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Books

Ascher, U. M. & Petzold, L. R. (1998). Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations. SIAM
Brenan, K. E., Campbell, S. L., & Petzold, L. R. (1996). The Numerical Solution of Initial Value Problems in Differential-Algebraic Equations. SIAM Classics Series

Papers

2022
gkac331.pdf Bilal Shaikh, Lucian P Smith, Dan Vasilescu, Gnaneswara Marupilla, Michael Wilson, Eran Agmon, Henry Agnew, Steven S Andrews, Azraf Anwar, Moritz E Beber, Frank T Bergmann, David Brooks, Lutz Brusch, Laurence Calzone, Kiri Choi, Joshua Cooper, John Detloff, Brian Drawert, Michel Dumontier, G Bard Ermentrout, James R Faeder, Andrew P Freiburger, Fabian Fröhlich, Akira Funahashi, Alan Garny, John H Gennari, Padraig Gleeson, Anne Goelzer, Zachary Haiman, Jan Hasenauer, Joseph L Hellerstein, Henning Hermjakob, Stefan Hoops, Jon C Ison, Diego Jahn, Henry V Jakubowski, Ryann Jordan, Matúš Kalaš, Matthias König, Wolfram Liebermeister, Rahuman S Malik Sheriff, Synchon Mandal, Robert McDougal, J Kyle Medley, Pedro Mendes, Robert Müller, Chris J Myers, Aurelien Naldi, Tung V N Nguyen, David P Nickerson, Brett G Olivier, Drashti Patoliya, Loïc Paulevé, Linda R Petzold, Ankita Priya, Anand K Rampadarath, Johann M Rohwer, Ali S Saglam, Dilawar Singh, Ankur Sinha, Jacky Snoep, Hugh Sorby, Ryan Spangler, Jörn Starruß, Payton J Thomas, David van Niekerk, Daniel Weindl, Fengkai Zhang, Anna Zhukova, Arthur P Goldberg, James C Schaff, Michael L Blinov, Herbert M Sauro, Ion I Moraru, Jonathan R Karr (2022). BioSimulators: a central registry of simulation engines and services for recommending specific tools. Nucleic Acids Research, gkac331
journal.pcbi_.1009830.pdf Jiang, R., Singh, P., Wrede, F., Hellander, A., & Petzold, L. (2022). Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods. PLoS Comput. Biol. 18(1): e1009830
rsos.211908.pdf Doering, G. N., Drawert, B., Lee, C., Pruitt, J. N., Petzold, L. R. and Dalnoki-Veress, K. (2022). Noise resistant synchronization and collective rhythm switching in a model of animal group locomotion. R. Soc. open sci.9211908211908
predicting_the_need_for_blood_transfusions.pdf Yuqing Wang, Yun Zhao, Linda Petzold Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning.
BCB '22: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
2021
an_analysis_of_relation_extraction_within_sentences_from_wet_lab_protocols.pdf Yang, X., Zhang, X., Zuo, J., Wilson, S., & Petzold, L. (2021). An Analysis of Relation Extraction within Sentences from Wet Lab Protocols. 2021 IEEE International Conference on Big Data (Big Data) Conference Proceedings.
accepted_preprint.pdf Jacob, B., Drawert, B., Yi, T-M, & Petzold, L. (2021). An arbitrary Lagrangian Eulerian smoothed particle hydrodynamics (ALE-SPH) method with a boundary volume fraction formulation for fluid-structure interaction. Engineering Analysis with Boundary Elements 128, 274-289.
