Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation

Defining the extent of gene function using ROC curvature

MOTIVATION

Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specific biological context, which can then be extended to the whole genome across different contexts, such as conditions, tissues or species, through machine learning methods. However, evaluating the performance of these methods remains a critical challenge. Here, we propose to evaluate the generalizability of gene characterizations through the shape of performance curves.

RESULTS

We identify Functional Equivalence Classes (FECs), subsets of annotated and unannotated genes that jointly drive performance, by assessing the presence of straight lines in ROC curves built from gene-centric prediction tasks, such as function or interaction predictions. FECs are widespread across data types and methods, they can be used to evaluate the extent and context-specificity of functional annotations in a data-driven manner. For example, FECs suggest that B cell markers can be decomposed into shared primary markers (10-50 genes), and tissue-specific secondary markers (100-500 genes). In addition, FECs suggest the existence of functional modules that span a wide range of the genome, with marker sets spanning at most 5% of the genome and data-driven extensions of Gene Ontology sets spanning up to 40% of the genome. Simple to assess visually and statistically, the identification of FECs in performance curves paves the way for novel functional characterization and increased robustness in the definition of functional gene sets.

AVAILABILITY AND IMPLEMENTATION

Code for analyses and figures is available at https://github.com/yexilein/pyroc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chromatin Network Analyses: Towards Structure-Function Relationships in Epigenomics

Recent technological advances have allowed us to map chromatin conformation and uncover the genome's spatial organization of the genome inside the nucleus. These experiments have revealed the complexities of genome folding, characterized by the presence of loops and domains at different scales, which can change across development and in different cell types. There is strong evidence for a relationship between the topological properties of chromatin contacts and cellular phenotype. Chromatin can be represented as a network, in which genomic fragments are the nodes and connections represent experimentally observed spatial proximity of two genomically distant regions in a specific cell type or biological condition. With this approach we can consider a variety of chromatin features in association with the 3D structure, investigating how nuclear chromatin organization can be related to gene regulation, replication, malignancy, phenotypic variability and plasticity. We briefly review the results obtained on genome architecture through network theoretic approaches. As previously observed in protein-protein interaction networks and many types of non-biological networks, external conditions could shape network topology through a yet unidentified structure-function relationship. Similar to scientists studying the brain, we are confronted with a duality between a spatially embedded network of physical contacts, a related network of correlation in the dynamics of network nodes and, finally, an abstract definition of function of this network, related to phenotype. We summarise major developments in the study of networks in other fields, which we think can suggest a path towards better understanding how 3D genome configuration can impact biological function and adaptation to the environment.

IMCC: A Novel Quantitative Approach Revealing Variation of Global Modular Map and Local Inter-Module Coordination Among Differential Drug's Targeted Cerebral Ischemic Networks

Stroke is a common disease characterized by multiple genetic dysfunctions. In this complex disease, detecting the strength of inter-module coordination (genetic community interaction) and subsequent modular rewiring is essential to characterize the reactive biosystematic variation (biosystematic perturbation) brought by multiple-target drugs, whose effects are achieved by hitting on a series of targets (target profile) jointly. Here, a quantitative approach for inter-module coordination and its transition, named as IMCC, was developed. Applying IMCC to mouse cerebral ischemia–related gene microarray, we investigated a holistic view of modular map and its rewiring from ischemic stroke to drugs (baicalin, BA; ursodeoxycholic acid, UA; and jasminoidin, JA) perturbation states and locally identified the cooperative pathological module pair and its dissection. Our result suggested the global modular map in cerebral ischemia exhibited a characteristic “core–periphery” architecture, and this architecture was rewired by the effective drugs heterogeneously: BA and UA converged modules into an intensively connected integrity, whereas JA diverged partial modules and widened the remaining inter-module paths. Locally, the PMP dissociation brought by drugs contributed to the reversion of the pathological condition: the focus of the cellular function shift from survival after nervous system injury into development and repair, including neurotrophin regulation, hormone releasing, and chemokine signaling activation. The core targets and mechanisms were validated by in vivo experiments. Overall, our result highlights the holistic inter-module coordination rearrangement rather than a target or a single module that brings phenotype alteration. This strategy may lead to systematically explore detailed variation of inter-module pharmacological action mode of multiple-target drugs, which is the principal problem of module pharmacology for network-based drug discovery.

