About

Andrew McCulloch is a Distinguished Professor in Bioengineering at UC San Diego, holding the Shu Chien Chancellor's Endowed Chair in Engineering and Medicine. He is the Director of the UC San Diego Institute for Engineering in Medicine and the Interfaces Graduate Training Program. His research focuses on in-vitro, in-vivo, and computational cardiac mechanics and physiology in health and disease. Professor McCulloch directs the Cardiac Mechanics Research Group, where his team investigates the relationships between the cellular and extracellular structure of cardiac muscle and the electrical and mechanical function of the heart during ventricular remodeling and cardiac arrhythmia. His work involves using experimental and computational models, genetically engineered animal models, tissue engineering techniques such as microlithography and microfluidics, and computational modeling combined with fluorescence optical mapping to study excitation-contraction coupling and mechanoelectric feedback in cardiac tissue. Additionally, he explores signal transduction pathways related to calcium cycling and hypertrophy, along with developing new technologies for high-throughput cardiac phenotyping in model organisms. Professor McCulloch joined UC San Diego's faculty in 1987, served as the department chair, and is a senior fellow at the San Diego Supercomputer Center. He is also a member of the Whitaker Institute for Biomedical Engineering and the UCSD/Salk Institute for Molecular Medicine. He co-founded Insilicomed, a company developing predictive modeling tools for medical applications. He earned his Ph.D. in engineering science and physiology from the University of Auckland in 1986, was an NSF Presidential Young Investigator from 1991-96, and has been a Fellow of the American Institute for Medical and Biological Engineering since 1997.

Research topics

• Biology
• Pathology
• Medicine
• Computer Science
• Biomedical engineering
• Computational biology
• Genetics
• Cell biology
• Materials science
• Chemistry
• Nanotechnology
• Biophysics
• Intensive care medicine
• Cardiology
• Bioinformatics

Selected publications

• PO-01-140 ARTIFICIAL INTELLIGENCE GUIDED DETERMINATION OF CHAMBER OF ORIGIN OF ATRIAL FLUTTER AND SEPARATION FROM ATRIAL FIBRILLATION

  Heart Rhythm · 2026-04-01

  article
  PublisherDOI

• PO-01-101 ACUTE ENDOCARDIAL AND HYBRID ENDO-EPICARDIAL PULSED-FIELD ABLATION WITH NOVEL “HYDRA” CATHETERS: PRECLINICAL EXPERIENCE AT CHARLES UNIVERSITY, PRAGUE

  Heart Rhythm · 2026-04-01

  article
  PublisherDOI

• From FAIR to CURE: guidelines for computational models of biological systems

  npj Systems Biology and Applications · 2026-03-27 · 1 citations

  articleOpen access

  Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community should strive to automate as many of the guidelines as possible.

  PublisherOA PDFDOI

• MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction

  IEEE Transactions on Medical Imaging · 2026-01-01

  preprintOpen access

  Cardiac Magnetic Resonance (CMR) imaging is widely used to personalize heart models for cardiac digital twin analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations result in data loss and measurement inaccuracies, hindering the capture of detailed anatomical structures. In this work, we introduce MorphiNet, a novel network that reproduces heart anatomy learned from high-resolution Computed Tomography (CT) images, unpaired with CMR images. MorphiNet encodes the anatomical structure as gradient fields, deforming template meshes into patient-specific geometries. A multilayer graph subdivision network refines these geometries while maintaining dense point correspondence, suitable for downstream computational analysis. MorphiNet achieved the strongest overall trade-off in bi-ventricular myocardium reconstruction on CMR patients with tetralogy of Fallot, with 0.3 higher Dice score and 2.6 lower Hausdorff distance compared to the best existing template-based methods, while achieving comparable geometric accuracy to neural implicit function methods on CT data at 50× faster inference. Cross-dataset validation on the Automated Cardiac Diagnosis Challenge confirmed robust generalization, achieving a 0.7 Dice score with 30% improvement over previous template-based approaches. We validate our anatomical learning approach through the successful restoration of missing cardiac structures and demonstrate significant improvement over standard Loop subdivision. Motion tracking experiments further confirm MorphiNet's capability for cardiac function analysis, including ejection-fraction estimates that correctly identify myocardial dysfunction in tetralogy of Fallot patients. Code and checkpoints are available at https://github.com/MalikTeng/MorphiNetV2.

  PublisherOA PDFDOI

• Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations

  arXiv (Cornell University) · 2025-12-22

  preprintOpen access

  Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates, that provides a common anatomical reference frame for the human heart. Our method predicts NIHCs directly from a limited number of 2D segmentations (sparse acquisition) and subsequently decodes them into dense 3D segmentations and high-resolution meshes at arbitrary output resolution. Trained on a large dataset of 5,000 cardiac meshes, the model achieves high reconstruction accuracy on clinical contours, with mean Euclidean surface errors of 2.51$\pm$0.33 mm in a diseased cohort (n=4549) and 2.3$\pm$0.36 mm in a healthy cohort (n=5576). The NIHC representation enables anatomically coherent reconstruction even under severe slice sparsity and segmentation noise, faithfully recovering complex structures such as the valve planes. Compared with traditional pipelines, inference time is reduced from over 60 s to 5-15 s. These results demonstrate that NIHCs constitute a robust and efficient anatomical representation for patient-specific 3D cardiac reconstruction from minimal input data.