s12859-021-04065-z.pdf Jiang, R. M., Pourzanjani, A. A., Cohen, M. J., & Petzold, L. (2021). Associations of Longitudinal D-Dimer and Factor II on Early Trauma Survival Risk. BMC Bioinformatics 22, 122
20-ba1202.pdf Pourzanjani, A. A., Jiang, R. M., Mitchell, B., Atzberger, P. J., & Petzold, L. R. (2021). Bayesian Inference over the Stiefel Manifold via the Givens Representation. Bayesian Anal. 16(2): 639-666
bertsurv-bert_based_survival_models_for_predicting_outcomes_for_trauma_patients.pdf Zhao, Y., Hong, Q., Zhang, X., Deng, Y., Wang, Y., & Petzold, L. (2021). BERTSurv: BERT based Survival Models for Predicting Outcomes for Trauma Patients. ICDM 2021 Conference Proceedings
journal.pcbi_.1007971.pdf Banavar, S. P., Trogdon, M., Drawert, B., Yi, T-M, Petzold, L. R., & Campas, O. (2021). Coordinating Cell Polarization and Morphogenesis Through Mechanical Feedback. PLoS Comput. Biol. 17(1): e1007971
domain_adaptation_for_trauma_mortality_prediction_in_ehrs_with_feature_disparity.pdf Zhang, X., Li, S., Cheng, Z., Callcut, R., & Petzold, L. (2021). Domain Adaptation for Trauma Mortality Prediction in EHRs with Feature Disparity. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Conference Proceedings.
ml_mof.pdf Wang, Y.*, Zhao, Y.*, Callcut, R., & Petzold, L. (2021). Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients. ICDM 2021 Conference Proceedings
empirical_quantitative_analysis_of_covid-19_forecasting_models.pdf Zhao, Y., Wang, Y., Liu, J., Xia, Z., Xu, Z., Hong, Q., Zhou, Z., & Petzold, L. (2021). Empirical Quantitative Analysis of COVID-19 Forecasting Models. 2021 International Conference on Data Mining Workshops (ICDMW) Conference Proceedings.
btab061.pdf Jiang, R., Jacob, B., Geiger, M., Matthew, S., Rumsey, B., Singh, P., Wrede, F., Yi, T-M, Drawert, B., Hellander, A., & Petzold, L. (2021). Epidemiological modeling in StochSS Live!. Bioinformatics, 2021, 1-2.
s41598-021-94282-6.pdf Guzman, E., Cheng, Z., Hansma, P. K., Tovar, K. R., Petzold, L. R., & Kosik, K. S. (2021). Extracellular Detection of Neuronal Coupling. Sci. Rep. 11, 14733
motor_adaptation.pdf Mitchell, B., Marneweck, M., Grafton, S., & Petzold, L. (2021). Motor Adaptation via Distributional Learning. J. of Neural Eng. 18(4)
2020
journal.pone_.0233640.pdf Wu, T. B., Orfeo, T. Moore, H. B., Sumislawski, J. J., Cohen, M. J., & Petzold, L. R. (2020). Computational Model of Tranexamic Acid on Urokinase Mediated Fibrinolysis. PLoS ONE 15(5):e0233640
icdm2020_camera_ready.pdf Zhao, Y., Ly, F., Hong, Q., Cheng, Z., Santander, T., Yang, H. T., Hansma, P. K., & Petzold, L. (2020). How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment. IEEE ICDM 2020 Workshops Proceedings
peng2020_article_multiscalemodelingmeetsmachine.pdf Peng, G. C. Y., Alber, M., Tepole, A. B., Cannon, W. R., De, S., Dura-Bernal, S., Garikipati, K., Karniadakis, G., Lytton, W. W., Perdikaris, P., Petzold, L. & Kuhl, E. (2020). Multiscale Modeling Meets Machine Learning: What Can We Learn?. Arch. Computat. Methods Eng. 28, 1017-1037.
fncom-14-00004.pdf Ghaffari, H., Grant, S. C., Petzold, L. R., and Harrington, M. G. (2020). Regulation of CSF and Brain Tissue Sodium Levels by the Blood-CSF and Blood-Brain Barriers During Migraine. Front. Comput. Neurosci. 14:4
biokdd2020_yun.pdf Zhao, Y., Jiang, R., Xu, Z., Guzman, E., Hansma, P. K., & Petzold, L. (2020). Scalable Bayesian Functional Connectivity Inference for Multi-Electrode Array Recordings. BioKDD 2020 Conference Proceedings
2019
mea_dl.pdf Zhao, Y., Guzman, E., Audouard, M., Cheng, Z., Hansma, P. K., Kosik, K. S., & Petzold, L. (2019). A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings. Proceedings of the 2019 International Conference on Data Mining
hybrid_sdpd_and_ssa.pdf Drawert, B., Jacob, B., Li, Z., Yi, T-M, & Petzold, L. (2019). A Hybrid Smoothed Dissipative Particle Dynamics (SDPD) Spatial Stochastic Simulation Algorithm (sSSA) for Advection-Diffusion-Reaction Problems. J. Comp. Phys. 378, pp. 1-17.
