods
Abstract

Genetic circuits are designed to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-throughput RNAseq measurements collected from wild-type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics., Comment: 4 pages Accepted in 2019 IEEE Biomedical Circuits and Systems Conference

Published
2019

2. Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

Author

Guan, Xiangnan, Sun, Duanchen, Lu, Eric, Urrutia, Joshua A, Reiter, Robert Evan, Rettig, Matthew, Evans, Christopher P, Lara, Primo, Gleave, Martin, Beer, Tomasz M, Thomas, George V, Huang, Jiaoti, Aggarwal, Rahul R, Quigley, David A, Foye, Adam, Chen, William S, Youngren, Jack, Weinstein, Alana S, Stuart, Joshua M, Feng, Felix Y, Small, Eric J, Xia, Zheng, and Alumkal, Joshi J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Human Genome, Genetics, Biotechnology, Cancer Genomics, Prostate Cancer, Urologic Diseases, Benzamides, Biomarkers, Tumor, Biopsy, Chromosomes, Human, Pair 17, DNA Copy Number Variations, Disease-Free Survival, Drug Resistance, Neoplasm, Humans, Male, Nitriles, Phenylthiohydantoin, Prostate, Prostatic Neoplasms, Castration-Resistant, RNA-Seq, Survival Analysis, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

PurposeThe purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing.Experimental designOne hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide (n = 64) or who had enzalutamide-resistant mCRPC (n = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing. We analyzed the genomes and transcriptomes of these mCRPC tumors.ResultsCopy number loss was more common than gain in enzalutamide-resistant tumors. Specially, we identified 124 protein-coding genes that were more commonly lost in enzalutamide-resistant samples. These 124 genes included eight putative tumor suppressors located at nine distinct genomic regions. We demonstrated that focal deletion of the 17q22 locus that includes RNF43 and SRSF1 was not present in any patient with enzalutamide-naïve mCRPC but was present in 16% (6/37) of patients with enzalutamide-resistant mCRPC. 17q22 loss was associated with lower RNF43 and SRSF1 expression and poor overall survival from time of biopsy [median overall survival of 19.3 months in 17q22 intact vs. 8.9 months in 17q22 loss, HR, 3.44 95% confidence interval (CI), 1.338-8.867, log-rank P = 0.006]. Finally, 17q22 loss was linked with activation of several targetable factors, including CDK1/2, Akt, and PLK1, demonstrating the potential therapeutic relevance of 17q22 loss in mCRPC.ConclusionsCopy number loss is common in enzalutamide-resistant tumors. Focal deletion of chromosome 17q22 defines a previously unappreciated molecular subset of enzalutamide-resistant mCRPC associated with poor clinical outcome.

Published
2020

3. Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with enzalutamide resistance

Author

Alumkal, Joshi J, Sun, Duanchen, Lu, Eric, Beer, Tomasz M, Thomas, George V, Latour, Emile, Aggarwal, Rahul, Cetnar, Jeremy, Ryan, Charles J, Tabatabaei, Shaadi, Bailey, Shawna, Turina, Claire B, Quigley, David A, Guan, Xiangnan, Foye, Adam, Youngren, Jack F, Urrutia, Joshua, Huang, Jiaoti, Weinstein, Alana S, Friedl, Verena, Rettig, Matthew, Reiter, Robert E, Spratt, Daniel E, Gleave, Martin, Evans, Christopher P, Stuart, Joshua M, Chen, Yiyi, Feng, Felix Y, Small, Eric J, Witte, Owen N, and Xia, Zheng

Subjects
Urologic Diseases, Genetics, Prostate Cancer, Aging, Cancer, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Aged, Aged, 80 and over, Antineoplastic Agents, Benzamides, Drug Resistance, Neoplasm, Gene Expression Profiling, Humans, Male, Middle Aged, Nitriles, Phenylthiohydantoin, Prostate-Specific Antigen, Prostatic Neoplasms, Castration-Resistant, Receptors, Androgen, enzalutamide, resistance, androgen receptor, stemness
Abstract

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline

Published
2020

5. LSD1 activates a lethal prostate cancer gene network independently of its demethylase function

Author

Sehrawat, Archana, Gao, Lina, Wang, Yuliang, Bankhead, Armand, McWeeney, Shannon K., King, Carly J., Schwartzman, Jacob, Urrutia, Joshua, Bisson, William H., Coleman, Daniel J., Joshi, Sunil K., Kim, Dae-Hwan, Sampson, David A., Weinmann, Sheila, Kallakury, Bhaskar V. S., Berry, Deborah L., Haque, Reina, Van Den Eeden, Stephen K., Sharma, Sunil, Bearss, Jared, Beer, Tomasz M., Thomas, George V., Heiser, Laura M., and Alumkal, Joshi J.

