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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 397 records · Page 22Linked to original sources
bioRxiv2025

Transcriptional competition biases the effects of second messengers in E. coli

Cells regulate gene expression by balancing transcriptional resources across different functional groups of genes. In Escherichia coli, second messengers such as ppGpp and cAMP control ribosome biogenesis and metabolic gene expression, respectively. While these regulators are typically studied in isolation, we provide a theory showing that their effects are intertwined due to global transcriptional competition. Using experimental data from RelA overexpression and a mechanistic modeling framework, we show that ppGpp-mediated repression of ribosomal genes competes for transcriptional resources with cAMP-driven activation of catabolic genes. This competition reshapes proteome allocation in a way that transcends individual regulators. Our findings challenge common modeling assumptions about transcription factor action and revive classical ideas suggesting that large-scale gene regulation should be studied within the broader context of resource availability, with implications for understanding cellular regulation across diverse biological systems, beyond bacterial physiology.

systems biology↗
bioRxiv2025

Non-enzymatic error correction in self-replicators without extraneous energy supply

Accurate propagation of sequence information in nucleic acids is central to the evolutionary dynamics of self-replicating systems. Modern biological systems achieve high fidelity using enzymes that actively correct errors through energy-driven mechanisms. However, such complex machinery was absent under prebiotic conditions. Here, we present a theoretical model of error correction in self-replicating heteropolymers that requires neither enzymes nor an extraneous energy supply. The model relies solely on the free-energy gradient driving strand growth and requires asymmetric cooperativity -- a kinetic asymmetry known to promote unidirectional elongation. Despite its simplicity, we demonstrate that this minimal model facilitates kinetic discrimination between correct and incorrect base pair incorporations, and for specific set of parameters, reproduces the error ratio of [~] 10-4, experimentally observed in passive base selection processes. It replicates key features observed in DNA error correction, including stalling, fraying, next-nucleotide effects, and the speed-accuracy trade-off. Our results provide plausible answers for longstanding questions, such as the energy source for the enhanced base selectivity of passive DNA polymerases and the role of thermodynamics and kinetics of phosphodiester bond formation in error correction. We show that catalysis of the phosphodiester bond plays a central role in error correction, even without explicit enzymatic structural discrimination. This observation points to a plausible pathway for accurate oligomer synthesis under prebiotic conditions, driven solely by the thermodynamic gradient favoring strand elongation. More broadly, the model highlights how persistent molecular order can emerge from non-equilibrium dynamics - a central requirement for emergence of life.

biochemistry↗
bioRxiv2025

Ecosystems as adaptive living circuits

Unlike many physical nonequilibrium systems, in biological systems, the coupling to external energy sources is not a fixed parameter but adaptively controlled by the system itself. We do not have theoretical frameworks that allow for such adaptability. As a result, we cannot understand emergent behavior in living systems where structure formation and non-equilibrium drive coevolve. Here, using ecosystems as a model of adaptive systems, we develop a framework of living circuits whose architecture changes adaptively with the energy dissipated in each circuit edge. We find that unlike traditional nonequilibrium systems, living circuits exhibit a phase transition from equilibrium death to a nonequilibrium dissipative state beyond a critical driving potential. This transition emerges through a feedback mechanism that saves the weakest edges by routing dissipation through them, even though the adaptive rule locally rewards the strongest dissipating edges. Despite lacking any global optimization principle, living circuits achieve near-maximal dissipation, with higher drive promoting more complex circuits. Our work establishes ecosystems as paradigmatic examples of living circuits whose structure and dissipation are tuned through local adaptive rules.

ecology↗
bioRxiv2025

OpenDVP: An experimental and computational framework for community-empowered deep visual proteomics

