Biology subjects

Maslov, S.

Publications and source records attributed to Maslov, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Survivorship Bias Explains Age-Dependent Extinction in Fossil Genera

Fossil genera exhibit pronounced age-dependent extinction: older genera are more likely to survive extinction events than younger ones. Many biological mechanisms have been proposed that could make lineages progressively more resistant to extinction through time. However, the same pattern can also arise from survivorship bias: as more vulnerable genera are progressively eliminated, the surviving pool becomes increasingly enriched in robust lineages, even if the properties of individual lineages never change. Using the Sepkoski marine genus compendium, we construct a quantitative model based solely on survivorship bias and ask whether additional age-dependent changes are required to explain the fossil record. This model with a single fitting parameter quantitatively reproduces the full set of age-conditioned survival probabilities, together with the overall dependence of extinction risk on genus age and the genus lifetime distribution. Allowing extinction resistance to change systematically through time yields no measurable improvement, indicating that, at least at the aggregate statistical level, explicit age-dependent changes in lineage properties are not required to explain the observed patterns of extinction. These results show that our minimal one-parameter model provides a quantitative null model for age-dependent extinction, against which proposed biological mechanisms can now be tested.

paleontology↗

bioRxiv2025

Resource ratio fluctuations drive the evolution of microbial metabolic strategies

Microorganisms employ diverse metabolic strategies to cope with environmental variability, yet the evolutionary conditions that favor specialists, hierarchical utilizers, or co-utilizers remain poorly understood. Here, we investigate how fluctuations in resource supply ratios drive the evolution of metabolic strategies. By simulating microbial community assembly in environments with variable resource ratios, we show that diverse metabolic strategies emerge as evolutionary optima in different fluctuation regimes. We find that environments with balanced resource supply select for specialists, whereas imbalanced environments select for generalists. As the magnitude of fluctuations in resource supply ratios progressively increases, generalist strategies smoothly shift from those of hierarchical utilizers to co-utilizers. We identify the duration of temporal niches as the mechanistic driver that selects for reduced lag times over maximal growth rates. These findings demonstrate how the temporal structure of resource availability can spontaneously generate the three distinct metabolic strategies observed in nature.

ecology↗

bioRxiv2025

Single-cell heterogeneity in interferon induction potential is heritable and governed by variation in cell state

Type I and III interferons (IFNs) are among the first lines of defense against viral infection, yet they are generally only produced by a tiny fraction of infected cells. Here, we show that cellular heterogeneity in IFN induction potential upon treatment with immunostimulatory RNA is not due to variability in sensing of stimuli but instead is shaped by heterogeneity in tonic cell signaling state. Using complementary single-cell approaches, we found that baseline variation in the c-Jun N-terminal kinase (JNK) and activator protein (AP)-1 transcription factor families correlated with IFNL1 expression predisposition. We further show that drug-based inhibition of JNK signaling virtually eliminates the innate antiviral response to immunostimulatory RNA. Finally, we show that single cell heterogeneity in IFN induction potential is heritable and stably maintained over numerous generations. Together, our study emphasizes the influence of intrinsic variability in cell state on innate immune regulation and IFN induction heterogeneity.

systems biology↗

bioRxiv2025

Protein Language Models Capture Structural and Functional Epistasis in a Zero-Shot Setting

Protein language models (PLMs) learn from large collections of natural sequences and achieve striking success across prediction tasks, yet it remains unclear what biological principles underlie their representations. We use epistasis, the dependence of a mutations effect on its sequence context, as a lens to probe what PLMs capture about proteins. Comparing PLM-derived scores with deep mutational scanning data, we find that epistasis emerges naturally from pretrained models, without supervision on experimental fitness. Raw model scores align with residue-residue contacts, indicating that PLMs internalize structural proximity. Applying a nonlinear transformation to bring model outputs onto the experimental scale, however, shifts the signal toward functional couplings between distant sites. These findings show that PLMs capture both structural and functional dependencies from sequence data alone, and that epistasis provides a powerful window into the biological principles embedded in their representations.

