Nanopores are versatile single-molecular sensors, but their utility is fundamentally constrained by stochastic translocation dynamics warping any encoded information. We resolve it by shifting from time-domain analysis to a learned latent-space mapping via a contrastive encoder trained exclusively on simulated signals from a physics-informed model. This encoder maps solid-state nanopore signals of engineered DNA barcodes into an interpretable molecular coordinate system. The learned representation is responsive to structural barcode parameters while remaining invariant to acquisition conditions and translocation conformation, allowing data pooling across devices. Molecule identification requires a single pass through the encoder, reducing computational cost by three orders of magnitude relative to alignment-based methods. We experimentally validate through mixture quantification, rare-variant detection, consensus barcode reconstruction, and real-time signal acquisition. This shift from temporal analysis to mapping structural coordinates into a latent space changes the paradigm behind analyzing stochastic sensor signals by linking classification to interpretable encoded molecular information.

Gene regulatory networks govern cellular fate decisions through multistable dynamics. The genetic toggle switch is a canonical model of such behaviour; yet, the impact of cell division on its dynamics remains poorly understood. We derive analytical separatrices for a simplified Boolean toggle switch with and without division. We show that division can redirect trajectories with identical initial conditions to opposing stable states, and we define a region of disagreement where fate decisions are predicted incorrectly if division is neglected. Our results imply that division can fundamentally reshape fate boundaries in multistable regulatory networks.

Determining an appropriate sample size for a study is a crucial step in planning scientific research. Appropriate sample size planning avoids both inadequate and inflated sample sizes. Inflated sample sizes wastes resources, time and effort of human subjects, and lives of experimental animals. Inadequate sample sizes, a much more common problem, wastes even more resources through the inability to detect biologically meaningful differences and encourages questionable research practices like $p$-hacking. Microbiome studies are particularly challenged by small sample sizes, particularly in studies of human subjects or expensive animal models. In practice, the statistical power of taxa within a differential abundance study is influenced by the effect size (typically quantified as fold change), mean abundance of individual taxa, and the number of samples. We present a novel approach for sample size calculation for differential abundance studies as a function of effect size, mean abundance and statistical power of taxa. Our method is implemented in the power.nb R package, available at https://michaelagronah.com/power.nb/articles/stub.html. We applied our model for sample size calculation using estimates of mean abundance and fold change of taxa obtained from thirty real-world microbiome datasets. Our results showed that differential abundance microbiome studies require larger sample sizes than are currently prevalent in the literature to achieve adequate statistical power. Our framework will help researchers make informed decisions about appropriate sample sizes.

Transformers are widely used as a general-purpose substrate for learning complex correlations between a large collection of coupled variables, but their internal mechanisms have remained mysterious. We introduce a theory of a deep transformer as a mean-field interacting system that implements distributed inference, subject to constraints on communication, locality and depth. We show that such a system can exploit internal state representations ('function vectors') to infer a latent context variable at increasingly finer scales over its layers. In an in-context regression task, the theory predicts a non-trivial relationship between non-Gaussian, hierarchical structure in the latent context variable, and transformer depth. Predictions are tested using constrained linear attention transformers and demonstrate adaptive inference in deep architectures. Feedforward blocks and depth enable transformers to implement a much richer class of in-context learning algorithms than previously described.

Biophysical neuron models link measurements of neural activity to underlying cellular mechanisms. Yet, a central challenge is that the kinetics of many ion channels are poorly characterized, and practical simplifications -- omitting channels or reducing morphological detail -- introduce systematic gaps between model and biology. Bridging these gaps requires approaches that can flexibly discover unmodeled dynamics while preserving mechanistic interpretability. Here, we introduce a hybrid modeling framework that embeds neural ordinary differential equations into conductance-based biophysical models to capture unknown currents or mis-specified channel kinetics. By parameterizing the neural ODE in terms of voltage-dependent steady-state and time-constant functions, we recover interpretable gating dynamics directly from voltage recordings without assuming a functional form. We show that the hybrid model fits the gating kinetics of 2400 ion channel models and recovers unknown gating dynamics from single current-clamp recordings, generalizing to out-of-distribution stimulus regimes under realistic inputs and parameter misspecification. We also use our method to reduce a multicompartment model of a cortical neuron into a single-compartment hybrid model with a learned axial current, yielding up to an order of magnitude lower computational cost. Together, our results establish a plug-and-play framework for selectively replacing unknown components of conductance-based models with neural ODEs while preserving their mechanistic structure.

