Computational Systems Biology Research Articles

High iodine promotes autoimmune thyroid disease by activating hexokinase 3 and inducing polarization of macrophages towards M1.

Autoimmune thyroid disease (AITD), the most common autoimmune disease, includes Graves' disease (GD) and Hashimoto's thyroiditis (HT). Currently, the pathogenesis of AITD is not fully understood. Our study aimed to examine the presence of macrophage polarization imbalance in AITD patients, to investigate whether high iodine can cause macrophage polarization imbalance, and to investigate the role of key genes of metabolic reprogramming in macrophage polarization imbalance caused by high iodine. We synergistically used various research strategies such as systems biology, clinical studies, cell culture and mouse disease models. Gene set enrichment analysis (GSEA) revealed that M1 macrophage hyperpolarization was involved in the pathogenesis of AITD. In vitro and in vivo experiments showed that high iodine can affect the polarization of M1 or M2 macrophages and their related cytokines. Robust rank aggregation (RRA) method revealed that hexokinase 3 (HK3) was the most aberrantly expressed metabolic gene in autoimmune diseases. In vitro and in vivo studies revealed HK3 could mediate macrophage polarization induced by high iodine. In summary, hyperpolarization of M1-type macrophages is closely related to the pathogenesis of AITD. High iodine can increase HK3 expression in macrophages and promote macrophage polarization towards M1. Targeting HK3 can inhibit M1 polarization induced by high iodine.

Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis.

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database.

The Involvement of Cell-Type-Specific Glycans in Hydra Temporary Adhesion Revealed by a Lectin Screen

Hydra is a freshwater solitary polyp, capable of temporary adhesion to underwater surfaces. The reversible attachment is based on an adhesive material that is secreted from its basal disc cells and left behind on the substrate as a footprint. Despite Hydra constituting a standard model system in stem cell biology and tissue regeneration, few studies have addressed its bioadhesion. This project aimed to characterize the glycan composition of the Hydra adhesive, using a set of 23 commercially available lectins to label Hydra cells and footprints. The results indicated the presence of N-acetylglucosamine, N-acetylgalactosamine, fucose, and mannose in the adhesive material. The labeling revealed a meshwork-like substructure in the footprints, implying that the adhesive is mainly formed by fibers. Furthermore, lectins might serve as a marker for Hydra cells and structures, e.g., many labeled as glycan-rich nematocytes. Additionally, some unexpected patterns were uncovered, such as structures associated with radial muscle fibers and endodermal gland cells in the hypostome of developing buds.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

In Silico Modeling and Quantification of Synergistic Effects of Multi-Combination Compounds: Case Study of the Attenuation of Joint Pain Using a Combination of Phytonutrients

The quantification of synergistic effects of multi-combination compounds is critical in developing “cocktails” that are efficacious. In this research, a method for in silico modeling and the quantification of synergistic effects of multi-combination compounds is applied for assessing a combination of phytonutrients for joint pain. Joint pain is the most prominent and disabling symptom of arthritis. Arthritic pain leads to a reduced quality of life. This research explores the efficacy of a synergistic combination of two plant-based flavonoids—apigenin and hesperidin—on joint pain. The study employs computational systems biology: (1) to identify biomolecular mechanisms of joint pain; (2) to identify the specific effects of apigenin and hesperidin, individually and in combination, on the mechanisms of joint pain; and (3) to predict the quantitative effects of apigenin and hesperidin, individually and in combination, on joint pain and whether these combination effects are synergistic or additive. Four molecular pathways that are affected by apigenin and hesperidin include the following: (1) arachidonic acid metabolism, (2) PGE2 signaling, (3) COX-2 synthesis, and (4) oxidative stress. The combination of apigenin and hesperidin significantly lowered PGE2 production, CGRP production, TRVP-1 synthesis, COX-2 production, and reactive oxygen species (ROS) production. Our results indicate that the apigenin and hesperidin combination synergistically affected four of the five modalities to attenuate joint pain.

Saccharomyces cerevisiae as a Model System for Eukaryotic Cell Biology, from Cell Cycle Control to DNA Damage Response.