Deep visual proteomics (DVP) is an emerging approach for cell type-specific and spatially resolved proteomics. However, its broad adoption has been constrained by the lack of an open-source end-to-end workflow in a community-driven ecosystem. Here, we introduce openDVP, an experimental and computational framework for simplifying and democratizing DVP. OpenDVP integrates open-source software for image analysis, including MCMICRO, QuPath, and Napari, and uses the scverse data formats AnnData and SpatialData for multi-omics integration. It offers two workflows: a fast-track pipeline requiring no image analysis expertise and an artificial intelligence (AI)-powered pipeline with recent algorithms for image pre-processing, segmentation, and spatial analysis. We demonstrate openDVPs versatility in three archival tissue studies, profiling human placenta, early-stage lung cancer, and locally relapsed breast cancer. In each study, our framework provided insights into health and disease states by integrating spatial single-cell phenotypes with exploratory proteomic data. Finally, we introduce deep proteomic profiling of cellular neighborhoods as a scalable approach to accelerate spatial discovery proteomics across biological systems.

systems biology↗
bioRxiv2025

Intrinsic dimensionality of single-cell transcriptomic data reveals potency landscapes during cell reprogramming

Cell potency, the ability of a cell to generate other cell types, is a fundamental property that drives development, regeneration, and reprogramming. Recent computational advances have enabled estimation of potency directly from single-cell RNA sequencing (scRNA-seq) data, with intrinsic dimensionality (ID) emerging as a promising, data-driven measure of transcriptional complexity linked to developmental potential. While ID offers an unbiased and interpretable framework, existing implementations have two main limitations: they have been applied in only a narrow range of biological contexts, and rely on clustering, which restricts resolution at the single-cell level. Here, we introduce IDEAS (Intrinsic Dimensionality Estimation Analysis of single-cell RNA sequencing data), a Python-based toolkit that computes both global and single-cell ID from scRNA-seq data, enabling potency scoring without requiring predefined clusters. IDEAS extends ID-based potency estimation to single cells, showing its usefulness in a new biological setting. It offers a clear and reliable way to study cell plasticity, simplifies ID score calculation, and makes the method easier to use across different biological systems, speeding up research on this approach to measuring cell potency. We apply IDEAS to multiple datasets of cellular reprogramming, a dynamic and heterogeneous process in which cells transition between identities. Our analysis reveals that ID scores effectively capture changes in potency, identify partially reprogrammed intermediates, and delineate alternative reprogramming trajectories.

systems biology↗
bioRxiv2025

Learning sequence-function relationships with scalable, interpretable Gaussian processes

AbstractUnderstanding the relationship between biological sequences, such as DNA, RNA or protein sequences, and their resulting phenotypes is one of the central goals of genetics. This task is complicated by epistasis, i.e., the context dependence of mutational effects. Advances in high-throughput phenotyping now make it possible to study these relationships at unprecedented scale, generating large datasets that measure phenotypes for tens or hundreds of thousands of sequences. However, standard regression models for analyzing such datasets often make unrealistic assumptions about the generalizability of mutational effects and epistatic coefficients across genetic backgrounds. Deep neural networks offer greater flexibility but suffer from limited interpretability and lack uncertainty quantification. Here, we introduce a family of interpretable Gaussian process models for sequence-function relationships that capture epistasis through flexible prior distributions that generalize classical theoretical models from the fitness landscape literature. In particular, these priors are parameterized by interpretable site-, allele-, and mutation-specific factors controlling the degree to which specific mutations decrease the predictability of the effects of other mutations. Using GPU acceleration to scale to large protein, RNA, and genome-wide SNP datasets, our models consistently deliver superior predictive performance while yielding interpretable parameters that both recover known features and uncover novel epistatic interactions. Overall, our methods provide new insights into the structure of the genotype-phenotype map and offer scalable, interpretable approaches for exploring complex genetic interactions across diverse biological systems.

systems biology↗
bioRxiv2025

Widespread transcriptional memory shapes heritable states and functional heterogeneity in cancer and stem cells

Recent studies show that non-genetic cellular heterogeneity, particularly through heritable cell states, fundamentally shapes cancer evolution and developmental trajectories. However, conventional single-cell transcriptomic snapshots lack temporal information needed to identify these heritable states. Here, we employ lineage-resolved single-cell transcriptomics to systematically map heritable cell states that persist across divisions, distinguishing them from transient fluctuations within a cell cycle. We uncover that heritable states are underpinned by widespread transcriptional memory, whereby gene expression is heritable, defining two classes of states in cancer and stem cells: clustered states, characterized by clustered gene expression, and latent states, marked by non-clustered gene expression. This memory shows unexpected conservation across cell types and conditions and is maintained by robust epigenetic mechanisms resistant to environmental perturbations. Functionally, memory genes predict critical behaviors including metastatic potential and lineage commitment, with latent-state genes often outperforming clustered-state genes. Our findings establish transcriptional memory as the molecular basis of heritable cellular heterogeneity, providing a framework for broadly understanding functional cellular variations across biological systems.

