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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Integration of Diverse Transcriptomics Datasets using Random Forest to Predict Universal Functional Pathways in Tfr Cells

MotivationT follicular regulatory (Tfr) cells are a specialized cell subset that controls humoral immunity. Despite a number of individual transcriptomic studies on these cells, core functional pathways have been difficult to uncover due to the substantial transcriptional overlap of these cells with other effector cell types, as well as transcriptional changes occurring due to disease settings. Developing a core transcriptional module for Tfr cells that integrates multiple cell type comparisons as well as diverse disease settings will allow a more accurate prediction of functional pathways. Researchers studying allergic reactions, immune responses to vaccines, autoimmunity and cancer could use this gene set to better understand the roles of Tfr cells in controlling disease progression. Additional cell types beyond Tfr cells that have similar features of transcriptomic complexity within diverse disease settings may also be studied using similar approaches. High-throughput sequencing technologies allow the generation of large datasets that require specific tools to best interpret the data. The development of a core transcriptional module for Tfr cells will allow investigators to determine if Tfr cells may have functional roles within their biological systems with little knowledge of Tfr biology. With this work, we have addressed the need of core gene modules to define specific subsets of immune cells. ResultsWe introduce an integrated "core Tfr cell gene module" that can be incorporated into GSEA analysis using various input sizes. The integrated core Tfr gene module was built using transcriptomic studies in Tfr cells from several different tissues, disease settings, and cell type comparisons. Random forest was used to integrate the transcriptomic studies to generate the core gene module. A GSEA gene set was formulated from the integrated core Tfr gene module for incorporation into end-user friendly GSEA. The gene sets are presented along with random genes taken from the GTEX data set and are presented as GMT files. The user can upload the gene set to the GSEA website or any gene set tool which takes GMT files. We also present the full results of the model including p-values calculated by random forest. This provides users with more flexibility in choosing a p-value cutoff that is most appropriate for the experimental setting. AvailabilityThe core Tfr gene sets are freely available at: https://github.com/alosdiallo/TFR_Model. We have also included all of the code and data used in developing these gene sets. The code and results are released under an MIT license. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗
bioRxiv2024

PRS-Net: Interpretable polygenic risk scores via geometric learning

Polygenic risk score (PRS) serves as a valuable tool for predicting the genetic risk of complex human diseases for individuals, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. We present PRS-Net, an interpretable deep learning-based framework designed to effectively model the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genomewide PRS at the single-gene resolution, and then it encapsulates gene-gene interactions for genetic risk prediction leveraging a graph neural network, thereby enabling the characterization of biological nonlinearity underlying complex diseases. An attentive readout module is specifically introduced into the framework to facilitate model interpretation and biological discovery. Through extensive tests across multiple complex diseases, PRS-Net consistently outperforms baseline PRS methods, showcasing its superior performance on disease prediction. Moreover, the interpretability of PRS-Net has been demonstrated by the identification of genes and gene-gene interactions that significantly influence the risk of Alzheimers disease and multiple sclerosis. In summary, PRS-Net provides a potent tool for parallel genetic risk prediction and biological discovery for complex diseases.

genetics↗
bioRxiv2024

Scalable and universal prediction of cellular phenotypes

Biological systems can be interrogated by perturbing individual components and observing the consequences across molecular, cellular, and phenotypic levels. The vast combinatorial space of possible perturbations and responses makes exhaustive experimentation infeasible. Recent advances in machine learning have shown that training on diverse datasets enables transfer learning across tasks, capturing patterns that generalize and improving performance on previously unseen problems. Inspired by this principle, we present Prophet, a transformer-based model pretrained on a vast, heterogeneous collection of perturbation experiments. This pretraining allows Prophet to predict the outcomes of untested genetic or chemical perturbations in novel cellular contexts, spanning phenotypes such as gene expression, viability, and morphology. By leveraging shared structure across apparently disconnected assays, Prophet provides a scalable framework for large-scale virtual screening and prioritization of informative experiments. Prophet consistently outperforms baseline models, including those trained on single phenotypes, showing that transfer learning between phenotypes not only is possible but improves predictive accuracy. Its capabilities extends to in vivo developmental systems, where it recapitulates known lineage biology and proposes new candidates. In a large-scale in silico screen for melanoma, Prophet identified and experimentally validated compounds with selective activity that mirrored clinically approved therapies, demonstrating its ability to transform perturbation biology into a predictive and scalable engine for therapeutic discovery.