neco_a_01219.pdf Mitchell, B. A., Lauharatanahirun, N., Garcia, J. O., Wymbs, N., Grafton, S., Vettel, J. M., & Petzold, L. R. (2019). A Minimum Free Energy Model of Motor Learning. Neural Computation 32, 1945-1963
s12976-019-0099-z.pdf Ghaffari, H., Varner, J. D., & Petzold, L. R. (2019). Analysis of the Role of Thrombomodulin in All-trans Retinoic Acid Treatment of Coagulation Disorders in Cancer Patients. Theoretical Biology and Medical Modelling 16(1):3
journal.pbio_.3000453-2.pdf Jang, J., Han, D., Golkaram, M., Audouard, M., Liu, G., Bridges, D., Hellander, S., Chialastri, A., Dey, S. S., Petzold, L. R., & Kosik, K. S. (2019). Control over single-cell distribution of G1 lengths by WNT governs pluripotency. PLoS Biol 17(9): e3000453.
digmed19.pdf Alber, M., Tepole, A. B., Cannon, W. R., De, S., Dura-Bernal, S., Garikipati, K., Karniadakis, G., Lytton, W. W., Perdikaris, P., Petzold, L., & Kuhl, E. (2019). Integrating machine learning and multiscale modeling--perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digital Medicine, 2:115
ptsd.pdf Dean, K. R., Hammamieh, R., Mellon, S. H., Abu-Amara, D., Flory, J. D., Guffanti, G., Wang, K., Daigle Jr., B. J., Gautam, A., Lee, I., Yang, R., Almli, L. M., Bersani, F. S., Chakraborty, N., Donohue, D., Kerley, K., Kim, T-K, Laska, E., Lee, M. Y., Lindqvist, D., Lori, A., Lu, L., Misganaw, B., Muhie, S., Newman, J., Price, N. D., Qin, S., Reus, V. I., Siegel, C., Somvanshi, P. R., Thakur, G. S., Zhou, Y., The PTSD Systems Biology Consortium, Hood, L., Ressler, K. J., Wolkowitz, O. M., Yehuda, R., Jett, M., Doyle III, F. J., & Marmar, C. (2019). Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder. Mol Psychiatry (2019)
arz135.pdf Doering, G. N., Sheehy, K. A., Lichtenstein, J. L. L., Drawert, B., Petzold, L. R., & Pruitt, J. N (2019). Sources of Intraspecific Variation in the Collective Tempo and Synchrony of Ant Societies. Behavioral Ecology, 30:6, 1682-1690
1-s2.0-s2352340918314963-main.pdf Drawert, B., Jacob, B., Li, Z., Yi, T-M, & Petzold, L. (2019). Validation Data for a Hybrid Smoothed Dissipative Particle Dynamics (SDPD) Spatial Stochastic Simulation Algorithm (sSSA) Method. Data in Brief, 22, pp. 11-15.
2018
1.5017840.pdf Bales, B., Petzold, L., Goodlet, B. R., Lenthe, W. C., & Pollock, T. M. (2018). Bayesian Inference of Elastic Properties with Resonant Ultrasound Spectroscopy. J. Acoust. Soc. Am., 143, pp. 71-83
10.10072fs10439-018-2031-9.pdf Wu, T. B., Wu, S., Buoni, M., Orfeo, T., Brummel-Ziedins, K., Cohen, M., & Petzold, L. (2018). Computational Model for Hyperfibrinolytic Onset of Acute Traumatic Coagulopathy. Ann. Biomed. Eng. pp. 1-10
1-s2.0-s0022509618303429-main.pdf Pro, J. W., Sehr, S., Lim, R. K., Petzold, L. R., & Begley, M. R. (2018). Conditions controlling kink crack nucleation out of, and delamination along, a mixed-mode interface crack. J. Mech. Phys. Solids 121, 480-495.
