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Genes are connected in complex networks of interactions where often the product of one gene is a transcription factor that alters the expression of another. Many of these networks are based on a few fundamental motifs leading to switches and oscillators of various kinds. And yet, there is more to the story than which transcription factors control these various circuits. These transcription factors are often themselves under the control of effector molecules that bind them and alter their level of activity. Traditionally, much beautiful work has shown how to think about the stability of the different states achieved by these fundamental regulatory architectures by examining how parameters such as transcription rates, degradation rates and dissociation constants tune the circuit, giving rise to behavior such as bistability. However, such studies explore dynamics without asking how these quantities are altered in real time in living cells as opposed to at the fingertips of the synthetic biologist's pipette or on the computational biologist's computer screen. In this paper, we make a departure from the conventional dynamical systems view of these regulatory motifs by using statistical mechanical models to focus on endogenous signaling knobs such as effector concentrations rather than on the convenient but more experimentally remote knobs such as dissociation constants, transcription rates and degradation rates that are often considered. We also contrast the traditional use of Hill functions to describe transcription factor binding with more detailed thermodynamic models. This approach provides insights into how biological parameters are tuned to control the stability of regulatory motifs in living cells, sometimes revealing quite a different picture than is found by using Hill functions and tuning circuit parameters by hand.

Background: Conventional cardiovascular magnetic resonance (CMR) cine sequences rely on binning reconstructions that average multiple heartbeats, an assumption that breaks down in arrhythmic patients where beat-to-beat variations lead to motion artifacts and loss of clinically relevant functional information. While 2D real-time imaging can capture individual heartbeats, a stack of 2D slices is sub-optimal to map the full complexity of incoherent cardiac dynamics during arrhythmia. We demonstrate the feasibility of 3D real-time motion field reconstruction for continuous beat-to-beat volumetric quantification in patients with premature ventricular contractions (PVC) using a free-running CMR protocol.

Methods: We extended CMR-MOTUS to jointly reconstruct real-time 3D motion fields and a motion-corrected reference image from continuously acquired data without breath-holds or ECG gating. A variable-density Cartesian sampling trajectory (OPRA) was used with a 3D spoiled gradient echo or balanced steady-state free precession sequence. The real-time volumetric beat-to-beat changes were quantified by propagating a single manual segmentation on the reference image, through all time frames using the reconstructed motion fields. The method was validated on a cardiac motion phantom with ground-truth static acquisitions and tested in 4 healthy volunteers and 4 patients with PVC. The ejection fraction (EF) was compared to ground-truth values for the phantom and to standard 2D real-time cine EF measurement techniques for in-vivo subjects.

Results: Reconstructed EF values of the phantom experiment showed good agreement with the ground-truth(EF = 22.1 ± 0.6% versus 21.9%). In healthy volunteers, the mean EF values were close to 2D reference measurements and narrow beat-to-beat EF distributions reflected normal physiological consistency. In PVC patients, the method revealed bimodal EF distributions, with the lower mode corresponding to PVC episodes where individual beats had substantially reduced ejection fractions. Simultaneously acquired ECG signals confirmed the temporal correspondence between volume irregularities and PVC episodes.

Conclusions: 3D real-time joint motion field and image reconstruction from a free-running CMR protocol enables continuous beat-to-beat volumetric quantification in arrhythmic patients, revealing functional heterogeneity that conventional single-beat and averaging measurements (binning and gating) obscure. The bimodal EF distributions observed in PVC patients quantify the true hemodynamic impact of arrhythmic episodes and may provide clinically relevant metrics for treatment monitoring and outcome prediction.