Published
2018

7. Prediction of whole-cell transcriptional response with machine learning

Author

Massachusetts Institute of Technology. Department of Biological Engineering, Eslami, Mohammed, Borujeni, Amin Espah, Eramian, Hamed, Weston, Mark, Zheng, George, Urrutia, Joshua, Corbet, Carolyn, Becker, Diveena, Maschhoff, Paul, Clowers, Katie, Cristofaro, Alexander, Hosseini, Hamid Doost, Gordon, D Benjamin, Dorfan, Yuval, Singer, Jedediah, Vaughn, Matthew, Gaffney, Niall, Fonner, John, Stubbs, Joe, Voigt, Christopher A, Yeung, Enoch, Massachusetts Institute of Technology. Department of Biological Engineering, Eslami, Mohammed, Borujeni, Amin Espah, Eramian, Hamed, Weston, Mark, Zheng, George, Urrutia, Joshua, Corbet, Carolyn, Becker, Diveena, Maschhoff, Paul, Clowers, Katie, Cristofaro, Alexander, Hosseini, Hamid Doost, Gordon, D Benjamin, Dorfan, Yuval, Singer, Jedediah, Vaughn, Matthew, Gaffney, Niall, Fonner, John, Stubbs, Joe, Voigt, Christopher A, and Yeung, Enoch

Abstract

Abstract Motivation Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. Results The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene’s dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. Availability and implementation The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. Supplementary information Supplem

Published
2023

8. Figure S4 from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

Author

Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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9. Supplementary Dataset S2 from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

Author

Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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10. Data from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

Author

Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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11. Supplementary Figure S3 from BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program

Author

Kim, Dae-Hwan, primary, Sun, Duanchen, primary, Storck, William K., primary, Welker Leng, Katherine, primary, Jenkins, Chelsea, primary, Coleman, Daniel J., primary, Sampson, David, primary, Guan, Xiangnan, primary, Kumaraswamy, Anbarasu, primary, Rodansky, Eva S., primary, Urrutia, Joshua A., primary, Schwartzman, Jacob A., primary, Zhang, Chao, primary, Beltran, Himisha, primary, Labrecque, Mark P., primary, Morrissey, Colm, primary, Lucas, Jared M., primary, Coleman, Ilsa M., primary, Nelson, Peter S., primary, Corey, Eva, primary, Handelman, Samuel K., primary, Sexton, Jonathan Z., primary, Aggarwal, Rahul, primary, Abida, Wassim, primary, Feng, Felix Y., primary, Small, Eric J., primary, Spratt, Daniel E., primary, Bankhead, Armand, primary, Rao, Arvind, primary, Gesner, Emily M., primary, Attwell, Sarah, primary, Lakhotia, Sanjay, primary, Campeau, Eric, primary, Yates, Joel A., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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12. Supplementary Table S3 from BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program

Author

Kim, Dae-Hwan, primary, Sun, Duanchen, primary, Storck, William K., primary, Welker Leng, Katherine, primary, Jenkins, Chelsea, primary, Coleman, Daniel J., primary, Sampson, David, primary, Guan, Xiangnan, primary, Kumaraswamy, Anbarasu, primary, Rodansky, Eva S., primary, Urrutia, Joshua A., primary, Schwartzman, Jacob A., primary, Zhang, Chao, primary, Beltran, Himisha, primary, Labrecque, Mark P., primary, Morrissey, Colm, primary, Lucas, Jared M., primary, Coleman, Ilsa M., primary, Nelson, Peter S., primary, Corey, Eva, primary, Handelman, Samuel K., primary, Sexton, Jonathan Z., primary, Aggarwal, Rahul, primary, Abida, Wassim, primary, Feng, Felix Y., primary, Small, Eric J., primary, Spratt, Daniel E., primary, Bankhead, Armand, primary, Rao, Arvind, primary, Gesner, Emily M., primary, Attwell, Sarah, primary, Lakhotia, Sanjay, primary, Campeau, Eric, primary, Yates, Joel A., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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13. Supplementary Data from BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program