Living cells utilize condensates to spatially concentrate molecules in response to dynamic signals. For instance, nuclear condensates respond to oscillations in transcription factor levels in the nucleoplasm, including those involved in repairing multiple DNA breaks. To understand how oscillating signals affect condensates, we analyze a theoretical model using numerical simulations and analytical theory. While passive dynamics would drive all molecules into a single condensate, we find that sufficiently fast oscillations stabilize multiple droplets, allowing control of their sizes. We thus reveal a new behavior of chemically active droplets, which could be exploited in synthetic applications.

Biomolecular sequence models are increasingly reused outside the studies in which they were introduced, but public checkpoints rarely preserve the execution context needed to inspect source-defined behavior, adapt models to new assays, compare models under shared task definitions or deploy biological predictions. MultiMolecule is an open-source Python ecosystem that turns heterogeneous RNA, DNA and protein sequence-model releases into complete, source-checked model-family implementations with shared loading, workflow and prediction interfaces. The Resource state reported here includes 53 complete model-family implementations with 112 standardized model checkpoints, together with 16 curated dataset resources released through 39 public dataset repositories and 10 user-facing prediction pipelines. Standardized components are linked to source provenance, conversion or preparation code, source-reference checks, Extended Data summaries and public documentation, allowing users to inspect what was standardized, what behavior was checked and how each component enters training, evaluation, inference or deployment. By shifting reuse from repository-specific checkpoints to executable implementations connected to standardized checkpoints, curated datasets, Runner workflows and biological prediction pipelines, MultiMolecule provides common infrastructure for preserving source-defined model behavior, adapting models to new assays, enabling controlled evaluation and deploying biomolecular predictions.

Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins. These models are built through post-training, yet how each stage shapes reasoning and generalization remains poorly understood. We study when post-training improves performance and when it induces over-specialization. Across genomics, transcriptomics, and proteins, we train and evaluate more than 100 biological reasoning models under controlled variation in backbone, continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL), measuring both in-domain (ID) and out-of-domain (OOD) performance. We find that each post-training stage reshapes generalization in a distinct way rather than contributing uniform gains. CPT improves downstream performance by aligning models with biological language. SFT consistently increases ID performance but causes OOD performance to peak early and decline as models fit the training distribution. RL, when applied to strong SFT checkpoints with aligned rewards, improves OOD performance and partially recovers generalization. These results show that biological reasoning does not improve monotonically with additional supervision or compute. Instead, performance depends on how training stages are composed. Under fixed post-training budgets, the strongest ID-OOD trade-off comes from brief SFT, larger RL allocations, and asymmetric adaptation capacity across stages.

This paper addresses real-time state estimation for a nine-compartment nonlinear COVID-19 epidemic model with two co-circulating strains, a super-spreader subpopulation, vaccination with waning immunity, hospitalization, and mortality. Time-varying transmission and vaccination rates are known inputs from a companion calibration, leaving the reconstruction of all nine states from three routinely reported observables: hospitalizations H, fatalities F, and vaccinated stock V. The contributions are theoretical rather than in the filter recursion. First, a Lie-derivative observability analysis yields, via a six-step derivation, the closed-form determinant |det(O9)| = delta_w * gamma_a^2 * kappa * rho2 * w1^2 * (delta_i - delta_p)^2 * |r1 - r2|, showing the level-2 codistribution is rank-deficient at the calibrated symmetric parameters (delta_i = delta_p, r1 = r2); the third Lie derivative restores full rank 9, with r2 the symmetry-breaking parameter. Second, an EKF is designed on the Euler-discretized dynamics with a closed-form 9x9 Jacobian and Joseph covariance update. Third, local exponential mean-square boundedness of the error is proved as a full theorem via the Reif-Gunther-Yaz-Unbehauen hypotheses, exploiting the bilinear drift and linear output to obtain a global-radius quadratic remainder bound that extends to bilinear-drift, linear-output systems. Fourth, the noise covariances are designed from calibration residuals and assessed by NEES and innovation-whiteness tests. All experiments use synthetic measurements from the calibrated model, so reported RMSE values (0.07%-2.72%) are methodology benchmarks, not predictive accuracy. A parameter-mismatch study shows measured and directly-coupled channels stay accurate under model error up to +/-30% while indirectly observed states degrade gracefully. The framework provides the state-feedback basis for future Model Predictive Control.

