Jeffrey J. Saucerman

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Primary Appointment

Professor of Biomedical Engineering, Biomedical Engineering

Education

  • PhD, Bioengineering, University of California at San Diego

Research Disciplines

Biophysics, Biotechnology, Cardiovascular Biology, Physiology

Research Interests

Roles of complex signaling networks involved in the regulation of cardiovascular function and disease

Research Description

Cell signaling networks coordinate a wide variety of cellular physiology and gene regulatory programs. Perturbations in these signaling networks contribute to the pathogenesis of many diseases, including cardiovascular disease, cancer and diabetes. One explanation for the remarkable ability of complex signaling networks to control the cell is the use of temporal and spatial strategies, such as feedback and compartmentation. Understanding of these sophisticated control mechanisms will require an integration of experimental and computational systems biology.
Our lab is particularly interested in the roles of complex signaling networks involved in the regulation of cardiovascular function and disease. We perform quantitative live-cell imaging of signaling dynamics and develop quantitative models to explain how signaling networks function. These systems approaches are currently helping us characterize mechanisms underlying regulation of cardiac contractility, ischemic heart disease, and pathways leading to cardiac growth. Such quantitative understanding will be critical for the future rational design of therapeutic agents for cardiovascular disease.
Current Projects:
1) Multi-scale integration from cell signaling networks to cardiac MRI
2) Nonlinear systems analysis of complex biochemical networks
3) Calcium signaling pathways regulating cardiac contractility and growth
4) Compartmentation of cAMP signaling in cardiac myocytes

Personal Statement

Cell signaling networks coordinate a wide variety of cellular physiology and gene regulatory programs. Perturbations in these signaling networks contribute to the pathogenesis of many diseases, including cardiovascular disease, cancer and diabetes. One explanation for the remarkable ability of complex signaling networks to control the cell is the use of temporal and spatial strategies, such as feedback and compartmentation. Understanding of these sophisticated control mechanisms will require an integration of experimental and computational systems biology.
Our lab is particularly interested in the roles of complex signaling networks involved in the regulation of cardiovascular function and disease. We perform quantitative live-cell imaging of signaling dynamics and develop quantitative models to explain how signaling networks function. These systems approaches are currently helping us characterize mechanisms underlying regulation of cardiac contractility, ischemic heart disease, and pathways leading to cardiac growth. Such quantitative understanding will be critical for the future rational design of therapeutic agents for cardiovascular disease.
Current Projects:
1) Multi-scale integration from cell signaling networks to cardiac MRI
2) Nonlinear systems analysis of complex biochemical networks
3) Calcium signaling pathways regulating cardiac contractility and growth
4) Compartmentation of cAMP signaling in cardiac myocytes

Training

  • Basic Cardiovascular Research Training Grant
  • Biotechnology Training Grant
  • Training in Cell and Molecular Biology
  • Training in Molecular Biophysics
  • Training in the Pharmacological Sciences

Selected Publications

2024

Nelson, A. R., Christiansen, S. L., Naegle, K. M., & Saucerman, J. J. (2024). Logic- based mechanistic machine learning on high- content images reveals how drugs differentially regulate cardiac fibroblasts. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 121(5). doi:10.1073/pnas.2303513121

Cao, S., Buchholz, K. S., Tan, P., Stowe, J. C., Wang, A., Fowler, A., . . . Mcculloch, A. D. (2024). Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes. AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY, 326(2), H370-H384. doi:10.1152/ajpheart.00562.2023

2023

Nelson, A. R., Christiansen, S. L., Naegle, K. M., & Saucerman, J. J. (2023). Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.. bioRxiv. doi:10.1101/2023.03.01.530599

Van de Graaf, M. W., Eggertsen, T. G., Zeigler, A. C., Tan, P. M., & Saucerman, J. J. (2023). Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models. JOURNAL OF PHYSIOLOGY-LONDON. doi:10.1113/JP284616

Nelson, A. R., Bugg, D., Davis, J., & Saucerman, J. J. (2023). Network model integrated with multi-omic data predicts MBNL1 signals that drive myofibroblast activation. ISCIENCE, 26(4). doi:10.1016/j.isci.2023.106502

