Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain

Vaccine efficacy and its quantification is a crucial concept for the proper design of public health vaccination policies. In this work we proposed a mathematical model to estimate the efficacy of the influenza vaccine in a real-word scenario. In particular, our model is a SEIR-type epidemiological model, which distinguishes vaccinated and unvaccinated populations. Mathematically, its dynamics is governed by a nonlinear system of ordinary differential equations, where the non-linearity arises from the effective contacts between susceptible and infected individuals. Two key aspects of this study is that we use a vaccine distribution over time that is based on real data specific to the elderly people in the Valencian Community and the calibration process takes into account that over one influenza season a specific proportion of the population becomes infected with influenza. To consider the effectiveness of the vaccine, the model incorporates a parameter, the vaccine attenuation factor, which is related with the vaccine efficacy against the influenza virus. With this framework, in order to calibrate the model parameters and to obtain an influenza vaccine efficacy estimation, we considered the 2016-2017 influenza season in the Valencian Community, Spain, using the influenza reported cases of vaccinated and unvaccinated. In order to ensure the model identifiability, we choose to deterministically calibrate the parameters for different scenarios and we find the one with the minimum error in order to determine the vaccine efficacy. The calibration results suggest that the influenza vaccine developed for 2016-2017 influenza season has an efficacy of approximately 76.7%, and that the risk of becoming infected is five times higher for an unvaccinated individual in comparison with a vaccinated one. This estimation partially agrees with some previous studies related to the influenza vaccine. This study presents a new integrated mathematical approach to study the influenza vaccine efficacy and gives further insight into this important public health topic.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase. The research employed high-throughput virtual screening and QSAR analysis were conducted to identify compounds with optimal drug-like properties, emphasizing adherence to ADMET parameters for further evaluation. Molecular dynamics simulations to analyze the binding interactions between Papaveroline and Staurosporine with Fyn Tyrosine Kinase over a 200-ns period. The study revealed detailed insights into the binding mechanisms and stability of the Papaveroline-Fyn complex, showcasing the compound's potential as an inhibitor of Fyn Tyrosine Kinase. Comparative analysis with natural compounds and a reference compound highlighted Papaveroline's unique characteristics and promising therapeutic implications for AD treatment. Overall, the findings underscore Papaveroline's potential as a valuable drug candidate for targeting Fyn Tyrosine Kinase in AD therapy, offering new avenues for drug discovery in neurodegenerative diseases. This study contributes to advancing our understanding of molecular interactions in AD pathogenesis and paves the way for further research and development in this critical area.

Human protein-protein interaction networks: A topological comparison review

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

Introducing critical proteins related to liver ischemia/reperfusion injury

Aim

The current study aimed to introduce the key proteins involved in liver ischemia/reperfusion (I/R) injury through protein-protein interaction (PPI) analysis.

Background

Liver transplantation (LT) is a well-known treatment for liver diseases that threaten patients with mortality. LT is a complex operation, and several risks, including liver I/R injury, affect its success. Improving LT requires detection of its molecular mechanism. Experiments have revealed that high throughput methods such as proteomics in combination with bioinformatics are useful tools for analyzing the molecular mechanism of disease.

Methods

The differentially expressed proteins (DEPs) involved in liver I/R injury were extracted from the literature. The queried DEPs plus the first 100 neighbors were included in a network through STRING database using Cytoscape software. Degree, betweenness centrality, closeness centrality, and stress were considered to determine the central nodes. The queried DEPs were assessed by action map analysis using the CluePedia application of Cytoscape software. The key proteins were identified by comparing network analysis and action map evaluation results.

Results

Six proteins, namely ALB, INS, GAPDH, CAT, IL6, and TNF, among the added first neighbors were determined as the central first neighbors. MPO, CRP, MMP9, and HMOX1 were selected as central DEPs among the queried proteins. Action map analysis confirmed the PPI findings. The final evaluation revealed that MMP9 in combination with CRP and HMOX1 plays a critical role in liver I/R injury.

Conclusion

The significant role of MMP9 in liver I/R injury was detected in this study. Two central proteins (CRP and HMOX1) were shown to have a regulatory effect on MMP9; CRP activated MMP9, while HMXO1 downregulated it.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model

MOTIVATION

Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information.

RESULTS

In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite.

