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Results for “Systems Biology”

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bioRxiv2018

Reproducible model development in the Cardiac Electrophysiology Web Lab

The modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models.

systems biology↗
bioRxiv2017

SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks

Background: The rapid growth of the number of mathematical models in Systems Biology fostered the development of many tools to simulate and analyse them. The reliability and precision of these tasks often depend on multiple repetitions and they can be optimised if executed as pipelines. In addition, new formal analyses can be performed on these repeat sequences, revealing important insights about the accuracy of model predictions.\n\nResults: Here we introduce SBpipe, an open source software tool for automating repetitive tasks in model building and simulation. Using basic configuration files, SBpipe builds a sequence of repeated model simulations or parameter estimations, performs analyses from this generated sequence, and finally generates a LaTeX/PDF report. The parameter estimation pipeline offers analyses of parameter profile likelihood and parameter correlation using samples from the computed estimates. Specific pipelines for scanning of one or two model parameters at the same time are also provided. Pipelines can run on multicore computers, Sun Grid Engine (SGE), or Load Sharing Facility (LSF) clusters, speeding up the processes of model building and simulation. SBpipe can execute models implemented in Copasi, Python or coded in any other programming language using Python as a wrapper module. Future support for other software simulators can be dynamically added without affecting the current implementation.\n\nConclusions: SBpipe allows users to automatically repeat the tasks of model simulation and parameter estimation, and extract robustness information from these repeat sequences in a solid and consistent manner, facilitating model development and analysis. The source code and documentation of this project are freely available at the web site: https://pdp10.github.io/sbpipe/.

systems biology↗
bioRxiv2025

Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention sets

MotivationThe integration of genome-scale metabolic and regulatory networks has received significant interest in the field of Cancer Systems Biology. However, the identification of lethal genetic interventions in these integrated models remains a substantially challenge due to the combinatorial explosion of potential solutions. To address this issue, we previously developed the genetic Minimal Cut Set (gMCS) framework, which is capable of computing synthetic lethal interactions - minimal sets of gene knockouts that are lethal for cellular proliferation- in genome-scale metabolic networks with signed directed acyclic regulatory pathways. Here, we present a novel formulation to calculate genetic Minimal Intervention Sets, gMISs, which incorporate interventions with both gene knockouts and knock-ins. ResultsWith our gMIS approach, we assessed the landscape of lethal genetic interactions in human cells and the impact of gene knock-in perturbations, which enable us to capture for the first-time genetic interventions beyond synthetic lethality, particularly synthetic dosage lethality and tumor suppressor gene complexes. We applied the concept of synthetic dosage lethality to predict essential genes in cancer and demonstrated a significant increase in sensitivity when compared to large-scale gene knockout screen data. Moreover, we conducted a tumor suppressor analysis in cancer cell lines and identified single gene knock-in strategies that block cellular proliferation. Finally, we illustrate the ability of our gMIS approach to elucidate potential targets for cancer research with several examples in hematological malignancies Availability and implementationThe Python package gMCSpy has been updated to include the functionalities to calculate gMISs. It can be accessed here: https://github.com/PlanesLab/gMCSpy.

systems biology↗
bioRxiv2024

Computational Microbiome Pharmacology Analysis Elucidates the Anti-Cancer Potential of Vaginal Microbes and Metabolites

