@conference {, title = {Distributed Control for Flocking Maneuvers via Acceleration-Weighted Neighborhooding}, booktitle = {Proceedings of ACC 2021, The 2021 American Control Conference}, year = {2021}, month = {06/2021}, author = {S. Roy and U. Mehmood and R. Grosu and S. A. Smolka and S. D. Stoller and A. Tiwari} } @conference {, title = {A Distributed Simplex Architecture for Multi-Agent Systems}, booktitle = {Proceedings of SETTA 2021, Symposium on Dependable Software Engineering}, year = {2021}, month = {11/2021}, author = {U. Mehmood and S. D. Stoller and R. Grosu and S. Roy and A. Damare and S. D. Smolka} } @article {, title = {Data-Driven Robust Control for a Closed-Loop Artificial Pancreas}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {17}, year = {2020}, month = {11/2020}, chapter = {1981}, author = {N. Paoletti and K. S. Liu and H. Chen and S. A. Smolka and S. Lin} } @article {, title = {Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics}, journal = {Frontiers in physiology}, volume = {11}, year = {2020}, month = {11/2020}, url = {https://scholar.google.com/citations?view_op=view_citation\&hl=en\&user=jKiNd-0AAAAJ\&sortby=pubdate\&citation_for_view=jKiNd-0AAAAJ:tuHXwOkdijsC}, author = {Pras Pathmanathan and Suran K Galappaththige and Jonathan M Cordeiro and Abouzar Kaboudian and Flavio H Fenton and Richard A Gray} } @article {, title = {Delay differential equation-based models of cardiac tissue: Efficient implementation and effects on spiral-wave dynamics}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, volume = {29}, year = {2019}, pages = {123128}, doi = {10.1063/1.5128240}, url = {https://doi.org/10.1063/1.5128240}, author = {Johnny Moreira Gomes and Marcelo Lobosco and Rodrigo Weber dos Santos and ELIZABETH M. CHERRY} } @conference {, title = {Declarative vs Rule-based Control for Flocking Dynamics}, booktitle = {Proceedings of the 33rd Annual ACM Symposium on Applied Computing}, year = {2018}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, isbn = {978-1-4503-5191-1}, doi = {10.1145/3167132.3167222}, url = {http://doi.acm.org/10.1145/3167132.3167222}, author = {Mehmood, Usama and Paoletti, Nicola and Phan, Dung and Grosu, Radu and Lin, Shan and Stoller, Scott D. and Tiwari, Ashish and Yang, Junxing and Smolka, Scott A.} } @article {, title = {Discordant Alternans as a Mechanism for Initiation of Ventricular Fibrillation In Vitro}, journal = {Journal of the American Heart Association}, volume = {7}, year = {2018}, month = {09}, abstract = {

Background-{\textemdash}Ventricular tachyarrhythmias are often preceded by short sequences of premature ventricular complexes. In a previous study, a restitution-based computational model predicted which sequences of stimulated premature complexes were most likely to induce ventricular fibrillation in canines in vivo. However, the underlying mechanism, based on discordant-alternans dynamics, could not be verified in that study. The current study seeks to elucidate the mechanism by determining whether the spatiotemporal evolution of action potentials and initiation of ventricular fibrillation in in vitro experiments are consistent with model predictions.

Methods and Results-{\textemdash}Optical mapping voltage signals from canine right-ventricular tissue (n=9) were obtained simultaneously from the entire epicardium and endocardium during and after premature stimulus sequences. Model predictions of action potential propagation along a 1-dimensional cable were developed using action potential duration versus diastolic interval data. The model predicted sign-change patterns in action potential duration and diastolic interval spatial gradients with posterior probabilities of 91.1\%, and 82.1\%, respectively. The model predicted conduction block with 64\% sensitivity and 100\% specificity. A generalized estimating equation logistic-regression approach showed that model-prediction effects were significant for both conduction block (P\<191015, coefficient 44.36) and sustained ventricular fibrillation (P=0.0046, coefficient, 1.63) events.

Conclusions-{\textemdash}The observed sign-change patterns favored discordant alternans, and the model successfully identified sequences of premature stimuli that induced conduction block. This suggests that the relatively simple discordant-alternans-based process that led to block in the model may often be responsible for ventricular fibrillation onset when preceded by premature beats. These observations may aid in developing improved methods for anticipating block and ventricular fibrillation. (J Am Heart Assoc. 2018;7:e007898. DOI: 10.1161/JAHA.117.007898.)