cell biology↗
bioRxiv2024

Molecular insights into dynamic protein structures by high-contrast crosslinking mass spectrometry

Proteins are comprised of structured domains and dynamic regions, and both are essential for biological function. However, studying dynamic regions is challenging using most structural biology methods, including crosslinking mass spectrometry. Here, we dramatically improve the usefulness of distance restraints from crosslinking MS by taking advantage of short-lived reactive species generated from diazirine-based photo-crosslinking. This leads to a clear view of complex topologies and conformational changes, including in dynamic regions. We demonstrate that photo-crosslinking MS data can be used to model flexible regions and conformational changes in the DNA repair complexes; Fanconi Anemia core complex and FANCD2-FANCI. In addition, we obtain new insights into the architecture and arrangement of the highly flexible CCR4-NOT mRNA deadenylation complex. The improved contrast of photo-crosslinking empowers structural biology by providing clearer structural insights into dynamic biological systems that have eluded other structural biology approaches.

molecular biology↗
bioRxiv2024

Entangled Evolution Model: Application to Adaptive Mutation

Adaptive mutation, the phenomenon where organisms appear to increase beneficial mutation rates in response to environmental stress, has challenged the traditional understanding of random mutations in evolutionary biology. Here, we present a novel computational model, the Entangled Evolution Model, which integrates quantum game theory and quantum walks to provide a new approach to understanding mutation dynamics. By applying the Clauser-Horne-Shimony-Holt (CHSH) quantum game within a modified Lotka-Volterra framework, we simulate how quantum entanglement can accelerate the rate of adaptive mutations in Escherichia coli populations. Our simulations reveal that entangled bacterial populations achieve successful adaptive mutations with an 85% probability, significantly surpassing the classical theoretical maximum. These results introduce a new perspective on computational approaches to evolutionary adaptation, offering an efficient algorithmic framework to explore the role of quantum effects in biological systems.

evolutionary biology↗
bioRxiv2025

The ten dollar proteome: low-cost, deep and quantitative proteome profiling of limited sample amounts using the Orbitrap Astral and timsTOF Ultra 2 mass spectrometers

Mass spectrometry (MS)-based proteomics remains technically demanding and prohibitively expensive for many large-scale or routine applications, with per-sample costs of hundreds of dollars or more. To democratize access to proteomics and facilitate its integration into more high-throughput multi-omic studies, we present a robust analytical framework for achieving in-depth, quantitative proteome profiling at a cost of approximately $10 per sample, termed the "$10 proteome." Using the Thermo Fisher Orbitrap Astral and Bruker timsTOF Ultra 2 mass spectrometers, we evaluated performance across sample inputs ranging from 200 pg to 100 ng and active gradient lengths from 5 to 60 minutes. Proteome coverage saturated within the low-nanogram input range, with [~]8000 protein groups quantified from as little as 10 ng of input and nearly 6000 protein groups from 200 pg. With already demonstrated low-cost one-pot sample preparation workflows that are appropriate for this sample input range, standardized MS acquisition settings, and high-throughput nanoLC operated at [~]10 min per sample, the $10 proteome becomes feasible. This study establishes a practical, scalable, and cost-effective foundation for global proteome profiling, paving the way for routine, large-scale applications in systems biology, clinical research and beyond.