The vaginal microbiomes role in risk, progression, and treatment of female cancers has been widely explored. Yet, there remains a need to develop methods to understand the interaction of microbiome factors with host cells and to characterize their potential therapeutic functions. To address this challenge, we developed a systems biology framework we term the Pharmacobiome for microbiome pharmacology analysis. The Pharmacobiome framework evaluates similarities between microbes and microbial byproducts and known drugs based on their impact on host transcriptomic cellular signatures. Here, we apply our framework to characterization of the Anti-Gynecologic Cancer Vaginal Pharmacobiome. Using published vaginal microbiome multi-omics data from the Partners PrEP clinical trial, we constructed vaginal epithelial gene signatures associated with each profiled vaginal microbe and metabolite. We compared these microbiome-associated host gene signatures to post-drug perturbation host gene signatures associated with 35 FDA-approved anti-cancer drugs from the Library of Integrated Network-based Cellular Signatures database to identify vaginal microbes and metabolites with high statistical and functional similarity to these drugs. We found that Lactobacilli and their metabolites can regulate host gene expression in ways similar to many anti-cancer drugs. Additionally, we experimentally tested our model prediction that taurine, a metabolite produced by L. crispatus, kills cancerous breast and endometrial cancer cells. Our study shows that the Pharmacobiome is a powerful framework for characterizing the anti-cancer therapeutic potential of vaginal microbiome factors with generalizability to other cancers, microbiomes, and diseases.

systems biology↗
bioRxiv2022

Proteome coverage after simultaneous proteo-metabolome liquid-liquid extraction

Proteo-metabolomics is essential in systems biology and simultaneous proteo-metabolome extraction by liquid-liquid extraction (SPM-LLE) allows extraction of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE it must be solubilized for quantitative proteomics. Solubilization and proteome extraction is a critical factor in the information that can be obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea with respect to proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol-chloroform based SPM-LLE. We also investigated the extent to which the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were very similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants (SDC, SDS) for quantitative proteomics is better when the proteome was extracted by direct cell lysis and not from an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet and by direct cell lysis.

systems biology↗
bioRxiv2018

WASABI: a dynamic iterative framework for gene regulatory network inference

Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. In conclusion, WASABI is a versatile algorithm which should help biologists to fully exploit the power of time-stamped single-cell data.

systems biology↗
bioRxiv2020

Understanding biochemical design principles with ensembles of canonical non-linear models

Systems biology applies concepts from engineering in order to understand biological networks. If such an understanding was complete, biologists would be able to design ad hoc biochemical components tailored for different purposes, which is the goal of synthetic biology. Needless to say that we are far away from creating biological subsystems as intricate and precise as those found in nature, but mathematical models and high throughput techniques have brought us a long way in this direction. One of the difficulties that still needs to be overcome is finding the right values for model parameters and dealing with uncertainty, which is proving to be an extremely difficult task. In this work, we take advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, the role of different components and the synergies between them can be better understood. We will address some of the difficulties often faced by ensemble modeling approaches, such as the need to sample a space whose size grows exponentially with the number of parameters, and establishing useful selection criteria. Some methods will be shown to reduce the predictions from many models into a set of understandable "design principles" that can guide us to improve or manufacture a biochemical network. Our proposed framework formulates models within standard formalisms in order to integrate information from different sources and minimize the dimension of the parameter space. Additionally, the mathematical properties of the formalism enable a partition of the parameter space into independent subspaces. Each of these subspaces can be paired with a set of criteria that depend exclusively on it, thus allowing a separate sampling/screening in spaces of lower dimension. By applying tests in a strict order where computationally cheaper tests are applied first to each subspace and applying computationally expensive tests to the remaining subset thereafter, the use of resources is optimized and a larger number of models can be examined. This can be compared to a complex database query where the order of the requests can make a huge difference in the processing time. The method will be illustrated by analyzing a classical model of a metabolic pathway with end-product inhibition. Even for such a simple model, the method provides novel insight. Author summaryA method is presented for the discovery of design principles, understood as recurrent solutions to evolutionary problems, in biochemical networks. The method takes advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, a set of simple rules can be identified that enables us to formulate new models that are known to perform well, a priori. By formulating the models within the framework of Biochemical Systems Theory (BST) we manage to overcome some of the obstacles often faced by ensemble modeling. Further analysis of the selected modeling with standard machine learning techniques enables the formulation of simple rules - design principles - for building good performing networks. We illustrate the method with a well-known case study: the unbranched pathway with end-product inhibition. The method manages to identify the known features of this well-studied pathway while providing additional guidelines on how the pathway kinetics can be tuned to achieve a desired functionality - e.g. demand vs supply control - as well as to identifying important tradeoffs between performance, robustness and and stability.