Khalilimeybodi, A., Riaz, M., Campbell, S. G., Omens, J. H., McCulloch, A. D., Qyang, Y., & Saucerman, J. J. (2023). Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 174, 1-14. doi:10.1016/j.yjmcc.2022.10.006

2022

Chowkwale, M., Lindsey, M. L., & Saucerman, J. J. (2023). Intercellular model predicts mechanisms of inflammation-fibrosis coupling after myocardial infarction. JOURNAL OF PHYSIOLOGY-LONDON, 601(13), 2635-2654. doi:10.1113/JP283346

Yoshida, K., Saucerman, J. J., & Holmes, J. W. (2022). Multiscale model of heart growth during pregnancy: integrating mechanical and hormonal signaling. BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, 21(4), 1267-1283. doi:10.1007/s10237-022-01589-y

Young, A., Bradley, L. A., Farrar, E., Bilcheck, H. O., Tkachenko, S., Saucerman, J. J., . . . Wolf, M. J. (2022). Inhibition of DYRK1a Enhances Cardiomyocyte Cycling After Myocardial Infarction. CIRCULATION RESEARCH, 130(9), 1345-1361. doi:10.1161/CIRCRESAHA.121.320005

Harris, A. R., Esparza, S., Azimi, M. S., Cornelison, R., Azar, F. N., Llaneza, D. C., . . . Munson, J. M. (2022). Platinum Chemotherapy Induces Lymphangiogenesis in Cancerous and Healthy Tissues That Can be Prevented With Adjuvant Anti-VEGFR3 Therapy. FRONTIERS IN ONCOLOGY, 12. doi:10.3389/fonc.2022.801764

Hota, S. K., Rao, K. S., Blair, A. P., Khalilimeybodi, A., Hu, K. M., Thomas, R., . . . Bruneau, B. G. (2022). Brahma safeguards canalization of cardiac mesoderm differentiation. NATURE, 602(7895), 129-+. doi:10.1038/s41586-021-04336-y

Gorick, C. M., Saucerman, J. J., & Price, R. J. (2022). Computational model of brain endothelial cell signaling pathways predicts therapeutic targets for cerebral pathologies. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 164, 17-28. doi:10.1016/j.yjmcc.2021.11.005

2021

Rogers, J. D., Holmes, J. W., Saucerman, J. J., & Richardson, W. J. (2021). Mechano-chemo signaling interactions modulate matrix production by cardiac fibroblasts.. Matrix biology plus, 10, 100055. doi:10.1016/j.mbplus.2020.100055

Chavkin, N. W., Sano, S., Wang, Y., Oshima, K., Ogawa, H., Horitani, K., . . . Walsh, K. (2021). The Cell Surface Receptors Ror1/2 Control Cardiac Myofibroblast Differentiation. JOURNAL OF THE AMERICAN HEART ASSOCIATION, 10(13). doi:10.1161/JAHA.120.019904

McCulloch, A. D., Grandi, E., & Saucerman, J. J. (2021). Computational models of cardiovascular regulatory mechanisms. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 155, 111. doi:10.1016/j.yjmcc.2021.01.009

Grabowska, M. E., Chun, B., Moya, R., & Saucerman, J. J. (2021). Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 155, 66-77. doi:10.1016/j.yjmcc.2021.02.014

Zeigler, A. C., Chandrabhatla, A. S., Christiansen, S. L., Nelson, A. R., Holmes, J. W., & Saucerman, J. J. (2021). Network model-based screen for FDA-approved drugs affecting cardiac fibrosis. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 10(4), 377-388. doi:10.1002/psp4.12599

Liu, X., Zhang, J., Zeigler, A. C., Nelson, A. R., Lindsey, M. L., & Saucerman, J. J. (2021). Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues. JOURNAL OF IMMUNOLOGY, 206(4), 883-891. doi:10.4049/jimmunol.1901444

Estrada, A. C., Yoshida, K., Saucerman, J. J., & Holmes, J. W. (2021). A multiscale model of cardiac concentric hypertrophy incorporating both mechanical and hormonal drivers of growth. BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, 20(1), 293-307. doi:10.1007/s10237-020-01385-6

