Biology subjects

Kutz, J. N.

Publications and source records attributed to Kutz, J. N..

12 recordsLinked to original sources

bioRxiv2025

Pervasive context-dependent effects in the genetic architecture of complex and quantitative traits revealed by a powerful multiparent mapping population in yeast

The genetic dissection of complex traits remains a major challenge in basic and biomedical research, but is essential for understanding the molecular pathways that shape phenotypic variation and for developing predictive models of trait and disease susceptibility. Here, we leverage a novel multiparent mapping population of budding yeast, CYClones, comprising 9,344 haploid strains derived from eight genetically diverse founders, to identify quantitative trait loci (QTL) and systematically investigate the genetic architecture of growth rates across ten environmental conditions. In total, we identified 349 QTL (ranging from 19 to 49 QTL per growth condition) that explained between 61% and 100% of narrow sense heritability across traits. The high power and resolution of CYClones revealed that growth traits exhibited distinct, condition-specific genetic architectures with extensive allelic heterogeneity, where a QTL was the result of multiple tightly linked causal variants. We also observed pleiotropy among QTL with complex, trait-dependent allele effects that are also consistent with allelic heterogeneity. Genetic complexity varied widely, with some traits showing nearly Mendelian architectures, while others were highly polygenic. Introgressed loci played a prominent role in the landscape of growth rate QTL, including one that is localized to a 2.4 kb interval in the PCA1 cadmium transporter and explains 72% of variation in cadmium resistance and another large-effect QTL that alters growth on galactose via complex non-additive interactions with the transcriptional regulator GAL3. In both cadmium and galactose conditions, we show that allelic variation at a small number of loci stratifies the population into regulatory or physiological subgroups, each with distinct genetic architectures, a phenomenon we term allele-dependent stratification. Collectively, our results provide novel insights into the genetics of growth rates in budding yeast, the architectural features of genetic complexity, and demonstrate that CYClones is a powerful platform for revealing the molecular basis of complex trait variation.

genetics↗

bioRxiv2025

Learning the bistable cortical dynamics of the sleep-onset period

Humans just dont fall asleep like a log - or step-function. Rather, the sleep-onset period (SOP) exhibits dynamic and non-monotonous changes of electroencephalogram (EEG) with high, and so far poorly understood, intra- and inter-individual variability. Computational models of the sleep regulation network have suggested that the transition to sleep can be viewed as a noisy bifurcation at a saddle point which is determined by an underlying control signal or "sleep drive". However, such models do not describe how internal control signals in the SOP can produce rapid switches between stable wake and sleep states, nor how these state-space changes are translated in the macroscopic EEG. Here, we propose a minimally-parameterized stochastic dynamical model, in which one slowly-varying control parameter drives the wake-to-sleep transition while exhibiting noise-driven bistability. We provide a procedure for estimating the parameters of the model given single observations of experimental sleep EEG data, and show that it can reproduce a wide variety of SOP phenomenology. Using the model to analyze a pre-existing sleep EEG dataset, we find that the estimated model parameters correlate with subjective sleepiness reports. These results suggest that the bistable characteristics of the SOP can serve as biomarkers for tracking intra- and inter-individual variability of sleep-onset disorders. Author summaryRecent neuroscience research has showed a growing interest in understanding the complexity of how we fall asleep. Electroencephalographic (EEG) recordings of the sleep onset period show all the hallmarks of a noise-driven bistable system, but there currently exists no computational model that can be fitted on experimental data to understand this behavior. In this paper, we propose a minimally-parameterized model, which dynamics corresponds to the motion of an noisy overdamped particle in a slowly tilting bistable landscape, as well as a way to fit it to an individuals sleep-onset EEG. We show that the fitted parameters of individual participants correlate with their subjective reports of sleepiness, suggesting that the model can capture important aspects of inter-individual variability, as well as provide potential biomarkers for sleep-onset disorders.