Within the biological, physical, and social sciences, there are two broad quantitative traditions: statistical and mathematical modeling. Both traditions have the common pursuit of advancing our scientific knowledge, but these traditions have developed largely independently using distinct languages and inferential frameworks. This paper uses the notion of identification from causal inference, a field originating from the statistical modeling tradition, to develop a shared language. I first review foundational identification results for statistical models and then extend these ideas to mathematical models. Central to this framework is the use of bounds, ranges of plausible numerical values, to analyze both statistical and mathematical models. I discuss the implications of this perspective for the interpretation, comparison, and integration of different modeling approaches, and illustrate the framework with a simple pharmacodynamic model for hypertension. To conclude, I describe areas where the approach taken here should be extended in the future. By formalizing connections between statistical and mathematical modeling, this work contributes to a shared framework for quantitative science. My hope is that this work will advance interactions between these two traditions.

From soil to the gut, communities composed of thousands of microbes perform functions such as carbon sequestration and immune system regulation. Here, we introduce a data-driven approach that explains how community function can be traced to just a few groups of microbes or genes. In gut communities, our neural-network based clustering algorithm correctly recovers known functional groups. In the ocean metagenome, it distills ~500 gene modules down to three sparse groups highlighting survival strategies at different depths. In soils, it distills ~4400 bacterial species into two groups that enter a mathematical model of nitrate metabolism. By combining interpretable ML with strain isolation and sequencing experiments, we connect the metabolic specialization of each group to community-wide responses to perturbations. This integrated approach yields simple structure-function maps of microbiomes, allowing the discovery of molecular mechanisms underlying human and environmental health. More broadly, we illustrate how to do function-informed dimensionality reduction in biology.

Protein representation learning has advanced rapidly with the scale-up of sequence and structure supervision, but most models still encode proteins either as per-residue token sequences or as single global embeddings. This overlooks a defining property of protein organization: proteins are built from recurrent, evolutionarily conserved substructures that concentrate biochemical activity and mediate core molecular functions. Although substructures such as domains and functional sites are systematically cataloged, they are rarely used as training signals or representation units in protein models. We introduce Magneton, an environment for developing substructure-aware protein models. Magneton provides (1) a dataset of 530,601 proteins annotated with over 1.7 million substructures spanning 13,075 types, (2) a training framework for incorporating substructures into existing protein models, and (3) a benchmark suite of 13 tasks probing representations at the residue, substructural, and protein levels. Using Magneton, we develop substructure-tuning, a supervised fine-tuning method that distills substructural knowledge into pretrained protein models. Across state-of-the-art sequence- and structure-based models, substructure-tuning improves function prediction, yields more consistent representations of substructure types never observed during tuning, and shows that substructural supervision provides information that is complementary to global structure inputs. The Magneton environment, datasets, and substructure-tuned models are all openly available at https://github.com/rcalef/magneton.

Purpose: Tissue-engineered vascular grafts (TEVG) have shown promise in advancing vascular reconstructions. However, precise in vivo implantation is challenging, and it is unclear how deviations in location and size affect hemodynamics. This study aims to 1) compare preoperative designs and postoperative anatomies of TEVG in an in vivo study to evaluate discrepancies and 2) investigate the impact of graft displacement and size on hemodynamics by virtually simulating implantation scenarios that are informed by in vivo postoperative results.

Methods: Designed and postoperative geometries of four porcine aortas were compared to measure the mismatch in implantation location and graft shape. These results informed a virtual TEVG implantation study. TEVG location, orientation, and size were varied to investigate the effects on the final aorta shape and hemodynamics. Anastomosis of TEVG was simulated using finite element modeling. Key hemodynamic metrics were obtained from virtual implantations and actual postoperative anatomies using computational fluid dynamics.

Results: Our in vivo study showed that TEVGs can experience up to 6.9 mm displacement and a 38° rotational shift post-implantation, leading to discrepancies in pressure drop (2.5 mmHg, 50%) and time-averaged wall shear stress (7.2 Pa, 72%) compared to predictions. Virtual TEVG implantation showed that peak systolic pressure drop (PSPD) was most sensitive to translation in the inferior-superior direction and rotation about the anterior-posterior axis. Size mismatch had a greater impact on time-averaged wall shear stress (TAWSS) (85%) than PSPD (23%). Additionally, virtual anastomosis simulations improved aortic shape predictions by 27.5%.

