Systems Biology View Research Articles

Recursive expectation-maximization clustering: A method for identifying buffering mechanisms composed of phenomic modules

Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

Linking transcriptomic and proteomic data on the level of protein interaction networks

Integration and joint analysis of omics profiles derived on the genome, transcriptome, proteome and metabolome levels is a natural next step in realizing a Systems Biology view of cellular processes. However, merging, e.g. mRNA concentration and protein abundance profiles, is not straightforward, as a direct overlap of differentially regulated/abundant features, resulting from transcriptomics and proteomics, is for various reasons limited. We present the procedures for integrating omics profiles at the level of protein interaction networks, exemplified by using transcriptomic and proteomic data sets characterizing chronic kidney disease. On the level of direct feature overlap, only a limited number of genes and proteins were found to be significantly affected following a separate transcript and protein profile analysis, including a collagen subtype and uromodulin, both being described in the context of renal failure. On the level of protein pathway and process categories, this minor overlap increases substantially, identifying cell structure, cell adhesion, as well as immunity and defense mechanisms as jointly populated with features individually identified as relevant in transcriptomics and proteomics experiments. Mapping diverse data sources characterizing a given phenotype under the analysis on directed and also undirected protein interaction networks serves in joint functional interpretation of omics data sets.

Quantitative Genetic Interactions Reveal Biological Modularity

Traditionally, research has been reductionist, characterizing the individual components of biological systems. But new technologies have increased the size and scope of biological data, and systems approaches have broadened the view of how these components are interconnected. Here, we discuss how quantitative mapping of genetic interactions enhances our view of biological systems, allowing their deeper interrogation across different biological scales.

Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks

The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level.

Bone morphogenetic protein signaling and arthritis

Chronic joint diseases have a major impact on society as patients suffer from pain and disability. The spectrum of arthritic disorders is wide including autoimmune and autoinflammatory diseases such as rheumatoid arthritis, ankylosing spondylitis and related spondyloarthritides but also the more prevalent osteoarthritic diseases. The latter appear to be mainly the consequence of injury, strain and aging in a predisposing genetic background. The therapeutic options for chronic inflammatory and immune joint diseases have greatly increased over the last decade by the use of targeted anti-cytokine or anti-immune cell drugs. However, such a shift towards successful treatment has not been achieved for osteoarthritis. In addition, control of inflammation does not equal cure of the disease as relapse occurs as soon as the treatment is interrupted, and only limited tissue repair has been observed. Bone morphogenetic proteins are potent regulators of cell proliferation, differentiation and apoptosis and they have come into the spotlight in arthritis research. Here, we summarize the recent data on the role of bone morphogenetic proteins in joint protection and repair and but also their potential disease promoting or controlling roles. These data are presented in the context of a systems biology view of joint diseases based on their histomorphological phenotype rather than on existing clinical classifications.

Birch pollen allergen Bet v 1 binds to and is transported through conjunctival epithelium in allergic patients

Previous work in type-I pollen allergies has mainly focused on lymphocytes and immune responses. Here, we begin to analyse with a systems biology view the differences in conjunctival epithelium obtained from healthy and allergic subjects. Transcriptomics analysis combined with light and electron microscopic analysis of birch pollen allergen Bet v 1 located within conjunctival epithelial cells and tissues from birch allergic subjects and healthy controls was carried out. Bet v 1 pollen allergen bound to conjunctival epithelial cells within minutes after the exposure even during the nonsymptomatic winter season only in allergic, but not in healthy individuals. Light- and electron microscopy showed that Bet v 1 was transported through the epithelium within lipid rafts/caveolae and reached mast cells only in allergic patients, but not in healthy individuals. Transcriptomics yielded 22 putative receptors expressed at higher levels in allergic epithelium compared with healthy specimens. A literature search indicated that out of these receptors, eight (i.e. 37%) were associated with lipid rafts/caveolae, which suggested again that Bet v 1 transport is lipid raft/caveola-dependent. We show a clear difference in the binding and uptake of Bet v 1 allergen by conjunctival epithelial cells in allergic vs healthy subjects and several putative lipid raft/caveolar receptors were identified, which could mediate or be co-transported with this entry. The application of discovery driven methodologies on human conjunctival epithelial cells and tissues can provide new hypotheses worth a further analysis to the molecular mechanisms of a complex multifactorial disease such as type-I birch pollen allergy.