2020

Zeigler, A. C., Nelson, A. R., Chandrabhatla, A. S., Brazhkina, O., Holmes, J. W., & Saucerman, J. J. (2020). Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction. MATRIX BIOLOGY, 91-92, 136-151. doi:10.1016/j.matbio.2020.03.007

Khalilimeybodi, A., Paap, A. M., Christiansen, S. L. M., & Saucerman, J. J. (2020). Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy. PLOS COMPUTATIONAL BIOLOGY, 16(12). doi:10.1371/journal.pcbi.1008490

Yoshida, K., Saucerman, J., & Holmes, J. (2020). Multiscale model of heart growth during pregnancy: Integrating mechanical and hormonal signaling. doi:10.1101/2020.09.18.302067

Cao, S., Aboelkassem, Y., Wang, A., Valdez-Jasso, D., Saucerman, J. J., Omens, J. H., & McCulloch, A. D. (2020). Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 378(2173). doi:10.1098/rsta.2019.0336

2019

Zeigler, A., Nelson, A., Chandrabhatla, A., Brazhkina, O., Holmes, J., & Saucerman, J. (2019). Computational Model Predicts Paracrine and Intracellular Drivers of Fibroblast Phenotype After Myocardial Infarction. doi:10.1101/840017

Saucerman, J. J., Tan, P. M., Buchholz, K. S., McCulloch, A. D., & Omens, J. H. (2019). Mechanical regulation of gene expression in cardiac myocytes and fibroblasts. NATURE REVIEWS CARDIOLOGY, 16(6), 361-378. doi:10.1038/s41569-019-0155-8

Liu, X., Zhang, J., Zeigler, A., Nelson, A., Lindsey, M., & Saucerman, J. (2019). Network analysis reveals a distinct axis of macrophage activation in response to conflicting inflammatory cues. doi:10.1101/844464

Rikard, S. M., Athey, T. L., Nelson, A. R., Christiansen, S. L. M., Lee, J. -J., Holmes, J. W., . . . Saucerman, J. J. (2019). Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling. FRONTIERS IN PHYSIOLOGY, 10. doi:10.3389/fphys.2019.01481

2018

Woo, L. A., Tkachenko, S., Ding, M., Plowright, A. T., Engkvist, O., Andersson, H., . . . Saucerman, J. J. (2019). High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 127, 204-214. doi:10.1016/j.yjmcc.2018.12.015

Xu, P., Damschroder, D., Zhang, M., Ryall, K. A., Adler, P. N., Saucerman, J. J., . . . Yan, Z. (2019). Atg2, Atg9 and Atg18 in mitochondrial integrity, cardiac function and healthspan in Drosophila. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 127, 116-124. doi:10.1016/j.yjmcc.2018.12.006

Frank, D. U., Sutcliffe, M. D., & Saucerman, J. J. (2018). Network-based predictions of in vivo cardiac hypertrophy. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 121, 180-189. doi:10.1016/j.yjmcc.2018.07.243

Mouton, A. J., DeLeon-Pennell, K. Y., Gonzalez, O. J. R., Flynn, E. R., Freeman, T. C., Saucerman, J. J., . . . Lindsey, M. L. (2018). Mapping macrophage polarization over the myocardial infarction time continuum. BASIC RESEARCH IN CARDIOLOGY, 113(4). doi:10.1007/s00395-018-0686-x

Santolini, M., Romay, M. C., Yukhtman, C. L., Rau, C. D., Ren, S., Saucerman, J. J., . . . Karma, A. (2018). A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure. NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 4. doi:10.1038/s41540-018-0046-3

Sutcliffe, M. D., Tan, P. M., Fernandez-Perez, A., Nam, Y. -J., Munshi, N. V., & Saucerman, J. J. (2018). High content analysis identifies unique morphological features of reprogrammed cardiomyocytes. SCIENTIFIC REPORTS, 8. doi:10.1038/s41598-018-19539-z

2017

Tan, P. M., Buchholz, K. S., Omens, J. H., McCulloch, A. D., & Saucerman, J. J. (2017). Predictive model identifies key network regulators of cardiomyocyte mechano-signaling. PLOS COMPUTATIONAL BIOLOGY, 13(11). doi:10.1371/journal.pcbi.1005854

