IL-23 induces CLEC5A+ IL-17A+ neutrophils and elicit skin inflammation associated with psoriatic arthritis.

IL-23-activation of IL-17 producing T cells is involved in many rheumatic diseases. Herein, we investigate the role of IL-23 in the activation of myeloid cell subsets that contribute to skin inflammation in mice and man. IL-23 gene transfer in WT, IL-23RGFP reporter mice and subsequent analysis with spectral cytometry show that IL-23 regulates early innate immune events by inducing the expansion of a myeloid MDL1+CD11b+Ly6G+ population that dictates epidermal hyperplasia, acanthosis, and parakeratosis; hallmark pathologic features of psoriasis. Genetic ablation of MDL-1, a major PU.1 transcriptional target during myeloid differentiation exclusively expressed in myeloid cells, completely prevents IL-23-pathology. Moreover, we show that IL-23-induced myeloid subsets are also capable of producing IL-17A and IL-23R+MDL1+ cells are present in the involved skin of psoriasis patients and gene expression correlations between IL-23 and MDL-1 have been validated in multiple patient cohorts. Collectively, our data demonstrate a novel role of IL-23 in MDL-1-myelopoiesis that is responsible for skin inflammation and related pathologies. Our data open a new avenue of investigations regarding the role of IL-23 in the activation of myeloid immunoreceptors and their role in autoimmunity.

Adaptive laboratory evolution for improved tolerance of isobutyl acetate in Escherichia coli.

Previously, Escherichia coli was engineered to produce isobutyl acetate (IBA). Titers greater than the toxicity threshold (3 g/L) were achieved by using layer-assisted production. To avoid this costly and complex method, adaptive laboratory evolution (ALE) was applied to E. coli for improved IBA tolerance. Over 37 rounds of selective pressure, 22 IBA-tolerant mutants were isolated. Remarkably, these mutants not only tolerate high IBA concentrations, they also produce higher IBA titers. Using whole-genome sequencing followed by CRISPR/Cas9 mediated genome editing, the mutations (SNPs in metH, rho and deletion of arcA) that confer improved tolerance and higher titers were elucidated. The improved IBA titers in the evolved mutants were a result of an increased supply of acetyl-CoA and altered transcriptional machinery. Without the use of phase separation, a strain capable of 3.2-fold greater IBA production than the parent strain was constructed by combing select beneficial mutations. These results highlight the impact improved tolerance has on the production capability of a biosynthetic system.

Understanding the Formation and Mechanism of Anticipatory Responses in Escherichia coli.

Microorganisms often live in complex habitats, where changes in the environment are predictable, providing an opportunity for microorganisms to learn, anticipate the upcoming environmental changes and prepare in advance for better survival and growth. One such environment is the mammalian intestine, where the abundance of different carbon sources is spatially distributed. In this study, we identified seven spatially distributed carbon sources in the mammalian intestine and tested whether Escherichia coli exhibits phenotypes that are consistent with an anticipatory response given their spatial order and abundance within the mammalian intestine. Through RNA-Seq and RT-PCR validation measurements, we found that there was a 67% match in the expression patterns between the measured phenotypes and what would otherwise be expected in the case of anticipatory behavior, while 83% and 0% were in agreement with the homeostatic and random response, respectively. To understand the genetic and phenotypic basis of the discrepancies between the expected and measured anticipatory responses, we thoroughly investigated the discrepancy in D-galactose treatment and the expression of maltose operon in E. coli. Here, the expected anticipatory response, based on the spatial distribution of D-galactose and D-maltose, was that D-galactose should upregulate the maltose operon, but it was the opposite in experimental validation. We performed whole genome random mutagenesis and screening and identified E. coli strains with positive expression of maltose operon in D-galactose. Targeted Sanger sequencing and mutation repair identified that the mutations in the promoter region of malT and in the coding region of the crp gene were the factors responsible for the reversion in the association. Further, to identify why positive association in the D-galactose treatment and the expression of the maltose operon did not evolve naturally, fitness measurements were performed. Fitness experiments demonstrated that the fitness of E. coli strains with a positive association in the D-galactose treatment and the expression of the maltose operon was 12% to 20% lower than that of the wild type strain.

Novel classification of axial spondyloarthritis to predict radiographic progression using machine learning.