Modular Reorganization of Signaling Networks during the Development of Colon Adenoma and Carcinoma

Network science is an emerging tool in systems biology and oncology, providing novel, system-level insight into the development of cancer. The aim of this project was to study the signaling networks in the process of oncogenesis to explore the adaptive mechanisms taking part in the cancerous transformation of healthy cells. For this purpose, colon cancer proved to be an excellent candidate as the preliminary phase, and adenoma has a long evolution time. In our work, transcriptomic data have been collected from normal colon, colon adenoma, and colon cancer samples to calculating link (i.e., network edge) weights as approximative proxies for protein abundances, and link weights were included in the Human Cancer Signaling Network. Here we show that the adenoma phase clearly differs from the normal and cancer states in terms of a more scattered link weight distribution and enlarged network diameter. Modular analysis shows the rearrangement of the apoptosis- and the cell-cycle-related modules, whose pathway enrichment analysis supports the relevance of targeted therapy. Our work enriches the system-wide assessment of cancer development, showing specific changes for the adenoma state.

Modeling cell population dynamics

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Modeling cell population dynamics

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Insights into archaeal chaperone machinery: a network-based approach

Molecular chaperones are a diverse group of proteins that ensure proteome integrity by helping the proteins fold correctly and maintain their native state, thus preventing their misfolding and subsequent aggregation. The chaperone machinery of archaeal organisms has been thought to closely resemble that found in humans, at least in terms of constituent players. Very few studies have been ventured into system-level analysis of chaperones and their functioning in archaeal cells. In this study, we attempted such an analysis of chaperone-assisted protein folding in archaeal organisms through network approach using Picrophilus torridus as model system. The study revealed that DnaK protein of Hsp70 system acts as hub in protein-protein interaction network. However, DnaK protein was present only in a subset of archaeal organisms and absent from many archaea, especially members of Crenarchaeota phylum. Therefore, a similar network was created for another archaeal organism, Sulfolobus solfataricus, a member of Crenarchaeota. The chaperone network of S. solfataricus suggested that thermosomes played an integral part of hub proteins in archaeal organisms, where DnaK was absent. We further compared the chaperone network of archaea with that found in eukaryotic systems, by creating a similar network for Homo sapiens. In the human chaperone network, the UBC protein, a part of ubiquitination system, was the most important module, and interestingly, this system is known to be absent in archaeal organisms. Comprehensive comparison of these networks leads to several interesting conclusions regarding similarities and differences within archaeal chaperone machinery in comparison to humans.

Multiscale effects of heating and cooling on genes and gene networks

Fluctuating environments such as changes in ambient temperature represent a fundamental challenge to life. Cells must protect gene networks that protect them from such stresses, making it difficult to understand how temperature affects gene network function in general. Here, we focus on single genes and small synthetic network modules to reveal four key effects of nonoptimal temperatures at different biological scales: (i) a cell fate choice between arrest and resistance, (ii) slower growth rates, (iii) Arrhenius reaction rates, and (iv) protein structure changes. We develop a multiscale computational modeling framework that captures and predicts all of these effects. These findings promote our understanding of how temperature affects living systems and enables more robust cellular engineering for real-world applications.

Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae. We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.

Quantitative Determination of Flexible Pharmacological Mechanisms Based On Topological Variation in Mice Anti-Ischemic Modular Networks

Targeting modules or signalings may open a new path to understanding the complex pharmacological mechanisms of reversing disease processes. However, determining how to quantify the structural alteration of these signalings or modules in pharmacological networks poses a great challenge towards realizing rational drug use in clinical medicine. Here, we explore a novel approach for dynamic comparative and quantitative analysis of the topological structural variation of modules in molecular networks, proposing the concept of allosteric modules (AMs). Based on the ischemic brain of mice, we optimize module distribution in different compound-dependent modular networks by using the minimum entropy criterion and then calculate the variation in similarity values of AMs under various conditions using a novel method of SimiNEF. The diverse pharmacological dynamic stereo-scrolls of AMs with functional gradient alteration, which consist of five types of AMs, may robustly deconstruct modular networks under the same ischemic conditions. The concept of AMs can not only integrate the responsive mechanisms of different compounds based on topological cascading variation but also obtain valuable structural information about disease and pharmacological networks beyond pathway analysis. We thereby provide a new systemic quantitative strategy for rationally determining pharmacological mechanisms of altered modular networks based on topological variation.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism

Background

The avalanche of integromics and panomics approaches shifted the deciphering of aging mechanisms from single molecular entities to communities of them. In this orientation, we explore the cardiac aging mechanisms – risk factor for multiple cardiovascular diseases - by capturing the micronome synergism and detecting longevity signatures in the form of communities (modules).

