Poster Abstracts
1. Quantum Operator-Based Real Autoencoder (QOBRA): A Quantum Autoencoder Algorithm for De Novo Molecular Design
Authors: Yue Yu, Francesco Calcagno, Haote Li, Victor S. Batista
Affiliations: Yale University; University of Bologna
We introduce a variational quantum autoencoder tailored for de novo molecular design named QOBRA (Quantum Operator-Based Real-Amplitude autoencoder). QOBRA leverages quantum circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoder and decoder operators enable unitary transformations and a generative process that ensures accurate reconstruction as well as novelty, uniqueness, and validity of the generated samples. We showcase the capabilities of QOBRA as applied to de novo design of Ca2+, Mg2+, and Zn2+-binding metalloproteins after training the generative model with a modest dataset.
2. VivariumEcoli: A Modular Framework for Predictive Whole-Cell Modeling
Authors: Neyamat Khan Tanvir, Eran Agmon
Affiliations: Systems Biology Graduate Program, University of Connecticut Health Center, Farmington, CT, USA 06030; Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, USA 06030
Predictive, mechanistic models of whole-cell activity are critical for understanding how E. coli combines several regulatory, metabolic, and signaling modules to survive and adapt in dynamic environment. Here, we introduce vEcoli, a modular, dynamical framework that integrates genome-scale metabolism with kinetic sub-models of transcriptional, post-transcriptional, translational, and post-translational control. In vEcoli, basic biological operations are encapsulated as discrete “processes” (e.g., metabolism, transcription, translation, replication, and division), which interact via shared “stores” that hold molecular state. Composites allow for hierarchical model creation, while workflows and experiments facilitate repeatable configuration and large-scale parameter sweeps. vEcoli’s simulation engine combines constraint-based flux analysis of genome-scale metabolism with kinetic sub-models of macromolecular synthesis and regulatory networks, capturing both steady-state growth phenotypes and dynamic stress responses like nutrient shifts or antibiotic perturbations. Initial applications show accurate recapitulation of growth rates, metabolite pools, and proteome allocation under a variety of circumstances. Whole-cell modeling holds the promise of uniting diverse cellular processes into a single predictive framework.
3. Astral Architecture Can Enhance Mechanical Strength of Cytoskeletal Networks by Modulating Percolation Thresholds
Authors: Brady Berg, Jun Allard
Affiliations: Mathematical, Computational & Systems Biology, UCI; Department of Mathematics; Department of Physics & Astronomy; Center for Complex Biological Systems, UCI
A repeated pattern in cytoskeletal architecture is the aster, in which a number of F-actin filaments emerge star-shaped from a central node. Aster-based structures occur in cytoplasmic actin, the early stages of the cytokinetic ring in yeast, and in the context of biomimetic materials engineering. In this work, we use computational simulation to show that there is an optimal number of filaments per aster that maximizes rigidity, even at a fixed density of F-actin. This nonlinear dependence holds for both the shear and extensional moduli. At physiological parameters, the maximum corresponds approximately to the same filaments-per-aster observed in recent super-resolution images of cortical F-actin. Furthermore, we find that increasing filaments-per-aster leads to dramatic increases in the sample-to-sample variability in network rigidity. We explain both effects using percolation theory, wherein the probability that a given network is productively connected exhibits a sharp dependence on parameters.
4. Elucidating the Role of ELAC-2 in Regulating Mitochondrial Function via a Novel Anterograde Response
Authors: Bharat Vivan Thapa, James Held, Chloe Hecht, Maulik Patel
Affiliations: Department of Biological Sciences, Vanderbilt University
Mitochondria are semiautonomous organelles essential for energy production, macromolecule biosynthesis, and signaling. Given these diverse functions, efficient communication between mitochondria and the nucleus is essential for maintaining cellular homeostasis. Eukaryotic cells have evolved elaborate crosstalk between the two organelles, broadly categorized into retrograde (mitochondria to nucleus) and anterograde (nucleus to mitochondria) signaling to ensure optimal function. Retrograde signaling, which consists of adaptive responses such as mitochondrial unfolded protein response (UPRmt), is well-characterized. In contrast, anterograde signaling involving preventative responses remains comparatively underexplored. We have uncovered a novel anterograde response involving ELAC-2, a tRNA processing enzyme, in regulating mitochondrial membrane potential.