systems biology↗
bioRxiv2017

Network Architecture and Mutational Sensitivity of the C. elegans Metabolome

A fundamental issue in evolutionary systems biology is understanding the relationship between the topological architecture of a biological network, such as a metabolic network, and the evolution of the network. The rate at which an element in a metabolic network accumulates genetic variation via new mutations depends on both the size of the mutational target it presents and its robustness to mutational perturbation. Quantifying the relationship between topological properties of network elements and the mutability of those elements will facilitate understanding the variation in and evolution of networks at the level of populations and higher taxa.\n\nWe report an investigation into the relationship between two topological properties of 29 metabolites in the C. elegans metabolic network and the sensitivity of those metabolites to the cumulative effects of spontaneous mutation. The relationship between several measures of network centrality and sensitivity to mutation is weak, but point estimates of the correlation between network centrality and mutational variance are positive, with only one exception. There is a marginally significant correlation between core number and mutational heritability. There is a small but significant negative correlation between the shortest path length between a pair of metabolites and the mutational correlation between those metabolites.\n\nPositive association between the centrality of a metabolite and its mutational heritability is consistent with centrally-positioned metabolites presenting a larger mutational target than peripheral ones, and is inconsistent with centrality conferring mutational robustness, at least in toto. The weakness of the correlation between shortest path length and the mutational correlation between pairs of metabolites suggests that network locality is an important but not overwhelming factor governing mutational pleiotropy. These findings provide necessary background against which the effects of other evolutionary forces, most importantly natural selection, can be interpreted.

systems biology↗
bioRxiv2018

Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series

The reconstruction of gene regulatory networks from time resolved gene expression measurements is a key challenge in systems biology with applications in health and disease. While the most popular network inference methods are based on unsupervised learning approaches, supervised learning methods have proven their potential for superior reconstruction performance. However, obtaining the appropriate volume of informative training data constitutes a key limitation for the success of such methods.\n\nHere, we introduce a supervised learning approach to detect gene-gene regulation based on exclusively synthetic training data, termed surrogate learning, and show its performance for synthetic and experimental time-series. We systematically investigate different simulation configurations of biologically representative time-series of transcripts and augmentation of the data with a measurement model. We compare the resulting synthetic datasets to experimental data, and evaluate classifiers trained on them for detection of gene-gene regulation from experimental time-series. For classifiers, we consider hybrid convolutional recurrent neural networks, random forests and logistic regression, and evaluate the reconstruction performance of different simulation settings, data pre-processing and classifiers.\n\nWhen training and test time-courses are generated from the same distribution, we find that the largest tested neural network architecture achieves the best performance of 0.448 {+/-} 0.047 (mean {+/-} std) in maximally achievable F1 score over all datasets outperforming random forests by 32.4 % {+/-} 14 % (mean {+/-} std). Reconstruction performance is sensitive to discrepancies between synthetic training and test data, highlighting the importance of matching training and test data domains. For an experimental gene expression dataset from E.coli, we find that training data generated with measurement model, multi-gene perturbations, but without data standardization is best suited for training classifiers for network reconstruction from the experimental test data. We further demonstrate superiority to multiple unsupervised, state-of-the-art methods for networks comprising 20 genes of the experimental data from E.coli (average AUPR best supervised = 0.22 vs best unsupervised = 0.07).\n\nWe expect the proposed surrogate learning approach to be broadly applicable. It alleviates the requirement for large, difficult to attain volumes of experimental training data and instead relies on easily accessible synthetic data. Successful application for new experimental conditions and other data types is only limited by the automatable and scalable process of designing simulations which generate suitable synthetic data.

systems biology↗
bioRxiv2019

Cell cycle regulation of mitochondrial protein import revealed by genome-scale pooled bimolecular fluorescence complementation screening