This study examines how deficiencies in one brain connectome modality propagate to the other, using the Krakencoder as a simulation framework. Structural and functional connectomes from 702 healthy participants in the Human Connectome Project were analyzed, with the impact of each of the Yeo-7 functional networks assessed separately. Seven scenarios were considered, each involving the removal of a single network while the remaining networks were preserved. The resulting perturbations in cross-modal predictions were quantified using three complementary metrics: KL divergence on eigenvalue spectra, Frobenius norm, and Wasserstein distance. In addition, the persistence of sex-specific information within the predicted connectomes was evaluated. Across all metrics and both prediction directions, the Default Mode Network produced the largest perturbations, whereas the Somatomotor network yielded the smallest. Sex differences in network-level perturbation signatures were subtle, with the best result being an accuracy of 66.09% from connectomes predicted under network-removal conditions. In contrast, connectomes predicted from intact inputs achieved substantially higher sex classification accuracy, reaching up to 84.76%. These findings confirm that full predicted connectomes retain considerably more sex-discriminative information than perturbation-derived signatures alone.

Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood. Existing mechanistic interpretability methods based on sparse autoencoders and transcoders primarily focus on protein representation learning models and do not capture the computation required for autoregressive generation. Here, we introduce ProGenMech, a mechanistic interpretability framework for generative protein language models that extends cross-layer transcoders (CLTs) to ProGen3, a sparse Mixture-of-Experts model trained for both causal generation and span infilling. Unlike per-layer approaches, CLTs reconstruct each layer using sparse latent variables from all preceding layers, enabling faithful recovery of inter-layer generative computation. We further develop a zero-shot circuit discovery framework to identify sparse latent circuits responsible for protein generation and fitness prediction. In causal generation and zero-shot fitness estimation tasks, ProGenMech outperforms local transcoder baselines in recovering ProGen3's probability distribution and functional scoring behavior, while matching the original model's generative distribution in span infilling tasks. Moreover, the recovered circuits reveal biologically meaningful motifs and functional regions associated with conserved sequence patterns and protein fitness landscapes, establishing a foundation for interpretable and steerable protein generation.

Public neurophysiological datasets are increasingly accessible but remain hard to reuse: turning one into a trained model still takes thousands of lines of code for download, loading, format repair, windowing, and evaluation, and a dataset that meets metadata standards can still fail to load. EEG-Dash is a software resource that catalogues 791 publicly archived recordings (39,778 participants, over 86,051 hours) spanning electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG (iEEG), electromyography (EMG), and functional near-infrared spectroscopy (fNIRS) from the OpenNeuro and NEMAR archives. It exposes each dataset as an importable, queryable class that preserves signal attributes and loads into machine-learning workflows without custom code, delegating signal handling to MNE-Python, windowing to Braindecode, and format compliance to the official Brain Imaging Data Structure (BIDS) validator. A metadata-first registry adds semantic search, a format-repair layer, automatic dataset-level tags drawn from each source publication, and a feature-extraction framework. The catalogue, with per-record loadability and compliance metadata, supports benchmarking, model development, and cross-dataset analysis.