mitchell_et_al-2018-scientific_reports.pdf Mitchell, B. A. & Petzold, L. R. (2018). Control of Neural Systems at Multiple Scales Using Model-free, Deep Reinforcement Learning. Scientific Reports 8:10721
10.1007-s11661-018-4575-6.pdf Goodlet, B. R., Mills, L., Bales, B., Charpagne, M-A, Murray, S. P., Lenthe, W. C., Petzold, L., & Pollock, T.M. (2018). Elastic Properties of Novel Co- and CoNi-Based Superalloys Determined through Bayesian Inference and Resonant Ultrasound Spectroscopy. Metall. and Mat. Trans. A
stancon_alzheimers.pdf Pourzanjani, A. A., Bales, B. B., Harrington, M., & Petzold, L. R. (2018). Flexible Modeling of Alzheimer's Disease Progression with I-Splines. StanCon 2018 Proceedings.
iccabs-koupaee.pdf Koupaee, M., Zhang, Y., Wu, T. B., Cohen, M., & Petzold, L. (2018). Identification of Disease States for Trauma Patients using Commonly Available Hospital Data. 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)
s12976-018-0088-7.pdf Ghaffari, H. & Petzold, L. R. (2018). Identification of Influential Proteins in the Classical Retinoic Acid Signaling Pathway. Theoretical Biology and Medical Modelling, 15:16.
fungi.pdf Wilken, S. E., Saxena, M., Petzold, L. R., & O'Malley, M.A. (2018). In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi. Processes 2018, 6, 7.
mechanical_feedback.pdf Banavar, S. P., Gomez, C., Trogdon, M., Petzold, L. R., Yi, T-M, & Campas, O. (2018). Mechanical Feedback Coordinates Cell Wall Expansion and Assembly in Yeast Mating Morphogenesis. PLoS Comput. Biol. 14(1):e1005940
sage_paper_1.4.pdf McBride, D. & Petzold, L. (2018). Model-based Inference of a Directed Network of Circadian Neurons. J. Biological Rhythms, 33(5), 515-522
1326.full_.pdf Camona-Alocer, V., Abel, J. H., Sun, T. C., Petzold, L. R., Doyle III, F. J., Simms, C. L., & Herzog, E. D. (2018). Ontogeny of Circadian Rhythms and Synchrony in the Suprachiasmatic Nucleus. J. Neurosci. 38(6): 1326-1334
nature_neuroscience_s41593-018-0265-3.pdf Nowakowski, T. J., Rani, N., Golkaram, M., Zhou, H. R., Alvarado, B., Huch, K., West, J. A., Leyrat, A., Pollen, A. A., Kriegstein, A. R., Petzold, L. R., & Kosik, K. S. (2018). Regulation of Cell-type-specific Transcriptomes by microRNA Networks During Human Brain Development. Nature Neuroscience 21, 1784–1792.
stancon_coag.pdf Pourzanjani, A. A., Wu, T. B., Bales, B. B., & Petzold, L. R. (2018). Relating Disparate Measures of Coagulapathy Using Unorthodox Data: A Hybrid Mechanistic-Statistical Approach. StanCon 2018 Proceedings.
siamcse.pdf Rüde, U., Willcox, K., McInnes, L. C., & DeSterck, H. (2018). Research and Education in Computational Science and Engineering. SIAM Review, 60(3), 707-754.
journal.pcbi_.1006241.pdf Trogdon, M., Drawert, B., Gomez, C., Banavar, S. P., Yi, T-M., Campas, O., & Petzold, L. R. (2018). The Effect of Cell Geometry on Polarization in Budding Yeast. PLoS Comput. Biol. 14(6):e1006241
2017
07843624.pdf Abel, J., Drawert, B., Hellander, A. & Petzold, L. R. (2017). GillesPy: A Python Package for Stochastic Model Building and Simulation. IEEE Life Sciences Letters, Vol. 2, No. 3, pp. 35-38.