Shim, J. V., Chun, B., van Hasselt, J. G. C., Birtwistle, M. R., Saucerman, J. J., & Sobie, E. A. (2017). Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics. FRONTIERS IN PHYSIOLOGY, 8. doi:10.3389/fphys.2017.00651

Laker, R. C., Drake, J. C., Wilson, R. J., Lira, V. A., Lewellen, B. M., Ryall, K. A., . . . Yan, Z. (2017). Ampk phosphorylation of Ulk1 is required for targeting of mitochondria to lysosomes in exercise-induced mitophagy. NATURE COMMUNICATIONS, 8. doi:10.1038/s41467-017-00520-9

Janes, K. A., Chandran, P. L., Ford, R. M., Lazzara, M. J., Papin, J. A., Peirce, S. M., . . . Lauffenburger, D. A. (2017). An engineering design approach to systems biology. INTEGRATIVE BIOLOGY, 9(7), 574-583. doi:10.1039/c7ib00014f

2016

Zeigler, A. C., Richardson, W. J., Holmes, J. W., & Saucerman, J. J. (2016). A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 94, 72-81. doi:10.1016/j.yjmcc.2016.03.008

Lindsey, M. L., Saucerman, J. J., & DeLeon-Pennell, K. Y. (2016). Knowledge gaps to understanding cardiac macrophage polarization following myocardial infarction. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE, 1862(12), 2288-2292. doi:10.1016/j.bbadis.2016.05.013

2015

Zeigler, A. C., Richardson, W. J., Holmes, J. W., & Saucerman, J. J. (2016). Computational modeling of cardiac fibroblasts and fibrosis. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 93, 73-83. doi:10.1016/j.yjmcc.2015.11.020

Ryall, K. A., & Saucerman, J. J. (2015). Automated Microscopy of Cardiac Myocyte Hypertrophy: A Case Study on the Role of Intracellular α-Adrenergic Receptors. NUCLEAR G-PROTEIN COUPLED RECEPTORS: METHODS AND PROTOCOLS, 1234, 123-134. doi:10.1007/978-1-4939-1755-6_11

2014

Amanfu, R. K., & Saucerman, J. J. (2014). Modeling the Effects of β1-Adrenergic Receptor Blockers and Polymorphisms on Cardiac Myocyte Ca2+ Handling. MOLECULAR PHARMACOLOGY, 86(2), 222-230. doi:10.1124/mol.113.090951

Ryall, K. A., Bezzerides, V. J., Rosenzweig, A., & Saucerman, J. J. (2014). Phenotypic screen quantifying differential regulation of cardiac myocyte hypertrophy identifies CITED4 regulation of myocyte elongation. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 72, 74-84. doi:10.1016/j.yjmcc.2014.02.013

Greenwald, E. C., Polanowska-Grabowska, R. K., & Saucerman, J. J. (2014). Integrating Fluorescent Biosensor Data Using Computational Models. FLUORESCENT PROTEIN-BASED BIOSENSORS: METHODS AND PROTOCOLS, 1071, 227-248. doi:10.1007/978-1-62703-622-1_18

Laker, R. C., Xu, P., Ryall, K. A., Sujkowski, A., Kenwood, B. M., Chain, K. H., . . . Yan, Z. (2014). A Novel MitoTimer Reporter Gene for Mitochondrial Content, Structure, Stress, and Damage in Vivo. JOURNAL OF BIOLOGICAL CHEMISTRY, 289(17), 12005-12015. doi:10.1074/jbc.M113.530527

Greenwald, E. C., Redden, J. M., Dodge-Kafka, K. L., & Saucerman, J. J. (2014). Scaffold State Switching Amplifies, Accelerates, and Insulates Protein Kinase C Signaling. JOURNAL OF BIOLOGICAL CHEMISTRY, 289(4), 2353-2360. doi:10.1074/jbc.M113.497941

Saucerman, J. J., Greenwald, E. C., & Polanowska-Grabowska, R. (2014). Mechanisms of cyclic AMP compartmentation revealed by computational models. JOURNAL OF GENERAL PHYSIOLOGY, 143(1), 39-48. doi:10.1085/jgp.201311044