Aligning neural activity across subjects offers the promise of discovering shared computational principles and generalizable decoders. However, traditional alignment methods require shared stimuli across subjects, a constraint that limits applicability to naturalistic paradigms with limited or non-overlapping data. We introduce a Multi-Encoder-Decoder Variational Autoencoder (MED-VAE) that achieves cross-subject alignment without shared stimuli by anchoring representations to a common scaffold provided by a pretrained ANN. Using the Natural Scenes Dataset, we show that MED-VAE creates common latent spaces with superior semantic organisation, achieving higher cross-subject alignment than common methods while maintaining robust generalisation to held-out stimuli where traditional methods degrade. Reconstructing from these common spaces back to each subject's original neural space, MED-VAE preserves equal stimulus-driven signal in its cross-subject latent space. Finally, we show that this superior alignment directly enables cross-subject neural prediction, as demonstrated via cross-subject image decoding. In summary, we introduce a framework to identify generalisable common subspaces for cross-subject predictions and downstream tasks, demonstrated here for visual cortex responses to static images.

Since Schrodinger's \emph{What Is Life?}, the physical basis of biological organization has been understood in terms of the interplay between matter, energy, and information. Subsequent developments in molecular biology, information theory, nonequilibrium thermodynamics, and evolutionary theory have clarified how hereditary information is stored, maintained, and modified through natural selection. Here, we extend this program by asking what minimal physical principles are required for adaptable life. We propose six postulates governing adaptive living systems: the existence of an entropy source, longevity of information, fast response to environmental change, repeatable operation, energetic efficiency, and networks of multiple interacting switches. These principles are introduced as a minimal foundation for biological information processing and adaptation. We examine their implications and compare them with observations across multiple levels of biological organization, including genetic inheritance, epigenetic regulation, cellular signaling, neural computation, metabolic networks, and ecological systems. The resulting framework suggests that adaptability emerges from the interplay of energy flow, information storage, information processing, and natural selection in systems maintained far from thermodynamic equilibrium. Although the proposed principles are qualitative and not yet predictive, they provide a unified perspective on the physical constraints governing adaptive behavior and offer a starting point for the development of a quantitative theory of adaptable life.

Evolving populations both respond to and reshape their environments, making fitness landscapes dynamic rather than static. We present a minimal eco-evolutionary model that couples replicator dynamics for a population density with a regenerating resource-driven landscape through a single environmental sensitivity parameter. This allows evolving populations to generate and ride self-induced selection gradients, enabling directed motion in trait space even on initially flat landscapes. Our analysis reveals sustained oscillations, chaotic dynamics, and evolutionary branching. To explain these, we derive reduced dynamical equation that extend Fisher's fundamental theorem to deformable landscapes by incorporating curvature-driven variance dynamics and environmental feedback. Together, these results show how populations actively reshape and self-propel themselves on regenerating landscapes.

Assembly theory is an experimental and theoretical framework that introduces a metrological approach to detecting life, with potential applications across diverse substrates. Its two central observables are assembly index and copy number. The assembly index is the minimum number of joining operations required to construct an object from its elementary parts; for molecules, it can be measured using mass spectrometry, infrared spectroscopy, and NMR. Copy number is the abundance of a given distinguishable object within a sample. A key empirical result of the theory is that high assembly index combined with high copy number constitutes a signature that cannot arise abiotically, and this has been validated experimentally in application to molecular biosignatures. The foundational theoretical concept underlying these results is the assembly space, which encodes the causal possibilities determinable from observed objects, with the assembly index the shortest path to them given the physical constraints of a given substrate. Here, we provide a generalized formalism to describe assembly spaces and tools for assembly index approximations. We begin by reviewing the applications of assembly theory across molecules, minerals and atmospheres, and then introduce a general, substrate-independent formal definition of assembly spaces and assembly indices. We develop a unified path hierarchy framework to clarify relationships among the various representations of assembly spaces and assembly paths that appear in the literature on molecular assembly. Finally, we show how formal grammar algorithms can be adapted to efficiently bound assembly index calculations and provide clarification on the utility of such approximations, with the goal to increase the accessibility of tools to explore this emerging area for a broader group of researchers across chemistry, biology, and complexity science.

