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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


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.
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
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.
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, pages1784–1792.
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. To Appear 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
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. To appear IEEE/ACM Transactions on Computational Biology and Bioinformatics
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.
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.
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
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.
international_journal_of_high_performance_computing_applications-2015-lim-1094342015593395.pdf Lim, R. K., Pro, J. W., Begley, M. R., Utz, M., & Petzold, L. R. (2015). High-performance Simulation of Fracture in Idealized "Brick and Mortar" Composites Using Adaptive Monte Carlo Minimization on the GPU. Int. J. High Perform. C. 1-14
daigle_2015.pdf Daigle, Jr., B. J., Soltani, M., Petzold, L. R. & Singh, A. (2015). Inferring Single-Cell Gene Expression Mechanisms Using Stochastic Simulation. Bioinformatics, 31(9), 2015, 1428-1435
manuscript.pdf Pourzanjani, A., Herzog, E. D., & Petzold, L. R. (2015). On the Inference of Functional Circadian Networks Using Granger Causality. PLoS ONE 10(9):e0137540
physreve.91.023312.pdf Hellander, S., Hellander, A., & Petzold, L. (2015). Reaction Rates for Mesoscopic Reaction-Diffusion Kinetics. Phys. Rev. E 91, 023312.
thakur_etal_molbiosystems_2015.pdf Thakur, G. S., Daigle Jr., B. J., Dean, K. R., Zhang, Y., Rodriguez-Fernandez, M., Hammamieh, R., Yang, R., Jett, M., Palma, J., Petzold, L. R., & Doyle III, F. J. (2015). Systems Biology Approach to Understanding Post-Traumatic Stress Disorder. Mol. BioSyst., 2015, 11, 980.

doi:10.1039/C4MB00404C  |  PMID: 25627823 [PubMed - in process]

1-s2.0-s2352431615001054-main.pdf Pro, J. W., Lim, R. K., Petzold, L. R., Utz, M., & Begley, M. R. (2015). The Impact of Stochastic Microstructures on the Macroscopic Fracture Properties of Brick and Mortar Composites. Extreme Mechanics Letters 5 (2015) 1-9
IFAC14_2031_FI.pdf Thakur, G., Daigle Jr., B., Petzold, L. R. & Doyle, F. (2014). A Multivariate Ensemble Approach for Identification of Biomarkers: Application to Breast Cancer. Proceedings of the 19th IFAC World Congress.
ELFPT_main.pdf Bezzola, A., Bales, B. B., Alkire, R. C. & Petzold, L. R. (2014). An Exact and Efficient First Passage Time Algorithm for Reaction-Diffusion Processes on a 2D-Lattice. J. Comp. Phys., 256, pp. 183-197.
BiPClustering.pdf Griesemer, M., Young, C., Robinson, A. S. & Petzold, L. R. (2014). BiP Clustering Facilitates Protein Folding in the Endoplasmic Reticulum. PLoS Comput. Biol. 10(7): e1003675.
main.pdf Zhang, Y., Daigle, Jr., B. J., Ferrigno, L., Cohen, M., & Petzold, L. (2014). Data-Driven Mortality Prediction for Trauma Patients. Invited Refereed Presentation at NIPS MLCB Workshop
LocalError.pdf Hellander, A., Lawson, M., Drawert, B. & Petzold, L. (2014). Local Error Estimates for Adaptive Simulation of the Reaction-Diffusion Master Equation via Operator Splitting. J. Comp. Phys. 266, pp. 89-100.
NumericalScaling.pdf Bezzola, A., Bales, B. B., Petzold, L. R. & Alkire, R. C. (2014). Numerical Scaling Studies of Kinetically-Limited Electrochemical Nucleation and Growth with Accelerated Stochastic Simulations. J. Electrochem. Soc. 161(8), E3001-E3008.
OpinionMining.pdf Bhattacharjee, K. & Petzold, L. R. (2014). Probabilistic User-level Opinion Detection on Online Social Networks. Proceedings of the International Conference on Social Informatics (SocInfo).
Hes1_dimerisation.pdf Sturrock, M., Hellander, A., Aldakheel, S., Petzold, L. R. & Chaplain, M. A. J. (2014). The Role of Dimerisation and Nuclear Transport in the Hes1 Gene Regulatory Network. Bull. Math Biol. 76(4):766-98.
TimeDependentPropensity.pdf Fu, J., Wu, S., Li, H. & Petzold, L. R. (2014). The Time Dependent Propensity Function for Acceleration of Spatial Stochastic Simulation of Reaction-Diffusion Systems. J. Comp. Phys. 274, pp. 524–549.
Validity.pdf Gillespie, D., Petzold, L. R. & Seitaridou, E. (2014). Validity Conditions for Stochastic Chemical Kinetics in Diffusion-Limited Systems. J. Chem. Phys. 140, 054111.