A central focus of systems biology is the functional mapping of protein-protein interactions under physiological conditions. Here we describe MaGiCaL-BiFC, a lentivirus-based bimolecular fluorescence protein-fragment complementation approach for the high-throughput, genome-scale identification of protein-protein interactions in mammalian cells. After developing and validating this methodology using known protein-protein interaction pairs, we constructed genome-scale pooled BiFC libraries using the human ORFeome cDNA collection. These pooled libraries, containing [~] 12,000 unique human cDNAs, were used to screen for candidate interaction partners of the mitochondrial transmembrane protein TOMM22. Following infection of cells with the TOMM22 bait and the pooled cDNA libraries, cells harboring candidate TOMM22 interacting proteins were isolated from the cell pool via fluorescence activated cell sorting, and identified via microarray analysis. This approach identified several known interaction partners of TOMM22, as well as novel physical and functional partners that link the mitochondrial network to proteins involved in diverse cellular processes. Notably, protein kinase CK2 was identified as a novel physical interaction partner of human TOMM22. We found that this association occurs preferentially during mitosis and involves direct phosphorylation of TOMM22, an event that may lead to attenuation of mitochondrial protein import. Together, this data contributes to the growing body of evidence suggesting eloquent coordination between cell cycle progression and mitochondrial physiology. Importantly, through high-throughput screening and focused validation, our study demonstrates the power of the MaGiCaL-BiFC approach to uncover novel functional protein-protein interactions, including those involving proteins with membrane-spanning domains, or of a transient nature, all within their native cellular environment.

systems biology↗
bioRxiv2018

Qualitative modeling of signaling networks in replicative senescence by selecting optimal node and arc sets

Signaling networks are an important tool of modern systems biology and drug development. Here, we present a new methodology to qualitatively model signaling networks by combining experimental data and prior knowledge about protein connectivity. Unlike other methods, our approach does not focus solely on selecting which reactions are involved but also on whether a protein is present. This allows the user to model more complicated experiments and incorporate more knowledge into the model. To demonstrate the capabilities of our method we compared the signaling networks of young and replicative senescent human primary HFL-1 fibroblasts, whose differences are expected to be due mainly to changes in the expression of the proteins rather than the reactions involved. The resulting networks indicate that, compared to young cells, aged cells are not as responsive to insulin stimulation and activate pathways that establish and maintain senescence.\n\nAuthor summaryCells have developed a complex network of biochemical reactions to monitor their environment and react to changes. Although multiple pathways, tuned to identify specific stimuli, have been discovered, it is generally understood that the signaling process typically involves multiple pathways and is context depended. Consequently, reconstructing the signaling network utilized by cells at any given moment is not a trivial task. In this article, we report on a novel logic-based method for identifying signaling network by combining experimental data with prior knowledge about the connectivity of the involved proteins. Unlike other methods proposed so far, our method uses data to evaluate the presence or absence of reactions and proteins alike. We reconstructed and compared the signaling network of human primary HFL-1 fibroblasts as they undergo replicative senescence in the presence of 6 different stimuli. The resulting networks indicate that, compared to young cells, senescent cells are not responsive to insulin stimulation and activate pathways that are known to establish and maintain senescence.

systems biology↗
bioRxiv2020

The impact of rare and common genetic variation in theInterleukin-1 pathway for human cytokine responses