Conclusion: Our results highlight the sensitivity of key hemodynamic metrics to graft implantation location and size mismatch. By quantifying displacement ranges and their impacts during surgery, surgeons can make informed decisions.

Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schrödinger bridge matching, effectively learn mappings between two distributions by modeling a single stochastic path. However, these methods are inherently limited to unimodal transitions and cannot capture branched or divergent evolution from a common origin to multiple distinct modes. To address this, we introduce Branched Schrödinger Bridge Matching (BranchSBM), a novel framework that learns branched Schrödinger bridges. BranchSBM parameterizes multiple time-dependent velocity fields and growth processes, enabling the representation of population-level divergence into multiple terminal distributions. We show that BranchSBM is not only more expressive but also essential for tasks involving multi-path surface navigation, modeling cell fate bifurcations from homogeneous progenitor states, and simulating diverging cellular responses to perturbations.

This collection comprises the abstracts presented during poster, power pitch and oral sessions at the Inaugural Conference of the International Society for Tractography (IST Conference 2025), held in Bordeaux, France, from October 13-16, 2025. The conference was designed to foster meaningful exchange and collaboration between disparate fields. The overall focus was on advancing research, innovation, and community in the common fields of interest: neuroanatomy, tractography methods and scientific/clinical applications of tractography. The included abstracts cover the latest advancements in tractography, Diffusion MRI, and related fields including new work on; neurological and psychiatric disorders, deep brain stimulation targeting, and brain development. This landmark event brought together world-leading experts to discuss critical challenges and chart the future direction of the field.

The brain can only be fully understood through the lens of the behavior it generates-a guiding principle in modern neuroscience research that nevertheless presents significant technical challenges. Many studies capture behavior with cameras, but video analysis approaches typically rely on specialized models requiring extensive labeled data. We address this limitation with BEAST (BEhavioral Analysis via Self-supervised pretraining of Transformers), a novel and scalable framework that pretrains experiment-specific vision transformers for diverse neuro-behavior analyses. BEAST combines masked autoencoding with temporal contrastive learning to effectively leverage unlabeled video data. Through comprehensive evaluation across multiple species, we demonstrate improved performance in three critical neuro-behavioral tasks: extracting behavioral features that correlate with neural activity, and pose estimation and action segmentation in both the single- and multi-animal settings. Our method establishes a powerful and versatile backbone model that accelerates behavioral analysis in scenarios where labeled data remains scarce.

Mixtures of linear dynamical systems (MoLDS) provide a path to model time-series data that exhibit diverse temporal dynamics across trajectories. However, its application remains challenging in complex and noisy settings, limiting its effectiveness for neural data analysis. Tensor-based moment methods can provide global identifiability guarantees for MoLDS, but their performance degrades under noise and complexity. Commonly used expectation-maximization (EM) methods offer flexibility in fitting latent models but are highly sensitive to initialization and prone to poor local minima. Here, we propose a tensor-based method that provides identifiability guarantees for learning MoLDS, which is followed by EM updates to combine the strengths of both approaches. The novelty in our approach lies in the construction of moment tensors using the input-output data to recover globally consistent estimates of mixture weights and system parameters. These estimates can then be refined through a Kalman EM algorithm, with closed-form updates for all LDS parameters. We validate our framework on synthetic benchmarks and real-world datasets. On synthetic data, the proposed Tensor-EM method achieves more reliable recovery and improved robustness compared to either pure tensor or randomly initialized EM methods. We then analyze neural recordings from the primate somatosensory cortex while a non-human primate performs reaches in different directions. Our method successfully models and clusters different conditions as separate subsystems, consistent with supervised single-LDS fits for each condition. Finally, we apply this approach to another neural dataset where monkeys perform a sequential reaching task. These results demonstrate that MoLDS provides an effective framework for modeling complex neural data, and that Tensor-EM is a reliable approach to MoLDS learning for these applications.