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Results for “systems biology”

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bioRxiv2024

Mapping the attractor landscape of Boolean networks

Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram describes this interplay in a discrete analog of Waddingtons epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. We present a new approach to succession diagram construction for asynchronously updated Boolean networks, implemented in the biologists Boolean attractor landscape mapper, biobalm, a Python 3 library. We compare the performance of biobalm to similar tools and find a substantial performance increase in succession diagram construction, attractor identification, and attractor control. To illustrate the utility of biobalm, we perform the most comprehensive comparative analysis to date of the succession diagram structure in experimentally-validated Boolean network models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small succession diagrams, indicating simple decision structures. In contrast, non-random models from the literature are enriched in extremely large succession diagrams, indicating an abundance of decision points in their dynamics and suggesting the presence of complex Waddington landscapes in nature.

systems biology↗

bioRxiv2022

CHRRT: boosting coordinate hit-and-run with rounding by thinning

Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling convex polytopes uniformly, a highly relevant use-case in systems biology, we here demonstrate that thinning generally boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. We benchmark CHRR with thinning (CHRRT) with simplices and constrained-based metabolic networks with up to thousands of dimensions. With appropriate thinning, CHRRT offers a substantial increase in computational efficiency compared to unthinned CHRR, in our examples of up to three orders of magnitude, as measured by the effective sample size per time (ESS/t). Our experiments reveal that the performance gain of CHRRT by optimal thinning grows substantially with polytope (effective model) dimension. Based on our experiments, we provide practically useful advice for tuning thinning to efficient and effective use of compute resources. Besides allocating computational resources optimally to permit sampling convex polytopes uniformly to convergence in a fraction of time, exploiting thinning unlocks investigating hitherto intractable models under limited computational budgets. CHRRT thereby paves the way to keep pace with progressing model sizes within the existing constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.

systems biology↗

bioRxiv2025

Integrated Cross-Organ Transcriptomic Analysis Uncovers Conserved Gene Signatures Predictive of Allograft Rejection

Long-term transplant success is limited by allograft rejection, a complex process traditionally studied on an organ-specific basis. To establish a unified framework beyond organ-specific studies, we performed a network-based systems biology analysis of transcriptomic data from 672 liver, kidney, and heart transplant biopsies to identify a conserved, pan-organ molecular framework of rejection. By constructing and comparing organ-specific gene co-expression networks, we identified a consensus, six-module immune cascade that captures the hierarchical nature of the alloimmune response. In addition, we also uncovered a highly conserved 24-gene cell cycle signature consistently upregulated in rejecting allografts, implicating cellular proliferation as a core feature of rejection pathology. From this framework, we derived a 172-gene immune signature and applied machine learning models to assess its predictive performance, achieving accuracy comparable to established benchmarks. We further refined this to a minimal, high-performance 20-gene immune signature (AUC > 0.96). Both the immune and cell cycle signatures demonstrated robust, pan-organ utility when independently validated in a lung transplant cohort (n=243). Collectively, these findings define a pan-organ molecular framework for rejection and highlight cell cycle dysregulation as a conserved hallmark, offering a foundation for standardized, cross-organ diagnostic platforms to improve allograft surveillance and patient outcomes.

systems biology↗

bioRxiv2022

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

systems biology↗

bioRxiv2023

Logic-Based Modeling of Inflammatory Macrophage Crosstalk with Glomerular Endothelial Cells in Diabetic Kidney Disease