OBJECTIVES: Prediction and determination of drug efficacy for radiographic progression is limited by the heterogeneity inherent in axial spondyloarthritis (axSpA). We investigated whether unbiased clustering analysis of phenotypic data can lead to coherent subgroups of axSpA patients with a distinct risk of radiographic progression. METHODS: A group of 412 patients with axSpA was clustered in an unbiased way using a agglomerative hierarchical clustering method, based on their phenotype mapping. We used a generalised linear model, naïve Bayes, Decision Trees, K-Nearest-Neighbors, and Support Vector Machines to construct a consensus classification method. Radiographic progression over 2 years was assessed using the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). RESULTS: axSpA patients were classified into three distinct subgroups with distinct clinical characteristics. Sex, smoking, HLA-B27, baseline mSASSS, uveitis, and peripheral arthritis were the key features that were found to stratifying the phenogroups. The three phenogroups showed distinct differences in radiographic progression rate (p<0.05) and the proportion of progressors (p<0.001). Phenogroup 2, consisting of male smokers, had the worst radiographic progression, while phenogroup 3, exclusively suffering from uveitis, showed the least radiographic progression. The axSpA phenogroup classification, including its ability to stratify risk, was successfully replicated in an independent validation group. CONCLUSIONS: Phenotype mapping results in a clinically relevant classification of axSpA that is applicable for risk stratification. Novel coupling between phenotypic features and radiographic progression can provide a glimpse into the mechanisms underlying divergent and shared features of axSpA.

Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily.

To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of Escherichia coli class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications. RESULTS: We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses cell nodes that have been specifically designed to capture the dependencies and non-linear dynamics that exist in gene networks. These two key features make the GNN architecture capable to capture complex relationships without the need of large training datasets. As a result, GNNs were 40% more accurate on average than competing architectures (MLP, RNN, BiRNN) when compared on hundreds of curated and inferred transcription modules. Our results argue that GNNs can become the architecture of choice when building predictors of gene expression from exponentially growing corpus of genome-wide transcriptomics data. AVAILABILITY AND IMPLEMENTATION: https://github.com/IBPA/GNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Short- and Long-Term Transcriptomic Responses of Escherichia coli to Biocides: a Systems Analysis.

The mechanisms of the bacterial response to biocides are poorly understood, despite their broad application. To identify the genetic basis and pathways implicated in the biocide stress response, we exposed Escherichia coli populations to 10 ubiquitous biocides. By comparing the transcriptional responses between a short-term exposure (30 min) and a long-term exposure (8 to 12 h) to biocide stress, we established the common gene and pathway clusters that are implicated in general and biocide-specific stress responses. Our analysis revealed a temporal choreography, starting from the upregulation of chaperones to the subsequent repression of motility and chemotaxis pathways and the induction of an anaerobic pool of enzymes and biofilm regulators. A systematic analysis of the transcriptional data identified a zur-regulated gene cluster to be highly active in the stress response against sodium hypochlorite and peracetic acid, presenting a link between the biocide stress response and zinc homeostasis. Susceptibility assays with knockout mutants further validated our findings and provide clear targets for downstream investigation of the implicated mechanisms of action.IMPORTANCE Antiseptics and disinfectant products are of great importance to control and eliminate pathogens, especially in settings such as hospitals and the food industry. Such products are widely distributed and frequently poorly regulated. Occasional outbreaks have been associated with microbes resistant to such compounds, and researchers have indicated potential cross-resistance with antibiotics. Despite that, there are many gaps in knowledge about the bacterial stress response and the mechanisms of microbial resistance to antiseptics and disinfectants. We investigated the stress response of the bacterium Escherichia coli to 10 common disinfectant and antiseptic chemicals to shed light on the potential mechanisms of tolerance to such compounds.

Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is therapeutically challenging due to patient heterogeneity and variability. Herein we describe a novel integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts that can be used to provide predictive insights on drug responses. A normalized compendium consisting of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets was build and compared with similar datasets derived from OA patients and healthy controls. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. RA-relevant pathway activation scores and four machine learning classification techniques supported the generation of a predictive model of patient treatment response. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets. DEG-based subgrouping revealed 3 distinct RA patient clusters with distinct activity signatures for RA-relevant pathways. In the case of infliximab, we constructed a classifier of drug response that was highly accurate with an AUC/AUPR of 0.92/0.86. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients.

Compendium of skin molecular signatures identifies key pathological features associated with fibrosis in systemic sclerosis.