ocx032.pdf Torshizi, A. D. & Petzold, L. R. (2017). Graph-based Semi-Supervised Learning with Genomic Data Integration Using Condition-Responsive Genes Applied to Phenotype Classification. J. Am. Med. Inform. Assoc. 25(1), 2018, 99-108.
nips_.pdf Pourzanjani, A. A., Jiang, R. M., & Petzold, L. R. (2017). Improving the Identifiability of Neural Networks for Bayesian Inference. Proceedings of NIPS Workshop on Bayesian Deep Learning
1.5002773.pdf Hellander, S., Hellander, A., & Petzold, L. (2017). Mesoscopic-microscopic Spatial Stochastic Simulation with Automatic System Partitioning. J. Chem. Phys. 147, 234101.
icnaam16_als_drawert_petzold.pdf Drawert, B., Thakore, N., Mitchell, B., Pioro, E., Ravits, J., & Petzold, L. R. (2017). Modeling the Neuroanatomic Propagation of ALS in the Spinal Cord. AIP Conference Proceedings 1863, 500002
1-s2.0-s0925231217305581-main.pdf Torshizi, A. D., Petzold, L., & Cohen, M. (2017). Multivariate Soft Repulsive System Identification for Constructing Rule-based Classification Systems: Application to Trauma Clinical Data. Neurocomputing 245, pp. 77-85.
1.4975167.pdf Hellander, S. & Petzold, L. (2017). Reaction Rates for Reaction-Diffusion Kinetics on Unstructured Meshes. J. Chem. Phys. 146, 064101
bales_2017_modelling_simul._mater._sci._eng._25_045009.pdf Bales, B., Pollock, T., & Petzold, L. (2017). Segmentation-Free Image Processing and Analysis of Precipitate Shapes in 2D and 3D. Modelling Simul. Mater. Sci. Eng. 25, 045009
07887681.pdf Torshizi, A. D. & Petzold, L. (2017). Sparse Pathway-Induced Dynamic Network Biomarker Discovery for Early Warning Signal Detection in Complex Diseases. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(3), 1028-1034.
main.pdf Zhang, Y., Jiang, R., & Petzold, L. (2017). Survival Topic Models for Predicting Outcomes for Trauma Patients. 2017 IEEE 33rd International Conference on Data Engineering (ICDE)
scirep.pdf Golkaram, M., Jang, J., Hellander, S., Kosik, K. S., & Petzold, L. R. (2017). The Role of Chromatin Density in Cell Population Heterogeneity during Stem Cell Differentiation. Scientific Reports, 7(1), 13307
understanding-coagulopathy-multi_6.pdf Porzanjani, A., Wu, T. B., Jiang, R. M., Cohen, M. J., & Petzold, L. R. (2017). Understanding Coagulopathy Using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace Approach. Proceedings of Machine Learning for Healthcare 2017, W&C Track Volume 68
Drawert, B., Griesemer, M., Petzold, L. R., Briggs, C. J. (2017). Using Stochastic Epidemiological Models to Evaluate Conservation Strategies for Endangered Amphibians. J. R. Soc. Interface 14: 20170480

Journal Version | doi: 10.1098/rsif.2017.0480 | PMCID: PMC5582134
tp2017129a.pdf Hammamieh, R., Chakraborty, N., Gautam, A., Muhie, S., Yang, R., Donohue, D., Kumar, R., Daigle, Jr., B. J., Zhang, Y., Amara, D. A., Miller, S-A., Srinivasan, S., Flory, J., Yehuda, R., Petzold, L., Wolkoxitz, O. M., Mellon, S. H., Hood, L., Doyle III, F. J., Marmar, C., & Jett, M. (2017). Whole Genome DNA Methylation Status Associated with Clinical PTSD Measure of OIF/OEF Veterans. Transl. Psychiatry (2017) 7, e1169.