Yang, J. H., Polanowska-Grabowska, R. K., Smith, J. S., Shields, C. W., & Saucerman, J. J. (2014). PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 66, 83-93. doi:10.1016/j.yjmcc.2013.11.001

2013

Kenwood, B. M., Weaver, J. L., Bajwa, A., Poon, I. K., Byrne, F. L., Murrow, B. A., . . . Hoehn, K. L. (2014). Identification of a novel mitochondria! uncoupler that does not depolarize the plasma membrane. MOLECULAR METABOLISM, 3(2), 114-123. doi:10.1016/j.molmet.2013.11.005

Sample, V., DiPilato, L. M., Yang, J. H., Ni, Q., Saucerman, J. J., & Zhang, J. (2013). Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP (vol 8, pg 375, 2012). NATURE CHEMICAL BIOLOGY, 9(6), 406. doi:10.1038/nchembio0613-406b

Saucerman, J. J. (2013). Modeling Mitochondrial ROS: A Great Balancing Act (vol 105, pg 1287, 2013). BIOPHYSICAL JOURNAL, 105(11), 2606. doi:10.1016/j.bpj.2013.11.001

Saucerman, J. J. (2013). Modeling Mitochondrial ROS: A Great Balancing Act. BIOPHYSICAL JOURNAL, 105(6), 1287-1288. doi:10.1016/j.bpj.2013.08.013

2012

Ryall, K. A., Holland, D. O., Delaney, K. A., Kraeutler, M. J., Parker, A. J., & Saucerman, J. J. (2012). Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling. JOURNAL OF BIOLOGICAL CHEMISTRY, 287(50), 42259-42268. doi:10.1074/jbc.M112.382937

Cui, W. -Y., Zhao, S., Polanowska-Grabowska, R., Wang, J., Wei, J., Dash, B., . . . Li, M. D. (2013). Identification and Characterization of Poly(I:C)-induced Molecular Responses Attenuated by Nicotine in Mouse Macrophages. MOLECULAR PHARMACOLOGY, 83(1), 61-72. doi:10.1124/mol.112.081497

Ryall, K. A., & Saucerman, J. J. (2012). Automated imaging reveals a concentration dependent delay in reversibility of cardiac myocyte hypertrophy. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 53(2), 282-290. doi:10.1016/j.yjmcc.2012.04.016

Sample, V., DiPilato, L. M., Yang, J. H., Ni, Q., Saucerman, J. J., & Zhang, J. (2012). Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP. NATURE CHEMICAL BIOLOGY, 8(4), 375-382. doi:10.1038/NCHEMBIO.799

Yang, J. H., & Saucerman, J. J. (2012). Phospholemman is a negative feed-forward regulator of Ca2+ in β-adrenergic signaling, accelerating β-adrenergic inotropy. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 52(5), 1048-1055. doi:10.1016/j.yjmcc.2011.12.015

Cui, W. Y., Chang, S. L., Polanowska-Grabowska, R., Saucerman, J. J., & Li, M. D. (2012). Nicotine Suppresses TLR3-mediated Inflammation Through a Calcium Signaling Mechanism. JOURNAL OF NEUROIMMUNE PHARMACOLOGY, 7, S35.

Raynor, L. L., Saucerman, J. J., Akinola, M. O., Lake, D. E., Moorman, J. R., & Fairchild, K. D. (2012). Cytokine screening identifies NICU patients with Gram-negative bacteremia. PEDIATRIC RESEARCH, 71(3), 261-266. doi:10.1038/pr.2011.45

Saucerman, J. J. (2012). Cardiac biexcitability: Two ways to catch a wave. HEART RHYTHM, 9(1), 123-124. doi:10.1016/j.hrthm.2011.09.001

2011

Bass, G. T., Ryall, K. A., Katikapalli, A., Taylor, B. E., Dang, S. T., Acton, S. T., & Saucerman, J. J. (2012). Automated image analysis identifies signaling pathways regulating distinct signatures of cardiac myocyte hypertrophy. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 52(5), 923-930. doi:10.1016/j.yjmcc.2011.11.009

