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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Metabolic and signalling network map integration: application to cross-talk studies and omics data analysis in cancer

BackgroundThe interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by creating an integrated resource of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease.\n\nResultsWe combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration. In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated ACSN-ReconMap 2.0 resource is accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated resource was applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified.\n\nConclusionsAs knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 resource will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases.

systems biology↗

bioRxiv2021

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies

Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition, and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 18 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

systems biology↗

bioRxiv2025

Systems modeling and uncertainty quantification of AMP-activated protein kinase signaling

AMP-activated protein kinase (AMPK) plays a key role in restoring cellular metabolic homeostasis after energy stress. Importantly, AMPK acts as a hub of metabolic signaling, integrating multiple inputs and acting on numerous downstream targets to activate catabolic processes and inhibit anabolic ones. Despite the importance of AMPK signaling, unlike other well-studied pathways, such as MAPK/ERK or NF-{kappa}B, only a handful of mechanistic models of AMPK signaling have been developed. Epistemic uncertainty in the biochemical mechanism of AMPK activity, combined with the complexity of the AMPK pathway, makes model development particularly challenging. Here, we leveraged uncertainty quantification methods and recently developed AMPK biosensors to construct a new, data-informed model of AMPK signaling. Specifically, we applied Bayesian parameter estimation and model selection to ensure that model predictions and assumptions are well-constrained to measurements of AMPK activity using recently developed AMPK biosensors. As an application of the new model, we predicted AMPK activity in response to exercise-like stimuli. We found that AMPK acts as a time- and exercise-dependent integrator of its input. Our results highlight how uncertainty quantification can facilitate model development and address epistemic uncertainty in a complex signaling pathway, such as AMPK. This work shows the potential for future applications of uncertainty quantification in systems biology to drive new biological insights by incorporating state-of-the-art experimental data at all stages of model development.

systems biology↗

bioRxiv2018

A Monte Carlo method for in silico modeling and visualization of Waddington’s epigenetic landscape with intermediate details

Waddingtons epigenetic landscape is a classic metaphor for describing the cellular dynamics during the development modulated by gene regulation. Quantifying Waddingtons epigenetic landscape by mathematical modeling would be useful for understanding the mechanisms of cell fate determination. A few computational methods have been proposed for quantitative modeling of landscape; however, to model and visualize the landscape of a high dimensional gene regulatory system with realistic details is still challenging. Here, we propose a Monte Carlo method for modeling the Waddingtons epigenetic landscape of a gene regulatory network (GRN). The method estimates the probability distribution of cellular states by collecting a large number of time-course simulations with random initial conditions. By projecting all the trajectories into a 2-dimensional plane of dimensions i and j, we can approximately calculate the quasi-potential U (xi, xj) = -ln P (xi, xj), where P (xi, xj) is the estimated probability of an equilibrium steady state or a non-equilibrium state. A state with locally maximal probability corresponds to a locally minimal potential and such a state is called an attractor. Compared to the state-of-the-art methods, our Monte Carlo method can quantify the global potential landscape (or emergence behavior) of GRN for a high dimensional system. The same topography of landscape can be produced from deterministic or stochastic time-course simulations. The potential landscapes show that not only attractors represent stability, but the paths between attractors are also part of the stability or robustness of biological systems. We demonstrate the novelty and reliability of our method by plotting the potential landscapes of a few published models of GRN. Besides GRN-driven landscapes of cellular dynamics, the algorithm proposed can also be applied to studies of global dynamics (or emergence behavior) of other dynamical systems.

systems biology↗

bioRxiv2026

A Perfect Soldier: the black soldier fly as a microbial-mediated physiological resilience model .