neuroscience↗

bioRxiv2025

Shallow Recurrent Decoders for Neural and Behavioral Dynamics

Machine learning algorithms are affording new opportunities for building bio-inspired and data-driven models characterizing neural activity. Critical to understanding decision making and behavior is quantifying the relationship between the activity of neuronal population codes and individual neurons. We leverage a SHallow REcurrent Decoding (SHRED) architecture for mapping the dynamics of population codes to individual neurons and other proxy measures of neural activity and behavior. SHRED is a robust and flexible sensing strategy which allows for decoding the diversity of neural measurements with only a few sensor measurements. Thus estimates of whole brain activity, behavior and individual neurons can be constructed with only a few neural time-series recordings. This opens up the potential for using non-invasive, or minimally invasive, measurements for estimating difficult to achieve, or invasive, large scale brain and neural recordings. SHRED is constructed from a temporal sequence model, which encodes the temporal dynamics of limited sensor data in multiple scenarios, and a shallow decoder, which reconstructs the corresponding high-dimensional neuronal and/or behavioral states. We demonstrate the capabilities of the method on a number of model organisms including C. elegans, mouse, zebrafish, and human biolocomotion.

neuroscience↗

bioRxiv2024

Human motion data expansion from arbitrary sparsesensors with shallow recurrent decoders

Advances in deep learning and sparse sensing have emerged as powerful tools for monitoring human motion in natural environments. We develop a deep learning architecture, constructed from a shallow recurrent decoder network, that expands human motion data by mapping a limited (sparse) number of sensors to a comprehensive (dense) configuration, thereby inferring the motion of unmonitored body segments. Even with a single sensor, we reconstruct the comprehensive set of time series measurements, which are important for tracking and informing movement-related health and performance outcomes. Notably, this mapping leverages sensor time histories to inform the transformation from sparse to dense sensor configurations. We apply this mapping architecture to a variety of datasets, including controlled movement tasks, gait pattern exploration, and free-moving environments. Additionally, this mapping can be subject-specific (based on an individuals unique data for deployment at home and in the community) or group-based (where data from a large group are used to learn a general movement model and predict outcomes for unknown subjects). By expanding our datasets to unmeasured or unavailable quantities, this work can impact clinical trials, robotic/device control, and human performance by improving the accuracy and availability of digital biomarker estimates.

systems biology↗

bioRxiv2023

Arousal as a universal embedding for spatiotemporal brain dynamics

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology--widely interpreted to reflect regionally-specific neural communication--are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

neuroscience↗

bioRxiv2023

Non-Stationary Dynamic Mode Decomposition

Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, DMD methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition (NS-DMD), that generalizes DMD by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.

neuroscience↗

bioRxiv2023

A machine learning approach to quantify individual gait responses to ankle exoskeletons

We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individuals response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), (p < 0.042)) than the Nominal model (median R2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

neuroscience↗

bioRxiv2023

Predicting the functional effect of compound heterozygous genotypes from large scale variant effect maps

BackgroundPathogenic variants in PHGDH, PSAT1, and PSPH cause a set of rare, autosomal recessive diseases known as serine biosynthesis defects. Serine biosynthesis defects present in a broad phenotypic spectrum that includes, at the severe end, Neu-Laxova syndrome, a lethal multiple congenital anomaly disease, intermediately in the form of infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end, as childhood disease with intellectual disability. However, because L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is highly actionable. MethodsRecently, our laboratory established a yeast-based assay for human PSAT1 function. We have now applied it at scale to assay the functional impact of 1,914 SNV-accessible amino acid substitutions. In addition to assaying the functional impact of individual variants in yeast haploid cells, we can assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. ResultsResults of our assays of individual variants (in haploid yeast cells) agree well with clinical interpretations and protein structure-function relationships, supporting the use of our data as functional evidence under the ACMG interpretation guidelines. Results from our diploid assay successfully distinguish patient genotypes from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements (in haploid yeast cells) to accurately predict the biallelic function (in diploid yeast cells) of ~1.8 million allele combinations corresponding to potential human genotypes. ConclusionsTaken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of a rare disease.