AVAILABILITY AND IMPLEMENTATION

The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

Reinvestigation of Passerini and Ugi scaffolds as multistep apoptotic inducers via dual modulation of caspase 3/7 and P53-MDM2 signaling for halting breast cancer

Selective induction of breast cancer apoptosis is viewed as the mainstay of various ongoing oncology drug discovery programs. Passerini scaffolds have been recently exploited as selective apoptosis inducers via a caspase 3/7 dependent pathway. Herein, the optimized Passerini caspase activators were manipulated to synergistically induce P53-dependent apoptosis via modulating the closely related P53-MDM2 signaling axis. The adopted design rationale and synthetic routes relied on mimicking the general thematic features of lead MDM2 inhibitors incorporating multiple aromatic rings. Accordingly, the cyclization of representative Passerini derivatives and related Ugi compounds into the corresponding diphenylimidazolidine and spiro derivative was performed, resembling the nutlin-based and spiro MDM-2 inhibitors, respectively. The study was also extended to explore the apoptotic induction capacity of the scaffold after simplification and modifications. MTT assay on MCF-7 and MDA-MB231 breast cancer cells compared to normal fibroblasts (WI-38) revealed their promising cytotoxic activities. The flexible Ugi derivatives 3 and 4, cyclic analog 8, Passerini adduct 12, and the thiosemicarbazide derivative 17 were identified as the study hits regarding cytotoxic potency and selectivity, being over 10-folds more potent (IC50 = 0.065-0.096 μM) and safer (SI = 4.4-18.7) than doxorubicin (IC50 = 0.478 μM, SI = 0.569) on MCF-7 cells. They promoted apoptosis induction via caspase 3/7 activation (3.1-4.1 folds) and P53 induction (up to 4 folds). Further apoptosis studies revealed that these compounds enhanced gene expression of BAX by 2 folds and suppressed Bcl-2 expression by 4.29-7.75 folds in the treated MCF-7 cells. Docking simulations displayed their plausible binding modes with the molecular targets and highlighted their structural determinants of activities for further optimization studies. Finally, in silico prediction of the entire library was computationally performed, showing that most of them could be envisioned as drug-like candidates.

Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model

It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Evolutionarily conserved gene expression patterns for affective disorders revealed using cross-species brain transcriptomic analyses in humans, rats and zebrafish

Widespread, debilitating and often treatment-resistant, depression and other stress-related neuropsychiatric disorders represent an urgent unmet biomedical and societal problem. Although animal models of these disorders are commonly used to study stress pathogenesis, they are often difficult to translate across species into valuable and meaningful clinically relevant data. To address this problem, here we utilized several cross-species/cross-taxon approaches to identify potential evolutionarily conserved differentially expressed genes and their sets. We also assessed enrichment of these genes for transcription factors DNA-binding sites down- and up- stream from their genetic sequences. For this, we compared our own RNA-seq brain transcriptomic data obtained from chronically stressed rats and zebrafish with publicly available human transcriptomic data for patients with major depression and their respective healthy control groups. Utilizing these data from the three species, we next analyzed their differential gene expression, gene set enrichment and protein-protein interaction networks, combined with validated tools for data pooling. This approach allowed us to identify several key brain proteins (GRIA1, DLG1, CDH1, THRB, PLCG2, NGEF, IKZF1 and FEZF2) as promising, evolutionarily conserved and shared affective 'hub' protein targets, as well as to propose a novel gene set that may be used to further study affective pathogenesis. Overall, these approaches may advance cross-species brain transcriptomic analyses, and call for further cross-species studies into putative shared molecular mechanisms of affective pathogenesis.

Deep learning and multi-omics approach to predict drug responses in cancer

BACKGROUND

Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features.

RESULTS

We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for [Formula: see text] values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC.

CONCLUSION

The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.

A hierarchy of biomolecular proportional-integral-derivative feedback controllers for robust perfect adaptation and dynamic performance

Proportional-Integral-Derivative (PID) feedback controllers are the most widely used controllers in industry. Recently, the design of molecular PID-controllers has been identified as an important goal for synthetic biology and the field of cybergenetics. In this paper, we consider the realization of PID-controllers via biomolecular reactions. We propose an array of topologies offering a compromise between simplicity and high performance. We first demonstrate that different biomolecular PI-controllers exhibit different performance-enhancing capabilities. Next, we introduce several derivative controllers based on incoherent feedforward loops acting in a feedback configuration. Alternatively, we show that differentiators can be realized by placing molecular integrators in a negative feedback loop, which can be augmented by PI-components to yield PID-controllers. We demonstrate that PID-controllers can enhance stability and dynamic performance, and can also reduce stochastic noise. Finally, we provide an experimental demonstration using a hybrid setup where in silico PID-controllers regulate a genetic circuit in single yeast cells.

Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation.