The yeast Saccharomyces cerevisiae has been used for bread making and beer brewing for thousands of years. In addition, its ease of manipulation, well-annotated genome, expansive molecular toolbox, and its strong conservation of basic eukaryotic biology also make it a prime model for eukaryotic cell biology and genetics. In this review, we discuss the characteristics that made yeast such an extensively used model organism and specifically focus on the DNA damage response pathway as a prime example of how research in S. cerevisiae helped elucidate a highly conserved biological process. In addition, we also highlight differences in the DNA damage response of S. cerevisiae and humans and discuss the challenges of using S. cerevisiae as a model system.

Species diversity and phylogeography of the Australoheros autrani group (Teleostei, Cichlidae) in eastern Brazil

Cichlid fishes are an important model system in evolutionary biology, primarily because of their exceptional diversity. However, while some cichlid assemblages, such as the ones of the African Great Lakes, have received considerable attention, others are not well studied, including many riverine species. Here, we focus on the Australoheros autrani species group and first report a new record of Australoheros in the upper Paranaíba River drainage, extending the known distribution range of this genus. Through Bayesian inference and maximum likelihood phylogenetic analyses of the mitochondrial cytochrome b gene of these specimens as well as available sequences, we assigned the newly discovered population to Australoheros barbosae. We corroborate the monophyly of the A. autrani species group and the presence of three species in the upper/middle Paraíba do Sul River basin as well as molecular diagnostic characters for each. Finally, we provide evidence for a recent expansion of A. barbosae.

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting

Abstract Summary The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

75 The Power of Theoretical and Practical Identifiability Analysis for Modeling (Micro-) Biological Processes

Abstract When modeling biological systems, one major step of the modeling exercise is the confrontation between the theory (the model) and the reality (the data). Such a confrontation passes through the resolution of the parameter identification (model calibration) problem, which aims at finding a set of parameters that best fits the variables predicted by the model to the data. The parameter identification step is often addressed like a downstream process. Using this approach, once the data are collected, the modeler has a minimal degree of freedom to improve the accuracy of her model. Instead, the modeler can identify opportunities for improving model accuracy if the parameter identification problem is addressed like an upstream process. For instance, one of these opportunities is to determine for a given experimental setup (before the actual data collection), whether the optimization problem is well-posed (that it is, if the problem has unique solution). Knowing this information beforehand is useful to set up an experimental protocol that guarantees the uniqueness of the parameter estimates. This question of uniqueness is related to the notion of structural (theoretical) identifiability. The modeler might also be interested in knowing whether the available data that will result from the experimental protocol will be informative enough to identify the model parameters accurately. The question of parameter accuracy based on the quality of available data is related to the notion of practical identifiability. This work aims to present the theoretical basis of structural and practical identifiability analysis. By capitalizing on the use of dedicated software tools, we will illustrate further the power of structural and practical identifiability analysis in the modeling exercise using relevant examples for the modeling of microbiological processes such as the rumen microbial fermentation.

New two-derivative implicit-explicit Runge-Kutta methods for stiff reaction-diffusion systems

Reaction-diffusion systems are extensively used in the mathematical modeling of biological and chemical systems to explain the Turing instability. Generally, reaction-diffusion systems are highly stiff in both reaction and diffusion terms. This paper discusses a new class of two-derivative implicit-explicit (IMEX) Runge-Kutta (RK) type methods for the numerical simulations of stiff reaction-diffusion systems. The present methods do not require numerical inversion of the coefficient matrix - computationally explicit. Stability properties of the developed methods are compared with the similar methods discussed in the literature. Moreover, accuracy and efficiency of the developed methods are validated by numerical simulations of spatiotemporal pattern formations for different reaction-diffusion systems, such as, phase-separation, Schnakenberg model and electrodeposition process.

Design centering enables robustness screening of pattern formation models.