}, keywords = {alternans, arrhythmia (mechanisms), computer-based model, electrophysiology, ventricular fibrillation}, doi = {10.1161/JAHA.117.007898}, author = {Munoz, Laura and R. M. Gelzer, Anna and Fenton, Flavio and Qian, Wei and Lin, WeiYe and F. Gilmour, Robert and F. Otani, Niels} } @article {, title = {Dynamic Network Model from Partial Observations}, journal = {CoRR}, volume = {abs/1805.10616}, year = {2018}, url = {http://arxiv.org/abs/1805.10616}, author = {Elahe Ghalebi K. and Baharan Mirzasoleiman and Radu Grosu and Jure Leskovec} } @conference {, title = {Data assimilation for cardiac electrical dynamics}, booktitle = {Proceedings of the 5th {International} {Conference} on {Computational} and {Mathematical} {Biomedical} {Engineering}}, year = {2017}, address = {Pittsburgh, PA}, abstract = {

New insights into the mechanisms underlying cardiac arrhythmias may be possible from a greater understanding of variables unobserved in experimental settings. When experimental observations are sparse, data assimilation can be effective at recovering a more complete system state estimate. We have recently shown using synthetic observations of one- and three-dimensional cardiac tissue that data assimilation can be used to recover state variables observed sparsely in space and time or unobserved. Here we extend these findings to show that data assimilation remains effective in reconstructing cardiac states with underlying system spatial heterogeneity and in the presence of significant model error.

}, author = {Cherry, E. M. and Cairns, D. I. and Holt, N. and LaVigne, N. S. and Fenton, F. H. and Hoffman, M. J.}, editor = {Nithiarasu, P. and Robertson, A. M.} } @conference {, title = {{Data-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties}}, booktitle = {Computational Methods in Systems Biology}, year = {2017}, publisher = {Springer}, organization = {Springer}, abstract = {

We present a fully closed-loop design for an artificial pancreas (AP) which regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction (e.g. in the form of meal announcements) with the patient. A major obstacle to achieving closed-loop insulin control is the uncertainty in those aspects of a patient{\textquoteright}s daily behavior that significantly affect blood glucose, especially in relation to meals and physical activity. To handle such uncertainties, we develop a data-driven robust model-predictive control framework, where we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These sets are then used in the controller and state estimator to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of this approach, without explicit meal announcements, to support high carbohydrate disturbances and to regulate glucose levels in large clusters of virtual patients learned from population-wide survey data.

}, doi = {10.1007/978-3-319-67471-1_13}, author = {Nicola Paoletti and Kin Sum Liu and Scott A. Smolka and Shan Lin} } @article {, title = {Distinguishing mechanisms for alternans in cardiac cells using constant-diastolic-interval pacing}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, volume = {27}, year = {2017}, pages = {093902}, abstract = {

Alternans, a proarrhythmic dynamical state in which cardiac action potentials alternate between long and short durations despite a constant pacing period, traditionally has been explained at the cellular level using nonlinear dynamics principles under the assumption that the action potential duration (APD) is determined solely by the time elapsed since the end of the previous action potential, called the diastolic interval (DI). In this scenario, APDs at a steady state should be the same provided that the preceding DIs are the same. Nevertheless, experiments attempting to eliminate alternans by dynamically adjusting the timing of pacing stimuli to keep the DI constant showed that alternans persisted, contradicting the traditional theory. It is now widely known that alternans also can arise from a different mechanism associated with intracellular calcium cycling. Our goal is to determine whether intracellular calcium dynamics can explain the experimental findings regarding the persistence of alternans despite a constant DI. For this, we use mathematical models capable of producing alternans through both voltage- and calcium-mediated mechanisms. We show that for voltage-driven alternans, action potentials elicited from a constant-DI protocol are always the same. However, in the case of calcium-driven alternans, the constant-DI protocol can result in alternans. Reducing the strength of the calcium instability progressively reduces and finally eliminates constant-DI alternans. Our findings suggest that screening for the presence of alternans using a constant-DI protocol has the potential for differentiating between voltage-driven and calcium-driven alternans.

}, issn = {1054-1500}, doi = {10.1063/1.4999354}, url = {http://aip.scitation.org/doi/10.1063/1.4999354}, author = {Cherry, Elizabeth M.} } @article {, title = {Dynamics of a human spiral wave}, journal = {Phys. Today}, volume = {70}, year = {2017}, month = {02/2017}, url = {https://scholar.google.com/citations?view_op=view_citation\&hl=en\&user=jKiNd-0AAAAJ\&cstart=20\&pagesize=80\&citation_for_view=jKiNd-0AAAAJ:BrmTIyaxlBUC}, author = {Andrea J Welsh and Edwin F Greco and Flavio H Fenton} }