Understanding the complex interplay between a host, its diet, and its microbiome is crucial for comprehending an organisms health and adaptability. Diet impacts both the host and microbiome, which then influence each other. We used black soldier fly larvae (Hermetia illucens) as a model to investigate this tripartite interaction due to its resilience and bioconversion capabilities. We analyzed life-history traits and metatranscriptomics in larvae fed three diets: carbohydrate-rich, protein-rich, and balanced. Our results showed that dietary macronutrients correlated with shifts in the microbial community and gene expression. The carbohydrate-rich diet, in particular, led to increased microbial diversity and carbohydrate metabolism transcripts. However, this diet also negatively affected larval weight and development, suggesting potential host control over the microbiome. Overall, black soldier fly performance was highest on the balanced diet. This study highlights the black soldier flys resilience and its value as a model for exploring host-diet-microbe interactions. Significance StatementUnderstanding the intricate interplay between an organism, its diet, and its microbiome is fundamental to health and adaptability. This complex tripartite relationship, where dietary macronutrients influence microbial communities and their gene expression, while the host maintains control, is crucial for addressing global challenges from sustainable food systems to personalized medicine. Using the black soldier fly as a resilience model, our metatranscriptomic study reveals how specific dietary shifts impact both host and microbial gene expression, providing mechanistic insights into nutrient utilization and adaptability with broad implications for diverse biological systems, including animals and humans.

systems biology↗

bioRxiv2021

SMGen: A generator of synthetic models of biochemical reaction networks

Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of Computational Systems Biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by both features (e.g., system connectivity, reaction discreteness) and non trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component, which generally results in a computationally demanding task. To avoid any burden in the execution time, SMGen exploits a Main-Worker paradigm to speed up the overall process. SMGen is also provided with a user-friendly Graphical User Interface that allows the user to easily set up all the parameters required to generate a set of synthetic models with any user-defined number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric Reaction-based Models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct high numbers of errors during the creation process of the RBMs, a circumstance that increases the overall running time. Still, SMGen can create synthetic models with 512 species and reactions in less than 7 seconds. The open-source code of SMGen is available on GitLab: https://gitlab.com/sgr34/smgen.

systems biology↗

bioRxiv2025

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition methods (Scanning SWATH, or more generally scanning DIA) but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a prototype Zeno trap Q-TOF instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows. Significance StatementThe advent of faster DIA proteomics methods paved the way for investigating increasingly large sample series, including patient cohorts and strain collections containing thousands of samples. Yet, recent improvements in DIA methods still entailed compromises between analytical sensitivity and selectivity. The presented combination of a scanning quadrupole with fast ion trapping in a Zeno Trap, coined ZT Scan DIA increases quantitative precision and accuracy in fast proteomics experiments. These features of ZT Scan DIA may benefit applications that deal with low input samples and high-throughput proteomic workflows in biomedical cohort studies and systems biology.

systems biology↗

bioRxiv2026

A general thermodynamic approach for model reduction of enzyme cycles and electrogenic transporters

Mathematical models of enzyme cycles form the basis of quantifying key features of metabolism and membrane transport. These models are often integrated into more comprehensive models such as whole-cell models to understand emergent behaviours between interacting components. However, it is currently computationally infeasible to simulate the full dynamical behaviour of every enzyme at a network scale. Model reduction is frequently used to improve computational efficiency, but in general, these approaches do not preserve physical and thermodynamic consistency. Here, we outline a general method for simplifying enzyme kinetics models while retaining mass, charge and energy balance. We base our approach on the bond graph, which is a general methodology for modelling biological systems from fundamental physical laws. This approach ensures that key physical constraints are enforced in every model, regardless of their complexity. Our thermodynamic model reduction framework is readily extended to electrogenic transporters through the coupling of chemical and electrical processes. Through the application of our approach to both hypothetical enzyme cycles and real data from the Na+/K+ ATPase, we show that it can rapidly screen for plausible network structures in circumstances where enzyme catalytic mechanisms may not be fully characterised, facilitating biological discovery and drug development.

systems biology↗

bioRxiv2025

Geometry-preserving vector field reconstruction of high-dimensional cell-state dynamics using ddHodge