bioinformatics↗

bioRxiv2025

Evolutionary chemical learning in dimerization networks

We present a novel framework for chemical learning based on Com- petitive Dimerization Networks (CDNs)-- systems in which multiple molecular species, e.g. proteins or DNA/RNA oligomers, reversibly bind to form dimers. We show that these networks can be trained in vitro through directed evolution, enabling the implementation of complex learning tasks such as multiclass classification without digital hardware or explicit parameter tuning. Each molecular species functions analogously to a neuron, with binding affinities acting as tunable synaptic weights. A training protocol involving mutation, selection, and amplification of DNA-based components allows CDNs to robustly discriminate among noisy input patterns. The resulting classifiers exhibit strong output contrast and high mutual information between input and output, especially when guided by a contrastenhancing loss function. Comparative analysis with in silico gradient descent training reveals closely correlated performance. These results establish CDNs as a promising platform for analog physical computation, bridging synthetic biology and machine learning, and advancing the development of adaptive, energy-efficient molecular computing systems. Significance StatementThis study introduces a new paradigm for learning based on chemical reaction networks rather than digital circuits. Using Competitive Dimerization Networks (CDNs)--biomolecular systems in which species reversibly bind to form dimers--complex classification tasks are learned through in vitro directed evolution. This approach eliminates the need for digital hardware or gradient-based optimization, relying instead on intrinsic molecular dynamics for computation. The resulting chemical classifiers achieve high fidelity and robustness to noise, with performance comparable to that of gradient descent training. These findings establish CDNs as a scalable, energy-efficient platform for molecular computing, suggesting broad potential applications in diagnostics, biosensing, synthetic biology, and nanotechnology, where programmable, adaptive chemical systems could serve as alternatives to conventional electronic processors.

bioengineering↗

bioRxiv2025

Intrinsic OASL expression licenses interferon induction during influenza A virus infection

Effective control of viral infection requires rapid induction of the innate immune response, especially the type I and type III interferon (IFN) systems. Despite the critical role of IFN induction in host defense, numerous studies have established that most cells fail to produce IFNs in response to viral stimuli. The specific factors that govern cellular heterogeneity in IFN induction potential during infection are not understood. To identify specific host factors that license some cells but not others to mount an IFN response to viral infection, we developed an approach for analyzing temporal scRNA-seq data of influenza A virus (IAV)-infected cells. This approach identified the expression of several interferon stimulated genes (ISGs) within pre-infection cells as correlates of IFN induction potential of those cells, post-infection. Validation experiments confirmed that intrinsic expression of the ISG OASL is essential for robust IFNL induction during IAV infection. Altogether, our findings reveal an important role for IFN-independent, intrinsic expression of ISGs in promoting IFN induction and provide new insights into the mechanisms that regulate cell-to-cell heterogeneity in innate immune activation.

immunology↗

bioRxiv2024

noSpliceVelo infers gene expression dynamics without separating unspliced and spliced transcripts

Modern single-cell transcriptomics has revolutionized biological research, but because of its destructive nature, it provides only static snapshots. Computational approaches that infer RNA velocity from the ratio of unspliced to spliced mRNA levels can be used to predict how gene expression changes over time. However, information about unspliced and spliced transcripts is not always available and may change on a timescale too short to accurately infer transitions between cellular states. Here we present noSpliceVelo, a novel technique for reconstructing RNA velocity without relying on unspliced and spliced transcripts. Instead, it exploits the temporal relationship between the variance and mean of bursty gene expression using a well-established biophysical model. When evaluated on datasets describing mouse pancreatic endocrinogenesis, mouse and human erythroid maturation, and neuronal stimulation in mouse embryonic cortex, noSpliceVelo performed comparably or better than scVelo, a splicing-based approach. In addition, noSpliceVelo inferred key biophysical parameters of gene regulation, specifically burst size and frequency, potentially distinguishing between transcriptional and epigenetic regulation.

bioinformatics↗

bioRxiv2024

Emergent ecological advantage of sequential metabolic strategies in complex microbial communities