BackgroundInterleukin(IL)-1 signaling is of major importance in human innate cytokine responses. Common variants in related genes have been linked to various inflammation-mediated diseases and stimulation-induced cytokine responses, but the role of rare variants remains to be elucidated. MethodsIn this study, we characterize the role of rare and common genetic variation, as identified by molecular inversion probe-based sequencing, in 48 genes related to the IL-1 pathway. Using a systems biology approach, we examined the inter-individual variability of in vitro stimulation-specific human cytokine responses from 463 healthy individuals of the Human Functional Genomics Project and assessed the role of rare and common genetic variants, separately and combined, by means of the Sequence Kernel Association Test. ResultsWe identified strong associations for rare genetic variants in NCF4 (adjP=7.2E-05) and CASP1 (adjP=3.0E-05) with IL-6 production in response to PHA and LPS stimulation, respectively. In addition, common variants in IL36A and IL38 were associated to both C. albicans-induced IL-1{beta} (IL36A adjP=0.0442; IL38 adjP=0.0092) and IL-6 production (IL36A adjP=0.0037; IL38 adjP=0.0082), an effect that was stronger at the subpathway level both for IL-1{beta} (adjP=0.0017) and IL-6 (adjP=1.8E-04). The common variant signature for the IL-1{beta} and IL-6 response to C. albicans was confirmed by an association with all anti-inflammatory genes (adjP=1.87E-03 and adjP=5.75E-04), and we validated this finding for non-coding common variants. Lastly, we identified a burden of rare variants in pro-inflammatory genes and LPS-induced IL-6 production (adjP=2.42E-04), and a new role for anti-inflammatory rare variants on S. aureus-stimulated IL-6 production (adjP=6.71E-03). ConclusionsIn conclusion, we show that both common and rare genetic variation in genes of the IL-1 pathway, separately and combined, differentially influence in vitro cytokine responses to various stimuli in healthy individuals. This study therefore accentuates potential mechanisms that are translatable into new hypothesis-driven characterization of common and rare variant involvement in a wide variety of inflammatory and immunological mechanisms and diseases.

systems biology↗
bioRxiv2020

Benchmarking kinetic models of Escherichia coli metabolism

Predicting phenotype from genotype is the holy grail of quantitative systems biology. Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli. We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance and applicability to various scenarios. This study can help to guide further development of kinetic models, and to demonstrate how to apply such models in real-world setting, ultimately enabling the design of efficient cell factories. Author summaryKinetic modeling is a promising method to predict cell metabolism. Such models provide mechanistic description of how concentrations of metabolites change in the cell as a function of time, cellular environment and the genotype of the cell. In the past years there have been several kinetic models published for various organisms. We want to assess how reliably models of Escherichia coli metabolism could predict cellular metabolic state upon genetic or environmental perturbations. We test selected models in the ways that represent common metabolic engineering practices including deletion and overexpression of genes. Our results suggest that all published models have tradeoffs and the model to use should be chosen depending on the specific application. We show in which cases users could expect the best performance from published models. Our benchmarking study should help users to make a better informed choice and also provides systematic training and testing dataset for model developers.

systems biology↗
bioRxiv2020

The Molecular Landscape of the Aging Mouse Liver

Systems biology approaches often use inferred networks of gene expression and metabolite data to identify regulatory factors and pathways connected with phenotypic variance. Generally, study-specific multi-layer "Omics" datasets are used to contextualize generic molecular networks. In this regard separating upstream causal mechanisms, downstream biomarkers, and incidental correlations remains a significant challenge, yet it is essential for designing mechanistic experiments. To address this, we designed a study following a population of 2157 individuals from 89 isogenic BXD mouse strains across their lifespan to identify molecular interactions among genotype, environment, age (GxExA) and metabolic fitness. Each strain was separated into two cohorts, one fed low fat (6% cal/fat) and the other high fat (60% cal/fat) diets. Tissues were collected for 662 individuals (309 cohorts) diverging across age (7, 12, 18, and 24 months), diet, sex, and strain. Transcriptome, proteome, and metabolome data were generated for liver. Of these we identified linear relations among these molecular data with lifespan for the same genomes of mice (Roy et al. 2020), and we defined [~]1100 novel protein-coding genes associated with longevity. We knocked down the ortholog of Ctsd in C. elegans. The treatment reduced longevity both in wildtype and in mutant long-lived strains, thus validating the prediction. Next, to assess the molecular impact of GxExA on gene expression, the multi-omics data was parsed into metabolic networks where connectivity varied due to the independent variables. Differences in edge strengths connecting nodes in these molecular networks according to each variable enabled causal inference by using stability selection, with roughly 21% of novel gene-pathway connections being causally affected by diet and/or age. For instance, Chchd2 is activated by aging and drives changes in the proteasome, oxidative phosphorylation, and mitochondrial translation transcriptional networks. Together, we have developed a large multi-omics resource for studying aging in the liver, and a resource for turning standard associations into causal networks.