5. Cellular Protrusions as Wave Propagations Coupled with Membrane Curvatures
Authors: Yiyan Lin, Siyu Ye, Saki Takayanagi, Takanari Inoue, Huaqing Cai, Miho Iijima, Mike Piacentino, Peter N. Devreotes
Affiliations: Departments of Cell Biology, Johns Hopkins School of Medicine, Baltimore, MD; Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
Cell migration is a vital process in embryonic development and morphogenesis. While cells typically move by extending actin-based protrusions at the front and contracting at the rear, they can form a wide variety of protrusions—such as lamellipodia, filopodia, pseudopodia, macropinosomes, and blebs. This diversity has often been attributed to distinct molecular pathways. However, emerging evidence points to a common underlying mechanism involving cortical signaling waves—spatiotemporal propagations of signaling activity across the cell cortex—increasingly linked to protrusion dynamics. Recently, we showed that small, reversible perturbations to the signaling network can rapidly switch cells between cup-like and lamellipodia-like protrusions, corresponding to predictable changes in the size and duration of cortical waves. These findings suggest that different protrusions may exist along a continuum, governed by a shared excitable signaling system.
6. Sst2 Is Essential for Pre-Morphogenic Gradient Sensing in Mating Yeast
Authors: Alanda Kelly; David Stone
Affiliations: University of Illinois, Chicago
Chemotropism is directed cell growth in response to chemical gradients. Although this process has been studied for decades, how cells accurately interpret and respond to shallow, complex, and dynamic chemical gradients is not fully understood. The yeast mating response is chemotropic: cells of opposite mating type signal their position to potential partners by secreting mating pheromones. We have proposed a deterministic gradient sensing model that explains how yeast cells detect and orient toward their mating partners. Using an intrinsic polarity site, cells assemble a gradient-tracking machine (GTM) at the plasma membrane composed of signaling, polarity, and trafficking proteins. A key function of this system is the activation of G-protein coupled receptors and their associated G-proteins. Activation of this complex enables the GTM to direct vesicle delivery and carry new receptor and G-protein toward the gradient source in a process called tracking. The negative regulator of G-protein signaling, Sst2, catalyzes the inactivation of the Gα subunit. Our results indicate that Sst2 plays a critical role in gradient tracking.
7. Engineering a Genomically Recoded Organism with One Stop Codon
Authors: Michael W. Grome, Michael T. A. Nguyen, Daniel W. Moonan, Kyle Mohler, Kebron Gurara, Shenqi Wang, Colin Hemez, Benjamin Stenton, Yunteng Cao, Felix Radford, Maya Kornaj, Jaymin Patel, Maisha Prome, Svetlana Rogulina, David Sozanski, Jesse Tordoff, Jesse Rinehart, Farren J. Isaacs
Affiliations: Yale University departments and institutes as listed in the original text
The genetic code is conserved across all domains of life, yet exceptions have revealed variations in codon assignments and associated translation factors. Inspired by this natural malleability, synthetic approaches have demonstrated whole-genome replacement of synonymous codons to construct genomically recoded organisms (GROs) with alternative codes. Here, we describe construction and characterization of the first GRO—“Ochre”—to fully compress a translational function into a single codon. We replaced 1,195 TGA stop codons with synonymous TAA within ΔTAG Escherichia coli C321.ΔA. We then engineered release factor 2 and tRNA Trp to mitigate native UGA recognition, translationally isolating four codons for non-degenerate functions. This rendered UAA as the sole stop codon, UGG for tryptophan, and reassigned UAG and UGA for multi-site incorporation of two distinct non-standard amino acids into single proteins with >99% accuracy.
8. Generative Vision-Based Modeling for Mechanistic Inference on Spatial Dynamical Data
Authors: Jun Won Park, Kangyu Zhao, Sanket Rane
Affiliations: Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
Biological systems commonly exhibit complex spatiotemporal patterns whose underlying generative mechanisms pose a significant analytical challenge. Traditional approaches to spatiodynamic inference rely on dimensionality reduction through summary statistics, which sacrifice complexity and interdependent structure intrinsic to these data in favor of parameter identifiability. To address this, we developed a simulation-based inference framework that employs vision transformer-driven variational encoding to generate compact representations of the data, exploiting the inherent contextual dependencies. These representations are subsequently integrated into a likelihood-free Bayesian approach for parameter inference. Through the integration of generative modeling and Bayesian principles, our approach provides a unified inference framework that captures and analyzes both spatial and temporal patterns in multivariate dynamical systems.