Author

Kim, Dae-Hwan, primary, Sun, Duanchen, primary, Storck, William K., primary, Welker Leng, Katherine, primary, Jenkins, Chelsea, primary, Coleman, Daniel J., primary, Sampson, David, primary, Guan, Xiangnan, primary, Kumaraswamy, Anbarasu, primary, Rodansky, Eva S., primary, Urrutia, Joshua A., primary, Schwartzman, Jacob A., primary, Zhang, Chao, primary, Beltran, Himisha, primary, Labrecque, Mark P., primary, Morrissey, Colm, primary, Lucas, Jared M., primary, Coleman, Ilsa M., primary, Nelson, Peter S., primary, Corey, Eva, primary, Handelman, Samuel K., primary, Sexton, Jonathan Z., primary, Aggarwal, Rahul, primary, Abida, Wassim, primary, Feng, Felix Y., primary, Small, Eric J., primary, Spratt, Daniel E., primary, Bankhead, Armand, primary, Rao, Arvind, primary, Gesner, Emily M., primary, Attwell, Sarah, primary, Lakhotia, Sanjay, primary, Campeau, Eric, primary, Yates, Joel A., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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14. Supplementary Figure Legend from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

Author

Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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15. Supplementary Table S8-S12 from BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program

Author

Kim, Dae-Hwan, primary, Sun, Duanchen, primary, Storck, William K., primary, Welker Leng, Katherine, primary, Jenkins, Chelsea, primary, Coleman, Daniel J., primary, Sampson, David, primary, Guan, Xiangnan, primary, Kumaraswamy, Anbarasu, primary, Rodansky, Eva S., primary, Urrutia, Joshua A., primary, Schwartzman, Jacob A., primary, Zhang, Chao, primary, Beltran, Himisha, primary, Labrecque, Mark P., primary, Morrissey, Colm, primary, Lucas, Jared M., primary, Coleman, Ilsa M., primary, Nelson, Peter S., primary, Corey, Eva, primary, Handelman, Samuel K., primary, Sexton, Jonathan Z., primary, Aggarwal, Rahul, primary, Abida, Wassim, primary, Feng, Felix Y., primary, Small, Eric J., primary, Spratt, Daniel E., primary, Bankhead, Armand, primary, Rao, Arvind, primary, Gesner, Emily M., primary, Attwell, Sarah, primary, Lakhotia, Sanjay, primary, Campeau, Eric, primary, Yates, Joel A., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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16. Supplementary Table S1 from Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

Author

Guan, Xiangnan, primary, Sun, Duanchen, primary, Lu, Eric, primary, Urrutia, Joshua A., primary, Reiter, Robert Evan, primary, Rettig, Matthew, primary, Evans, Christopher P., primary, Lara, Primo, primary, Gleave, Martin, primary, Beer, Tomasz M., primary, Thomas, George V., primary, Huang, Jiaoti, primary, Aggarwal, Rahul R., primary, Quigley, David A., primary, Foye, Adam, primary, Chen, William S., primary, Youngren, Jack, primary, Weinstein, Alana S., primary, Stuart, Joshua M., primary, Feng, Felix Y., primary, Small, Eric J., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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17. Supplementary Table S2 S4 S6 S7 from BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program

Author

Kim, Dae-Hwan, primary, Sun, Duanchen, primary, Storck, William K., primary, Welker Leng, Katherine, primary, Jenkins, Chelsea, primary, Coleman, Daniel J., primary, Sampson, David, primary, Guan, Xiangnan, primary, Kumaraswamy, Anbarasu, primary, Rodansky, Eva S., primary, Urrutia, Joshua A., primary, Schwartzman, Jacob A., primary, Zhang, Chao, primary, Beltran, Himisha, primary, Labrecque, Mark P., primary, Morrissey, Colm, primary, Lucas, Jared M., primary, Coleman, Ilsa M., primary, Nelson, Peter S., primary, Corey, Eva, primary, Handelman, Samuel K., primary, Sexton, Jonathan Z., primary, Aggarwal, Rahul, primary, Abida, Wassim, primary, Feng, Felix Y., primary, Small, Eric J., primary, Spratt, Daniel E., primary, Bankhead, Armand, primary, Rao, Arvind, primary, Gesner, Emily M., primary, Attwell, Sarah, primary, Lakhotia, Sanjay, primary, Campeau, Eric, primary, Yates, Joel A., primary, Xia, Zheng, primary, and Alumkal, Joshi J., primary

Published
2023
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18. Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Author