Adaptation often requires the assembly of favorable combinations of mutations that are individually deleterious. As a result, populations may remain trapped in low-fitness genetic states even when higher-fitness genotypes exist. Recombination plays a dual role in this process because it can both generate and disrupt advantageous multilocus combinations. Previous work showed that the balance between selection and recombination determines whether populations cross fitness valleys or persist in low-fitness states associated with demographic decline. We study this problem in a three-locus model consisting of two selected loci and a recombination modifier locus. The modifier has no direct effect on fitness but alters the recombination rate between the selected loci, allowing recombination itself to evolve. We characterize the fixation states of the system and derive explicit conditions for the local stability of the low-fitness fixation set. Stability depends on selection strength, recombination among selected loci, recombination between the modifier and selected loci, and modifier composition. In the classical two-locus model, stability depends on a single recombination parameter. By contrast, the modifier model generates a continuum of fixation states whose stability varies with modifier frequency. Populations with identical selected-haplotype frequencies can therefore differ in stability solely because they differ in modifier composition. We further show that modifier polymorphism can either stabilize or destabilize the low-fitness state, depending on the relative magnitudes of modifier-dependent recombination rates. These results demonstrate that genetic variation affecting recombination alters evolutionary outcomes not only by changing the formation of favorable multilocus combinations but also by changing the stability of alternative evolutionary states.

Therapeutic peptides occupy a valuable design space between small molecules and biologics, but their development requires satisfying several competing constraints at once: solubility, hemolytic activity, and nonspecific surface fouling are governed by overlapping sequence features, so improving one property often degrades another. Computational design addresses this by pairing generative models with sequence-based property predictors, iteratively proposing and refining candidates. However, these components are typically wired together as monolithic scripts that are difficult to inspect, extend, or reuse, and they often refine sequences by natural-language reasoning rather than by tracking the evolving multi-property state of each candidate. We present Pepti-Agent, a closed-loop, peptide-specific framework that exposes generation, property prediction, and single-residue mutation as independently inspectable Model Context Protocol (MCP) tools. A large language model controller invokes these tools and consults live predictor output between calls, so refinement is guided by each sequence's current property profile rather than by language reasoning alone. Task-specific PeptideGPT models generate candidates, ProtBERT-based classifiers score solubility, hemolysis, and non-fouling, and two interchangeable mutation operators propose sequence edits. By recording a per-step trace of controller decisions, predictor outputs, and accepted mutations, Pepti-Agent offers a reproducible substrate for benchmarking multi-objective design strategies and for prioritizing candidates for experimental validation.

The effective reproduction number ($R_t$) is widely used to track epidemic dynamics in real time. The standard estimation framework uses "instantaneous $R_t$," defined via the renewal equation, which relates new infections to past infections through a generation interval distribution. Compartmental models like SEIR yield a seemingly distinct quantity, "mechanistic $R_t$," based on the effective contact rate and duration of infectiousness. We prove these two definitions are equivalent under homogeneous mixing, the standard assumption in compartmental modeling. We also derive the generation interval distribution implied by SEIR dynamics. A practical consequence is that generation intervals, often treated as assumption-light inputs to renewal equation estimators, in fact encode specific compartmental structure.

Motivation: BWA-MEM remains a popular short-read mapper especially for the purpose of variant calling. Several groups have accelerated this algorithm as it has been the performance bottleneck of many current workflows. However, constrained by the original design, these drop-in replacements could only achieve limited speedup. Breaking changes to BWA-MEM are required for further improvement. Results: We developed minibwa for aligning short and accurate long reads against a reference genome. It combines BWA-MEM variable-length seeding with minimap2 chaining and base alignment. It speeds up BWA-MEM2 further with additional prefetch for seeding, new heuristics to skip unnecessary mate rescue and reduced effort in highly repetitive regions where reads would anyway be wrongly mapped due to structural changes. Minibwa is about four times as fast as BWA-MEM and over twice as fast as BWA-MEM2 at comparable accuracy. It also natively supports directional bisulfite sequencing data to high mapping accuracy. Availability and implementation: https://github.com/lh3/minibwa