Diabetic kidney disease is a complication in one out of three patients with diabetes. Aberrant glucose metabolism in diabetes leads to structural and functional damage in glomerular tissue and a systemic inflammatory immune response. Complex cellular signaling is at the core of metabolic and functional derangement. Unfortunately, the mechanism underlying the role of inflammation in glomerular endothelial cell dysfunction during diabetic kidney disease is not fully understood. Mathematical models in systems biology allow the integration of experimental evidence and cellular signaling networks to understand mechanisms involved in disease progression. This study developed a logic-based ordinary differential equations model to study inflammatory crosstalk between macrophages and glomerular endothelial cells during diabetic kidney disease progression using a protein signaling network stimulated with glucose and lipopolysaccharide. This modeling approach reduced the biological parameters needed to study signaling networks. The model was fitted to and validated against available biochemical data from in vitro experiments. The model identified mechanisms for dysregulated signaling in macrophages and glomerular endothelial cells during diabetic kidney disease. In addition, the influence of signaling interactions on glomerular endothelial cell morphology through selective knockdown and downregulation was investigated. Simulation results showed that partial knockdown of VEGF receptor 1, PLC-{gamma}, adherens junction proteins, and calcium partially recovered the intercellular gap width between glomerular endothelial cells. These findings contribute to understanding signaling and molecular perturbations that affect the glomerular endothelial cells in the early stage of diabetic kidney disease. NEW & NOTEWORTHYThe work provides a novel analysis of signaling crosstalk between macrophages and glomerular endothelial cells in the early stage of diabetic kidney disease. A logic-based mathematical modeling approach identified vital signaling molecules and interactions that regulate glucose-mediated inflammation in the glomerular endothelial cells and cause endothelial dysfunction in the diabetic kidney. Simulated interactions among vascular endothelial growth factor receptor 1, nitric oxide, calcium, and junction proteins significantly affect the intercellular gap between glomerular endothelial cells.

systems biology↗

bioRxiv2023

An Automated Model Annotation System (AMAS) for SBML Models

MotivationAnnotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. ResultsWe present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g., species, reactions) by specifying the reference database (e.g., ChEBI for species) and the match score function (e.g., string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has sub-second response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AvailabilityOur project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license. Contacthsauro@uw.edu Supplementary informationSupplementary data are available online.

systems biology↗

bioRxiv2025

Proteomic Signatures of Hepatitis B Virus Mutations Reveal Genotype-Specific Host Responses and Biomarker Candidates

Hepatitis B virus (HBV) remains a global health challenge, with viral genetic heterogeneity and mutation-driven resistance complicating treatment outcomes. While previous genomic and transcriptomic studies have characterized HBV mutations, the proteomic consequences of these variants remain underexplored. In this study, we applied liquid chromatography- mass spectrometry (LC-MS)-based proteomics and systems biology approaches to serum samples from 60 HBV-infected patients, stratified by mutation-defined genotype signatures. Four genotype groups were generated, including those harboring mutation related to liver disease progression (rt269/s184) and basal core promoter/precore mutations (A1762T/G1896A). Comparative analyses of 406 high-abundance plasma proteins revealed distinct proteomic signatures, particularly in the ##AG group lacking mutation related to liver disease progression motifs. This group exhibited elevated CRISPLD2 and HSPD1 expression, implicating a dual axis of anti-inflammatory buffering and chaperone-mediated viral processes. Network and co-expression analyses identified modules enriched in focal adhesion, extracellular matrix remodeling, angiogenesis, and PI3K/Akt signaling--pathways tightly linked to hepatocarcinogenesis. Protein-protein interaction enrichment further highlighted disruption of chaperone networks, cytoskeletal regulation, and unfolded protein response. These findings provide molecular evidence for genotype-specific host-virus interactions, nominate CRISPLD2 and HSPD1 as biomarker candidates, and suggest therapeutic strategies targeting PI3K/Akt and microenvironmental pathways. Our results underscore the value of proteomics in refining genotype-informed risk stratification and personalized management in chronic HBV.

systems biology↗

bioRxiv2020

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine. Author summaryReconstructing gene regulatory network (GRN) is an essential question in systems biology. The lack of temporal information in sample-based transcriptomic data leads to a major challenge for inferring GRN and its translation to precision medicine. To address the above challenge, we propose to decode the latent temporal information underlying cancer progression via ordering patient samples based on transcriptomic similarity, and design a latent-temporal progression-based Bayesian method to infer GRNs from sample-based transcriptomic data of cancer patients. The advantages of our method include its capability to infer causal GRNs (with directed and signed edges) and its robustness to the measurement variability in the data. Performance evaluation using both simulated data and real data demonstrate that our method outperforms other existing methods in both pseudotime inference and GRN inference. Our method is then applied to reconstruct EMT regulatory networks in bladder cancer and to identify key regulators underlying progression of breast cancer. Importantly, the predicted key regulators/interactions are experimentally validated. Our study suggests that inferring dynamic progression trajectory from static expression data of tumor samples helps to uncover regulatory mechanisms underlying cancer progression and to discovery key regulators which may be used as candidate drug targets.