systems biology↗
bioRxiv2025

Beyond Arrhenius: Nonlinear and negative temperature scaling of biological rates from multi-step mechanisms

Temperature shapes all biological processes, particularly during the early development of ectothermic organisms. A widely used framework for describing temperature dependence is the Arrhenius equation, which predicts an exponential increase in rates with temperature. However, biological rates often deviate from this prediction when measured across broader temperature ranges. While negative apparent activation energies are often attributed to protein denaturation, this cannot explain similar behavior observed at temperatures where enzymes remain stable. These broader scaling patterns remain mechanistically unexplained. Here we present a general Markov chain framework for modeling biological timing as cascades of reversible, temperature-dependent steps. Applying this model to 121 published datasets spanning diverse species, biological timescales, and temperature ranges, we find that a consistent three-zone scaling pattern emerges: Arrhenius-like behavior at low and high temperatures, and a quadratic exponential regime at intermediate temperatures. We show that this pattern arises naturally from differences in activation energies across steps in the network. The quadratic exponential regime is an emergent feature of averaging across many steps and is robust to variation across network realizations. In contrast, Arrhenius-like scaling at the extremes tends to be more variable and originates from smaller sub-networks. Apparent negative activation energies can emerge naturally from the dynamics of multi-step networks, even in the absence of protein denaturation. Our framework provides a unified mechanistic explanation for diverse temperature-scaling behaviors in biology and may help predict how developmental and physiological processes respond to environmental change. Although we focus on development, the model is broadly applicable to biological systems governed by multi-step reaction networks.

systems biology↗
bioRxiv2025

The role of contact guidance and ECM remodelling in cancer invasion: a computational study

The extracellular matrix (ECM) is a complex network of fibrous proteins and other macromolecules that provides both structural support and directional cues that regulate cancer cell invasion and tumour progression. Its fibre organisation plays a critical role in directing migration through contact guidance, while cancer cells simultaneously remodel the matrix through biochemical and mechanical interactions. However, the interplay between ECM architecture, chemical gradients, and matrix remodelling remains poorly understood. Mathematical modelling offers a powerful approach to explore how ECM architecture regulates this process. We present a hybrid computational model, implemented in PhysiCell, that integrates a discrete agent-based cell model with a continuous representation of the chemical microenvironment and ECM microstructure. In an advance over previous efforts, we adapt several mechanisms to a fibre-focused representation of the ECM, including "cell-front" ECM sensing reflecting protrusion-driven engagement of the matrix, contact-guided cell movement via ECM fibre orientation integrated with chemotaxis bias, and proliferation regulated by oxygen and mechanical pressure. In addition, we introduce a new mechanical mechanism for ECM density displacement alongside degradation to simulate how cells redistribute matrix fibres. Simulations reveal how the interplay between initial fibre alignment, anisotropy (fibre-fibre alignment correlation), and fibre reorientation capacity affects invasion, and how competing mechanical and chemical cues influence the invasive potential of tumour cells. Furthermore, our results demonstrate that the balance between degradation and displacement strongly affects invasion dynamics, with high displacement promoting the formation of dense ECM rims around tumour spheroids, whereas increased degradation enables greater invasive spread. This work provides mechanistic insights into bidirectional interactions between cancer cells and the surrounding ECM, highlighting how structural remodelling of the ECM influences the invasive potential of cancer cells. Author summaryCancer cells invade surrounding tissue by interacting with the extracellular matrix (ECM), a fibrous network of proteins that provides both mechanical support and directional cues for migration. Experiments have shown that the orientation of ECM fibres can either promote or hinder invasion, but it remains difficult to disentangle the underlying mechanisms because cells both respond to and actively remodel the matrix. To address this challenge, we developed a computational model that simulates how cancer cells migrate through and reshape the ECM. The model combines individual cell behaviour with a representation of ECM structure, including fibre orientation, anisotropy (fibre-fibre alignment correlation), and density. Our simulations show that invasion depends on the initial fibre orientation and on chemical cues. We also introduce a mechanism that allows cells to mechanically displace the matrix, revealing how the balance between matrix degradation and physical pushing can generate either compact tumour growth or sparse invasion. These results help explain how physical interactions between cells and their environment shape tumour invasion. More broadly, the framework provides a tool to explore how mechanical and chemical signals together regulate collective cell migration in cancer and other biological systems.