METHODS

In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks.

RESULTS

We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways.

CONCLUSIONS

The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.

Wet-dry cycles protect surface-colonizing bacteria from major antibiotic classes

Diverse antibiotic compounds are abundant in microbial habitats undergoing recurrent wet-dry cycles, such as soil, root and leaf surfaces, and the built environment. These antibiotics play a central role in microbial warfare and competition, thus affecting population dynamics and the composition of natural microbial communities. Yet, the impact of wet-dry cycles on bacterial response to antibiotics has been scarcely explored. Using the bacterium E. coli as a model organism, we show through a combination of experiments and computational modeling, that wet-dry cycles protect bacteria from beta-lactams. This is due to the combined effect of several mechanisms including tolerance induced by high salt concentrations and slow cell-growth, which are inherently associated with microscopic surface wetness-a hydration state typical to 'dry' periods. Moreover, we find evidence for a cross-protection effect, where lethal doses of antibiotic considerably increase bacterial survival during the dry periods. This work focuses on beta-lactams, yet similar protection was observed for additional major antibiotic classes. Our findings shed new light on how we understand bacterial response to antibiotics, with broad implications for population dynamics, interspecies interactions, and the evolution of antibiotic resistance in vast terrestrial microbial habitats.

PlantPathMarks (PPMdb): an interactive hub for pathways-based markers in plant genomes

Over the past decade, the problem of finding an efficient gene-targeting marker set or signature for plant trait characterization has remained challenging. Many databases focusing on pathway mining have been released with one major deficiency, as they lack to develop marker sets that target only genes controlling a specific pathway or certain biological process. Herein, we present the PlantPathMarks database (PPMdb) as a comprehensive, web-based, user-friendly, and interactive hub for pathway-based markers in plant genomes. Based on our newly developed pathway gene set mining approach, two novel pathway-based marker systems called pathway gene-targeted markers (PGTMs) and pathway microsatellite-targeted markers (PMTMs) were developed as a novel class of annotation-based markers. In the PPMdb database, 2,690,742 pathway-based markers reflecting 9,894 marker panels were developed across 82 plant genomes. The markers include 691,555 PGTMs and 1,999,187 PMTMs. Across these genomes, 165,378 enzyme-coding genes were mapped against 126 KEGG reference pathway maps. PPMdb is furnished with three interactive visualization tools (Map Browse, JBrowse and Species Comparison) to visualize, map, and compare the developed markers over their KEGG reference pathway maps. All the stored marker panels can be freely downloaded. PPMdb promises to create a radical shift in the paradigm of the area of molecular marker research. The use of PPMdb as a mega-tool represents an impediment for non-bioinformatician plant scientists and breeders. PPMdb is freely available at http://ppmdb.easyomics.org.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

Background

Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.

Results

In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.

Conclusions

We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

Modeling and analyzing the transmission dynamics of visceral leishmaniasis

In this paper, we develop a mathematical model to study the transmission dynamics of visceral leishmaniasis. Three populations: dogs, sandflies and humans, are considered in the model. Based on recent studies, we include vertical transmission of dogs in the spread of the disease. We also investigate the impact of asymptomatic humans and dogs as secondary reservoirs of the parasites. The basic reproduction number and sensitivity analysis show that the control of dog-sandfly transmission is more important for the elimination of the disease. Vaccination of susceptible dogs, treatment of infective dogs, as well as control of vertical transmission in dogs are effective prevention and control measures for visceral leishmaniasis.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

Evolution of computational models in BioModels Database and the Physiome Model Repository

BACKGROUND

A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable.

METHODS

We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database.

RESULTS

We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub https://github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at https://hub.docker.com/r/binfalse/bives-statsgenerator/ . The website https://most.bio.informatik.uni-rostock.de/ provides interactive access to model versions and their evolutionary statistics.

CONCLUSION

The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model's provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users.

Metabolite profiling of red and blue potatoes revealed cultivar and tissue specific patterns for anthocyanins and other polyphenols