systems biology↗

bioRxiv2024

The regulatory architecture of the primed pluripotent cell state

Although numerous studies have focused on defining transcriptional cell states in normal and disease contexts, the gene regulatory architecture that governs and defines specific mammalian cell states remains poorly understood. Here we present an integrative computational and experimental systems biology approach to elucidate the regulatory architecture of a conserved cell state of critical importance in development and stem cell biology, namely primed state pluripotency. We have used an unbiased approach to analyze protein activity profiles from mouse epiblast stem cells (EpiSCs), leading to identification and experimental confirmation of 132 transcription factors that are master regulators (MRs) of primed state pluripotency. These MRs include known as well as novel factors, many of which were further validated for their role in lineage-specific differentiation using CRISPR-mediated functional assays. To assemble a comprehensive regulatory network, we silenced each of the 132 MRs to assess their effects on the other MRs and their transcriptional targets, yielding a network of 1,273 MR[->]MR interactions. Network architecture analyses revealed four functionally distinct MR modules (communities), largely independent of lineage-specific differentiation, and identified key Speaker and Mediator MRs based on their hierarchical rank and centrality in mediating information flow in the pluripotent cell. Taken together, our findings elucidate the de-centralized logic of a "communal interaction" model in which the balanced activities of four MR communities maintain pluripotency, and define the primed pluripotent cell state in terms of its transcriptional regulatory network.

systems biology↗

bioRxiv2025

Rif-seq reveals Caulobacter crescentus mRNA decay is globally coordinated with transcription and translation

While transcription and translation have been shown to be coordinated with mRNA decay across various single-gene studies, their global coordination remains poorly defined. Therefore, we performed Rif-seq experiments in C. crescentus to measure genome-wide mRNA lifetimes and analyzed the impact of transcription and translation. Based upon the RNAP elongation speed, we identified that approximately 20% of mRNAs were cotranscriptionally degraded. We generated absolute quantitative estimates of mRNA copy numbers in C. crescentus, a useful systems biology resource, revealing that some gene categories have coordinated mRNA turnover. To investigate translations impact on mRNA decay, we found that translation efficiency measured by ribosome profiling correlates with mRNA lifetime. We compared the 5 P cleavage sites to ribosome occupancy and found that cleavage sites occur preferentially in regions of low ribosome occupancy. Using the translation initiation inhibitor retapamulin, which traps ribosomes at the start codon, and chloramphenicol, which arrests elongating ribosomes, shows that chloramphenicol leads to global mRNA stabilization. Surprisingly, we find that the codon adaptation index is inversely correlated with mRNA lifetime, suggesting slow translation elongation may be stabilizing mRNAs from decay. We confirmed the roles of translation initiation and elongation on mRNA lifetimes by generating synthetic YFP and mCherry reporter mRNAs. Taken together, mRNA decay is globally interconnected with transcription and translation. Highlights[bullet] Quantitative analysis yields absolute mRNA abundance and half-lives for C. crescentus [bullet]Identification of 47 new stable ncRNAs [bullet]Both transcription and translation are globally coordinated with mRNA decay [bullet]Translation initiation and elongation impact mRNA decay through ribosome occupancy

systems biology↗

bioRxiv2018

NeoDTI: Neural integration of neighborinformation from a heterogeneous network fordiscovering new drug-target interactions

MotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.\n\nResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.\n\nAvailability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI.\n\nContactzengjy321@tsinghua.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2017

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the \"angiogenic signal\" produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumors response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

systems biology↗

bioRxiv2026

A Multiscale Computational Architecture to Study Signaling Dynamics at Cell-Cell Interfaces