9. Modeling Reveals the Strength of Weak Interactions in Stacked Trimeric Ring Assembly
Authors: Leonila Lagunes, Eric J. Deeds
Affiliations: University of California, Los Angeles
Cell and organismal viability rely on macromolecular machines regulating many vital processes. An extremely common motif is a stacked ring-like topology, such as in the proteasome or the ubiquitin conjugating enzyme E2. In this work, we developed a mathematical model of stacked trimer assembly that accounts for different binding affinities between and within rings. Our main finding is that deadlock—a severe form of kinetic trapping—can be extremely long, lasting for days or longer. Deadlock is worst when all the interfaces have high binding affinities. Therefore, we predict that evolutionary pressures select against stacked trimers having strong binding affinities throughout.
10. Membrane Segmentation in Noisy Data via Physics-Informed Neural Networks
Authors: Atsushi Matsuda, Christopher T. Lee, Matthew Akamatsu
Affiliations: University of Washington; University of California, San Diego
Membrane segmentation is a critical step in the analysis of cryo-electron tomography data, enabling structural and biophysical insights into cellular organelles. While recent advances in image processing and deep learning have enabled automated segmentation, these methods often struggle under low signal-to-noise conditions. We developed a physics-informed neural network (PINN) that reconstructs membrane structures from tomographic images. Rather than relying solely on image features, the model is trained to satisfy both image fidelity and physical consistency, where the latter is defined by the principles of membrane mechanics. We found that incorporating physical constraints significantly improves segmentation quality under noisy conditions.
11. Integrating Qualitative Data via Mathematical Modeling Reconciles Discordant Observations and Offers a Candidate Mechanism for Intracellular Regulation of BRUTUS in Arabidopsis
Authors: Ghizelle Jane E. Abarro, Dipali Srivastava, DT Flaherty, Terri A. Long, Belinda S. Akpa
Affiliations: University of Tennessee Knoxville
Iron is an essential nutrient but becomes toxic when present in excess. In Arabidopsis thaliana, the putative Fe sensor BRUTUS (BTS) directly binds Fe, and BTS knockdown mutants accumulate more Fe, suggesting that the protein negatively regulates Fe uptake. Experimental efforts to elucidate the interplay between Fe status and BTS activity have yielded discordant observations and contradictory conclusions. Using mathematical modeling as a formal, testable sense-making strategy, we sought to determine whether there exists a kinetic regime under which Fe-responsive stability and translocation of BTS could concurrently explain this group of empirical observations. Through an iterative modeling-experimentation loop, we reconciled seemingly contradictory empirical observations via a novel systems mechanism wherein BTS persistence varies with Fe-binding stoichiometry.
12. Mechanisms of Protein Self-Assembly: From Microtubule Dynamics to Membrane Localization
Authors: Smriti Chhibber, Margaret Johnson
Affiliations: Department of Biophysics, Johns Hopkins University
Microtubules are dynamic cytoskeletal filaments whose growth and shrinkage are tightly regulated by GTP hydrolysis. We simulate the growth of individual protofilaments composed of two distinct tubulin dimers on a 2D lattice using a Gillespie algorithm that accounts for lateral and longitudinal interactions, as well as hydrolysis dynamics. In parallel, we investigate how biological assemblies are enhanced by membrane localization, where confinement to a 2D surface increases effective molecular concentration and promotes self-assembly. Using a thermodynamic framework and NERDSS, a spatial rule-based simulation platform, we compare protein assembly in 3D solution versus 2D membrane environments.
13. Functional Implications of Biomolecular Condensate Size Distribution
Authors: Aniruddha Chattaraj, Eugene I. Shakhnovich
Affiliations: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
Biomolecular condensates are phase-separated sub-cellular structures that spatially control biochemistry in many systems. For a given condensate type, the existence of multiple droplets inside living cells is a peculiar phenomenon not compatible with equilibrium statistical mechanics. We combined Langevin dynamics, reaction-diffusion simulation, and dynamical systems theory to show that a multiple-condensate state can indeed be a functional strategy to optimize biochemical output. Using Arp2/3-mediated actin nucleation as an example, we showed that actin polymerization is maximum at an optimal number of condensates. Our analysis reveals the functional significance of condensate size distribution.