Metabolite profiling of tuber flesh and peel for selected colored potato varieties revealed cultivar and tissue specific profiles of anthocyanins and other polyphenols with variations in composition and concentration. Starchy tubers of Solanum tuberosum are a staple crop and food in many countries. Among cultivated potato varieties a huge biodiversity exists, including an increasing number of red and purple colored cultivars. This coloration relates to the accumulation of anthocyanins and is supposed to offer nutritional benefits possibly associated with the antioxidative capacity of anthocyanins. However, the anthocyanin composition and its relation to the overall polyphenol constitution in colored potato tubers have not been investigated closely. This study focuses on the phytochemical characterization of the phenolic composition of a variety of colored potato tubers, both for peel and flesh tissues. First, liquid chromatography (LC) separation coupled to UV and mass spectrometry (MS) detection of polyphenolic compounds of potato tubers from 57 cultivars was used to assign groups of potato cultivars differing in their anthocyanin and polyphenol profiles. Tissues from 19 selected cultivars were then analyzed by LC separation coupled to multiple reaction monitoring (MRM) to detect quantitative differences in anthocyanin and polyphenol composition. The measured intensities of 21 anthocyanins present in the analyzed potato cultivars and tissues could be correlated with the specific tuber coloration. Besides secondary metabolites well-known for potato tubers, the metabolic profiling led to the detection of two anthocyanins not described for potato tuber previously, which we tentatively annotated as pelargonidin feruloyl-xylosyl-glucosyl-galactoside and cyanidin 3-p-coumaroylrutinoside-5-glucoside. We detected significant correlations between some of the measured metabolites, as for example the negative correlation between the main anthocyanins of red and blue potato cultivars. Mainly hydroxylation and methylation patterns of the B-ring of dihydroflavonols, leading to the formation of specific anthocyanidin backbones, can be assigned to a distinct coloring of the potato cultivars and tuber tissues. However, basically the same glycosylation and acylation reactions occur regardless of the main anthocyanidin precursor present in the respective red and blue/purple tissue. Thus, the different anthocyanin profiles in red and blue potato cultivars likely relate to superior regulation of the expression and activities of hydroxylases and methyltransferases rather than to differences for downstream glycosyl- and acyltransferases. In this regard, the characterized potato cultivars represent a valuable resource for the molecular analysis of the genetic background and the regulation of anthocyanin side chain modification.

Modeling Immune Response to Leishmania Species Indicates Adenosine As an Important Inhibitor of Th-Cell Activation

Infection by Leishmania protozoan parasites can cause a variety of disease outcomes in humans and other mammals, from single self-healing cutaneous lesions to a visceral dissemination of the parasite. The correlation between chronic lesions and ecto-nucleotidase enzymes activity on the surface of the parasite is addressed here using damage caused in epithelial cells by nitric oxide. In order to explore the role of purinergic metabolism in lesion formation and the outcome of the infection, we implemented a cellular automata/lattice gas model involving major immune characters (Th1 and Th2 cells, IFN-γ, IL-4, IL-12, adenosine-Ado-, NO) and parasite players for the dynamic analysis of the disease progress. The model were analyzed using partial ranking correlation coefficient (PRCC) to indicate the components that most influence the disease progression. Results show that low Ado inhibition rate over Th-cells is shared by L. major and L. braziliensis, while in L. amazonensis infection the Ado inhibition rate over Th-cells reaches 30%. IL-4 inhibition rate over Th-cell priming to Th1 independent of IL-12 are exclusive of L. major. The lesion size and progression showed agreement with published biological data and the model was able to simulate cutaneous leishmaniasis outcomes. The sensitivity analysis suggested that Ado inhibition rate over Th-cells followed by Leishmania survival probability were the most important characteristics of the process, with PRCC of 0.89 and 0.77 respectively. The simulations also showed a non-linear relationship between Ado inhibition rate over Th-cells and lesion size measured as number of dead epithelial cells. In conclusion, this model can be a useful tool for the quantitative understanding of the immune response in leishmaniasis.

More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.

External Noise and External Signal Induced Transition of Gene Switch and Coherence Resonance in the Genetic Regulatory System

The transition of gene switch induced by external noises (multiplicative external noise and additive external noise) and external signals is investigated in the genetic regulatory system. Results show that the state-to-state transition of gene switch as well as resonant behaviors, such as the explicit coherence resonance (ECR), implicit coherence resonance (ICR) and control parameter coherence biresonance (CPCBR), can appear when noises are injected into the genetic regulatory system. The ECR is increased with the increase of the control parameter value when starting from the supercritical Hopf bifurcation parameter point, and there exists a critical control parameter value for the occurrence of ECR. However, the ICR is decreased as the control parameter value is increased when starting from the subcritical Hopf bifurcation point. In particular, the coherence of ECR is higher and more sensitive to noise than that of ICR. When an external signal is introduced into the system, the enhancement or suppression of the CPCBR and the number of peaks strongly depend on the frequency and amplitude of the external signal. Furthermore, the gene regulation system can selectively enhance or decrease the noise-induced oscillation signals at preferred frequency and amplitude of an external signal.