OBJECTIVES: Treatment of patients with systemic sclerosis (SSc) can be challenging because of clinical heterogeneity. Integration of genome-scale transcriptomic profiling for patients with SSc can provide insights on patient categorisation and novel drug targets. METHODS: A normalised compendium was created from 344 skin samples of 173 patients with SSc, covering an intersection of 17 424 genes from eight data sets. Differentially expressed genes (DEGs) identified by three independent methods were subjected to functional network analysis, where samples were grouped using non-negative matrix factorisation. Finally, we investigated the pathways and biomarkers associated with skin fibrosis using gene-set enrichment analysis. RESULTS: We identified 1089 upregulated DEGs, including 14 known genetic risk factors and five potential drug targets. Pathway-based subgrouping revealed four distinct clusters of patients with SSc with distinct activity signatures for SSc-relevant pathways. The inflammatory subtype was related to significant improvement in skin fibrosis at follow-up. The phosphoinositide-3-kinase-protein kinase B (PI3K-Akt) signalling pathway showed both the closest correlation and temporal pattern to skin fibrosis score. COMP, THBS1, THBS4, FN1, and TNC were leading-edge genes of the PI3K-Akt pathway in skin fibrogenesis. CONCLUSIONS: Construction and analysis of normalised skin transcriptomic compendia can provide useful insights on pathway involvement by SSc subsets and discovering viable biomarkers for a skin fibrosis index. Particularly, the PI3K-Akt pathway and its leading players are promising therapeutic targets.

Benzalkonium Chlorides: Uses, Regulatory Status, and Microbial Resistance.

Benzalkonium chlorides (BACs) are chemicals with widespread applications due to their broad-spectrum antimicrobial properties against bacteria, fungi, and viruses. This review provides an overview of the market for BACs, as well as regulatory measures and available data on safety, toxicity, and environmental contamination. We focus on the effect of frequent exposure of microbial communities to BACs and the potential for cross-resistant phenotypes to emerge. Toward this goal, we review BAC concentrations in consumer products, their correlation with the emergence of tolerance in microbial populations, and the associated risk potential. Our analysis suggests that the ubiquitous and frequent use of BACs in commercial products can generate selective environments that favor microbial phenotypes potentially cross-resistant to a variety of compounds. An analysis of benefits versus risks should be the guidepost for regulatory actions regarding compounds such as BACs.

Population collapse and adaptive rescue during long-term chemostat fermentation.

Microbial fermentation is an essential process for research and industrial applications, yet our understanding of cellular dynamics during long-term fermentation is limited. Here, we report a reproducible phenomenon of abrupt population collapse followed by a rapid population rescue that was observed during long-term chemostat cultivations, for various strains of Escherichia coli in minimal media. Through genome resequencing and whole-genome transcriptional profiling of replicate runs over time, we identified that changes in the tRNA and carbon catabolic genes are the genetic basis of this phenomenon. Since current fermentation models are unable to capture the observed dynamics, we present an extended model that takes into account critical biological processes during fermentation, and we further validated carbon source predictions through forward experimentation. This study extends the predictability of current models for microbial fermentation and adds to our system-level knowledge of cellular adaptation during this crucial biotechnological process.

Integrated omics analyses of retrograde signaling mutant delineate interrelated stress-response strata.

To maintain homeostasis in the face of intrinsic and extrinsic insults, cells have evolved elaborate quality control networks to resolve damage at multiple levels. Interorganellar communication is a key requirement for this maintenance, however the underlying mechanisms of this communication have remained an enigma. Here we integrate the outcome of transcriptomic, proteomic, and metabolomics analyses of genotypes including ceh1, a mutant with constitutively elevated levels of both the stress-specific plastidial retrograde signaling metabolite methyl-erythritol cyclodiphosphate (MEcPP) and the defense hormone salicylic acid (SA), as well as the high MEcPP but SA deficient genotype ceh1/eds16, along with corresponding controls. Integration of multi-omic analyses enabled us to delineate the function of MEcPP from SA, and expose the compartmentalized role of this retrograde signaling metabolite in induction of distinct but interdependent signaling cascades instrumental in adaptive responses. Specifically, here we identify strata of MEcPP-sensitive stress-response cascades, among which we focus on selected pathways including organelle-specific regulation of jasmonate biosynthesis; simultaneous induction of synthesis and breakdown of SA; and MEcPP-mediated alteration of cellular redox status in particular glutathione redox balance. Collectively, these integrated multi-omic analyses provided a vehicle to gain an in-depth knowledge of genome-metabolism interactions, and to further probe the extent of these interactions and delineate their functional contributions. Through this approach we were able to pinpoint stress-mediated transcriptional and metabolic signatures and identify the downstream processes modulated by the independent or overlapping functions of MEcPP and SA in adaptive responses.

The Genetic and Transcriptional Basis of Short and Long Term Adaptation across Multiple Stresses in Escherichia coli.

