Key gene modules and hub genes associated with pyrethroid and organophosphate resistance in Anopheles mosquitoes: a systems biology approach.

Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions.

Design Principles for Perfect Adaptation in Biological Networks with Nonlinear Dynamics.

Establishing a mapping between the emergent biological properties and the repository of network structures has been of great relevance in systems and synthetic biology. Adaptation is one such biological property of paramount importance that promotes regulation in the presence of environmental disturbances. This paper presents a nonlinear systems theory-driven framework to identify the design principles for perfect adaptation with respect to external disturbances of arbitrary magnitude. Based on the prior information about the network, we frame precise mathematical conditions for adaptation using nonlinear systems theory. We first deduce the mathematical conditions for perfect adaptation for constant input disturbances. Subsequently, we translate these conditions to specific necessary structural requirements for adaptation in networks of small size and then extend to argue that there exist only two classes of architectures for a network of any size that can provide local adaptation in the entire state space, namely, incoherent feed-forward (IFF) structure and negative feedback loop with buffer node (NFB). The additional positiveness constraints further narrow the admissible set of network structures. This also aids in establishing the global asymptotic stability for the steady state given a constant input disturbance. The proposed method does not assume any explicit knowledge of the underlying rate kinetics, barring some minimal assumptions. Finally, we also discuss the infeasibility of certain IFF networks in providing adaptation in the presence of downstream connections. Moreover, we propose a generic and novel algorithm based on non-linear systems theory to unravel the design principles for global adaptation. Detailed and extensive simulation studies corroborate the theoretical findings.

Simulated complexes formed from a set of postsynaptic proteins suggest a localised effect of a hypomorphic Shank mutation.

BACKGROUND: The postsynaptic density is an elaborate protein network beneath the postsynaptic membrane involved in the molecular processes underlying learning and memory. The postsynaptic density is built up from the same major proteins but its exact composition and organization differs between synapses. Mutations perturbing protein: protein interactions generally occurring in this network might lead to effects specific for cell types or processes, the understanding of which can be especially challenging. RESULTS: In this work we use systems biology-based modeling of protein complex distributions in a simplified set of major postsynaptic proteins to investigate the effect of a hypomorphic Shank mutation perturbing a single well-defined interaction. We use data sets with widely variable abundances of the constituent proteins. Our results suggest that the effect of the mutation is heavily dependent on the overall availability of all the protein components of the whole network and no trivial correspondence between the expression level of the directly affected proteins and overall complex distribution can be observed. CONCLUSIONS: Our results stress the importance of context-dependent interpretation of mutations. Even the weakening of a generally occurring protein: protein interaction might have well-defined effects, and these can not easily be predicted based only on the abundance of the proteins directly affected. Our results provide insight on how cell-specific effects can be exerted by a mutation perturbing a generally occurring interaction even when the wider interaction network is largely similar.

Reprogramming of Energy Metabolism in Human PKD1 Polycystic Kidney Disease: A Systems Biology Analysis.

Multiple alterations of cellular metabolism have been documented in experimental studies of autosomal dominant polycystic kidney disease (ADPKD) and are thought to contribute to its pathogenesis. To elucidate the molecular pathways and transcriptional regulators associated with the metabolic changes of renal cysts in ADPKD, we compared global gene expression data from human PKD1 renal cysts, minimally cystic tissues (MCT) from the same patients, and healthy human kidney cortical tissue samples. We found gene expression profiles of PKD1 renal cysts were consistent with the Warburg effect with gene pathway changes favoring increased cellular glucose uptake and lactate production, instead of pyruvate oxidation. Additionally, mitochondrial energy metabolism was globally depressed, associated with downregulation of gene pathways related to fatty acid oxidation (FAO), branched-chain amino acid (BCAA) degradation, the Krebs cycle, and oxidative phosphorylation (OXPHOS) in renal cysts. Activation of mTORC1 and its two target proto-oncogenes, HIF-1α and MYC, was predicted to drive the expression of multiple genes involved in the observed metabolic reprogramming (e.g., GLUT3, HK1/HK2, ALDOA, ENO2, PKM, LDHA/LDHB, MCT4, PDHA1, PDK1/3, MPC1/2, CPT2, BCAT1, NAMPT); indeed, their predicted expression patterns were confirmed by our data. Conversely, we found AMPK inhibition was predicted in renal cysts. AMPK inhibition was associated with decreased expression of PGC-1α, a transcriptional coactivator for transcription factors PPARα, ERRα, and ERRγ, all of which play a critical role in regulating oxidative metabolism and mitochondrial biogenesis. These data provide a comprehensive map of metabolic pathway reprogramming in ADPKD and highlight nodes of regulation that may serve as targets for therapeutic intervention.