Cummins, Breschine, primary, Vrana, Justin, additional, Moseley, Robert C, additional, Eramian, Hamed, additional, Deckard, Anastasia, additional, Fontanarrosa, Pedro, additional, Bryce, Daniel, additional, Weston, Mark, additional, Zheng, George, additional, Nowak, Joshua, additional, Motta, Francis C, additional, Eslami, Mohammed, additional, Johnson, Kara Layne, additional, Goldman, Robert P, additional, Myers, Chris J, additional, Johnson, Tessa, additional, Vaughn, Matthew W, additional, Gaffney, Niall, additional, Urrutia, Joshua, additional, Gopaulakrishnan, Shweta, additional, Biggers, Vanessa, additional, Higa, Trissha R, additional, Mosqueda, Lorraine A, additional, Gameiro, Marcio, additional, Gedeon, Tomáš, additional, Mischaikow, Konstantin, additional, Beal, Jacob, additional, Bartley, Bryan, additional, Mitchell, Tom, additional, Nguyen, Tramy T, additional, Roehner, Nicholas, additional, and Haase, Steven B, additional

Published
2023
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20. Computational Prediction of Synthetic Circuit Function Across Growth Conditions

Author

Cummins, Breschine, primary, Moseley, Robert C, additional, Deckard, Anastasia, additional, Weston, Mark, additional, Zheng, George, additional, Bryce, Daniel, additional, Nowak, Joshua, additional, Gameiro, Marcio, additional, Gedeon, Tomas, additional, Mischaikow, Konstantin, additional, Beal, Jacob, additional, Johnson, Tessa, additional, Vaughn, Matthew, additional, Gaffney, Niall I, additional, Gopaulakrishnan, Shweta, additional, Urrutia, Joshua, additional, Goldman, Robert P, additional, Bartley, Bryan, additional, Nguyen, Tramy T, additional, Roehner, Nicholas, additional, Mitchell, Tom, additional, Vrana, Justin D, additional, Clowers, Katie J, additional, Maheshri, Narendra, additional, Becker, Diveena, additional, Mikhalev, Ekaterina, additional, Biggers, Vanessa, additional, Higa, Trissha, additional, Mosqueda, Lorraine, additional, and Haase, Steven B., additional

Published
2022
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21. Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Author

Cummins, Breschine, primary, Vrana, Justin, additional, Moseley, Robert C., additional, Eramian, Hamed, additional, Deckard, Anastasia, additional, Fontanarrosa, Pedro, additional, Bryce, Daniel, additional, Weston, Mark, additional, Zheng, George, additional, Nowak, Joshua, additional, Motta, Francis C., additional, Eslami, Mohammed, additional, Johnson, Kara Layne, additional, Goldman, Robert P., additional, Myers, Chris J., additional, Johnson, Tessa, additional, Vaughn, Matthew W., additional, Gaffney, Niall, additional, Urrutia, Joshua, additional, Gopaulakrishnan, Shweta, additional, Biggers, Vanessa, additional, Higa, Trissha R., additional, Mosqueda, Lorraine A., additional, Gameiro, Marcio, additional, Gedeon, Tomáš, additional, Mischaikow, Konstantin, additional, Beal, Jacob, additional, Bartley, Bryan, additional, Mitchell, Tom, additional, Nguyen, Tramy T., additional, Roehner, Nicholas, additional, and Haase, Steven B., additional

Published
2022
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22. A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists

Author

Garcia, Benjamin J, primary, Urrutia, Joshua, additional, Zheng, George, additional, Becker, Diveena, additional, Corbet, Carolyn, additional, Maschhoff, Paul, additional, Cristofaro, Alexander, additional, Gaffney, Niall, additional, Vaughn, Matthew, additional, Saxena, Uma, additional, Chen, Yi-Pei, additional, Gordon, D Benjamin, additional, and Eslami, Mohammed, additional

Published
2022
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23. Prediction of whole-cell transcriptional response with machine learning

Author

Eslami, Mohammed, primary, Borujeni, Amin Espah, additional, Eramian, Hamed, additional, Weston, Mark, additional, Zheng, George, additional, Urrutia, Joshua, additional, Corbet, Carolyn, additional, Becker, Diveena, additional, Maschhoff, Paul, additional, Clowers, Katie, additional, Cristofaro, Alexander, additional, Hosseini, Hamid Doost, additional, Gordon, D Benjamin, additional, Dorfan, Yuval, additional, Singer, Jedediah, additional, Vaughn, Matthew, additional, Gaffney, Niall, additional, Fonner, John, additional, Stubbs, Joe, additional, Voigt, Christopher A, additional, and Yeung, Enoch, additional