The differentiation potency of cells is governed by dynamic changes in gene expression, which can be inferred from single-cell RNA sequencing (scRNA-seq) data. While velocity-based approaches have been used to analyze cell state changes as vector fields, extracting acceleration (change of change) information remains challenging because of the sparsity and high dimensionality of the data. Here, we developed ddHodge, a framework based on Hodge decomposition for precise vector field reconstruction. ddHodge accurately recovers all basic components of the vector field, namely, the gradient, curl, and divergence, including the acceleration of the cell state, as second-order derivatives, even from biased and sparse samples. Furthermore, we extend the method to approximate high-dimensional gene expression dynamics on lower-dimensional data manifolds. By applying ddHodge to scRNA-seq data from mouse embryogenesis, we revealed that the gene expression dynamics during development follow a gradient system shaped by potential landscapes, which has not previously been validated with real data. Furthermore, we quantified differentiation potency as cell state stability on the basis of the divergence and identified key genes that drive potency. Our general computational framework for analyzing complex biological systems can elucidate cell fate decisions in developmental processes.

systems biology↗

bioRxiv2021

A virtual host model of Mycobacterium tuberculosis infection identifies early immune events as predictive of infection outcomes

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the worlds deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.

systems biology↗

bioRxiv2025

Self-driven Biological Discovery through Automated Hypothesis Generation and Experimental Validation

Automation is transforming scientific discovery by enabling systematic exploration of complex hypotheses. Large language models (LLMs) perform well across diverse tasks and promise to accelerate research, but often struggle to interact with logical structures. Here we present a framework integrating LLM-based agents with laboratory automation, guided by a logical scaffold incorporating symbolic relational learning, structured vocabularies, and experimental constraints. This integration reduces output incoherence and improves reliability in automated workflows. We couple this AI-driven approach to automated cell-culture and metabolomics platforms, enabling hypothesis validation and refinement, yielding a flexible system for scientific discovery. We validate the system in Saccharomyces cerevisiae, identifying novel interactions, including glutamate-induced synergistic growth inhibition in spermine-treated cells and aminoadipates partial rescue of formic-acid stress. All hypotheses, experiments, and data are captured in a graph database employing controlled vocabularies. Existing ontologies are extended, and a novel representation of scientific hypotheses is presented using description logics. This work enables a more reliable, machine-driven discovery process in systems biology.

systems biology↗

bioRxiv2022

Uncovering potential interventions for pancreatic cancer patients via mathematical modeling

Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository at https://github.com/drplaugher/PCC_Mutations.

systems biology↗

bioRxiv2022

Context-aware information selection and model recommendation with ACCORDION

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, context-specific, and useful subset (15-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.

systems biology↗

bioRxiv2022

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

MotivationThe accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as Flux Balance Analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. ResultsRelative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into Flux Balance Analysis predictions of a multi-tissue, diel model of Arabidopsis thalianas central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C Metabolic Flux Analysis (MFA) compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps, as measured by weighted averaged percent error values, dropped from between 169-180% and 94-103% in high light and low light conditions, respectively, to between 10-12% and 9-11%, depending on the gene expression dataset used. The incorporation of gene expression data into the modeling process also substantially altered the predicted carbon and energy economy of the plant. AvailabilityCode is available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights Contactyairhill@msu.edu

systems biology↗

bioRxiv2017

Adapting machine-learning algorithms to design gene circuits

Biological systems rely on complex networks, such as transcriptional circuits and protein-protein interaction networks, to perform a variety of functions e.g. responding to stimuli, directing cell fate, or patterning an embryo. Mathematical models are often used to ask: given some network, what function does it perform? However, we often want precisely the opposite i.e. given some circuit - either observed in vivo, or desired for some engineering objective - what biological networks could execute this function? Here, we adapt optimization algorithms from machine learning to rapidly screen and design gene circuits capable of performing arbitrary functions. We demonstrate the power of this approach by designing circuits (1) that recapitulate important in vivo phenomena, such as oscillators, and (2) to perform complex tasks for synthetic biology, such as counting noisy biological events. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.