systems biology↗
bioRxiv2025

SmartHisto: Bayesian Active Learning for Histology Images

Accurate and efficient characterization of biological images is crucial for advancing systems biology and medical research. Recent advancements in deep learning and image processing have enabled neural network models to rapidly accelerate image analysis by utilizing large expert-annotated datasets. However, in histopathology, the size of whole-slide images makes expert annotation expensive, limiting the acquisition of sufficiently large annotated datasets and posing a major challenge for developing automated, AI-driven image analysis pipelines. To address this limitation, we propose a novel active learning-based framework to train image segmentation models interactively. Our approach employs a Bayesian neural network to identify informative regions in unlabeled images rather than entire images, making expert labeling more cost-effective. We validate our framework on multiple benchmark datasets spanning different staining techniques and magnifications, demonstrating substantial reductions in annotation effort. Notably, our method achieves a mean IoU of 0.75, significantly outperforming competing approaches, which average 0.60. Author summaryHistopathology is fundamental to investigating tissue and immune responses, host-pathogen interactions, and disease mechanisms. However, histopathology is highly resource-intensive and requires specialized training, dramatically increasing the costs of annotating whole-slide images and, consequently, the expenses of large-scale studies involving numerous labs and specialists. We developed a computational tool to overcome these challenges, implementing a robust uncertainty-based sampling algorithm in conjunction with a next-generation Bayesian Convolutional Neural Network. This algorithm can be used for hypothesis testing and discovery by reducing the reliance on large, precisely annotated training datasets required in automated image analysis pipelines. The base model, when trained to identify lung tissue types using a small set of annotated images, outperforms state-of-the-art models and can efficiently annotate thousands of images much more quickly than a human. Models trained by the proposed algorithm will serve as a standardized approach for pathologists and disease researchers to train automated image segmentation pipelines for large-scale histopathology.

bioinformatics↗
bioRxiv2018

Parameter estimation and identifiability in a neural population model for electro-cortical activity

Electroencephalography (EEG) provides a non-invasive measure of brain electrical activity. Neural population models, where large numbers of interacting neurons are considered collectively as a macroscopic system, have long been used to understand features in EEG signals. By tuning dozens of input parameters describing the excitatory and inhibitory neuron populations, these models can reproduce prominent features of the EEG such as the alpha-rhythm. However, the inverse problem, of directly estimating the parameters from fits to EEG data, remains unsolved. Solving this multi-parameter non-linear fitting problem will potentially provide a real-time method for characterizing average neuronal properties in human subjects. Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling. We estimate how much is learned about individual parameters by computing Kullback-Leibler divergences between posterior and prior distributions for each parameter. Results indicate that only a single parameter, that determining the dynamics of inhibition, is directly identifiable, while other parameters have large, though correlated, uncertainties. We show that the eigenvalues of the Fisher information matrix are roughly uniformly spaced over a log scale, indicating that the model is sloppy, like many of the regulatory network models in systems biology. These eigenvalues indicate that the system can be modeled with a low effective dimensionality, with inhibition being prominent in driving system behavior.\n\nAuthor summaryElectroencephalography (EEG), where electrodes are used to measure electric potential on the outside of the scalp, provides a simple, non-invasive way to study brain activity. Physiological interpretation of features in EEG signals has often involved use of collective models of neural populations. These neural population models have dozens of input parameters to describe the properties of inhibitory and excitatory neurons. Being able to estimate these parameters by direct fits to EEG data holds the promise of providing a real-time non-invasive method of inferring neuronal properties in different individuals. However, it has long been impossible to fit these nonlinear, multi-parameter models effectively. Here we describe fits of a 22-parameter neural population model to EEG spectra from 82 different subjects, all exhibiting alpha-oscillations. We show how only one parameter, that describing inhibitory dynamics, is constrained by the data, although all parameters are correlated. These results indicate that inhibition plays a central role in the generation and modulation of the alpha-rhythm in humans.