MotivationAccess to unprecedented amounts of quantitative biological data allows us to build and test biochemically accurate reaction–diffusion models of intracellular processes. However, any increase in model complexity increases the number of unknown parameters and, thus, the computational cost of model analysis. To efficiently characterize the behavior and robustness of models with many unknown parameters remains, therefore, a key challenge in systems biology.ResultsWe propose a novel computational framework for efficient high-dimensional parameter space characterization of reaction–diffusion models in systems biology. The method leverages the Lp-Adaptation algorithm, an adaptive-proposal statistical method for approximate design centering and robustness estimation. Our approach is based on an oracle function, which predicts for any given point in parameter space whether the model fulfills given specifications. We propose specific oracles to efficiently predict four characteristics of Turing-type reaction–diffusion models: bistability, instability, capability of spontaneous pattern formation and capability of pattern maintenance. We benchmark the method and demonstrate that it enables global exploration of a model’s ability to undergo pattern-forming instabilities and to quantify robustness for model selection in polynomial time with dimensionality. We present an application of the framework to pattern formation on the endosomal membrane by the small GTPase Rab5 and its effectors, and we propose molecular mechanisms underlying this system.Availability and implementationOur code is implemented in MATLAB and is available as open source under https://git.mpi-cbg.de/mosaic/software/black-box-optimization/rd-parameter-space-screening.Supplementary information Supplementary data are available at Bioinformatics online.

Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.

Integrating Text Mining into the Curation of Disease Maps.

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.

S-25-04 Reproducibility in systems biology modelling – sometimes

S-25-04 Reproducibility in systems biology modelling – sometimes

South West Atlantic salt marshes as model systems for community and ecosystem ecology

Just as some species are used as model systems in organismal biology (e.g., physiology, genetics), many ecosystems are commonly used as model systems in ecology. Salt marshes, for instance, are great models to perform manipulative field experiments, and thus, were historically used to understand the drivers of community and ecosystem function. Decades of experimental work, indeed, made a strong contribution to community ecology as a discipline, but most of the emerged hypotheses and models were grounded in a few sites. When studies from new sites came onboard, looking to enlarge generalities, their results challenged the prevailing ideas. Here, we review more than 25 years of intense experimentation in South West Atlantic salt marshes, which helped not only to increase the knowledge about salt marsh functioning, but also to expand this knowledge beyond salt marshes helping to refine community and ecosystem function theory. We show that results coming from SW Atlantic marshes significantly contribute to understand 1) the separate and interactive effect of biotic and abiotic stress for species distribution and even for ecosystem stability, 2) the integrated role of species that can function as ecosystem engineers and as consumers, 3) the balance between stochastic and deterministic forces as drivers of community structure and 4) the regulation of cross-ecosystem fluxes. Nevertheless, we believe SW Atlantic salt marshes still have a lot more to offer, not only as conceptual models that help satisfy our intellectual curiosity, but also as key ecosystems that provide valuable benefits to our societies.

Integrating operating conditions to develop a neural network for predicting organics removal and power density in an earthen microbial fuel cell treating leachate

Nowadays, the acceptability of artificial neural networks (ANNs) to develop models for biological treatment systems has been broadened instead of the complex mechanistic mathematical model. The present study attempts to train neural networks to predict organics removal and power output from an earthen microbial fuel cell treating acidogenic leachate obtained from kitchen waste. To model the effluent chemical oxygen demand (COD) and volatile fatty acid (VFA) values and power density, the study used four input parameters: influent COD, and VFA, hydraulic retention time, and anode electrode surface area. Among the multiple algorithms employed, the Bayesian inference proved to deliver a winning model to forecast the response variable with an overall high coefficient of determination of 0.9890. This high correlation for a biological system employing a real substrate, i.e. leachate, ensures the model’s reproducibility and adequacy. Additionally, the statistical error criteria for model efficacy showed acceptable values. Besides, the study performs validation runs with an error of approx. 11% with conservative predictions. Ultimately, the ANN model fared well in predicting the response for effluent COD and power output when compared with kinetic study (Han-Levenspiel) and statistical equation (from ANOVA) to the experimental values.