A common objection to artificial or simulated consciousness is that a simulated brain is no more conscious than simulated water is wet. We address this from the perspective of Intrinsic Computational Functionalism (ICF): if consciousness is computationally constituted, it depends not on externally imposed descriptions but on the computational structures a system physically realizes in virtue of its own causal-dynamical organization. In previous work we developed Canonical Functionalism as a mathematically precise special case of this anti-interpretivist program, identifying functional states by their complete future input-output roles under a fixed interface. Here we argue that this input-output construction, though important, is incomplete: as a behavioral boundary case of ICF, it makes lookup tables and unfolded systems that preserve the same boundary behavior canonically equivalent. A consciousness-relevant canonical representation must instead include internal mechanisms, interventions, and joint readouts belonging to the relevant intrinsic organization. We therefore define a mechanism-enriched canonical structure and use it to formulate Intrinsic Causal-Computational Realization (ICCR), a realization relation preserving physical implementation, intrinsic state individuation, transition structure, intervention profiles, and the relevant agent-body-world boundary. The central result is conditional: if conscious properties are invariants of intrinsic causal-computational organization, then any system satisfying ICCR realizes the same consciousness-relevant properties, whether biological, artificial, or simulated. We discuss objections including biological naturalism and integrated information theory. We conclude that to deny consciousness to a simulation, one must identify a consciousness-relevant intrinsic causal-computational structure that the simulation fails to realize.

Temporal Interference (TI) stimulation enables deep brain targeting, yet precise optimization tools for mouse models remain limited. We developed a computational optimization tool integrating mouse head modeling with the optimization algorithm to optimize stimulation strategies for predefined target regions. By balancing target intensity and spatial focality, the optimized strategy significantly outperformed empirical baselines. For the CA3-CA1 target, it achieved a 7-fold intensity increase (10.29 vs. 2.89 V/m) under iso-focality conditions. Conversely, for the Dentate Gyrus, it improved spatial confinement ($r_{0.5}$ reduced from 3.99 to 3.54 mm) while maintaining comparable intensity. Cross-model validation on a standardized Sim4Life phantom further confirmed the framework's robustness. This approach offers a powerful tool for enhancing the precision and reproducibility of preclinical TI stimulation studies.

A system of coupled oscillators provides a fundamental framework for modeling a wide range of physical and biological phenomena. In neuroscience, the central nervous system exhibits synchronized oscillatory activity with adjacent brain regions, giving rise to traveling wave dynamics for instance during sleep. Similarly, in the gastrointestinal system, neuromuscular cells coordinate their oscillations to generate propagating waves of slow wave activity. To estimate probability distributions of multivariate phase relationships, existing approaches typically rely on equilibrium thermodynamics, expressing the system in a Boltzmann form through a pairwise exponential family distribution. However, these assumptions are often violated in real-world systems, which are inherently dynamic and frequently transition between equilibrium and non-equilibrium regimes. To address this, we propose an efficient method for estimating the probability distribution of coupled oscillators that does not assume thermodynamic equilibrium. Using a Langevin dynamics-based construction, the approach enables accurate modeling even in non-equilibrium regimes. The maximum likelihood estimation method is shown to have a closed form algebraic solution in the high sampling rate regime, a condition commonly satisfied by modern data acquisition systems, which makes it readily applicable in practice. We demonstrate its robustness on simulated data, where it outperforms existing approaches in non-equilibrium settings, and further illustrate its utility for characterizing dynamic brain traveling waves in response to brain stimulation and in hypothesis testing within the context of electrophysiologic recordings of the human stomach.