systems biology↗

bioRxiv2022

A dynamic HIF1alpha-PPARgamma circuit controls a paradoxical adipocyte regulatory landscape

Hypoxia-induced upregulation of HIF1 triggers adipose tissue dysfunction and insulin resistance in obese patients. HIF1 closely interacts with PPAR{gamma}, the master regulator of adipocyte differentiation and lipid accumulation, but there are conflicting results how this co-regulation controls the excessive lipid accumulation that drives adipocyte dysfunction. Using single-cell imaging and modeling, we find that, surprisingly, HIF1 both promotes and represses lipid accumulation during adipogenesis. We show that the opposing roles of HIF1 are isolated from each other and depend on when HIF1 increases relative to the positive-feedback mediated upregulation of PPAR{gamma} that drives adipocyte differentiation. A theoretical model incorporating our findings resolves conflicting prior results and suggests that three network nodes before and after the isolation step have to be synergistically targeted in therapeutic strategies to revert hypoxia-mediated adipose tissue dysfunction in obesity. TeaserA systems biology approach detangles the effect of hypoxic and adipogenic regulators on lipid accumulation in adipocytes.

systems biology↗

bioRxiv2023

An ensemble of Gleditsia sinensis Lam. and gut microbiota against alcoholic liver disease

Gleditsia sinensis Lam. (GSL) is a medicinal herb and a noticeable resource of possessing hepatic protective agents such as alcoholic liver disease (ALD). At present, it has been documented that gut microbiota (GM) is related directly to etiology of ALD. Nevertheless, the bioactive molecules in GSL, favorable GM, targets, and key mechanism(s) against ALD are yet to be revealed. Hence, we integrated the significant four components to clarify the nuanced pathogenesis with help of network pharmacology (NP) concept. We retrieved significant metabolites via gutMGene and constructed GSL or GM-Signaling pathways-Targets-Metabolites (GGSTM) networks. Finally, molecular docking test (MDT) was performed to verify the key findings. The gutMGene suggested that 16 GM and 6 metabolites were related to the two signaling pathways through GGSTM networks. Both MDT and frontier molecular orbitals (FMO) theory revealed the most stable conformers: equol from Lactobacillus paracasei JS1 on IL6, Bauer-7-en-3-one, and Urs-12-en-3-one from GSL on PPARA, PPARD, and PPARG, respectively. In conclusion, this study sheds light on the combinatorial effects of GM, and GSL in treating ALD via systems biology concept.

systems biology↗

bioRxiv2024

C1QA is an invariant biomarker for tissue macrophages

Macrophages play a pivotal role in immune responses, particularly in the context of combating microbial threats within tissues. The identification of reliable biomarkers associated with macrophage function is essential for understanding their diverse roles in host defense. This study investigates the potential of C1QA as an invariant biomarker for tissue macrophages, focusing on its correlation with the anti-microbial pathway. C1QA, a component of the complement system, has been previously implicated in various immune functions. Our research delves into the specific association of C1QA with tissue-resident macrophages and its implications in the context of anti-microbial responses. Through comprehensive systems biology and Boolean analysis of gene expression, we aim to establish C1QA as a consistent and reliable marker for identifying tissue macrophages. Furthermore, we explore the functional significance of C1QA in the anti-microbial pathway. This research seeks to provide valuable insights into the molecular mechanisms underlying the anti-microbial functions of tissue macrophages, with C1QA emerging as a potential key player in this intricate regulatory network. Understanding the relationship between C1QA, tissue macrophages, and the anti-microbial pathway could pave the way for the development of targeted therapeutic strategies aimed at enhancing the hosts ability to combat infections. Ultimately, our findings contribute to the expanding knowledge of macrophage biology and may have implications for the diagnosis and treatment of infectious diseases. One Sentence SummaryC1QA is a fundamental biomarker of tissue macrophages

systems biology↗