Neisseria species as pathobionts in bronchiectasis

Neisseria species are frequently identified in the bronchiectasis microbiome, but they are regarded as respiratory commensals. Using a combination of human cohorts, next-generation sequencing, systems biology, and animal models, we show that bronchiectasis bacteriomes defined by the presence of Neisseria spp. associate with poor clinical outcomes, including exacerbations. Neisseria subflava cultivated from bronchiectasis patients promotes the loss of epithelial integrity and inflammation in primary epithelial cells. Invivo animal models of Neisseria subflava infection and metabolipidome analysis highlight immunoinflammatory functional gene clusters and provide evidence for pulmonary inflammation. The murine metabolipidomic data were validated with human Neisseria-dominant bronchiectasis samples and compared with disease in which Pseudomonas-, an established bronchiectasis pathogen, is dominant. Metagenomic surveillance of Neisseria across various respiratory disorders reveals broader importance, and the assessment of the home environment in bronchiectasis implies potential environmental sources of exposure. Thus, we identify Neisseria species as pathobionts in bronchiectasis, allowing for improved risk stratification in this high-risk group.

Comparing Petri net-based models of biological systems using Holmes.

The first and necessary step in systems approach to study biological phenomena is building a formal model. One of the possibilities is to construct a model based on Petri nets. They have an intuitive graphical representation on one hand, and on the other, can be analyzed using formal mathematical methods. Finding homologies or conserved processes playing important roles in various biological systems can be done by comparing models. The ones expressed as Petri nets are especially well-suited for such a comparison, but there is a lack of software tools for this task. To resolve this problem, a new analytical tool has been implemented in Holmes application and described in this article. It offers four different comparison methods, i.e. the ones based on t-invariants, decomposition, graphlets and branching vertices. Available at http://www.cs.put.poznan.pl/mradom/Holmes/holmes.html. Supplementary data are available at Bioinformatics online.

The path via pathway-based approaches towards safety assessment: A concise review

For decades, chemical safety assessment has been proposed to shift from animal testing to in vitro testing systems in response to the call for the 3R. In Europe, the answer was to combine various information sources in integrated testing strategies (ITS); In the US, it was in 2007 when the landmark report by the National Research Council put forward a vision of in vitro toxicity testing paradigm. Since then, efforts to develop pathway-based assessment framework have been on the track. In 2010, systems biology brought out a conceptual framework called adverse outcome pathway (AOP), which took one step further from toxicity pathway to regulatory toxicology. Computational modeling, high-throughput screening, high-content omics have all been approached to facilitate this progress. This paper briefly reviewed the achievement of pathway-based chemical assessment since 2007, discussed potential pitfalls and challenges that mechanism-driven chemical assessment may undergo, and presented future perspectives of safety assessment that is to be based on computational system biology.

An integrative transcriptome analysis reveals potential predictive, prognostic biomarkers and therapeutic targets in colorectal cancer

BackgroundA deep understanding of potential molecular biomarkers and therapeutic targets related to the progression of colorectal cancer (CRC) from early stages to metastasis remain mostly undone. Moreover, the regulation and crosstalk among different cancer-driving molecules including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs) in the transition from stage I to stage IV remain to be clarified, which is the aim of this study.MethodsWe carried out two separate differential expression analyses for two different sets of samples (stage-specific samples and tumor/normal samples). Then, by the means of robust dataset analysis we identified distinct lists of differently expressed genes (DEGs) for Robust Rank Aggregation (RRA) and weighted gene co-expression network analysis (WGCNA). Then, comprehensive computational systems biology analyses including mRNA-miRNA-lncRNA regulatory network, survival analysis and machine learning algorithms were also employed to achieve the aim of this study. Finally, we used clinical samples to carry out validation of a potential and novel target in CRC.ResultsWe have identified the most significant stage-specific DEGs by combining distinct results from RRA and WGCNA. After finding stage-specific DEGs, a total number of 37 DEGs were identified to be conserved across all stages of CRC (conserved DEGs). We also found DE-miRNAs and DE-lncRNAs highly associated to these conserved DEGs. Our systems biology approach led to the identification of several potential therapeutic targets, predictive and prognostic biomarkers, of which lncRNA LINC00974 shown as an important and novel biomarker.ConclusionsFindings of the present study provide new insight into CRC pathogenesis across all stages, and suggests future assessment of the functional role of lncRNA LINC00974 in the development of CRC.