Biology subjects

Dixit, P. D.

Publications and source records attributed to Dixit, P. D..

16 recordsLinked to original sources

bioRxiv2026

Kinetic proofreading decouples signal strength and range in paracrine gradient formation

Spatial gradients of signaling molecules pattern multicellular tissues with high precision. The canonical synthesis-diffusion-degradation (SDD) framework imposes a tradeoff on these gradients: ligand-receptor interactions that generate downstream signaling activity are also responsible for consuming the ligand. Correspondingly, at a fixed ligand synthesis rate, raising ligand-receptor affinity increases local signal strength at the expense of spatial range, and lowering it extends range at the expense of strength. Recent live-imaging measurements appear to violate this seemingly fundamental tradeoff, with low-affinity ligands of the epidermal growth factor receptor (EGFR) diffusing farther and driving spatially broader signaling activity compared to high-affinity ligands. Here we explain these observations with a model of multi-step ligand processing at the receptor, and show that the activity-range tradeoff is a consequence of receptor architecture rather than a physical necessity. When receptors process ligand through a multi-step phosphorylation cascade with kinetic-proofreading-like resetting, the states that generate activity decouple from those that consume ligand, and signaling activity and range increase together over a finite window of ligand residence time. This lets cells tune how far a signal travels independently of how strongly it acts through tuning signaling parameters. Realistic EGFR parameters place the low-affinity ligands in this window. Because multi-site phosphorylation and preferential degradation of the active receptor recur across multiple receptor families, kinetic proofreading may be a general strategy for controlling signaling range. Significance StatementCells coordinate by releasing molecules that bind receptors on neighboring cells. For a fixed supply, how strongly a signal acts and how far it spreads are locked together: tight binding gives a strong response but the molecule is captured near its source, while weak binding spreads farther but signals feebly. Yet recent imaging of epidermal growth factor receptor ligands shows the opposite: weak binders activate a broader field of cells. We show this limit reflects how receptors read the signal, not physics. A receptor that processes a bound molecule through several steps, and can release it partway, separates the states that signal from those that destroy it. Cells, and engineers, can then set a signals reach independently of its strength.

biophysics↗

bioRxiv2026

Metagenome-scale Modeling to Assess Microbiome Metabolic Complementarity for Precision Microbiota Transplantation Therapies

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable, in part because existing computational models do not fully capture the metabolic compatibility between donor and recipient communities. Here, we present a metagenome-scale metabolic modeling framework that quantifies metabolic niche complementarity between donor and recipient microbiomes to predict transplantation outcomes. Using MICOM-derived community metabolic models, we show that donor taxa whose metabolic flux profiles are more dissimilar from the recipient community engraft at significantly higher rates in both murine and human FMT cohorts. In a human IBS trial, metabolic models accurately predicted post-FMT community composition via leave-one-out cross-validation and recapitulated disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to demonstrate a platform for personalized donor screening. This screen identified super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition, as quantified by a flux-derived ecological network balance index that strongly predicted engraftment potential. This framework provides a mechanistic, scalable tool for rational donor-recipient matching that could guide personalized microbiome-based therapies.

microbiology↗

bioRxiv2026

Endocytosis shapes extracellular chemical gradients in autonomous cell-cell attraction

Receptor-mediated ligand endocytosis is traditionally viewed as a negative feedback mechanism for signal attenuation. Here we show that ligand removal can paradoxically enhance directional information in autonomous cell-cell attraction. Many cell systems migrate toward one another in the absence of externally imposed gradients, implying that secretion, diffusion, and uptake must themselves generate usable directional cues. We develop a surface-resolved theory of a finite-sized detector exposed to a nearby source and derive analytical expressions for the steady-state ligand field. The resulting concentration profiles are governed by a single dimensionless Damkohler number that compares receptor-mediated endocytosis to diffusive ligand transport. Increasing ligand removal lowers extracellular ligand concentrations and reduces absolute concentration differences across the detector surface, but preferentially enhances relative surface anisotropy. Thus, destroying the signal can increase the usable information encoded in relative gradients. Incorporating nonlinear downstream processing reveals a tradeoff between contrast enhancement and signal depletion that yields a well-defined optimal endocytosis rate, in a regime consistent with experimentally measured receptor internalization kinetics. These results recast receptor-mediated endocytosis as an extracellular information-processing mechanism that reshapes self-generated gradients to enhance directional information.