systems biology↗
bioRxiv2021

Kinetic and data-driven modeling of pancreatic β-cell central carbon metabolism and insulin secretion

Pancreatic {beta}-cells respond to increased extracellular glucose levels by initiating a metabolic shift. That change in metabolism is part of the process of glucose-stimulated insulin secretion and is of particular interest in the context of diabetes. However, we do not fully understand how the coordinated changes in metabolic pathways and metabolite products influence insulin secretion. In this work, we apply systems biology approaches to develop a detailed kinetic model of the intracellular central carbon metabolic pathways in pancreatic {beta}-cells upon stimulation with high levels of glucose. The model is calibrated to published metabolomics datasets for the INS1 823/13 cell line, accurately capturing the measured metabolite fold-changes. We first employed the calibrated mechanistic model to estimate the stimulated cells fluxome. We then used the predicted network fluxes in a data-driven approach to build a partial least squares regression model. By developing the combined kinetic and data-driven modeling framework, we gain insights into the link between {beta}-cell metabolism and glucose-stimulated insulin secretion. The combined modeling framework was used to predict the effects of common anti-diabetic pharmacological interventions on metabolite levels, flux through the metabolic network, and insulin secretion. Our simulations reveal targets that can be modulated to enhance insulin secretion. The model is a promising tool to contextualize and extend the usefulness of metabolomics data and to predict dynamics and metabolite levels that are difficult to measure in vitro. In addition, the modeling framework can be applied to identify, explain, and assess novel and clinically-relevant interventions that may be particularly valuable in diabetes treatment. Author SummaryDiabetes is among the most common chronic illnesses, occurring when the {beta}-cells in the pancreas are unable to produce enough insulin to properly manage the bodys blood sugar levels. {beta}-cells metabolize nutrients to produce energy needed for insulin secretion in response to high glucose, and there is a potential to harness {beta}-cell metabolism for treating diabetes. However, {beta}-cell metabolism is not fully characterized. We have developed a computational modeling framework to better understand the relationship between cellular metabolism and insulin production in the pancreatic {beta}-cell. With this modeling framework, we are able to simulate metabolic perturbations, such as the knockdown of the activity of a metabolic enzyme, and predict the effect on the metabolic network and on insulin production. This work can therefore be applied to investigate, in a time- and cost-efficient manner, {beta}-cell metabolism and predict effective therapies that target the cells metabolic network.

systems biology↗
bioRxiv2026

Scalable biophysical constraints for physiologically consistent metabolic states

Systems biology aims to develop predictive models that connect molecular mechanisms to cellular behavior. Genome-scale metabolic models are among the most widely used frameworks for integrating stoichiometric, thermodynamic, and omics-derived information to predict feasible metabolic phenotypes. However, cellular metabolism operates on timescales governed by enzyme kinetics and by the relationship between metabolic fluxes and metabolite pool sizes. In steady-state metabolic models, this relationship can be expressed in terms of metabolite turnover rates, defined as flux-to-pool-size ratios that quantify how rapidly metabolite pools are renewed. As a result, physiologically consistent steady-state solutions should not only satisfy mass-balance and thermodynamic constraints but also exhibit turnover rates consistent with enzyme-mediated cellular dynamics. Current constraint-based approaches can admit many steady-state flux-concentration states that do not account for turnover rates, resulting in phenotypes incompatible with realistic metabolic dynamics, even when multiple types of data are imposed. Here, we present METEOR-K, an optimization framework that links steady-state metabolic fluxes to metabolite concentrations via turnover rate constraints to identify dynamically plausible flux-concentration reference states. Because these constraints reshape the feasible solution space, we also introduce turnover-rate-aware sampling strategies to efficiently explore the resulting feasible region. We applied METEOR-K to models of increasing scope and scale, including a reduced glycolysis pathway, anaerobic E. coli, and near-genome-scale ovarian cancer models. METEOR-K narrowed the admissible steady-state solution space, reduced uncertainty in feasible flux-concentration states, and improved local dynamic behavior. In nonlinear ODE simulations of bioreactor cultivation and drug-response scenarios, METEOR-K-derived states produced intracellular response times compatible with growth-supporting metabolic operation and perturbation recovery. Overall, these results establish metabolite turnover rates as scalable biophysical constraints that improve the physiological consistency of steady-state metabolic modeling. Because turnover rates encode flux-to-pool-size timescale constraints, METEOR-K moves part of physiological-consistency assessment upstream of kinetic parameterization, yielding better-suited flux-concentration reference states for kinetic modeling and dynamic prediction.