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder.

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.

Revisiting the evolution of bow-tie architecture in signaling networks.

Bow-tie architecture is a layered network structure that has a narrow middle layer with multiple inputs and outputs. Such structures are widely seen in the molecular networks in cells, suggesting that a universal evolutionary mechanism underlies the emergence of bow-tie architecture. The previous theoretical studies have implemented evolutionary simulations of the feedforward network to satisfy a given input-output goal and proposed that the bow-tie architecture emerges when the ideal input-output relation is given as a rank-deficient matrix with mutations in network link intensities in a multiplicative manner. Here, we report that the bow-tie network inevitably appears when the link intensities representing molecular interactions are small at the initial condition of the evolutionary simulation, regardless of the rank of the goal matrix. Our dynamical system analysis clarifies the mechanisms underlying the emergence of the bow-tie structure. Further, we demonstrate that the increase in the input-output matrix reduces the width of the middle layer, resulting in the emergence of bow-tie architecture, even when evolution starts from large link intensities. Our data suggest that bow-tie architecture emerges as a side effect of evolution rather than as a result of evolutionary adaptation.

Systems modeling of oncogenic G-protein and GPCR signaling reveals unexpected differences in downstream pathway activation.

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.

MetaboReport: from metabolomics data analysis to comprehensive reporting.

MOTIVATION: Metabolomics, as an essential tool in systems biology, is now widely accessible to researchers of all levels. Yet challenges remain in data analysis and result interpretation. To address these challenges, we introduced MetaboReport, a versatile and interactive web app that simplifies metabolomics experiment design, data preprocessing, exploration, statistical analysis, visualization, and reporting. RESULTS: MetaboReport produces a comprehensive HTML report, including project details, an introduction, interactive plots and tables, statistical results and an in-depth explanations and interpretation of the results. MetaboReport is particularly tailored for research labs and metabolomics core facilities that provide metabolomics services, allowing them to efficiently manage and document different metabolomics projects, and effectively report the metabolomics results to users. AVAILABILITY AND IMPLEMENTATION: MetaboReport is freely accessible on https://metaboreport.com, with source code available on GitHub (https://github.com/YonghuiDong/MetReport). Alternatively, users can install MetaboReport as a standalone desktop app (https://metaboreport.sourceforge.io).

Canalization reduces the nonlinearity of regulation in biological networks.

Biological networks, such as gene regulatory networks, possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated into biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased, and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we compare published Boolean biological network models with different ensembles of null models and show that the abundance of canalization in biological networks can almost completely explain their recently postulated high approximability. Moreover, an analysis of random N-K Kauffman models reveals a strong dependence of approximability on the dynamical robustness of a network.

Systems genetics of metabolic health in the BXD mouse genetic reference population.

Susceptibility to metabolic syndrome (MetS) is dependent on genetics, environment, and gene-by-environment interactions, rendering the study of underlying mechanisms challenging. The majority of experiments in model organisms do not incorporate genetic variation and lack specific evaluation criteria for MetS. Here, we derived a continuous metric, the metabolic health score (MHS), based on standard clinical parameters and defined its molecular signatures in the liver and circulation. In human UK Biobank, the MHS associated with MetS status and was predictive of future disease incidence, even in individuals without MetS. Using quantitative trait locus analyses in mice, we found two MHS-associated genetic loci and replicated them in unrelated mouse populations. Through a prioritization scheme in mice and human genetic data, we identified TNKS and MCPH1 as candidates mediating differences in the MHS. Our findings provide insights into the molecular mechanisms sustaining metabolic health across species and uncover likely regulators. A record of this paper's transparent peer review process is included in the supplemental information.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors.