2016
1.4967338.pdf Drawert, B., Hellander, S., Trogdon, M., Yi, T-M, & Petzold, L. (2016). A Framework for Discrete Stochastic Simulation on 3D Moving Boundary Domains. J. Chem. Phys., 145, 184113.
07583664.pdf Thakur, G. S., Daigle, Jr., B. J., Qian, M., Dean, K. R., Zhang, Y., Yang, R., Kim, T-K, Wu, X., Li, M., Lee, I. Petzold, L. R., & Doyle III, F. J. (2016). A Multimetric Evaluation of Stratified Random Sampling for Classification: A Case Study. IEEE Life Sciences Letters 2(4).
aes_paper.pdf Lim, R. K., Petzold, L. R., & Koc, C. K. (2016). Bitsliced High-Performance AES-ECB on GPUs. LNCS 9100, pp. 125-133
pnas.pdf Abel, J. H., Meeker, K., Granados-Fuentes, D., St. John, P. C., Wang, T. J., Bales, B. B., Doyle III, F. J., Herzog, E. D., & Petzold L. R. (2016). Functional Network Inference of the Suprachiasmatic Nucleus. Proceedings of the National Academy of Science.
art3a10.11862fs12911-016-0360-x.pdf Zhang, Y. Wu, T. B., Daigle, Jr., B. J., Cohen, M., & Petzold, L. (2016). Identification of Disease States Associated with Coagulopathy in Trauma. BMC Medical Informatics and Decision Making, 16:124
pcbi.1005122.pdf Golkaram, M., Hellander, S., Drawert, B., & Petzold, L. R. (2016). Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion. PLoS Comput. Biol. 12(11):e1005122
15m1014784.pdf Drawert, B., Trogdon, M., Toor, S., Petzold, L., & Hellander, A. (2016). MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME. SIAM J. Sci. Comput., 38(3), C179-C202
physreve.93.013307.pdf Hellander, S., & Petzold, L. (2016). Reaction Rates for a Generalized Reaction-Diffusion Master Equation. Phys. Rev. E 93, 013307.
journal.pcbi_.1005220.pdf Drawert, B., Hellander, A., Bales, B., Banerjee, D., Bellesia, G., Daigle, Jr., B. J., Douglas, G., Gu, M., Gupta, A., Hellander, S., Horuk, C., Nath, D., Takkar, A., Wu, S., Lötstedt, P., Krintz, C., & Petzold, L. R. (2016). Stochastic Simulation Service: Bridging the Gap Between the Compuational Expert and the Biologist. PLoS Comput. Biol. 12(12): e1005220
2015
interface_latex.pdf Lawson, M. J., Petzold, L., & Hellander, A. (2015). Accuracy of the Michaelis-Menten Approximation When Analysing Effects of Molecular Noise. J. R. Soc. Interface 12:20150054.
1.4921638.pdf Wu, S., Fu, J., & Petzold, L. R. (2015). Adaptive Deployment of Model Reductions for Tau-Leaping Simulation. J. Chem. Phys. 142, 204108
temporal_opdetection-2.pdf Bhattacharjee, K., & Petzold, L. (2015, December). Detecting Opinions in a Temporally Evolving Conversation on Twitter. Proceedings of the International Conference on Social Informatics (SocInfo), Beijing, China.
b325.pdf Doostparast, A. Petzold, L., & Cohen, M. (2015, November). Direct Higher Order Fuzzy Rule-based Classification System: Application in Mortality Prediction. Proceedings of the IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2015), Washington D. C.
abel-gillespy-fosbe-vf.pdf Abel, J. H., Drawert, B., Hellander, A., & Petzold, L. R. (2015, August). GillesPy: A Python Package for Stochastic Model Building and Simulation. FOSBE 2015 Conference Proceedings, Boston, MA.
gpubasedsims.pdf Pro, J. W., Lim, R. K., Petzold, L. R., Utz, M., & Begley, M. R. (2015). GPU-Based Simulations of Fracture in Idealized Brick and Mortar Composites. J. Mech. Phys. Solids 80, 68-85.

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