For this, we developed a meta-analysis scheme that integrates transcriptome expression data from multiple cardiac-specific independent studies in mouse and human along with proteome and micronome interaction data in the form of multiple independent weighted networks. Modularization of each weighted network produced modules, which in turn were further analyzed so as to define consensus modules across datasets that change substantially during lifespan. Also, we established a metric that determines - from the modular perspective - the synergism of microRNA-microRNA interactions as defined by significantly functionally associated targets.

Results

The meta-analysis provided 40 consensus integromics modules across mouse datasets and revealed microRNA relations with substantial collective action during aging. Three modules were reproducible, based on homology, when mapped against human-derived modules. The respective homologs mainly represent NADH dehydrogenases, ATP synthases, cytochrome oxidases, Ras GTPases and ribosomal proteins. Among various observations, we corroborate to the involvement of miR-34a (included in consensus modules) as proposed recently; yet we report that has no synergistic effect. Moving forward, we determined its age-related neighborhood in which HCN3, a known heart pacemaker channel, was included. Also, miR-125a-5p/-351, miR-200c/-429, miR-106b/-17, miR-363/-92b, miR-181b/-181d, miR-19a/-19b, let-7d/-7f, miR-18a/-18b, miR-128/-27b and miR-106a/-291a-3p pairs exhibited significant synergy and their association to aging and/or cardiovascular diseases is supported in many cases by a disease database and previous studies. On the contrary, we suggest that miR-22 has not substantial impact on heart longevity as proposed recently.

Conclusions

We revised several proteins and microRNAs recently implicated in cardiac aging and proposed for the first time modules as signatures. The integromics meta-analysis approach can serve as an efficient subvening signature tool for more-oriented better-designed experiments. It can also promote the combinational multi-target microRNA therapy of age-related cardiovascular diseases along the continuum from prevention to detection, diagnosis, treatment and outcome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1256-3) contains supplementary material, which is available to authorized users.

Quasispecies theory for evolution of modularity

Biological systems are modular, and this modularity evolves over time and in different environments. A number of observations have been made of increased modularity in biological systems under increased environmental pressure. We here develop a quasispecies theory for the dynamics of modularity in populations of these systems. We show how the steady-state fitness in a randomly changing environment can be computed. We derive a fluctuation dissipation relation for the rate of change of modularity and use it to derive a relationship between rate of environmental changes and rate of growth of modularity. We also find a principle of least action for the evolved modularity at steady state. Finally, we compare our predictions to simulations of protein evolution and find them to be consistent.

Mapping knowledge translation and innovation processes in Cancer Drug Development: the case of liposomal doxorubicin

We explored how the knowledge translation and innovation processes are structured when theyresult in innovations, as in the case of liposomal doxorubicin research. In order to map the processes, a literature network analysis was made through Cytoscape and semantic analysis was performed by GOPubmed which is based in the controlled vocabularies MeSH (Medical Subject Headings) and GO (Gene Ontology). We found clusters related to different stages of the technological development (invention, innovation and imitation) and the knowledge translation process (preclinical, translational and clinical research), and we were able to map the historic emergence of Doxil as a paradigmatic nanodrug. This research could be a powerful methodological tool for decision-making and innovation management in drug delivery research.

Biological network analysis with CentiScaPe: centralities and experimental dataset integration

The growing dimension and complexity of available experimental data generating biological networks has increased the need for tools allowing to categorize nodes by their topological relevance in biological networks. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes for the identification of the most important nodes of a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be also integrated with data sets from lab experiments, such as expression or phosphorylation levels of the proteins represented in the network, using the graphical features of the tool. This opens a new perspective in the analysis of biological networks, since integration of topological analysis with lab experimental data can increase the predictive power of a bioinformatical analysis.

Biological network analysis with CentiScaPe: centralities and experimental dataset integration

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.