Intercellular communication is governed by the spatiotemporal dynamics of protein complexes at the cell-cell interface. However, conventional static interaction models fail to incorporate key physical constraints, such as steric hindrance, spatial compartmentalization, and dimensionality reduction that regulate complex assembly in vivo. To bridge the gap between static network topology and dynamic systems biology, we developed a multi-scale computational framework. We first identified a highly conserved, Fibroblast Growth Factor Receptor 1 (FGFR1)-centered cell adhesion and signaling motif by analyzing a diverse set of human cell-cell interfaces. We then constructed a multi-layer spatial stochastic simulator to recapitulate and interrogate the dynamic behavior of this network motif at cell-cell interfaces. Atomic-resolution structural models of the protein complexes within the motif were further generated using AlphaFold to define interaction rules for the stochastic simulations by categorizing binding interfaces. Our results show that the structural arrangement of cell-cell adhesion complexes controls how FGFR1 receptors cluster at the cell-cell interface, effectively dividing the membrane into distinct functional microdomains. Competition from decoy receptors further regulates this process by capturing receptors before they can participate in signaling. Even small changes in binding affinity can therefore alter receptor organization and disrupt normal signal transduction, which may contribute to human disease. By integrating macro-scale interactomics, atomic-level structural bioinformatics, and mesoscale stochastic modeling, this study reveals how structural interaction rules, combined with spatial constraints, shape the formation and function of intercellular signaling networks.

systems biology↗

bioRxiv2023

Systems-ecology designed bacterial consortium protects from severe Clostridioides difficile infection

Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in treatment and prevention of recurrent Clostridioides difficile infection (rCDI). FMT has recently been associated with adverse outcomes such as inadvertent transfer of antimicrobial resistance, necessitating development of more targeted bacteriotherapies. To address this challenge, we developed a novel systems biology pipeline to identify candidate probiotic strains that would be predicted to interrupt C. difficile pathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we identified those metabolic pathways most associated with successful FMTs and reconstructed the metabolism of encoding species to simulate interactions with C. difficile. This analysis resulted in predictions of high levels of cross-feeding for amino acids in species most associated with FMT success. Guided by these in silico models, we assembled consortia of bacteria with increased amino acid cross-feeding which were then validated in vitro. We subsequently tested the consortia in a murine model of CDI, demonstrating total protection from severe CDI through decreased toxin levels, recovered gut microbiota, and increased intestinal eosinophils. These results support the novel framework that amino acid cross-feeding is likely a critical mechanism in the initial resolution of CDI by FMT. Importantly, we conclude that our predictive platform based on predicted and testable metabolic interactions between the microbiota and C. difficile led to a rationally designed biotherapeutic framework that may be extended to other enteric infections.

systems biology↗

bioRxiv2024

High-throughput proteome profiling with low variation in a multi-center study using dia-PASEF

High throughput proteomics is gaining increasing traction as it facilitates screening of large sample cohorts required in clinical research and systems biology studies. Recent developments in mass spectrometry-based proteomics resulted in improved hardware and software providing deep proteome coverage, robustness, and scale accessible to a wide range of laboratories. Here, we benchmark dia-PASEF, a data-independent acquisition scheme that integrates trapped ion mobility with high scan speed, with a high-resolution time-of-flight mass analyzer (timsTOF HT) for the deep proteome analysis of a human cell line applying short 5-minute gradients. To show intra-and interlaboratory reproducibility, we performed a multi-laboratory study including 11 sites. We demonstrate that on average 7,072 protein groups and 99,835 peptides were identified in human chronic myelogenous leukemia cells on the timsTOF HT with low variation. Our results underline that dia-PASEF data acquisition combined with reproducible chromatography enables high robustness and data consistency across instruments and laboratories, which is a prerequisite for translational biomedical insights.

systems biology↗

bioRxiv2018

PP-2, a src-kinase inhibitor, is a potential corrector for F508del-CFTR in cystic fibrosis

Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The most common mutation in CF, an in-frame deletion of phenylalanine 508, leads to a trafficking defect and endoplasmic reticulum retention of the protein where it becomes targeted for degradation. Successful clinical deployments of ivacaftor and ivacaftor/lumacaftor combination have been an exciting translational development in treating CF. However, their therapeutic effects are variable between subjects and remain insufficient. We used the Library of Integrated Network-based Cellular Signatures (LINCS) database as our chemical pool to screen for candidates. For in silico screening, we integrated connectivity mapping and CF systems biology to identify candidate therapeutic compounds for CF. Following in silico screening, we validated our candidate compounds with (i) an enteroid-based compound screening assay using CF ({Delta}F508/{Delta}F508-CFTR) patient-derived enteroids, (ii) short-circuit current analysis using polarized CF primary human airway epithelial cells and (iii) Western blots to measure F508-del-CFTR protein maturation. We identified 184 candidate compounds with in silico screening and tested 24 of them with enteroid-based forskolin-induced swelling (FIS) assay. The top hit compound was PP2, a known src-kinase inhibitor that induced swelling in enteroid comparable to known CF corrector (lumacaftor). Further validation with Western blot and short-circuit current analysis showed that PP-2 could correct mutant CFTR mis-folding and restore CFTR-mediated transmembrane current. We have identified PP2, a known src-kinase inhibitor, as a novel corrector of {Delta}F508-CFTR. Based on our studies and previous reports, src kinase inhibition may represent a novel paradigm of multi-action therapeutics - corrector, anti-inflammatory, and anti-infective - in CF.

systems biology↗

bioRxiv2025

Conditional Universal Differential Equations Simultaneously Capture Population Level Dynamics and Inter-individual Variation in Human C-peptide Production

Universal differential equations (UDEs) are an emerging approach in biomedical systems biology, integrating physiology-driven mathematical models with machine learning for data-driven model discovery in areas where knowledge of the underlying physiology is limited. However, current approaches to training UDEs do not directly accommodate heterogeneity in the underlying data. As a data-driven approach, UDEs are also vulnerable to overfitting and consequently cannot sufficiently generalise to heterogeneous populations. We propose a conditional UDE (cUDE) where we assume that the structure and weights of the embedded neural network are common across individuals, and introduce a conditioning parameter that is allowed to vary between individuals. In this way, the cUDE architecture can accommodate inter-individual variation in data while learning a generalisable network representation. We demonstrate the effectiveness of the cUDE as an extension of the UDE framework by training a cUDE model of c-peptide production. We show that our cUDE model can accurately describe postprandial c-peptide levels in individuals with normal glucose tolerance, impaired glucose tolerance, and type 2 diabetes mellitus. Furthermore, we show that the conditional parameter captures relevant inter-individual variation. Subsequently, we use symbolic regression to derive a generalisable analytical expression for c-peptide production.

systems biology↗

bioRxiv2025

Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimers Disease

Alzheimers Disease (AD) is marked by the accumulation of pathology, neuronal loss, and gliosis and frequently accompanied by cognitive decline. Understanding brain cell interactions is key to identifying new therapeutic targets to slow its progression. Here, we used systems biology methods to analyze single-nucleus RNA sequencing (snRNASeq) data generated from dorsolateral prefrontal cortex (DLPFC) tissues of 424 participants in the Religious Orders Study or the Rush Memory and Aging Project (ROSMAP). We identified modules of co-regulated genes in seven major cell types, assigned them to coherent cellular processes, and assessed which modules were associated with AD traits such as cognitive decline, tangle density, and amyloid-{beta} deposition. Coexpression network structure was conserved in the majority of modules across cell types, but we also found distinct communities with altered connectivity, especially when compared to bulk RNASeq, suggesting cell-specific gene co-regulation. These coexpression modules can also capture signatures of cell subpopulations and be influenced by cell proportions. Using a Bayesian network framework, we modeled the direction of relationships between the modules and AD progression. We highlight two key modules, a microglia module (mic_M46), associated with tangles; and an astrocyte module (ast_M19), associated with cognitive decline. Our work provides cell-specific molecular networks modeling the molecular events leading to AD.

systems biology↗