neuroscience↗
bioRxiv2026

Adaptive-optics lattice light-sheet microscopy enables correlative single-molecule imaging of endogenous proteins in native environments

Resolving molecular organization and dynamics in native environments remains challenging, as single-molecule imaging requires high sensitivity and spatiotemporal resolution while preserving 3D cellular context. Here, we present ALPINE (Adaptive-optics Lattice light-sheet microscopy for endogenous Protein Imaging in Native Environments), a modular framework for quantitative single-molecule imaging into complex biological samples. ALPINE combines adaptive-optics lattice light-sheet microscopy, programmable PSF shaping and acquisition stabilization, an engineered cell-specific labelling toolkit for endogenous cell-surface proteins and analysis pipelines assigning molecular localizations and trajectories to 3D cellular compartments. Labelling endogenous AMPA receptors in intact brain tissue, we demonstrate aberration-corrected 3D single-molecule localization and nanoscale organization within neuronal compartments. Importantly, fast optical sectioning of lattice light-sheet illumination further enables 3D single-molecule tracking in live tissue and quantification of molecular dynamics within native cellular environment. Together, ALPINE provides a modular approach to investigate how endogenous molecules are organized and dynamically regulated in complex biological systems, with components adaptable to diverse biological questions and imaging modalities.

neuroscience↗
bioRxiv2024

A modular model of immune response as a computational platform to investigate a pathogenesis of infection disease

The COVID-19 pandemic significantly transformed the field of mathematical modeling in immunology. International collaboration among numerous research groups yielded a substantial amount of experimental data, which greatly facilitated model validation and led to the development of new mathematical models. The aim of the study is an improvement of system understanding of the immune response to SARS-CoV-2 infection based on the development of a modular mathematical model which provides a foundation for further research on host-pathogen interactions. We utilized the open-source BioUML platform to develop a model using ordinary, delay and stochastic differential equations. The model was validated using experimental data from middle-aged individuals with moderate COVID-19 progression, including measurements of viral load, antibodies, CD4+ and CD8+ T cells, and interleukin-6 levels. Parameter optimization and sensitivity analysis were conducted to refine the models accuracy. The model effectively reproduces moderate, severe, and critical COVID-19 progressions, consistent with experimental observations. We investigated the efficiency and contributions of innate and adaptive immunity in response to SARS-CoV-2 infection and assessed immune system behavior during co-infection with HIV and organ transplantation. Additionally, we studied therapy methods, such as interferon administration. The developed model can be employed as a framework for simulating other infectious diseases taking into account follow-up immune response. Author summaryDespite the significant progress reached in understanding of COVID-19, traditional methods still struggle to analyze and interpret the extensive and sometimes controversial experimental data on SARS-CoV-2 infection. Mathematical and systems biology approaches attempt to address this challenge by developing mathematical models of the immune response. We aimed not only to investigate the disease at a systemic level but also to provide a framework for further research on host-pathogen interactions, both existing and forthcoming. To achieve this, we constructed a model incorporating both innate and adaptive immunity, as well as cellular and humoral components. This together allowed us to conduct a series of in silico experiments, exploring the immune response across various levels and compartments. The results of these investigations offer valuable insights into the complex dynamics of the immune system and can guide future research and therapeutic strategies.

systems biology↗
bioRxiv2019

Formalizing metabolic-regulatory networks by hybrid automata

Computational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control.

systems biology↗
bioRxiv2023

Biological embedding of childhood adversity - a multi-omics perspective on stress regulation