Microbes exhibit short and long term responses when exposed to challenging environmental conditions. To what extent these responses are correlated, what their evolutionary potential is and how they translate to cross-stress fitness is still unclear. In this study, we comprehensively characterized the response of Escherichia coli populations to four abiotic stresses (n-butanol, osmotic, acidic, and oxidative) and their combinations by performing genome-scale transcriptional analysis and growth profiling. We performed an analysis of their cross-stress behavior which identified 15 cases of cross- protection and one case of cross vulnerability. To elucidate the evolutionary potential of stress responses to individual stresses and stress combinations, we re-sequenced E. coli populations evolved in those four environments for 500 generations. We developed and applied a network-driven method that integrates mutations and differential expression to identify core and stress-specific gene communities that are likely to have a phenotypic impact. Our results suggest that beyond what is expected from the general stress response mechanisms, cross-stress behavior arises both from common pathways, several including metal ion binding and glycolysis/gluconeogenesis, and stress-specific expression programs. The stress-specific dependences uncovered, argue that cross-stress behavior is ubiquitous and central to understanding microbial physiology under stressful conditions.

DeepPep: Deep proteome inference from peptide profiles.

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/.

Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems.

Evolutionary potential, cross-stress behavior and the genetic basis of acquired stress resistance in Escherichia coli.

Bacterial populations have a remarkable capacity to cope with extreme environmental fluctuations in their natural environments. In certain cases, adaptation to one stressful environment provides a fitness advantage when cells are exposed to a second stressor, a phenomenon that has been coined as cross-stress protection. A tantalizing question in bacterial physiology is how the cross-stress behavior emerges during evolutionary adaptation and what the genetic basis of acquired stress resistance is. To address these questions, we evolved Escherichia coli cells over 500 generations in five environments that include four abiotic stressors. Through growth profiling and competition assays, we identified several cases of positive and negative cross-stress behavior that span all strain-stress combinations. Resequencing the genomes of the evolved strains resulted in the identification of several mutations and gene amplifications, whose fitness effect was further assessed by mutation reversal and competition assays. Transcriptional profiling of all strains under a specific stress, NaCl-induced osmotic stress, and integration with resequencing data further elucidated the regulatory responses and genes that are involved in this phenomenon. Our results suggest that cross-stress dependencies are ubiquitous, highly interconnected, and can emerge within short timeframes. The high adaptive potential that we observed argues that bacterial populations occupy a genotypic space that enables a high phenotypic plasticity during adaptation in fluctuating environments.

Volatile Organic Compound-Based Predictive Modeling of Smoke Taint in Wine.

Smoke taint in wine has become a critical issue in the wine industry due to its significant negative impact on wine quality. Data-driven approaches including univariate analysis and predictive modeling are applied to a data set containing concentrations of 20 VOCs in 48 grape samples and 56 corresponding wine samples with a taster-evaluated smoke taint index. The resulting models for predicting the smoke taint index of wines are highly predictive when using as inputs VOC concentrations after log conversion in both grapes and wines (Pearson Correlation Coefficient PCC = 0.82; R2 = 0.68) and less so when only grape VOCs are used (Pearson Correlation Coefficient PCC = 0.76; R2 = 0.56), and the classification models also show the capacity for detecting smoke-tainted wines using both wine and grape VOC concentrations (Recall = 0.76; Precision = 0.92; F1 = 0.82) or using only grape VOC concentrations (Recall = 0.74; Precision = 0.92; F1 = 0.80). The performance of the predictive model shows the possibility of predicting the smoke taint index of the wine and grape samples before fermentation. The corresponding code of data analysis and predictive modeling of smoke taint in wine is available in the Github repository (https://github.com/IBPA/smoke_taint_prediction).

Discovery of Potent Glycosidases Enables Quantification of Smoke-Derived Phenolic Glycosides through Enzymatic Hydrolysis.

When grapes are exposed to wildfire smoke, certain smoke-related volatile phenols (VPs) can be absorbed into the fruit, where they can be then converted into volatile-phenol (VP) glycosides through glycosylation. These volatile-phenol glycosides can be particularly problematic from a winemaking standpoint as they can be hydrolyzed, releasing volatile phenols, which can contribute to smoke-related off-flavors. Current methods for quantitating these volatile-phenol glycosides present several challenges, including the requirement of expensive capital equipment, limited accuracy due to the molecular complexity of the glycosides, and the utilization of harsh reagents. To address these challenges, we proposed an enzymatic hydrolysis method enabled by a tailored enzyme cocktail of novel glycosidases discovered through genome mining, and the generated VPs from VP glycosides can be quantitated by gas chromatography-mass spectrometry (GC-MS). The enzyme cocktails displayed high activities and a broad substrate scope when using commercially available VP glycosides as the substrates for testing. When evaluated in an industrially relevant matrix of Cabernet Sauvignon wine and grapes, this enzymatic cocktail consistently achieved a comparable efficacy of acid hydrolysis. The proposed method offers a simple, safe, and affordable option for smoke taint analysis.

A flood-based information flow analysis and network minimization method for gene regulatory networks.

BACKGROUND: Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. RESULTS: This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. CONCLUSIONS: The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.