Microbes growing in fluctuating environments employ two key metabolic strategies: sequential (diauxic) utilization and co-utilization of nutrients. Most work has focused on understanding and comparing these strategies physiologically for the growth of single species, rather than ecologically for the assembly of complex natural communities. This is in part because of the lack of good metrics quantifying the fitness of different metabolic strategies in ecological contexts. Here, we present a new consumer-resource framework that incorporates dynamic proteome reallocation, and use it to compare the fitness of metabolic strategies during community assembly. We introduce two notions of fitness of a strategy in fluctuating environments: the time-averaged growth rate and the biomass-weighted prevalence of microbes using a given strategy. We find that sequential utilizers, although disadvantaged in pairwise competitions, gain a significant edge during community assembly -- an advantage that becomes more pronounced with increasing community diversity and the size of the species pool from which they are assembled. Low diversity communities resemble pairwise competitions and are dominated by co-utilizers, whereas high diversity, mature communities are dominated by the sequential utilizers. This shift is driven by two factors: the difference in lag times and the increased structural stability conferred by sequential strategies. Overall, our work provides several testable predictions about the co-occurrence patterns of microbes using different metabolic strategies.

ecology↗

bioRxiv2024

Higher-Order Interactions in Auxotroph Communities Enhance Their Resilience to Resource Fluctuations

Auxotrophs are prevalent in microbial communities, enhancing their diversity and stability--a counterintuitive effect considering their dependence on essential resources from other species. To address the ecological roles of auxotrophs, our study introduced a novel consumer-resource model that captures the complex higher-order interactions within these communities. We also developed an intuitive graphical and algebraic framework, which assesses the feasibility of auxotroph communities and their stability under resource fluctuations and biological invasions. Validated against experimental data from synthetic E. coli auxotroph communities, the model accurately predicted outcomes of community assembly. Our findings highlight the critical role of higher-order interactions and resource dependencies in maintaining the diversity and stability of microbial ecosystems dominated by auxotrophs.

ecology↗

bioRxiv2024

SPLICER: A Highly Efficient Base Editing Toolbox That Enables In Vivo Therapeutic Exon Skipping

Exon skipping technologies enable exclusion of targeted exons from mature mRNA transcripts, which has broad applications in molecular biology, medicine, and biotechnology. Existing exon skipping techniques include antisense oligonucleotides, targetable nucleases, and base editors, which, while effective for specific applications at some target exons, remain hindered by shortcomings, including transient effects for oligonucleotides, genotoxicity for nucleases and inconsistent exon skipping for base editors. To overcome these limitations, we created SPLICER, a toolbox of next-generation base editors consisting of near-PAMless Cas9 nickase variants fused to adenosine or cytosine deaminases for the simultaneous editing of splice acceptor (SA) and splice donor (SD) sequences. Synchronized SA and SD editing with SPLICER improves exon skipping, reduces aberrant outcomes, including cryptic splicing and intron retention, and enables skipping of exons refractory to single splice-site editing. To demonstrate the therapeutic potential of SPLICER, we targeted APP exon 17, which encodes the amino acid residues that are cleaved to form the A{beta} plaques in Alzheimers disease. SPLICER reduced the formation of A{beta}42 peptides in vitro and enabled efficient exon skipping in a mouse model of Alzheimers disease. Overall, SPLICER is a widely applicable and efficient toolbox for exon skipping with broad therapeutic applications.

bioengineering↗

bioRxiv2024

Coarse-Grained Model of Serial Dilution Dynamics in Synthetic Human Gut Microbiome

Many microbial communities in nature are complex, with hundreds of coexisting strains and the resources they consume. We currently lack the ability to assemble and manipulate such communities in a predictable manner in the lab. Here, we take a first step in this direction by introducing and studying a simplified consumer resource model of such complex communities in serial dilution experiments. The main assumption of our model is that during the growth phase of the cycle, strains share resources and produce metabolic byproducts in proportion to their average abundances and strain-specific consumption/production fluxes. We fit the model to describe serial dilution experiments in hCom2, a defined synthetic human gut microbiome with a steady-state diversity of 63 species growing on a rich media, using consumption and production fluxes inferred from metabolomics experiments. The model predicts serial dilution dynamics reasonably well, with a correlation coefficient between predicted and observed strain abundances as high as 0.8. We applied our model to: (i) calculate steady-state abundances of leave-one-out communities and use these results to infer the interaction network between strains; (ii) explore direct and indirect interactions between strains and resources by increasing concentrations of individual resources and monitoring changes in strain abundances; (iii) construct a resource supplementation protocol to maximally equalize steady-state strain abundances.