systems biology↗
bioRxiv2017

Impact of division rate and cell size on gene expression noise

The cell division rate, size, and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli d other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persistors cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding of circuits robustness across growth conditions is key for the effective design of synthetic biological systems.

systems biology↗
bioRxiv2017

Learning the pattern of epistasis linking genotype and phenotype in a protein

Understanding the pattern of epistasis - the non-independence of mutations - is critical for relating genotype and phenotype in biological systems. However, the complexity of potential epistatic interactions has limited approaches to this problem at any level. To develop practical strategies, we carried out a comprehensive experimental study of epistasis between all mutations that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein. The data demonstrate significant high-order epistatic interactions between mutations, but also reveals extraordinary sparsity, enabling novel experimental strategies and sequence-based statistical methods for learning the relevant epistasis. The sequence space linking the parental fluorescent proteins is functionally connected through paths of single mutations; thus, high-order epistasis in proteins is consistent with evolution through stepwise variation and selection. This work initiates a path towards characterizing epistasis in proteins in general.

systems biology↗
bioRxiv2017

Network modelling unravels mechanisms of crosstalk between ethylene and salicylate signalling in potato

To provide means for novel crop breeding strategies, it is crucial to understand the mechanisms underlying the interaction between plants and their pathogens. Network modelling represents a powerful tool that can unravel properties of complex biological systems. Here, we build on a reliable Arabidopsis (Arabidopsis thaliana L.) immune signalling model, extending it with the information from diverse publically available resources. The resulting prior knowledge network (20,012 nodes, 70,091 connections) was then translated to potato (Solanum tuberosum L.) and superimposed with an ensemble network inferred from potato time-resolved transcriptomics data. We used different network modelling approaches to generate specific hypotheses of potato immune signalling mechanisms. An interesting finding was the identification of a string of molecular events, illuminating the ethylene pathway modulation of the salicylic acid pathway through NPR1 gene expression. Functional validations confirmed this modulation, thus confirming the potential of our integrative network modelling approach for unravelling molecular mechanisms in complex systems.\n\nOne-sentence summaryAnalysis of integrated prior knowledge and ensemble networks highlights a novel connection between ethylene and salicylic acid signalling modules in potato.

systems biology↗
bioRxiv2018

Complexity-Aware Simple Modeling

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach -- complexity-aware simple modeling -- that can bridge the gap between the small- and large-scale approaches.\n\nHighlightsO_LISimple or small-scale models allow deduction of fundamental principles of biological systems\nC_LIO_LIDetailed or large-scale models can be quantitatively accurate but difficult to analyze\nC_LIO_LIComplexity-aware simple models can extract principles that are robust to the presence of unknown complex interactions\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC=\"FIGDIR/small/248419_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1cc6c8borg.highwire.dtl.DTLVardef@1ea1eb3org.highwire.dtl.DTLVardef@185a0b4org.highwire.dtl.DTLVardef@1f9c6b9_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG

systems biology↗
bioRxiv2018

Deciphering noise amplification and reduction in open chemical reaction networks

The impact of fluctuations on the dynamical behavior of complex biological systems is a longstanding issue, whose understanding would elucidate how evolutionary pressure tends to modulate intrinsic noise. Using the It[o] stochastic differential equation formalism, we performed analytic and numerical analyses of model systems containing different molecular species in contact with the environment and interacting with each other through mass-action kinetics. For networks of zero deficiency, which admit a detailed- or complex-balanced steady state, all molecular species are uncorrelated and their Fano factors are Poissonian. Systems of higher deficiency have non-equilibrium steady states and non-zero reaction fluxes flowing between the complexes. When they model homooligomerization, the noise on each species is reduced when the flux flows from the oligomers of lowest to highest degree, and amplified otherwise. In the case of hetero-oligomerization systems, only the noise on the highest-degree species shows this behavior.