biophysics↗

bioRxiv2025

Niche dimensionality drives microbial community structure

Niche dimensionality links environmental complexity to ecosystem structure. Although niche theory is often invoked in investigations of microbiomes, most models assume very high-dimensional coexistence, effectively sidestepping the role of dimensionality. However, direct estimates of niche dimensionality of microbiome have been lacking. Here we use joint species distribution modeling (JSDM) to infer niche dimensionality from relative abundance data alone. In paired 16S rRNA-metabolomics datasets, inferred dimensionality closely tracked the complexity of the metabolic environment, validating our abundance-only approach. Across nearly 200 human gut microbiome studies, lower dimensionality coincided with greater inter-species competition (metabolic niche overlap) and reduced biodiversity, consistent with the macroecological niche dimensionality hypothesis. Consumer/resource simulations reproduced these empirical relationships when both species-intrinsic metabolic tradeoffs and species-extrinsic environmental tradeoffs were imposed, with the latter dominating. Dimensionality was reduced in stressed or diet-simplified microbiomes and correlated broadly with ecological stress markers; negatively with prevalence of generalists and positively with microbial load. Together, these results establish niche dimensionality as a measurable and previously overlooked driver of microbial community structure.

ecology↗

bioRxiv2025

Decay in transcriptional information flow is a hallmark of cellular aging

Aging is marked by the progressive loss of cellular function, yet the organizing principles underlying this decline remain unclear. Although molecular fingerprints of aging are diverse, many converge on disruption of the interrelated and overlapping communication networks that coordinate molecular activity. Here, we apply information theory to quantify age-related corruption in gene regulation by modeling regulatory interactions between transcription factors (TFs) and their target genes (TGs) as a multi-input multi-output communication channel. Using an analytically tractable probabilistic model and single-cell RNA-sequencing data from multiple tissues, we find that the mutual information (a measure of information transfer) between TFs and TGs declines with age across all ten tissues analyzed, establishing loss of regulatory information transmission as a hallmark of aging. Structural analysis of the regulatory network reveals that aging degrades communication primarily through input distribution mismatch, reflecting a loss of coordinated TF activity, rather than channel corruption, or the inability of TFs to reliably activate or inhibit their targets. This mismatch is caused by increased network centralization and loss of stabilizing feedback motifs, leading to reduced robustness to random perturbations. Notably, in silico upregulation of a small set of TFs restores youthful information transfer and gene expression levels, suggesting that targeted reinforcement of key regulatory nodes may rejuvenate aged networks.

systems biology↗

bioRxiv2024

Receptor polarization through localized activity and global sensitization

Directional sensing enables eukaryotic cells to detect spatial gradients of extracellular ligands by comparing local signaling activity to an internal set point. While this capability is typically attributed to signaling pathways downstream of cell surface receptors, we present a reaction-diffusion model to argue that receptors themselves can perform directional sensing via a mechanism we term Localized Activity and Global Sensitization (LAGS). LAGS integrates common receptor signaling motifs: lateral diffusion, basal activity, and active receptor degradation. Without diffusion, receptor activity locally adapts to a ligand-independent set point via degradation-mediated integral feedback, leading to receptor depletion in ligand-rich regions. The introduction of lateral diffusion redistributes primarily ligand-free receptors across the cell surface, creating a spatial mismatch between local activity and feedback. This mismatch drives polarized receptor activity relative to the set point, while rapid ligand equilibration and receptor degradation protects it from diffusion. The model predicts an optimal diffusion coefficient that maximizes receptor polarization, and shows that cells respond to relative - not absolute - changes in ligand levels. Cooperative receptor interactions further amplify the polarized response. Finally, stochastic analysis identifies an optimal set point that maximizes the signal-to-noise ratio in polarization. A survey of kinetic parameters across diverse receptor systems supports LAGS as a general mechanism for receptor-mediated directional sensing. Significance statementCells navigate their spatially heterogeneous environments by detecting spatial gradients of external signals - a process known as directional sensing. While traditionally attributed to downstream signaling pathways, we argue that cell surface receptors can themselves perform directional sensing by leveraging lateral diffusion, basal activity, and active receptor degradation. Using a reaction-diffusion model, we identify a general mechanism of directional sensing which we call Localized Activity and Global Sensitization (LAGS). In LAGS, degradation-driven integral feedback and receptor diffusion together generate robust, spatially polarized receptor activity. Our results suggest that directional sensing can emerge directly from receptor-level dynamics, revealing a broadly applicable strategy encoded in the biophysics of diverse signaling systems.