Nonlinear effects of nanoparticles: biological variability from hormetic doses, small particle sizes, and dynamic adaptive interactions

Researchers are increasingly focused on the nanoscale level of organization where biological processes take place in living systems. Nanoparticles (NPs, e.g., 1-100 nm diameter) are small forms of natural or manufactured source material whose properties differ markedly from those of the respective bulk forms of the "same" material. Certain NPs have diagnostic and therapeutic uses; some NPs exhibit low-dose toxicity; other NPs show ability to stimulate low-dose adaptive responses (hormesis). Beyond dose, size, shape, and surface charge variations of NPs evoke nonlinear responses in complex adaptive systems. NPs acquire unique size-dependent biological, chemical, thermal, optical, electromagnetic, and atom-like quantum properties. Nanoparticles exhibit high surface adsorptive capacity for other substances, enhanced bioavailability, and ability to cross otherwise impermeable cell membranes including the blood-brain barrier. With super-potent effects, nano-forms can evoke cellular stress responses or therapeutic effects not only at lower doses than their bulk forms, but also for longer periods of time. Interactions of initial effects and compensatory systemic responses can alter the impact of NPs over time. Taken together, the data suggest the need to downshift the dose-response curve of NPs from that for bulk forms in order to identify the necessarily decreased no-observed-adverse-effect-level and hormetic dose range for nanoparticles.

Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

Homeopathic medications as clinical alternatives for symptomatic care of acute otitis media and upper respiratory infections in children

The public health and individual risks of inappropriate antibiotic prescribing and conventional over-the-counter symptomatic drugs in pediatric treatment of acute otitis media (AOM) and upper respiratory infections (URIs) are significant. Clinical research suggests that over-the-counter homeopathic medicines offer pragmatic treatment alternatives to conventional drugs for symptom relief in children with uncomplicated AOM or URIs. Homeopathy is a controversial but demonstrably safe and effective 200-year-old whole system of complementary and alternative medicine used worldwide. Numerous clinical studies demonstrate that homeopathy accelerates early symptom relief in acute illnesses at much lower risk than conventional drug approaches. Evidence-based advantages for homeopathy include lower antibiotic fill rates during watchful waiting in otitis media, fewer and less serious side effects, absence of drug-drug interactions, and reduced parental sick leave from work. Emerging evidence from basic and preclinical science research counter the skeptics' claims that homeopathic remedies are biologically inert placebos. Consumers already accept and use homeopathic medicines for self care, as evidenced by annual US consumer expenditures of $2.9 billion on homeopathic remedies. Homeopathy appears equivalent to and safer than conventional standard care in comparative effectiveness trials, but additional well-designed efficacy trials are indicated. Nonetheless, the existing research evidence on safety supports pragmatic use of homeopathy in order to “first do no harm” in the early symptom management of otherwise uncomplicated AOM and URIs in children.

An efficient immunization strategy for community networks

An efficient algorithm that can properly identify the targets to immunize or quarantine for preventing an epidemic in a population without knowing the global structural information is of obvious importance. Typically, a population is characterized by its community structure and the heterogeneity in the weak ties among nodes bridging over communities. We propose and study an effective algorithm that searches for bridge hubs, which are bridge nodes with a larger number of weak ties, as immunizing targets based on the idea of referencing to an expanding friendship circle as a self-avoiding walk proceeds. Applying the algorithm to simulated networks and empirical networks constructed from social network data of five US universities, we show that the algorithm is more effective than other existing local algorithms for a given immunization coverage, with a reduced final epidemic ratio, lower peak prevalence and fewer nodes that need to be visited before identifying the target nodes. The effectiveness stems from the breaking up of community networks by successful searches on target nodes with more weak ties. The effectiveness remains robust even when errors exist in the structure of the networks.

Tamoxifen integromics and personalized medicine: dynamic modular transformations underpinning response to tamoxifen in breast cancer treatment

Recent advances in pharmacogenomics technologies allow bold steps to be taken towards personalized medicine, more accurate health planning, and personalized drug development. In this framework, systems pharmacology network-based approaches offer an appealing way for integrating multi-omics data and set the basis for defining systems-level drug response biomarkers. On the road to individualized tamoxifen treatment in estrogen receptor-positive breast cancer patients, we examine the dynamics of the attendant pharmacological response mechanisms. By means of an "integromics" network approach, we assessed the tamoxifen effect through the way the high-order organization of interactome (i.e., the modules) is perturbed. To accomplish that, first we integrated the time series transcriptome data with the human protein interaction data, and second, an efficient module-detecting algorithm was applied onto the composite graphs. Our findings show that tamoxifen induces severe modular transformations on specific areas of the interactome. Our modular biomarkers in response to tamoxifen attest to the immunomodulatory role of tamoxifen, and further reveal that it deregulates cell cycle and apoptosis pathways, while coordinating the proteasome and basal transcription factors. To the best of our knowledge, this is the first report that informs the fields of personalized medicine and clinical pharmacology about the actual dynamic interactome response to tamoxifen administration.