The experience of adversity in childhood can have life-long consequences on health outcomes. In search of mediators of this relationship, alterations of bio-behavioral and cellular regulatory systems came into focus, including those dealing with basic gene regulatory processes. Systems biology oriented approaches have been proposed to gain a more comprehensive understanding of the complex multiple interrelations between and within layers of analysis. We used co-expression based, supervised and unsupervised single and multi-omics system approaches to investigate the influence of childhood adversity on gene expression, protein expression and DNA methylation in CD14+ monocytes of healthy adults before and after exposure to an experimental psychosocial stress protocol. Childhood adversity explained some variance at the single analyte level and within gene and protein co-expression structures. A single-omic, post stress gene expression model differentiated best between participants with a history of childhood adversity and controls in supervised analyses. In unsupervised analyses, a multi-omics based model showed best performance but separated participants based on sex only. Multi-omics analyses are a promising concept but might yield different results based on the specific approach taken and the omic-datasets supplied. Here, stress associated gene-expression pattern were most strongly associated with childhood adversity, and integrating multiple cellular layers did not results in better discriminatory performance. Currently, the capacity and yield of different omics-profiling methods might limit the full potential of integrative approaches.

systems biology↗
bioRxiv2016

System-wide quantitative proteomics of the metabolic syndrome in mice: genotypic and dietary effects

Advances in mass spectrometry have made the quantitative measurement of proteins across multiple samples a reality, allowing for the study of complex biological systems such as the metabolic syndrome. Although deregulation of lipid metabolism and increased hepatic storage of triacylglycerides are known to play a part in the onset of the metabolic syndrome, its molecular basis and dependency on dietary and genotypic factors are poorly characterized. Here, we used a rich experimental design with two different mouse strains, dietary and metabolic perturbations to generate a compendium of quantitative proteome data, using three mass spectrometry strategies. The data recapitulates known properties of the metabolic system and indicate differential molecular adaptation of the two mouse strains to perturbations, contributing to a better understanding of the metabolic syndrome. We show that high-quality, high-throughput proteomic datasets provide an unbiased broad overview of complex systems upon perturbation.\n\nHIGHLIGHTSO_LIRich experimental design with two mouse strains, dietary and metabolic perturbations\nC_LIO_LIThree mass spectrometry proteomic data collection strategies in one data compendium\nC_LIO_LISWATH-MS data with systematic perturbation provide a broad unbiased view of the system\nC_LIO_LIThe data reveals differential adaptation of molecular systems in two mouse strains\nC_LI

Systems Biology↗
bioRxiv2021

Cellular signaling beyond the Wiener-Kolmogorov limit

Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However, WK theory has one assumption that potentially limits its applicability: it relies on a linear, continuum description of the reaction dynamics. Despite this, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence the bound being broken. To accomplish this, we develop a theoretical framework for multi-level signaling cascades, including the possibility of feedback interactions between input and output. In the absence of feedback, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback, we rely on numerical solutions of the systems master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However the magnitude of the violation is biologically negligible, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds, its predictions for performance limits are excellent approximations, even for nonlinear systems.

systems biology↗
bioRxiv2017

A quantitative systems pharmacology analysis of KRAS G12C covalent inhibitors

The KRAS oncogene is the most common, activating, oncogenic mutation in human cancer. KRAS has proven difficult to target effectively. Two different strategies have recently been described for covalently targeting the most common activating KRAS mutant in lung cancer, KRAS G12C. Previously, we have developed a computational model of the processes that regulate Ras activation and this model has proven useful for understanding the complex behaviors of Ras signaling. Here, we use this model to perform a computational systems pharmacology analysis of KRAS G12C targeted covalent inhibitors. After updating our model to include Ras protein turnover, we verified the validity of our model for problems in this domain by comparing model behaviors with experimental behaviors. The model naturally reproduces previous experimental data, including several experimental observations that were interpreted as being contrary to conventional wisdom. Overall, this suggests that our model describes the Ras system well, including those areas where conventional wisdom struggles. We then used the model to investigate possible strategies to improve the ability of KRAS G12C inhibitors to inhibit Ras pathway signaling. We identify one, as of yet unexplored mechanism, that, if optimized, could improve the effectiveness of one class of KRAS inhibitor. We also simulated resistance to targeted therapies and found that resistance promoting mutations may reverse which class of KRAS G12C inhibitor inhibits the system better, suggesting that there may be value to pursuing both types of KRAS G12C inhibitors. Overall, this work demonstrates that systems biology approaches can provide insights that inform the drug development process.