biophysics↗

bioRxiv2024

Tunable ligand discrimination via kinetic sorting

Signaling receptors often encounter multiple ligands and haven been shown to respond selectively to generate appropriate, context-specific outcomes. At thermal equilibrium, ligand specificity is limited by the relative affinities of ligands for their receptors. Here, we present a non-equilibrium model in which receptors overcome thermodynamic constraints to preferentially signal from specific ligands while suppressing others. In our model, multi-site phosphorylation and active receptor degradation act in concert to regulate ligand specificity, with receptor degradation providing a previously under-appreciated layer of control. Here, ligand-bound receptors undergo sequential phosphorylation, with progression restarted by ligand unbinding or receptor turnover. High-affinity complexes are kinetically sorted toward degradation-prone states, while low-affinity complexes are sorted towards inactivated states, both limiting signaling. As a result, network activity is maximized for ligands with intermediate affinities. This mechanism explains paradoxical experimental observations in receptor tyrosine kinase (RTK) signaling, including non-monotonic dependence of signaling output on ligand affinity and kinase activity. Given the ubiquity of multi-site phosphorylation and ligand-induced degradation across signaling receptors, we propose that kinetic sorting may be a general non-equilibrium ligand-discrimination strategy used by multiple signaling receptors.

systems biology↗

bioRxiv2023

Phenomenological modeling reveals the emergent simplicity of host-associated microbiomes

A key step towards rational microbiome engineering is in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously capture compositions of host-associated microbiomes and host phenotypes. To that end, we present a generative model based on the mechanistic consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (inferred latent variables) while species resource preferences remain conserved. The same latent variables are used to model phenotypic states of hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address three salient questions in host-associated microbial ecologies: (1) which host phenotypes maximally constrain the composition of the host-associated microbiomes? (2) how context-specific are phenotype/microbiome associations, and (3) what are plausible microbiome compositions that correspond to desired host phenotypes? Our approach aids the analysis and design of microbial communities associated with host phenotypes of interest.

systems biology↗

bioRxiv2023

The ability to sense the environment is heterogeneously distributed in cell populations

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information theoretic framework to quantify the distribution of sensing abilities from single cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an "average cell". We verify these predictions using live cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells sensing abilities. This information theoretic framework will significantly improve our understanding of how cells sense in their environment.

systems biology↗

bioRxiv2022

GENERALIST: An efficient generative model for protein sequence families

Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting-related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. To that end, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. Compared to state-of-the-art methods, GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike other methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. GENERALIST will be an important tool to study protein sequence variability.

biophysics↗

bioRxiv2022

SMbiot: A Shared Latent Model for Microbiomes and their Hosts

The collective nature of the variation in host associated microbial communities suggest that they exhibit low dimensional characteristics. To identify these lower dimensional descriptors, we propose SMbiot (pronounced SIM BY OT): a Shared Latent Model for Microbiomes and their hosts. In SMbiot, latent variables embed host-specific microbial communities in a lower dimensional space and the corresponding features reflect controlling axes that dictate community compositions. Using data from different animal hosts, organ sites, and microbial kingdoms of life, we show that SMbiot identifies a small number of host-specific latent variables that accurately capture the compositional variation in host associated microbial communities. By using the same latents to describe hosts phenotypic states and the host-associated microbiomes, we show that the latent space embedding is informed by host physiology as well as the associated microbiomes. Importantly, SMbiot enables the quantification of host phenotypic differences associated with altered microbial community compositions in a host-specific manner, underscoring the context specificity of host-microbiome associations. SMbiot can also predict missing host metadata or microbial community compositions. This way, SMbiot is a concise quantitative method to understand the low dimensional collective behavior of host-associated microbiomes.