systems biology↗
bioRxiv2020

Consensus clustering for Bayesian mixture models

AO_SCPLOWBSTRACTC_SCPLOWCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness. In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. These clusters can help assign likely function to understudied genes, for example GAS3 clusters with histones active in S-phase, suggesting a role in DNA replication. Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

systems biology↗
bioRxiv2026

Kinome-wide CRISPR/Cas9-knockout screening reveals critical protein kinases in vasopressin V2-receptor signaling

Identification of signaling networks is an essential goal in systems biology. Here, we use CRISPR/Cas9 knockout screening (employing a whole kinome sgRNA library) to identify functionally critical protein kinases in a well-studied Gs-dependent G-protein coupled receptor (GPCR)-signaling model, namely the vasopressin V2 receptor (V2R) pathway. Screening was done using a specially-designed fluorescence-based reporter cell line with green-fluorescent protein (GFP) co-transcribed with Aqp2, a gene whose transcription is dependent on vasopressin-mediated activation of protein kinase A (PKA). Positive regulators (n=14) included PKA-catalytic subunit (Prkaca) and Dyrk1a (minibrain homolog). Negative regulators (n=12) included PKA-regulatory subunit type I, Stk11 (catalytic subunit of liver kinase B1 [LKB1] complex), and three TGF-{beta} receptor subunits (Tgfbr1, Tgfbr2, Tgfbr3) (see https://esbl.nhlbi.nih.gov/Databases/Kinome-CRISPR-screen/ for full list). Dyrk1a knockout cell lines failed to express AQP2 protein and exhibited a profound decrease in AQP2 mRNA. RNA-sequencing demonstrated widespread increases in cell-cycle transcripts, with a general defect in cell differentiation, accounting for AQP2 loss. TGF-{beta} exposure to un-transformed cells results in a profound decrease in V2R and AQP2 mRNA abundance along with multiple additional transcriptional targets of V2R-PKA signaling, consistent with prior findings in TGF-{beta}-mediated vasopressin escape. Stk11/LKB1 knockout lines displayed marked increases in AQP2 protein and mRNA, even in the absence of vasopressin. RNA-sequencing showed a marked similarity between the responses to Stk11/LKB1 deletion and vasopressin exposure in untransformed cells. Phospho-proteomic data point to opposing roles of Stk11/LKB1 and PKA in the regulation of cAMP-responsive transcriptional coactivator (CRTC) proteins in the transcriptional response to V2R-PKA signaling. Significance StatementCells throughout the body are regulated by extracellular signals like the hormone, vasopressin. Hormonal effects on cellular function are mediated by membrane receptors that trigger biochemical changes, often by inducing chemical modification of the amino acids making up individual proteins, such as addition of function-altering phosphate groups (phosphorylation). Protein phosphorylation is mediated by enzymes known as "protein kinases". Here, we have screened all known protein kinases using modern CRISPR/Cas9 technology to identify those involved in vasopressin action in the kidney. As expected from prior knowledge, the screen identified protein kinase A and one of its regulatory subunits, but also identified several protein kinases not previously implicated in vasopressin action in the kidney.

systems biology↗