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

Drug repurposing in MASLD and MASH-cirrhosis: Targets and treatment approaches based on pathways analysis.

Designing and predicting novel drug targets to accelerate drug discovery for treating metabolic dysfunction-associated steatohepatitis (MASH)-cirrhosis is a challenging task. The presence of superimposed (nested) and co-occurring clinical and histological phenotypes, namely MASH and cirrhosis, may partly explain this. Thus, in this scenario, each sub-phenotype has its own set of pathophysiological mechanisms, triggers, and processes. Here, we used gene/protein and set enrichment analysis to predict druggable pathways for the treatment of MASH-cirrhosis. Our findings indicate that the pathogenesis of MASH-cirrhosis can be explained by perturbations in multiple, simultaneous, and overlapping molecular processes. In this scenario, each sub-phenotype has its own set of pathophysiological mechanisms, triggers, and processes. Therefore, we used systems biology modeling to provide evidence that MASH and cirrhosis paradoxically present unique and distinct as well as common disease mechanisms, including a network of molecular targets. More importantly, pathway analysis revealed straightforward results consistent with modulation of the immune response, cell cycle control, and epigenetic regulation. In conclusion, the selection of potential therapies for MASH-cirrhosis should be guided by a better understanding of the underlying biological processes and molecular perturbations that progressively damage liver tissue and its underlying structure. Therapeutic options for patients with MASH may not necessarily be of choice for MASH cirrhosis. Therefore, the biology of the disease and the processes associated with its natural history must be at the forefront of the decision-making process.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning.

The traditional way of thinking about human diseases across clinical and narrow phenomics silos often masks the underlying shared molecular substrates across human diseases. One Health and planetary health fields particularly address such complexities and invite us to think across the conventional disease nosologies. For example, tuberculosis (TB) and lung cancer (LC) are major pulmonary diseases with significant planetary health implications. Despite distinct etiologies, they can coexist in a given community or patient. This is both a challenge and an opportunity for preventive medicine, diagnostics, and therapeutics innovation. This study reports a bioinformatics analysis of publicly available gene expression data, identifying overlapping dysregulated genes, downstream regulators, and pathways in TB and LC. Analysis of NCBI-GEO datasets (GSE83456 and GSE103888) unveiled differential expression of CEACAM6, MUC1, ADM, DYSF, PLOD2, and GAS6 genes in both diseases, with pathway analysis indicating association with lysine degradation pathway. Random forest, a machine-learning-based classification, achieved accuracies of 84% for distinguishing TB from controls and 83% for discriminating LC from controls using these specific genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of warfarin to GAS6. Taken together, the present study speaks of the pressing need to rethink clinical diagnostic categories of human diseases and that TB and LC might potentially share molecular substrates. Going forward, planetary health and One Health scholarship are poised to cultivate new ways of thinking about diseases not only across medicine and ecology but also across traditional diagnostic conventions.

Multistationarity questions in reduced versus extended biochemical networks.

We address several questions in reduced versus extended networks via the elimination or addition of intermediate complexes in the framework of chemical reaction networks with mass-action kinetics. We clarify and extend advances in the literature concerning multistationarity in this context, mainly from Feliu and Wiuf (J R Soc Interface 10:20130484, 2013), Sadeghimanesh and Feliu (Bull Math Biol 81:2428-2462, 2019), Pérez Millán and Dickenstein (SIAM J Appl Dyn Syst 17(2):1650-1682, 2018), Dickenstein et al. (Bull Math Biol 81:1527-1581, 2019). We establish general results about MESSI systems, which we use to compute the circuits of multistationarity for significant biochemical networks.

Machine learning for the advancement of genome-scale metabolic modeling.

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWK/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.

Novel drug targets and molecular mechanisms for sarcopenia based on systems biology.

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.

Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

MOTIVATION: Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. RESULTS: We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. AVAILABILITY AND IMPLEMENTATION: The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators.

BACKGROUND: Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. RESULTS: We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. CONCLUSION: These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.