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

Background

The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times.

Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies.

Results

The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network.

The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used.

Conclusions

The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1452-4) contains supplementary material, which is available to authorized users.

STON: exploring biological pathways using the SBGN standard and graph databases

Background

When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.

Results

We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.

Conclusion

STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1394-x) contains supplementary material, which is available to authorized users.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.

GAM: a web-service for integrated transcriptional and metabolic network analysis

Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM (‘genes and metabolites’): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/

Systems Biology - A Pivotal Research Methodology for Understanding the Mechanisms of Traditional Medicine

Objectives:

Systems biology is a novel subject in the field of life science that aims at a systems’ level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era.

Methods:

The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction.

Results:

The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted.

Conclusion:

Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

New targets for drug discovery against malaria

A mathematical model which predicts the intraerythrocytic stages of Plasmodium falciparum infection was developed using data from malaria-infected mice. Variables selected accounted for levels of healthy red blood cells, merozoite (Plasmodium asexual phase) infected red blood cells, gametocyte (Plasmodium sexual phase) infected red blood cells and a phenomenological variable which accounts for the mean activity of the immune system of the host. The model built was able to reproduce the behavior of three different scenarios of malaria. It predicts the later dynamics of malaria-infected humans well after the first peak of parasitemia, the qualitative response of malaria-infected monkeys to vaccination and the changes observed in malaria-infected mice when they are treated with antimalarial drugs. The mathematical model was used to identify new targets to be focused on drug design. Optimization methodologies were applied to identify five targets for minimizing the parasite load; four of the targets thus identified have never before been taken into account in drug design. The potential targets include: 1) increasing the death rate of the gametocytes, 2) decreasing the invasion rate of the red blood cells by the merozoites, 3) increasing the transformation of merozoites into gametocytes, 4) decreasing the activation of the immune system by the gametocytes, and finally 5) a combination of the previous target with decreasing the recycling rate of the red blood cells. The first target is already used in current therapies, whereas the remainders are proposals for potential new targets. Furthermore, the combined target (the simultaneous decrease of the activation of IS by gRBC and the decrease of the influence of IS on the recycling of hRBC) is interesting, since this combination does not affect the parasite directly. Thus, it is not expected to generate selective pressure on the parasites, which means that it would not produce resistance in Plasmodium.

Treatment of visceral leishmaniasis: model-based analyses on the spread of antimony-resistant L. donovani in Bihar, India

Background

Pentavalent antimonials have been the mainstay of antileishmanial therapy for decades, but increasing failure rates under antimonial treatment have challenged further use of these drugs in the Indian subcontinent. Experimental evidence has suggested that parasites which are resistant against antimonials have superior survival skills than sensitive ones even in the absence of antimonial treatment.

Methods and Findings

We use simulation studies based on a mathematical L. donovani transmission model to identify parameters which can explain why treatment failure rates under antimonial treatment increased up to 65% in Bihar between 1980 and 1997. Model analyses suggest that resistance to treatment alone cannot explain the observed treatment failure rates. We explore two hypotheses referring to an increased fitness of antimony-resistant parasites: the additional fitness is (i) disease-related, by causing more clinical cases (higher pathogenicity) or more severe disease (higher virulence), or (ii) is transmission-related, by increasing the transmissibility from sand flies to humans or vice versa.

Conclusions

Both hypotheses can potentially explain the Bihar observations. However, increased transmissibility as an explanation appears more plausible because it can occur in the background of asymptomatically transmitted infection whereas disease-related factors would most probably be observable. Irrespective of the cause of fitness, parasites with a higher fitness will finally replace sensitive parasites, even if antimonials are replaced by another drug.

The protozoan flagellate Leishmania donovani causes the neglected, life-threatening disease visceral leishmaniasis. Parasites are transmitted from man to man by the bite of the sand fly Phlebotomus argentipes, the vector of the disease. Pentavalent antimonials have been the mainstay of antileishmanial therapy for decades but rapidly increasing failure rates up to 65% observed between 1980 and 1997 in the state of Bihar, India, have challenged further use of these drugs. Comparative in vitro and in vivo experiments indicate that antimony-resistant parasites have a higher fitness than antimony-sensitive ones even in the absence of antimonial treatment. Simulation studies based on a previously published mathematical L. donovani transmission model suggest that resistance to antimonial treatment alone cannot explain the Bihar observations but that resistance together with higher fitness offers the potential to explain the data. After an antimony-resistant parasite with higher fitness has emerged, it will finally replace the antimony-sensitive ones, even in complete absence of antimonial treatment.