Perturbation centrality and turbine: a novel centrality measure obtained using a versatile network dynamics tool

Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions.

Nodes having a major influence to break cooperation define a novel centrality measure: game centrality

Cooperation played a significant role in the self-organization and evolution of living organisms. Both network topology and the initial position of cooperators heavily affect the cooperation of social dilemma games. We developed a novel simulation program package, called ‘NetworGame’, which is able to simulate any type of social dilemma games on any model, or real world networks with any assignment of initial cooperation or defection strategies to network nodes. The ability of initially defecting single nodes to break overall cooperation was called as ‘game centrality’. The efficiency of this measure was verified on well-known social networks, and was extended to ‘protein games’, i.e. the simulation of cooperation between proteins, or their amino acids. Hubs and in particular, party hubs of yeast protein-protein interaction networks had a large influence to convert the cooperation of other nodes to defection. Simulations on methionyl-tRNA synthetase protein structure network indicated an increased influence of nodes belonging to intra-protein signaling pathways on breaking cooperation. The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of nodes in the integration and regulation of complex systems. Game centrality may help to design more efficient interventions to cellular networks (in forms of drugs), to ecosystems and social networks.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Passing messages between biological networks to refine predicted interactions

Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

Integrative avenues for exploring the dynamics and evolution of protein interaction networks

Over the past decade, the study of protein interaction networks (PINs) has shed light on the organizing principles of living cells. However, PINs have been mostly mapped in one single condition. We outline three of the most promising avenues of investigation in this field, namely the study of first, how PINs are rewired by mutations and environmental perturbations; secondly, how inter-species interactions affect PIN achitectures; thirdly, what mechanisms and forces drive PIN evolution. These investigations will unravel the dynamics and condition dependence of PINs and will thus lead to a better functional annotation of network architecture. One major challenge to reach these goals is the integration of PINs with other cellular regulatory networks in the context of complex cellular phenotypes.

Advances in Integrative Nanomedicine for Improving Infectious Disease Treatment in Public Health

INTRODUCTION

Infectious diseases present public health challenges worldwide. An emerging integrative approach to treating infectious diseases is using nanoparticle (NP) forms of traditional and alternative medicines. Advantages of nanomedicine delivery methods include better disease targeting, especially for intracellular pathogens, ability to cross membranes and enter cells, longer duration drug action, reduced side effects, and cost savings from lower doses.

METHODS

We searched Pubmed articles in English with keywords related to nanoparticles and nanomedicine. Nanotechnology terms were also combined with keywords for drug delivery, infectious diseases, herbs, antioxidants, homeopathy, and adaptation.

RESULTS

NPs are very small forms of material substances, measuring 1-100 nanometers along at least one dimension. Compared with bulk forms, NPs' large ratio of surface-area-to-volume confers increased reactivity and adsorptive capacity, with unique electromagnetic, chemical, biological, and quantum properties. Nanotechnology uses natural botanical agents for green manufacturing of less toxic NPs.

DISCUSSION

Nanoparticle herbs and nutriceuticals can treat infections via improved bioavailability and antiinflammatory, antioxidant, and immunomodulatory effects. Recent studies demonstrate that homeopathic medicines may contain source and/or silica nanoparticles because of their traditional manufacturing processes. Homeopathy, as a form of nanomedicine, has a promising history of treating epidemic infectious diseases, including malaria, leptospirosis and HIV/AIDS, in addition to acute upper respiratory infections. Adaptive changes in the host's complex networks underlie effects.

CONCLUSIONS

Nanomedicine is integrative, blending modern technology with natural products to reduce toxicity and support immune function. Nanomedicine using traditional agents from alternative systems of medicine can facilitate progress in integrative public health approaches to infectious diseases.

Fungal Hsp90: a biological transistor that tunes cellular outputs to thermal inputs

Heat shock protein 90 (HSP90) is an essential, abundant and ubiquitous eukaryotic chaperone that has crucial roles in protein folding and modulates the activities of key regulators. The fungal Hsp90 interactome, which includes numerous client proteins such as receptors, protein kinases and transcription factors, displays a surprisingly high degree of plasticity that depends on environmental conditions. Furthermore, although fungal Hsp90 levels increase following environmental challenges, Hsp90 activity is tightly controlled via post-translational regulation and an autoregulatory loop involving heat shock transcription factor 1 (Hsf1). In this Review, we discuss the roles and regulation of fungal Hsp90. We propose that Hsp90 acts as a biological transistor that modulates the activity of fungal signalling networks in response to environmental cues via this Hsf1-Hsp90 autoregulatory loop.