The power and control of gravitropic movements in plants: a biomechanical and systems biology view

The study of gravitropic movements in plants has enjoyed a long history of research going back to the pioneering works of the 19th century and the famous book entitled 'The power of movement in plants' by Charles and Francis Darwin. Over the last few decades, the emphasis has shifted towards the cellular and molecular biology of gravisensing and the onset of auxin gradients across the organs. However, our understanding of plant movement cannot be completed before quantifying spatio-temporal changes in curvature and how they are produced through the motor process of active bending and controlled by gravisensing. This review sets out to show how combining approaches borrowed from continuum mechanics (kinematic imaging, structural modelling) with approaches from physiology and modern molecular biology has made it possible to generate integrative biomechanical models of the processes involved in gravitropism at several levels. The physiological and biomechanical bases are reviewed and two of the most complete integrative models of the gravireaction organ available are then compared, highlighting how the comparison between movements driven by differential growth and movements driven by reaction wood formation in woody organs has provided highly informative key insights. The advantages of these models as tools for analysing genetic control through quantitative process-based phenotyping as well as for identifying target traits for ecological studies are discussed. It is argued that such models are tools for a systems biology approach to gravitropic movement that has the potential to resolve at least some of the research questions raised 150 years ago.

Purpose and regulation of stem cells: a systems-biology view from the Caenorhabditis elegans germ line.

Stem cells are expected to play a key role in the development and maintenance of organisms, and hold great therapeutic promises. However, a number of questions must be answered to achieve an understanding of stem cells and put them to use. Here I review some of these questions, and how they relate to the model system provided by the Caenorhabditis elegans germ line, which is exceptional in its thorough genetic characterization and experimental accessibility under in vivo conditions. A fundamental question is how to define a stem cell; different definitions can be adopted that capture different features of interest. In the C. elegans germ line, stem cells can be defined by cell lineage or by cell commitment ('commitment' must itself be carefully defined). These definitions are associated with two other important questions about stem cells: their functions (which must be addressed following a systems approach, based on an evolutionary perspective) and their regulation. I review possible functions and their evolutionary groundings, including genome maintenance and powerful regulation of cell proliferation and differentiation, and possible regulatory mechanisms, including asymmetrical division and control of transit amplification by a developmental timer. I draw parallels between Drosophila and C. elegans germline stem cells; such parallels raise intriguing questions about Drosophila stem cells. I conclude by showing that the C. elegans germ line bears similarities with a number of other stem cell systems, which underscores its relevance to the understanding of stem cells.

Live and let die—A systems biology view on cell death

This contribution provides an overview of our work on modelling biological processes focussing on the example of programmed cell death, also called apoptosis. Apoptosis is a molecular programme present in all cells of multi-cellular organisms. It is crucial during development and for cell homoeostasis in the adult. Misregulation is implicated in severe diseases. We review an ordinary differential equation model describing core processes of apoptosis signalling and the idea of viewing the life and death decision as a bistable system. Then, we show how small differences in model parameters can give rise to observed population heterogeneities. Employing a new conceptional modelling framework, we further show how a single model can describe a population and how stable steady states then translate into invariant sets employing a local notion of stability.

ISOL@: an Italian SOLAnaceae genomics resource

BackgroundPresent-day ‘-omics’ technologies produce overwhelming amounts of data which include genome sequences, information on gene expression (transcripts and proteins) and on cell metabolic status. These data represent multiple aspects of a biological system and need to be investigated as a whole to shed light on the mechanisms which underpin the system functionality.The gathering and convergence of data generated by high-throughput technologies, the effective integration of different data-sources and the analysis of the information content based on comparative approaches are key methods for meaningful biological interpretations.In the frame of the International Solanaceae Genome Project, we propose here ISOLA, an Italian SOLAnaceae genomics resource.ResultsISOLA (available at ) represents a trial platform and it is conceived as a multi-level computational environment.ISOLA currently consists of two main levels: the genome and the expression level. The cornerstone of the genome level is represented by the Solanum lycopersicum genome draft sequences generated by the International Tomato Genome Sequencing Consortium. Instead, the basic element of the expression level is the transcriptome information from different Solanaceae species, mainly in the form of species-specific comprehensive collections of Expressed Sequence Tags (ESTs).The cross-talk between the genome and the expression levels is based on data source sharing and on tools that enhance data quality, that extract information content from the levels' under parts and produce value-added biological knowledge.ConclusionsISOLA is the result of a bioinformatics effort that addresses the challenges of the post-genomics era. It is designed to exploit ‘-omics’ data based on effective integration to acquire biological knowledge and to approach a systems biology view. Beyond providing experimental biologists with a preliminary annotation of the tomato genome, this effort aims to produce a trial computational environment where different aspects and details are maintained as they are relevant for the analysis of the organization, the functionality and the evolution of the Solanaceae family.