Greenwald, E. C., & Saucerman, J. J. (2011). Bigger, Better, Faster: Principles and Models of AKAP Anchoring Protein Signaling. JOURNAL OF CARDIOVASCULAR PHARMACOLOGY, 58(5), 462-469. doi:10.1097/FJC.0b013e31822001e3

Saucerman, J. J., & Bers, D. M. (2012). Calmodulin binding proteins provide domains of local Ca2+ signaling in cardiac myocytes. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY, 52(2), 312-316. doi:10.1016/j.yjmcc.2011.06.005

Soltis, A. R., & Saucerman, J. J. (2011). Robustness Portraits of Diverse Biological Networks Conserved Despite Order-Of-Magnitude Parameter Variation. BIOPHYSICAL JOURNAL, 100(3), 165.

Polanowska-Grabowska, R., Park, S. R., & Saucerman, J. J. (2011). Validating a Model of Nitric Oxide-Ca2+ Crosstalk in Cardiac Myocytes. BIOPHYSICAL JOURNAL, 100(3), 82.

Dang, S. T., & Saucerman, J. J. (2011). Netflux: Biological Network Modeling for Biologists and Students. BIOPHYSICAL JOURNAL, 100(3), 324-325.

Amanfu, R. K., Muller, J. B., & Saucerman, J. J. (2011). Automated Image Analysis of Cardiac Myocyte Ca2+ Dynamics. 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 4661-4664.

Amanfu, R. K., & Saucerman, J. J. (2011). Cardiac models in drug discovery and development: a review.. Critical reviews in biomedical engineering, 39(5), 379-395. doi:10.1615/critrevbiomedeng.v39.i5.30

Raynor, L. L., Saucerman, J. J., Akinola, M. O., Lake, D. E., Moorman, J. R., & Fairchild, K. D. (2011). CYTOKINE SCORE IDENTIFIES NICU PATIENTS WITH GRAM-NEGATIVE BACTEREMIA. PEDIATRIC RESEARCH, 70, 688. doi:10.1038/pr.2011.913

Holland, D. O., Krainak, N. C., & Saucerman, J. J. (2011). Graphical Approach to Model Reduction for Nonlinear Biochemical Networks. PLOS ONE, 6(8). doi:10.1371/journal.pone.0023795

Benedict, K. F., Mac Gabhann, F., Amanfu, R. K., Chavali, A. K., Gianchandani, E. P., Glaw, L. S., . . . Skalak, T. C. (2011). Systems Analysis of Small Signaling Modules Relevant to Eight Human Diseases. ANNALS OF BIOMEDICAL ENGINEERING, 39(2), 621-635. doi:10.1007/s10439-010-0208-y

Soltis, A. R., & Saucerman, J. J. (2011). Robustness portraits of diverse biological networks conserved despite order-of-magnitude parameter uncertainty. BIOINFORMATICS, 27(20), 2888-2894. doi:10.1093/bioinformatics/btr496

Yang, J. H., & Saucerman, J. J. (2011). Computational Models Reduce Complexity and Accelerate Insight Into Cardiac Signaling Networks. CIRCULATION RESEARCH, 108(1), 85-97. doi:10.1161/CIRCRESAHA.110.223602

Haggart, C. R., Bartell, J. A., Saucerman, J. J., & Papin, J. A. (2011). WHOLE-GENOME METABOLIC NETWORK RECONSTRUCTION AND CONSTRAINT-BASED MODELING. METHODS IN ENZYMOLOGY, VOL 500, 500, 411-433. doi:10.1016/B978-0-12-385118-5.00021-9

2010

Soltis, A. R., & Saucerman, J. J. (2010). Synergy between CaMKII Substrates and β-Adrenergic Signaling in Regulation of Cardiac Myocyte Ca2+ Handling. BIOPHYSICAL JOURNAL, 99(7), 2038-2047. doi:10.1016/j.bpj.2010.08.016

Kraeutler, M. J., Soltis, A. R., & Saucerman, J. J. (2010). Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model. BMC SYSTEMS BIOLOGY, 4. doi:10.1186/1752-0509-4-157