© 2014 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the authors and the American Institute of Physics. Journal Version  |  PMCID: PMC3977787

Neuropeptide.pdf An, S., Harang, R., Meeker, K., Granados-Fuentes, D., Tsai, C., Mazuski, C., Kim, J., Doyle III, F. J., Petzold, L. R. & Herzog, E. D. (2013). A Neuropeptide Speeds Circadian Entrainment by Reducing Intercellular Synchrony. Proceedings of the National Academy of Sciences.
Mouse_Model.pdf Yang, R., Daigle, Jr., B. J., Muhie, S. Y., Hammamieh, R., Jett, M., Petzold, L. & Doyle III, F. J. (2013). Core Modular Blood and Brain Biomarkers in Social Defeat Mouse Model for Post Traumatic Stress Disorder. BMC Systems Biology 2013, 7:80.
Pub199-1.pdf Kolpas, A., Busch, M., Li, H., Couzin, I. D., Petzold, L. R. & Moehlis, J. (2013). How the Spatial Position of Individuals Affects Their Influence on Swarms: A Numerical Comparison of Two Popular Swarm Dynamics Models. PLoS ONE 8(3): e58525.
I_Act.pdf Macropol, K., Bogdanov, P., Singh, A. K., Petzold, L. R. & Yan, X. (2013). I Act, Therefore I Judge: Network Sentiment Dynamics Based on User Activity Change. Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
Perspective.pdf Gillespie, D. T., Hellander, A. & Petzold, L. R. (2013). Perspective: Stochastic Algorithms for Chemical Kinetics. J. Chem. Phys. 138, 170901.

© 2013 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the authors and the American Institute of Physics. Journal Version  |  PMCID: PMC3656953

Polarization.pdf Lawson, M. J., Drawert, B., Khammash, M., Petzold, L. R. & Yi, T. (2013). Spatial Stochastic Dynamics Enable Robust Cell Polarization. PLoS Comput. Biol. 9(7): e1003139.
Time_Dependent.pdf Fu, J., Wu, S. & Petzold, L. R. (2013). Time Dependent Solution for Acceleration of Tau-Leaping. J. Comp. Phys. 235, 446-457.
URDME_StratUm.pdf Östberg, P., Hellander, A., Drawert, B., Elmroth, E., Holmgren, S. & Petzold, L. R. (2012). Abstractions for Scaling eScience Applications to Distributed Computing Environments: A StratUm Integration Case Study in Molecular Systems Biology. Proceedings of Bioinformatics 2012, International Conference on Bioinformatics Models, Methods, and Algorithms.
Accelerated.pdf Daigle, Jr., B. J., Roh, M. K., Petzold, L. R. & Niemi, J. (2012). Accelerated Maximum Likelihood Parameter Estimation for Stochastic Biochemical Systems. BMC Bioinformatics 2012, 13:68.
Automatic_Id.pdf Wu, S., Fe, J., Li, H. & Petzold, L. R. (2012). Automatic Identification of Model Reductions for Discrete Stochastic Simulation. J. Chem. Phys. 137(3), 034106.
COMBINER.pdf Yang, R., Daigle Jr., B., Petzold, L. R. & Doyle III, F. J. (2012). Core Module Biomarker Identification with Network Exploration for Breast Cancer Metastasis. BMC Bioinfomatics 13:12.
Core_module.pdf Yang, R., Daigle, Jr., B. J., Petzold, L. R. & Doyle III, F. J. (2012). Core Module Network Construction for Breast Cancer Metastasis. Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, China.