Evolution and RNA Relics. A Systems Biology View

The genetic code has evolved from its initial non-degenerate wobble version until reaching its present state of degeneracy. By using the stereochemical hypothesis, we revisit the problem of codon assignations to the synonymy classes of amino-acids. We obtain these classes with a simple classifier based on physico-chemical properties of nucleic bases, like hydrophobicity and molecular weight. Then we propose simple RNA (or more generally XNA, with X for D, P or R) ring structures that present, overlap included, one and only one codon by synonymy class as solutions of a combinatory variational problem. We compare these solutions to sequences of present RNAs considered as relics, with a high interspecific invariance, like invariant parts of (t)RNAs and micro-RNAs. We conclude by emphasizing some optimal properties of the genetic code.

Coupling and coordination in gene expression processes: a systems biology view

Genome-scale analyses have allowed us to progress beyond studying gene expression at the level of individual components of a given process by providing global information about functional connections between genes, mRNAs and their regulatory proteins. Such analyses have greatly increased our understanding of the interplay between different events in gene regulation and have highlighted previously unappreciated functional connections, including coupling between nuclear and cytoplasmic processes. Genome-wide approaches have also revealed extensive coordination within regulatory levels, such as the organization of transcription factors into regulatory motifs. Overall, these studies enhance our understanding of how the many components of the eukaryotic cell function as a system to allow both coordination and versatility in gene expression.

Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model

The transgenomic metabolic effects of exposure to either Lactobacillus paracasei or Lactobacillus rhamnosus probiotics have been measured and mapped in humanized extended genome mice (germ-free mice colonized with human baby flora). Statistical analysis of the compartmental fluctuations in diverse metabolic compartments, including biofluids, tissue and cecal short-chain fatty acids (SCFAs) in relation to microbial population modulation generated a novel top-down systems biology view of the host response to probiotic intervention. Probiotic exposure exerted microbiome modification and resulted in altered hepatic lipid metabolism coupled with lowered plasma lipoprotein levels and apparent stimulated glycolysis. Probiotic treatments also altered a diverse range of pathways outcomes, including amino-acid metabolism, methylamines and SCFAs. The novel application of hierarchical-principal component analysis allowed visualization of multicompartmental transgenomic metabolic interactions that could also be resolved at the compartment and pathway level. These integrated system investigations demonstrate the potential of metabolic profiling as a top-down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics.

Towards Personalized Radiation Therapy: Translation of a Mathematical Model of the Radiosensitivity Network to the Prediction of Clinical Radiation Response

Understanding the biological variables that influence the inherent radiosensitivity of tumors is central to the development of personalized medicine in radiation oncology. We hypothesized that a systems biology view of the radiation sensitivity network would impact our ability to define these variables.

Lineage Specification of Hematopoietic Stem Cells: Mathematical Modeling and Biological Implications

Lineage specification of hematopoietic stem cells is considered a progressive restriction in lineage potential. This view is consistent with observations that differentiation and lineage specification is preceded by a low-level coexpression of lineage specific, potentially antagonistic genes in early progenitor cells. This coexistence, commonly referred to as priming, disappears in the course of differentiation when certain lineage-restricted genes are upregulated while others are downregulated. Based on this phenomenological description, we propose a quantitative model that describes lineage specification as a competition process between different interacting lineage propensities. The competition is governed by environmental stimuli promoting a drift from a multipotent coexpression to the dominance of one lineage. The assumption of a context-dependent intracellular differentiation control is consistently embedded into our previously proposed model of hematopoietic stem cell organization. The extended model, which comprises self-renewal and lineage specification, is verified using available data on the lineage specification potential of primary hematopoietic stem cells and on the differentiation kinetics of the FDCP-mix cell line. The model provides a number of experimentally testable predictions. From our results, we conclude that lineage specification is best described as a flexible and temporally extended process in which lineage commitment emerges as the result of a sequence of small decision steps. The proposed model provides a novel systems biological view on the functioning of lineage specification in adult tissue stem cells and its connections to the self-renewal properties of these cells. Disclosure of potential conflicts of interest is found at the end of this article.