14. Enzyme Structure and Function at Extreme Temperatures
Authors: Catherine Le (presenter), Maanasa Panuganti, Melanie Cocco
Affiliations: Department of Molecular Biology and Biochemistry, UC Irvine
Bacterial enzymes have evolved to function across a temperature range exceeding 110°C, offering valuable insights to industrial enzyme engineering, biotherapeutic stabilization, genomics, and protein structure prediction. Our focus is Pol IV, a bypass polymerase expressed across prokaryotes from polar ice to deep-sea thermal vents. Using NMR spectroscopy, we have studied Pol IV from a thermophile and an enteric bacterium. We have compiled over 2,700 Pol IV sequences from bacteria thriving within four major temperature ranges, spanning more than 100°C. Our future goal is to use AI tools to identify sequence patterns conserved within each temperature range and to guide domain swapping and directed evolution of Pol IV variants for extreme hot or cold conditions.
15. The Effect of a Pheromone Protease on Yeast Gradient Sensing
Authors: Paul A. Urban, David E. Stone
Affiliations: Department of Biological Sciences, University of Illinois at Chicago
The mating response of the budding yeast S. cerevisiae relies on chemotropism. Haploid yeast cells of opposite mating types detect pheromone gradients produced by one another and polarize growth towards the gradient source. We are investigating how Bar1, a protease that degrades the pheromone α-factor, affects the yeast cell’s ability to gradient sense. We found that Bar1 is essential for gradient sensing and suggest that Bar1 enables wild-type levels of tracking through a cell-autonomous mechanism. These results support the hypothesis that Bar1 steepens the pheromone gradient along the gradient tracking machine, enabling accurate pre-morphogenic gradient sensing.
16. A Variational Deep-Learning Approach to Modeling Memory T Cell Dynamics
Authors: Christiaan H. van Dorp, Joshua I. Gray, Daniel H. Paik, Donna L. Farber, Andrew J. Yates
Affiliations: Columbia University Irving Medical Center departments as listed in the original text
Mechanistic models of dynamic, interacting cell populations have yielded many insights into immune responses. To tackle the challenge of confronting tractable mathematical models with high-dimensional data, we studied the development and persistence of lung-resident memory CD4 and CD8 T cells in mice infected with influenza virus. We developed an approach in which dynamical model parameters and the population structure are inferred simultaneously using deep learning and stochastic variational inference trained on single-cell flow-cytometry data directly. Our approach yields new insights into tissue-localized immune memory and provides a novel basis for interpreting time series of high-dimensional data.
17. Defining and Programming Transcriptional Activatability in Bacterial Promoters
Authors: Debora Tenenbaum, Chirangini Pukhrambam, Andalus Ayaz, Bryce Nickels, Justin B. Kinney
Affiliations: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory; Waksman Institute of Microbiology, Rutgers University
Transcription initiation is the primary control point of gene expression in bacteria, yet the sequence-to-function relationship governing this process remains incompletely understood. We investigate how promoter DNA sequences encode regulatory information by systematically characterizing transcription initiation by E. coli RNA polymerase using in vitro transcription assays on diverse promoter libraries. By varying RNA polymerase concentrations, we can dissect how specific promoter sequence elements modulate mechanistic steps of transcription initiation. We plan to combine biophysically motivated models with flexible deep learning approaches to facilitate mechanistic interpretation of sequence effects while capturing complex dependencies beyond current understanding.
18. Multiple Approaches to Identifying Tissue Correlates of Serum Antibody/Neutralization Titers Against SARS-CoV-2
Authors: Juliane Schröter, Christiaan H. van Dorp, Julia M. Davis-Porada, Donna L. Farber, Andrew J. Yates
Affiliations: Columbia University Irving Medical Center departments as listed in the original text
Infections and vaccinations elicit both humoral and cellular immune responses. We investigated how SARS-CoV-2-specific immune responses across tissues relate to systemic binding and neutralizing antibody levels in vaccinated individuals. Samples from 57 deceased vaccinated organ donors were analyzed across seven tissues. Antibody titers varied widely and were strongly associated with tissue-resident immune features, notably memory B cells and CD8+ T cells in lung-associated lymph nodes, lung, and spleen. These findings highlight the individualized nature of SARS-CoV-2 vaccine responses and the added value of tissue-based immune profiling.