How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning. Here, we leverage data released through the Machine Intelligence from Cortical Networks (MICrONS) program--a functional connectomics resource spanning multiple areas of mouse visual cortex, in which dense calcium imaging is co-registered with high-resolution electron microscopy reconstruction from the same animal--to build biologically grounded recurrent neural networks. Using neuronal spatial coordinates, anatomical connectivity, and function-derived relationships from nearly 12,000 coregistered excitatory neurons, we initialize recurrent weights and impose communication-aware spatial constraints during learning. Across three cognitive decision-making tasks, networks constrained by cortical structure and function consistently outperform baseline and partially constrained models. Functional weight initialization provides the largest gain, while real spatial embedding yields robust additional improvements across conditions. These biologically grounded networks also develop low-entropy, modular, and small-world organization, and retain strong performance even when recurrence is restricted to positive weights. Together, our results show that the machinery of cortex--its geometry, wiring, and functional structure--can be harnessed as a powerful inductive basis for building recurrent networks that learn more effectively while converging toward key organizational principles of biological computation.

Boolean models are widely used to characterize the dynamics of gene regulatory networks. However, their coarse state discretization limits their ability to capture complex continuous dynamics and continuous parameter dependencies. In this paper, we present a rigorous mathematical framework that embeds monotone Boolean models into a broader class of multilevel combinatorial models, which in turn embed into the Dynamic Signatures Generated by Regulatory Networks (DSGRN) methodology. We define the DSGRN parameter graph, which encodes the notion of parameter adjacency and is used to map Boolean functions to specific nodes within the DSGRN parameter space. We prove that these multilevel discrete update functions act as a multilevel refinement of monotone Boolean models. We demonstrate that purely Boolean models systematically underestimate network dynamics by missing crucial intermediate behaviors such as higher-order multistability and stable periodic orbits. We show that the DSGRN framework efficiently captures a strictly richer set of dynamics consistent with ordinary differential equations (ODEs), providing a mathematically rigorous and computationally viable bridge between discrete and continuous network modeling.

Modeling growth in bacterial cells is a major issue in systems and synthetic biology. Despite several growth rate functions proposed in the literature, most focus on nutrient composition without explicitly accounting for the possible perturbation provided by the expression of recombinant genes, an effect known as cell load or burden. On the other hand, mathematical models that attempt to provide mechanistic details on the phenomena, leveraging ribosome partitioning and nutrient availability, are generally too detailed and complex to be easily applied to the rational design of synthetic genetic circuits. A bottom-up approach is adopted herein to identify and analyze the minimal model structure, thereby unveiling the fundamental role of negative feedback in ribosomal synthesis in predicting the effects of cell load on both gene expression and growth rate. Indeed, to ensure cellular efficiency, ribosome synthesis must be finely regulated. While an increased number of ribosomes generally enhances protein production and cellular performance, their synthesis incurs a high energetic cost. For this reason, cells have evolved mechanisms to tightly control ribosome synthesis, avoiding unnecessary accumulation. One of the key regulatory strategies, usually neglected in previous cell models, involves a negative feedback loop that modulates the production of ribosomal components. This feedback ensures that ribosomes are produced only in the amount strictly needed, balancing functionality and energy expenditure. This work evaluates the individual contribution of this feedback under heterologous expression conditions using minimal gene-circuit models, explicitly linking ribosome allocation, hidden couplings between protein synthesis levels, and growth rate.

Genetic evidence is enriched among approved drug targets: in an observational analysis of 26,278 target-disease pairs from Open Targets and ChEMBL, targets with any genetic association had a 3.25-fold higher approval rate than those without (OR = 3.25, 95% CI 2.79-3.79, p = 1.91e-42). A target-level analysis accounting for non-independence of pairs sharing the same gene gave OR = 2.79 (bootstrap 95% CI 2.22-3.53); the oncology pair-level OR of 6.72 attenuates to 2.71 at the target level, illustrating how non-independence inflates area-specific estimates. The enrichment replicated in post-2015 approvals (OR = 3.51, p = 1.72e-8). Feature ablation across six evidence types revealed that literature mining alone accounts for most classifier performance (AUPRC = 0.099 versus 0.109 for all features), consistent with temporal leakage from post-approval publications. Excluding literature, remaining evidence types retain above-baseline signal (AUPRC = 0.084, 1.63x baseline). Sensitivity analyses bracket the pair-level OR between 3.25 and 4.93. Genetic evidence alone yields only a 1.0-percentage-point absolute AUPRC gain and the best model has poor calibration; the classifier has limited practical predictive value. We catalogue 1,433 genetically supported Phase 1/2 pairs as a hypothesis-generating resource. All findings are observational.