  PublisherDOI

• PO-04-105 OPTIMIZING ELECTRODE DESIGN FOR ENHANCED PULSED FIELD ABLATION: A COMPUTATIONAL AND EX VIVO STUDY

  Heart Rhythm · 2025-04-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• From FAIR to CURE: Guidelines for Computational Models of Biological Systems.

  PubMed · 2025-02-21 · 3 citations

  preprintOpen access

  are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.

  PublisherOA PDF

• Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations

  ArXiv.org · 2025-12-22

  articleOpen access

  Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates, that provides a common anatomical reference frame for the human heart. Our method predicts NIHCs directly from a limited number of 2D segmentations (sparse acquisition) and subsequently decodes them into dense 3D segmentations and high-resolution meshes at arbitrary output resolution. Trained on a large dataset of 5,000 cardiac meshes, the model achieves high reconstruction accuracy on clinical contours, with mean Euclidean surface errors of 2.51$\pm$0.33 mm in a diseased cohort (n=4549) and 2.3$\pm$0.36 mm in a healthy cohort (n=5576). The NIHC representation enables anatomically coherent reconstruction even under severe slice sparsity and segmentation noise, faithfully recovering complex structures such as the valve planes. Compared with traditional pipelines, inference time is reduced from over 60 s to 5-15 s. These results demonstrate that NIHCs constitute a robust and efficient anatomical representation for patient-specific 3D cardiac reconstruction from minimal input data.

  PublisherOA PDF

• Excitation and mechanical contraction of a 3D cardiomyocyte model

  Biophysical Journal · 2025-07-25 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Systems modeling of mitochondrial dynamics in different exercise regimes

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-28 · 2 citations

  preprintOpen access

  Abstract Exercise stimulates skeletal muscle signaling and mitochondrial metabolism. Emerging evidence shows that mitochondrial dynamics (i.e., fission and fusion) could be regulated by exercise. Yet, there are knowledge gaps on the following questions: (i) which upstream signals are necessary and sufficient to bias mitochondria toward fission versus fusion? (ii) How does cellular energy status and ROS partition control between DRP1 and MFN/OPA1? And (iii) which combinations of intensity and duration produce similar cytosolic signals but distinct mitochondrial remodeling? To address these gaps, we developed an integrative computational framework that connects exercise regimens to mitochondria fission-fusion machinery by linking blood-myofiber energetics in cytosol and mitochondria to skeletal muscle signaling network. The influence of three exercise regimen (i.e., sprint, resistance, and endurance) on mitochondrial fission and fusion was simulated. Classified qualitative validation of signaling network model against studies not used in developing the model achieved 80% accuracy. The model predicts regimen-specific dynamics starting with acute DRP1-driven fission during exercise followed by MFN1/2–OPA1-mediated re-fusion as energy stress declines, consistent with a cyclical triage-then-rebuild paradigm. Changes are most pronounced and sustained with endurance, sharp but brief with sprint, and minimal with resistance. Global sensitivity analysis identified AMPK/PGC-1α→MFN1/2 as dominant fusion drivers, ROS and AMPK→MFF/DRP1 as primary fission switches, and Ca²⁺-calmodulin, ERK, and LKB1/AMPK as shared regulators of fission and fusion. Our model also predicts that an endurance base, augmented with 1–2 weekly high intensity interval traning (HIIT)/ sprint interval training (SIT) sessions could maximize AMPK-ROS pulses and mitochondrial fission-fusion. This framework unifies muscle’s signaling logic with the energetic state to explain how intensity-volume combinations, bout spacing, and kinase modulation tune mitochondrial remodeling, yielding testable predictions for optimizing training and adjuvant therapies for enhanced human performance.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R29HL041603

  NIH · $505k · 1993

• NIH Grant P01HL043026

  NIH · $12.9M · 2007

• NIH Grant P01HL046345

  NIH · $36.2M · 2014

• NIH Grant R01HL041603

  NIH · $965k · 1999

• Integrative Bioengineering of Heart, Vessels, and Blood

  NIH · $3.6M · 2010–2021

Frequent coauthors

• Jeffrey H. Omens

  University of California, San Diego

  221 shared

• Roy C. P. Kerckhoffs

  University of California, San Diego

  63 shared

• Andrew G. Edwards

  University of California, San Diego

  49 shared

• Michael Regnier

  University of Washington

  43 shared

• Giovanni Paternostro

  Sanford Burnham Prebys Medical Discovery Institute

  42 shared

• Kevin P. Vincent

  University of California, San Diego

  41 shared

• Jeff Omens

  40 shared

• Anushka Michailova

  University of California, San Diego

  40 shared

Labs

• Cardiac Mechanics Research GroupPI

Awards & honors

• NSF Presidential Young Investigator (1991-96)
• Fellow of the American Institute for Medical and Biological…