19. Blood Digital Twins Developed Using Dynamic State Modeling of Single-Cell RNA-seq Data
Authors: Pancy Lwin, Juilee Thakar
Affiliations: URMC Departments of Microbiology and Immunology, Biostatistics and Computational Biology, and Biomedical Genetics
The development of blood digital twins represents a critical step toward personalized medicine and in-silico clinical trials. We present a dynamic modeling framework that leverages single-cell RNA-seq data to construct immune digital twins through Boolean network-based State Transition Graphs. By modeling intracellular signaling dynamics, we identify dominant attractors that represent stable cellular states and track pseudotime progression across disease-specific trajectories. In silico perturbation experiments reveal gene targets that significantly shift disease trajectories, offering insights into therapeutic vulnerabilities. This personalized modeling approach builds toward virtual clinical trials and precision immunotherapy.
20. A Genome-Complete Foundation for a Whole Human Epithelial Cell Model
Authors: Jonah R. Huggins, Atalanta Harley-Gasaway, Marc R. Birtwistle
Affiliations: Clemson University departments as listed in the original text
Whole-cell models have been described for multiple single-celled organisms but not yet for a human cell. We previously reported one of the largest models of a human epithelial cell that captures key proliferation and death pathways and single-cell heterogeneity using custom simulation algorithms. In a key step toward a whole human epithelial cell model, we are creating species for twenty-thousand functional human genes and their nascent products, resulting in a genome-complete foundation. This work provides a basis for scalable simulation, benchmarking, and broader community contribution.
21. Synergistic Bactericidal Pore Formation by Differential Targeting Membranes by Histones and AMPs
Authors: Yonghan Wu
Affiliations: Department of Physics and Astronomy, University of California, Irvine
Histone and antimicrobial peptides are crucial components of innate immunity, but the mechanism of their antimicrobial functions remains poorly understood. Using stochastic optical reconstruction microscopy and cryo-electron microscopy, we show that histone or AMPs alone do not damage membrane, but together they form bactericidal pores. To elucidate the kinetics, we developed a mathematical model of molecular translocation and pore formation across the four leaflets of bacterial inner and outer membranes. We found that the synergy arises from differential targeting of the membrane leaflets, providing insights into potential therapeutic strategies against gram-negative bacteria.
22. Metapages: A Platform for Reproducible, Shareable, Interactive, AI-Enhanced Mechanistic Modeling in the Browser
Authors: Dion Whitehead
Affiliations: metapages, LLC
As AI systems begin to rival domain experts in predictive performance, mechanistic modeling remains indispensable. We present the metapage platform, a web tool that helps modelers, experimentalists, and AI developers co-create executable, visual scientific simulations entirely in the browser. The platform addresses reproducibility, accessibility, and collaboration by linking datasets, code, and containerized compute environments with dynamic visualizations. AI models can be plugged in as swappable, interpretable components. Early use cases include protein design, cellular signaling, and morphogenesis modeling, each blending mechanistic insight with AI acceleration.
23. A Computational and Experimental Investigation of Cell-Cell Interactions Driving Tumor-Induced Bone Disease
Authors: Alexandra Gutierrez Vega, Natalie E. Bennett, Saja Alshafeay, Erik P. Beadle, Julie A. Rhoades, Leonard A. Harris
Affiliations: University of Arkansas and Vanderbilt University programs and departments as listed in the original text
Tumor-induced bone disease is a complex and poorly understood condition that arises when tumors metastasize to bone. We present a computational model of osteoblast and osteoclast interactions with tumor cells in the bone microenvironment. The model includes logistic tumor growth, TGF-beta enhancement of tumor proliferation and PTHrP synthesis, and osteoblast inhibition and osteoclast activation by tumor-secreted factors. Parameter values were estimated by fitting the model to in vivo time-course data. Our results provide insights into the mechanisms underlying tumor-induced bone disease and raise questions regarding a fundamental assumption of the prevailing “vicious cycle” model.