genetics↗

bioRxiv2022

The functional impact of 1,570 SNP-accessible missense variants in human OTC

Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare, but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Neonatal onset patients appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma and death, outcomes ameliorated by rapid diagnosis and treatment. Existing biochemical assays have limitations, including the sensitivity of the citrulline assays used in newborn screening panels. With prior knowledge of variant pathogenicity, DNA sequence-based diagnostics would provide an alternative screening method. Here, we develop a high throughput functional assay for human OTC and measure the impact of 1,570 variants, 84% of all SNP-accessible missense mutations. Our assay scores agree well with existing clinical significance calls, distinguishing known benign from pathogenic variants and variants with neonatal onset from late-onset disease presentation. Further, use of an intronless expression construct allows us to measure the impact of amino acid changes at splice sites independent of their effect on splicing, thereby separating the contribution of splicing and protein coding changes to aid the analysis of molecular mechanisms underlying pathogenicity. Finally, we assess the utility of our functional data on OTC variant curation by using the current ACMG/AMP guidelines to reclassify variants. Inclusion of our data as PS3/BS3 substantially improves variant interpretation. Thus, our dataset is of high clinical utility and illustrates the power of functional assays to inform interpretation of existing and novel genetic variation.

genetics↗

bioRxiv2020

A Data-driven Surrogate Model for Work Computation of a Periodically Forced Half-Sarcomere

Muscle force generation follows from molecular scale interactions that drive macroscopic behaviors and macroscopic processes that influence those at the molecular scale. A particuarly challenging issue is that models at the molecular level of organization are often quite difficult to apply to larger spatial scales. This is particularly true of moleuclar models driven by Monte-Carlo simulations. This challenge of multiscale dynamics requires methods to extract reduced order behaviors from detailed high-dimensional simulations. In this work we present a novel deterministic simulation method yielding accurate predictions of force-length behaviors of contracting muscle sarcomeres undergoing periodic length changes (work loops). The model maintains interpretability by tracking macroscopic state variables throughout the simulation while using data-driven representations of dynamics. Parameters of the data-driven dynamics are learned from trajectories from Monte-Carlo simulations of a half-sarcomere. Our method significantly reduces computational cost by tracking the state of the sarcomere in a course grained set of variables while maintaining accurate prediction of macroscopic level observables and time series for course grained variables. This allows for rapid sampling of the models output and builds towards the ability to scale to multiple-sarcomere simulations. Author SummaryWe develop a data-driven surrogate model for the dynamics of the half-sarcomere. This model achieves the same behavior with respect to force traces as more sophisticated Monte Carlo models at a substantially lower computational cost. The model is built by finding a course grained description of the full state space of the Monte Carlo simulation and learning dynamical models on the course grained space. Data-driven representations of the dynamics in the course grained space are trained using data from the full model. Data-driven models for forcing are also learned, and the result fed back into the dynamics. In doing so, the model seeks to replicate the effects of filament compliance on macro scale dynamics without explicitly tracking micro scale features. We withhold some input parameter regimes and demonstrate accurate reconstruction of course grained state and force traces using the data-driven model and given only knowledge of the initial condition and input. This work allows for faster computation of the forcing behavior of the half-sarcomere, as well as consistent representations of the course grained state variables. It is therefore promising as a step towards multi-sarcomere or even tissue scale models of skeletal muscle.

biophysics↗

bioRxiv2019

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

Transmission of dengue fever depends on a complex interplay of human, climate, and mosquito dynamics, which often change in time and space. It is well known that disease dynamics are highly influenced by a populations susceptibility to infection and microclimates, small-area climatic conditions which create environments favorable for the breeding and survival of the mosquito vector. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and adaptively in space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city-level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology, it may prove valuable for public-health decision making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.

epidemiology↗

bioRxiv2018

Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

neuroscience↗