Published
2021
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24. BET Bromodomain Inhibition Blocks an AR-Repressed, E2F1-Activated Treatment-Emergent Neuroendocrine Prostate Cancer Lineage Plasticity Program

Author

Kim, Dae-Hwan, primary, Sun, Duanchen, additional, Storck, William K., additional, Welker Leng, Katherine, additional, Jenkins, Chelsea, additional, Coleman, Daniel J., additional, Sampson, David, additional, Guan, Xiangnan, additional, Kumaraswamy, Anbarasu, additional, Rodansky, Eva S., additional, Urrutia, Joshua A., additional, Schwartzman, Jacob A., additional, Zhang, Chao, additional, Beltran, Himisha, additional, Labrecque, Mark P., additional, Morrissey, Colm, additional, Lucas, Jared M., additional, Coleman, Ilsa M., additional, Nelson, Peter S., additional, Corey, Eva, additional, Handelman, Samuel K., additional, Sexton, Jonathan Z., additional, Aggarwal, Rahul, additional, Abida, Wassim, additional, Feng, Felix Y., additional, Small, Eric J., additional, Spratt, Daniel E., additional, Bankhead, Armand, additional, Rao, Arvind, additional, Gesner, Emily M., additional, Attwell, Sarah, additional, Lakhotia, Sanjay, additional, Campeau, Eric, additional, Yates, Joel A., additional, Xia, Zheng, additional, and Alumkal, Joshi J., additional

Published
2021
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25. Prediction of whole-cell transcriptional response with machine learning.

Author

Eslami, Mohammed, Borujeni, Amin Espah, Eramian, Hamed, Weston, Mark, Zheng, George, Urrutia, Joshua, Corbet, Carolyn, Becker, Diveena, Maschhoff, Paul, Clowers, Katie, Cristofaro, Alexander, Hosseini, Hamid Doost, Gordon, D Benjamin, Dorfan, Yuval, Singer, Jedediah, Vaughn, Matthew, Gaffney, Niall, Fonner, John, Stubbs, Joe, and Voigt, Christopher A

Subjects
MACHINE learning, NUCLEOTIDE sequencing, SYSTEMS biology, SYNTHETIC biology, GENE regulatory networks, BACILLUS subtilis
Abstract

Motivation Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. Results The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R 2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis , using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. Availability and implementation The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics%5ftools. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2022
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26. toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists.

Author

Garcia, Benjamin J, Urrutia, Joshua, Zheng, George, Becker, Diveena, Corbet, Carolyn, Maschhoff, Paul, Cristofaro, Alexander, Gaffney, Niall, Vaughn, Matthew, Saxena, Uma, Chen, Yi-Pei, Gordon, D Benjamin, and Eslami, Mohammed

Subjects
*RNA sequencing, *SYNTHETIC biology, *MACHINE learning, *ELECTRONIC data processing, *TRANSCRIPTOMES
Abstract

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data. Graphical Abstract [ABSTRACT FROM AUTHOR]

Published
2022
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27. A data-driven method for quantifying the impact of a genetic circuit on its host

Author

Hasnain, Aqib, primary, Becker, Diveena, additional, Siba, Atsede, additional, Maheshri, Narendra, additional, Gordon, Ben, additional, Voigt, Chris, additional, Yeung, Enoch, additional, Sinha, Subhrajit, additional, Dorfan, Yuval, additional, Borujeni, Amin Espah, additional, Park, Yongjin, additional, Maschhoff, Paul, additional, Saxena, Uma, additional, Urrutia, Joshua, additional, and Gaffney, Niall, additional

Published
2019
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28. Copy number analysis to identify tumor suppressor genes associated with enzalutamide (Enza) resistance and poor prognosis in metastatic castration-resistant prostate cancer (mCRPC) patients.