systems biology↗
bioRxiv2022

Quantitative Relationship between Intracellular Metabolic Responses against Nutrient Conditions and Metabolic Inhibitions

Many previous studies have attempted to predict the metabolic states of cells assuming metabolic regulation is optimized through (sometimes artificial) evolution for some objective, e.g., growth rate or production of some metabolites. Conventional approaches, however, require identifying the microscopic details of individual metabolic reactions and the objective functions of cells, and their predictions sensitively depend on such details. In this study, we focus on the responses of metabolic systems to environmental perturbations, rather than their metabolic states themselves, and theoretically demonstrate a universal property of the responses independent of the systems details. With the help of a microeconomic theory, we show a simple relationship between intracellular metabolic responses against nutrient abundance and metabolic inhibition due to manipulation such as drug administration: these two experimentally measurable quantities show a proportional relationship with a negative coefficient. This quantitative relationship should hold in arbitrary metabolic systems as long as the law of mass conservation holds and cells are optimized for some objectives, but the true objective functions need not be known. Through numerical calculations using large-scale metabolic networks such as the E. coli core model, we confirmed that the relationship is valid from abstract to detailed models. Because the relationship provides quantitative predictions regarding metabolic responses without prior knowledge of systems, our findings have implications for experimental applications in microbiology, systems biology, metabolic engineering, and medicine, particularly for unexplored organisms or cells.

systems biology↗
bioRxiv2026

Molecular pixelation of the CAR T cell surface proteome

Immunotherapies using CAR T cells are revolutionizing B-cell acute lymphoblastic leukemia treatments. However, the majority of patients remain unresponsive, and chronic stimulation of T cells is a common contributor that reduces effector function and persistence. We apply Molecular Pixelation, a recently developed single-cell technology for characterizing cellular surface proteomes, to determine characteristic topological surface-based proteomic signatures of CAR T cell exhaustion. We analyze 76 surface proteins on 8504 CAR T cells at a single-cell level, collected from three donors and either stimulated once or repeatedly, six times over two weeks. The abundances, polarizations, and colocalizations of surface proteins can each distinguish CAR T cells that were stimulated acutely or chronically, and all but one marker with polarization changes increased in polarization. These data also reveal disrupted adhesion signatures of protein colocalization in the peripheral supramolecular activation complex (pSMAC) and increased CD37/CD82 colocalization after chronic stimulation. These Molecular Pixelation results convey new spatial signatures for proteomic polarization and colocalization on the cell surface that represent new cell-state axes for immunology and systems biology.

systems biology↗
bioRxiv2026

An extension of Modular Response Analysis for global perturbations and robust connectivity inference of gene regulatory networks.

Understanding how gene regulatory networks respond to global cell perturbations remains a central challenge in systems biology and network inference. Modular Response Analysis (MRA) provides a mathematical framework to infer gene-to-gene directed connectivity graphs from perturbation experiments; however, classical MRA captures direct gene-to-gene influences, and does not explicitly account for global stimuli that simultaneously change the graph. Here, we introduce MRA+, an extension of MRA, that incorporates the effect of global perturbations into gene-to-gene graph inference. MRA+ assumes a sequential experimental design in which targeted gene perturbations are followed by the application of a global stimulus, enabling the separation of connectivity changes from direct gene induction. The method estimates network connectivity under induced conditions and quantifies gene-specific induction strengths, which represent contributions to expression changes arising from mechanisms external to the inferred network. In the case of single-cell expression data, we present a bootstrap strategy to assess the robustness of inferred connectivity coefficients and propose a complementary criterion based on sign stability to interpret weak or non-significant estimates. Together, these developments provide a general framework for robust inference of gene connectivity graphs in the presence of global perturbations, applicable to diverse biological and experimental contexts.

systems biology↗