ecology↗

bioRxiv2023

The ecological consequences of microbial metabolic strategies in fluctuating environments

Microbes adopt a variety of metabolic strategies to consume resources in fluctuating environments, but most work has focused on understanding these strategies in the context of isolated species, rather than diverse natural communities. We systematically measure the feasibility, dynamical and structural stability of multispecies microbial communities adopting different metabolic strategies. Our results reveal key distinctions between the ecological properties of different metabolic strategies, showing that communities containing sequential utilizers are more resilient to resource fluctuations, but are less feasible than co-utilizing communities.

ecology↗

bioRxiv2023

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence- specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 102 - 103, a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers towards a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

evolutionary biology↗

bioRxiv2023

Predicting metabolic response to dietary intervention using deep learning

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

microbiology↗

bioRxiv2023

DR-BERT: A Protein Language Model to Annotate Disordered Regions

Despite their lack of a rigid structure, intrinsically disordered regions in proteins play important roles in cellular functions, including mediating protein-protein interactions. Therefore, it is important to computationally annotate disordered regions of proteins with high accuracy. Most popular tools use evolutionary or biophysical features to make predictions of disordered regions. In this study, we present DR-BERT, a compact protein language model that is first pretrained on a large number of unannotated proteins before being trained to predict disordered regions. Although it does not use any explicit evolutionary or biophysical information, DR-BERT shows a statistically significant improvement when compared to several existing methods on a gold standard dataset. We show that this performance is due to the information learned during pretraining and DR-BERTs ability to use contextual information. A web application for using DR-BERT is available at https://huggingface.co/spaces/nambiar4/DR-BERT and the code to run the model can be found at https://github.com/maslov-group/DR-BERT.

bioinformatics↗

bioRxiv2022

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a new method -- mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

microbiology↗

bioRxiv2022

FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important for the task of metabolic engineering of these organisms to produce desired chemicals in industrially scalable conditions. Most of the previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE - a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species: Saccharomyces cerevisiae, Neurospora crassa and Issatchenkia orientalis and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

bioinformatics↗

bioRxiv2022

Fine-scale diversity of microbial communities due to satellite niches in boom-and-bust environments

Recent observations have revealed that closely related strains of the same microbial species can stably coexist in natural and laboratory settings subject to boom-and-bust dynamics and serial dilutions, respectively. However, the possible mechanisms enabling the coexistence of only a handful of strains, but not more, have thus far remained unknown. Here, using a consumer-resource model of microbial ecosystems, we propose that by differentiating on Monod parameters characterizing microbial growth rates in high and low nutrient conditions, strains can coexist in patterns similar to those observed. In our model, boom-and-bust environments create satellite niches due to resource concentrations varying in time. These satellite niches can be occupied by closely related strains, thereby enabling their coexistence. We demonstrate that this result is valid even in complex environments consisting of multiple resources and species. In these complex communities, each species partitions resources differently and creates separate sets of satellite niches for their own strains. While there is no theoretical limit to the number of coexisting strains, in our simulations, we always find between 1 and 3 strains coexisting, consistent with experiments and observations. Author summaryRecent genomic data has exposed to us the remarkable spectrum of microbial diversity in natural communities surrounding us, which harbor not only hundreds of species, but also a handful of closely related strains within each of those species (termed "oligo-colonization"). While the mechanisms behind species coexistence are much better studied, the mechanisms behind the coexistence of closely related strains have remained understudied. Here, using a simple consumer-resource model, we show that if strains differ on their Monod growth parameters, they can coexist even on a single limiting resource, provided that the environments vary with time in boom-and-bust cycles. The Monod growth parameters describe how a strains growth rate changes with resource concentration, namely the half-maximal concentration and maximal growth rate. Simulations of our model show that both in simple and complex environments, even though an arbitrary number of strains can coexist, typically it is between 1 and 3 strains of a species that coexist over several randomly assembled communities, consistent with some experimental observations. This is because the allowed parameter space for coexistence shrinks significantly with the number of strains that coexist.

ecology↗