systems biology↗
bioRxiv2018

A bacterial growth law out of steady-state

Bacterial growth depends on numerous reactions, and yet follows surprisingly simple laws that inspired biologists for decades. Growth laws until now primarily dealt with steady-state exponential growth in constant conditions. However, bacteria in nature often face fluctuating environments, with nutritional upshifts and downshifts. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for strong upshifts using an optimal resource-allocation model that was previously calibrated at steady-state growth: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and robotic batch culture experiments, as well as previous data from several species, and find good agreement with the model predictions. The increase in growth rate after an upshift indicates that ribosomes have spare capacity. We demonstrate theoretically that spare ribosomal capacity has the cost of slow steady-state growth, but is beneficial in fluctuating environments because it prevents large overshoots in intracellular metabolites after an upshift and allows rapid response to change. We also provide predictions for downshifts for future experimental tests. Spare capacity appears in diverse biological systems, and the present study quantifies the optimal degree of spare capacity, which rises the slower the growth rate, and suggests that it can be precisely regulated.

systems biology↗
bioRxiv2018

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

Due to the complexity of biological systems, their heterogeneity, and the internal regulation of each cell and its surrounding, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful to understand such complex systems. However, very few of these tools, freely available and computationally efficient, are currently available. To fill this gap, we present here our open-source software, PhysiBoSS, which is built on two available software packages that focus on different scales: intracellular signalling using continuous-time markovian Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell).\n\nThe multi-scale feature of PhysiBoSS - its agent-based structure and the possibility to integrate any Boolean network to it - provide a flexible and computationally efficient framework to study heterogeneous cell population growth in diverse experimental set-ups. This tool allows one to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from genotype to phenotype. PhysiBoSS thus becomes very useful when studying population response to treatment, mutations effects, cell modes of invasion or isomorphic morphogenesis events.\n\nTo illustrate potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment in a 2-D cell population and in a tumour cell 3-D spheroid. We explored the effect of different treatment regimes and the behaviour and selection of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen. PhysiBoSS is freely available on GitHub (https://github.com/gletort/PhysiBoSS), and is distributed open source under the BSD 3-clause license. It is compatible with most Unix systems, and a Docker package (https://hub.docker.com/r/gletort/physiboss/) is provided to ease its deployment in other systems.

systems biology↗
bioRxiv2018

MoonFit, a minimal interface for fitting ODE dynamical models, bridging simulation by experimentalists and customization by C++ programmers

The modelling of biological systems often consists into differential equation models that need to be fitted to experimental data. During this complex process, the practical experience of the biologist and the theoretical abstraction of the modeller require back-and-forth refinements of the model, design of new experiments and inclusion of more data-points into the fitting procedure. Available optimization interfaces rarely simultaneously allow customizations by the programmer and the capacity for the biologist to perform simulations or optimizations with a simple interface.\n\nHere, we provide the C++ code of a graphical user interface based on a user defined minimal C++ ODE model class. The graphical interface allows to perform simulations and optimizations without any knowledge in programming. The code was designed minimal and modular to be easily modified, with maximal freedom to link customized optimization libraries, solver or hand-made scripts. Moonfit is powerful enough to fit and compare models with high dimensionality, multiple datasets, to automatize optimizations, and to perform iterative fittings using data interpolation. We believe this will ease the interaction between modellers and experimental partners.\n\nAvailability: Moonfit is freely available via c++ source code and accompanying scripts from gitlab.com/Moonfit/MoonLight.

systems biology↗
bioRxiv2018

Multilayer view of pathogenic SNVs in human interactome through in-silico edgetic profiling

Non-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present an systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, \"edgetic\", effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus, and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations.

systems biology↗