Author

Guan, Xiangnan, primary, Sun, Duan-Chen, additional, Lu, Eric, additional, Urrutia, Joshua A., additional, Reiter, Robert Evan, additional, Rettig, Matthew, additional, Evans, Christopher P., additional, Lara, Primo, additional, Gleave, Martin, additional, Beer, Tomasz M., additional, Thomas, George V., additional, Huang, Jiaoti, additional, Xia, Zheng, additional, and Alumkal, Joshi J., additional

Published
2019
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30. Abstract LB-240: LSD1 activates a lethal prostate cancer gene network independently of its demethylase function

Author

Sehrawat, Archana, primary, Kim, Dae-Hwan, additional, Gao, Lina, additional, Wang, Yuliang, additional, Bankhead, Armand, additional, McWeeney, Shannon, additional, King, Carly J., additional, Schwartzman, Jacob, additional, Tayou, Junior, additional, Urrutia, Joshua, additional, Bisson, William H., additional, Coleman, Daniel J., additional, Joshi, Sunil K., additional, Sampson, David A., additional, Weinmann, Sheila, additional, Kallakury, Bhaskar VS, additional, Berry, Deborah L., additional, Haque, Reina, additional, Eeden, Stephen K. Van Den, additional, Sharma, Sunil, additional, Bearss, Jared, additional, Beer, Tomasz M., additional, Thomas, George V., additional, Heiser, Laura M., additional, and Alumkal, Joshi J., additional

Published
2018
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31. Integrative molecular network analysis identifies emergent enzalutamide resistance mechanisms in prostate cancer

Author

King, Carly J., primary, Woodward, Josha, additional, Schwartzman, Jacob, additional, Coleman, Daniel J., additional, Lisac, Robert, additional, Wang, Nicholas J., additional, Van Hook, Kathryn, additional, Gao, Lina, additional, Urrutia, Joshua, additional, Dane, Mark A., additional, Heiser, Laura M., additional, and Alumkal, Joshi J., additional

Published
2017
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32. Abstract 2406: LSD1 promotes castration-resistant prostate cancer cell survival independently of the androgen receptor and of histone demethylation

Author

Sehrawat, Archana, primary, Gao, Lina, additional, Tayou, Junior, additional, Bankhead, Armand, additional, Heiser, Laura M., additional, King, Carly J., additional, Wang, Yuliang, additional, Schwartzman, Jacob, additional, Urrutia, Joshua, additional, Coleman, Daniel J., additional, Weinmann, Sheila, additional, Kallakury, Bhaskar V., additional, Berry, Deborah L., additional, Haque, Reina, additional, Eeden, Stephen K. Van Den, additional, Beer, Tomasz M., additional, Thomas, George V., additional, McWeeney, Shannon, additional, and Alumkal, Joshi J., additional

Published
2017
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34. Cellular androgen content influences enzalutamide agonism of F877L mutant androgen receptor

Author

Coleman, Daniel J., primary, Van Hook, Kathryn, additional, King, Carly J., additional, Schwartzman, Jacob, additional, Lisac, Robert, additional, Urrutia, Joshua, additional, Sehrawat, Archana, additional, Woodward, Josha, additional, Wang, Nicholas J., additional, Gulati, Roman, additional, Thomas, George V., additional, Beer, Tomasz M., additional, Gleave, Martin, additional, Korkola, James E., additional, Gao, Lina, additional, Heiser, Laura M., additional, and Alumkal, Joshi J., additional

Published
2016
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35. Androgen content and BET bromodomain proteins influence enzalutamide agonism of mutant F876L androgen receptor.

Author

Coleman, Daniel J., primary, Van Hook, Kathryn, additional, King, Carly J., additional, Schwartzman, Jacob, additional, Lisac, Robert, additional, Urrutia, Joshua, additional, Sehrawat, Archana, additional, Woodward, Josha, additional, Wang, Nicholas J., additional, Gulati, Roman, additional, Thomas, George V., additional, Beer, Tomasz M., additional, Gleave, Martin E., additional, Korkola, James E., additional, Gao, Lina, additional, Heiser, Laura M., additional, and Alumkal, Joshi J., additional

Published
2016
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36. Abstract 731: Integrative genomic analysis to identify emergent enzalutamide resistance mechanisms in castration-resistant prostate cancer

Author

Woodward, Josha, primary, King, Carly, additional, Coleman, Daniel, additional, Lisac, Robert, additional, Schwartzman, Jacob, additional, Wang, Nicholas, additional, Gleave, Martin, additional, Gray, Joe, additional, Thomas, George, additional, Beer, Tomasz M., additional, Van Hook, Katy, additional, Baertsch, Robert, additional, Goldstein, Ted, additional, Stuart, Josh, additional, Gao, Lina, additional, Urrutia, Joshua, additional, Heiser, Laura, additional, and Alumkal, Joshi J., additional

Published
2015
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