We construct a three-layer reaction-diffusion model of an autocatalytic chemical system in which raw molecules ($a_i$), catalytic proteins ($p_l$) and large RNA/protein ``genes'' ($W_p^{(k)}$) interact through a mass-action stoichiometry tensor $\mathrm{Coef}_{ijk}$ whose magnitude is modulated by the fold-stable activity of the largest polymers.Mass-action is broken by an $ε$-noise term so that the system is nonequilibrium. We compute the total entropy production $σ(t)$, the genetic Shannon entropy $S_\mathrm{gene}$ and the thermodynamic uncertainty relation (TUR) and thermodynamic speed limit (TSL) bounds on growth and evolution rates. The hierarchical model exhibits the expected co-occurrence of $σ_\mathrm{env}\!\uparrow$ and $S_\mathrm{gene}\!\downarrow$ predicted by Schrodinger's negentropy argument and reformulated as maximum-entropy-production-principle (MEPP)-driven adaptation. In contrast to a single kinetic-proofreading-like cycle, whose TUR products of $\sim 5$, matching the experimentally reported regime of the ribosome.The hierarchical model's TUR product sits $10^4$-$10^5$ above the universal bound of 2, and the TSL ratio sits $10^6$-$10^8$ above its bound of 1. And scaling number of molucules leaves the looseness intact for the hierarchical model but tightens it monotonically with particle number for the minimal model. We close by drawing an explicit correspondence between the autocatalytic system and diffusion-model training: $a_\mathrm{ext} \to a$ flux $ \Leftrightarrow $ data-information flow, $ \tanh(βWp - θ) $ $\Leftrightarrow$ score network, replication noise $\Leftrightarrow$ forward-diffusion noise, $ S_\mathrm{gene} \searrow $ $\Leftrightarrow$ $ H[q(θ|\mathcal{D})] \searrow $. All code and figures are available https://github.com/xiangze/DiverseCells/Hier_Autocatalysis

Do LLMs have emotions? A recent paper from Anthropic reports finding internal representations of emotion concepts in Claude Sonnet 4.5, concluding that the LLM has 'functional emotions.' We evaluate this claim against what is known about how emotions actually function in biological systems. We argue that emotions serve two core functions: the context-sensitive interpretation of situations, and the reorganization of processing across multiple systems in response to those interpretations. The Anthropic findings offer partial support for the first function, though the consistent, discrete emotional representations identified in Claude sit uneasily with affective neuroscience findings that human emotion is characterized by variable rather than uniform neural signatures. On the second function, the evidence is mixed: Claude's representations modulate output without producing the dynamic reorganization of attention, decision speed, and motivational state that defines emotion in biological systems. We close by proposing what it would take for an LLM to have emotions.

Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings. Designing descriptors that are both expressive and broadly applicable, however, remains challenging. We study persistent homology (PH) as a general-purpose representation for MD and introduce the masked Flood complex, a protein-tailored modification of a recently introduced simplicial complex construction that emphasizes inter-residue structure at low computational cost. Vectorized persistence diagrams then provide information-rich, geometry-aware summaries of protein conformations, which we evaluate on protein class prediction, frame-level observable regression, and Markov state model (MSM) estimation from learned low-dimensional coordinates in a single shared representation space. Results on the mdCATH dataset show that PH-based descriptors are competitive across tasks, with masked Flood PH yielding the most consistent overall performance. Further, when using topologically-informed MSMs as a drop-in replacement within the recent MarS-FM framework for generative modeling of protein conformations, we obtain consistently better ensemble statistics than MSMs based on physical observables. Finally, we explore the transferability of the generative model to qualitatively different, fast folding, proteins.