systems biology↗

bioRxiv2021

EMBED: a low dimensional reconstruction of gut microbiome dynamics based on ecological normal modes

Dimensionality reduction can offer unique insights into high dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa level are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic non-linear tensor factorization approach. Similar to normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. A very small number of ECNs can accurately approximate microbiome dynamics across multiple data sets. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.

systems biology↗

bioRxiv2020

SiGMoiD: A superstatistical generative model for binary data

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers identification of constraints and are computationally expensive to infer when the number of variables is large (N [~]100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same bath whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Notably, unlike current approaches, SiGMoiD allows to model collections of a very large number (N > 1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using several datasets spanning several time- and length-scales.

biophysics↗

bioRxiv2020

Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited

It is not well understood how physiological environmental conditions and nutrient availability influence cancer cell proliferation. Production of oxidized biomass, which requires regeneration of the cofactor NAD+, can limit cancer cell proliferation1-5. However, it is currently unclear which specific metabolic processes are constrained by electron acceptor availability, and how they affect cell proliferation. Here, we use computational and experimental approaches to demonstrate that de novo lipid biosynthesis can impose an increased demand for NAD+ in proliferating cancer cells. While some cancer cells and tumors synthesize a substantial fraction of their lipids de novo6, we find that environmental lipids are crucial for proliferation in hypoxia or when the mitochondrial electron transport chain is inhibited. Surprisingly, we also find that even the reductive glutamine carboxylation pathway to produce fatty acids is impaired when cancer cells are limited for NAD+. Furthermore, gene expression analysis of 34 heterogeneous tumor types shows that lipid biosynthesis is strongly and consistently negatively correlated with hypoxia, whereas expression of genes involved in lipid uptake is positively correlated with hypoxia. These results demonstrate that electron acceptor availability and access to environmental lipids can play an important role in determining whether cancer cells engage in de novo lipogenesis to support proliferation.

cancer biology↗

bioRxiv2018

Macroecological relationships in the dynamics of gut microbiota

The gut microbiome is now widely recognized as a dynamic ecosystem that plays an important role in health and disease1. While current sequencing technologies make it possible to estimate relative abundances of host-associated bacteria over time2, 3, the biological processes governing their dynamics remain poorly understood. Therefore, as in other ecological systems4, 5, it is important to identify quantitative relationships describing global aspects of gut microbiota dynamics. Here we use multiple high-resolution time series data obtained from humans and mice6-8 to demonstrate that despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, these patterns are highly similar to those previously observed across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations9-12 and the performance of publicly traded companies13. Specifically, we find power law relationships describing short- and long-term changes in gut microbiota abundances, species residence and return times, and the connection between the mean and variance of species abundances. The observed scaling relationships are altered in mice receiving different diets and affected by context-specific perturbations in humans. We use these macroecological relationships to reveal specific bacterial taxa whose dynamics are significantly affected by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding complex dynamics of microbial communities.

microbiology↗

bioRxiv2018

Quantification of human gut microbiota variability using replicate sampling and spike-in sequencing

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal dynamics remains an important challenge. Here we present DIVERS, a widely applicable method based on replicate sampling and spike-in sequencing that quantifies the contributions of temporal dynamics, spatial sampling variability and technical noise to the variances and covariances of absolute bacterial abundances. Using high resolution time series profiling, we apply DIVERS to the human gut microbiome. Our method reveals complex spatiotemporal dynamics of individual gut bacteria and unmasks key features of their behavior hidden from previous analyses.

microbiology↗