MedicCyc: a biochemical pathway database forMedicago truncatula

There is an imperative need to integrate functional genomics data to obtain a more comprehensive systems-biology view of the results. We believe that this is best achieved through the visualization of data within the biological context of metabolic pathways. Accordingly, metabolic pathway reconstruction was used to predict the metabolic composition for Medicago truncatula and these pathways were engineered to enable the correlated visualization of integrated functional genomics data. Metabolic pathway reconstruction was used to generate a pathway database for M. truncatula (MedicCyc), which currently features more than 250 pathways with related genes, enzymes and metabolites. MedicCyc was assembled from more than 225,000 M. truncatula ESTs (MtGI Release 8.0) and available genomic sequences using the Pathway Tools software and the MetaCyc database. The predicted pathways in MedicCyc were verified through comparison with other plant databases such as AraCyc and RiceCyc. The comparison with other plant databases provided crucial information concerning enzymes still missing from the ongoing, but currently incomplete M. truncatula genome sequencing project. MedicCyc was further manually curated to remove non-plant pathways, and Medicago-specific pathways including isoflavonoid, lignin and triterpene saponin biosynthesis were modified or added based upon available literature and in-house expertise. Additional metabolites identified in metabolic profiling experiments were also used for pathway predictions. Once the metabolic reconstruction was completed, MedicCyc was engineered to visualize M. truncatula functional genomics datasets within the biological context of metabolic pathways. freely accessible at http://www.noble.org/MedicCyc/

A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model.

Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short-chain fatty acids in cecum by GC-FID. Top-down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro-conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level.

Strategies for plasma proteomic profiling of cancers

Despite a voluminous literature on potential protein biomarkers and a compelling need for diagnostic tests based on biomarkers to detect cancers at much earlier, more treatable stages, progress has been limited. New methods and new instruments for analysis of differences in gene expression, gene methylation, and proteomics are being employed to try to accelerate the discovery phase. Given the heterogeneity of tumor mechanisms and the limitations of analytical methods, it is likely that a variety of strategies will be needed and will be complementary. That is the basis of this review of proteomic approaches. This article adopts a systems biology view, starting with mRNA transcripts in tumors and cultured tumor cells to detect mRNA overexpression, some of which will be correlated with protein overexpression. Some of those proteins may be secreted or released into proximal biofluids and plasma. Detection of low-abundance tumor proteins in the complex and dynamic mixture that is plasma requires combinations of increasingly powerful technologies. The biological amplification of protein signals through the immune system offers autoantibodies as potential biomarkers. Higher abundance proteins, including acute-phase reactants, may have practical value, especially if the proteins are modified as part of the cancer processes. Low molecular weight proteins, fragments, and peptides may offer complementary biomarkers. Promising biomarker candidates must be confirmed in independent studies. Then they must be submitted to higher-throughput methods practical for large-scale validation studies and, hopefully, for clinical and epidemiological applications. Standardized operating procedures for specimen handling, design and use of various reference standards, care to avoid bias and confounding, and guidelines for reporting findings and contributing datasets should enhance the prospects for predictive proteomic profiling of people at risk for cancers.

Modeling and simulation in signal transduction pathways: a systems biology approach

Modeling, the heart of systems biology, of complex processes (example: signal transduction) is a wide scientific discipline where many approaches from different areas are confronted with the aim of better understanding, identifying and modeling of complex data coming from various sources. The purpose of this paper is to introduce the basic steps of systems biology view towards signaling pathways, which mainly deals with the computational tools. The paper emphasizes the modeling and simulation approach in the signal transduction pathways using the topologies of the biochemical reactions with an overview of the different types of software platforms. Finally, we demonstrated the epidermal growth factor receptor signaling pathway model as an example to study the growth factor mediated signaling system with biological experiments. This paper will enables new comers to underline the strengths of the computational approaches towards signal transduction, as well as to highlight the systems biology research directions.

Scanning probe microscopy for the characterization of biomaterials and biological interactions.

The scanning probe microscopies provide a unique view of biological and biomedical systems at a nanoscale appropriate to appreciate molecular events. The advent of these methods has brought the ability to acquire quantitative information at the molecular level. Given the proliferation of microscopes and associated methods, the probability for important discoveries is high. If tempered with an appreciation for the potential for artifacts, the SPMs may revolutionize our view of biological systems and biomaterials interactions with those systems.