Effects of weak ties on epidemic predictability on community networks

Weak ties play a significant role in the structures and the dynamics of community networks. Based on the contact process, we study numerically how weak ties influence the predictability of epidemic dynamics. We first investigate the effects of the degree of bridge nodes on the variabilities of both the arrival time and the prevalence of disease, and find out that the bridge node with a small degree can enhance the predictability of epidemic spreading. Once weak ties are settled, the variability of the prevalence will display a complete opposite trend to that of the arrival time, as the distance from the initial seed to the bridge node or the degree of the initial seed increases. More specifically, the further distance and the larger degree of the initial seed can induce the better predictability of the arrival time and the worse predictability of the prevalence. Moreover, we discuss the effects of the number of weak ties on the epidemic variability. As the community strength becomes very strong, which is caused by the decrease of the number of weak ties, the epidemic variability will change dramatically. Compared with the case of the hub seed and the random seed, the bridge seed can result in the worst predictability of the arrival time and the best predictability of the prevalence.

Large-scale functional organization of long-range chromatin interaction networks

Chromatin interactions play important roles in transcription regulation. To better understand the underlying evolutionary and functional constraints of these interactions, we implemented a systems approach to examine RNA polymerase-II-associated chromatin interactions in human cells. We found that 40% of the total genomic elements involved in chromatin interactions converged to a giant, scale-free-like, hierarchical network organized into chromatin communities. The communities were enriched in specific functions and were syntenic through evolution. Disease-associated SNPs from genome-wide association studies were enriched among the nodes with fewer interactions, implying their selection against deleterious interactions by limiting the total number of interactions, a model that we further reconciled using somatic and germline cancer mutation data. The hubs lacked disease-associated SNPs, constituted a nonrandomly interconnected core of key cellular functions, and exhibited lethality in mouse mutants, supporting an evolutionary selection that favored the nonrandom spatial clustering of the least-evolving key genomic domains against random genetic or transcriptional errors in the genome. Altogether, our analyses reveal a systems-level evolutionary framework that shapes functionally compartmentalized and error-tolerant transcriptional regulation of human genome in three dimensions.

A model for homeopathic remedy effects: low dose nanoparticles, allostatic cross-adaptation, and time-dependent sensitization in a complex adaptive system

Background

This paper proposes a novel model for homeopathic remedy action on living systems. Research indicates that homeopathic remedies (a) contain measurable source and silica nanoparticles heterogeneously dispersed in colloidal solution; (b) act by modulating biological function of the allostatic stress response network (c) evoke biphasic actions on living systems via organism-dependent adaptive and endogenously amplified effects; (d) improve systemic resilience.

Discussion

The proposed active components of homeopathic remedies are nanoparticles of source substance in water-based colloidal solution, not bulk-form drugs. Nanoparticles have unique biological and physico-chemical properties, including increased catalytic reactivity, protein and DNA adsorption, bioavailability, dose-sparing, electromagnetic, and quantum effects different from bulk-form materials. Trituration and/or liquid succussions during classical remedy preparation create “top-down” nanostructures. Plants can biosynthesize remedy-templated silica nanostructures. Nanoparticles stimulate hormesis, a beneficial low-dose adaptive response. Homeopathic remedies prescribed in low doses spaced intermittently over time act as biological signals that stimulate the organism’s allostatic biological stress response network, evoking nonlinear modulatory, self-organizing change. Potential mechanisms include time-dependent sensitization (TDS), a type of adaptive plasticity/metaplasticity involving progressive amplification of host responses, which reverse direction and oscillate at physiological limits. To mobilize hormesis and TDS, the remedy must be appraised as a salient, but low level, novel threat, stressor, or homeostatic disruption for the whole organism. Silica nanoparticles adsorb remedy source and amplify effects. Properly-timed remedy dosing elicits disease-primed compensatory reversal in direction of maladaptive dynamics of the allostatic network, thus promoting resilience and recovery from disease.

Summary

Homeopathic remedies are proposed as source nanoparticles that mobilize hormesis and time-dependent sensitization via non-pharmacological effects on specific biological adaptive and amplification mechanisms. The nanoparticle nature of remedies would distinguish them from conventional bulk drugs in structure, morphology, and functional properties. Outcomes would depend upon the ability of the organism to respond to the remedy as a novel stressor or heterotypic biological threat, initiating reversals of cumulative, cross-adapted biological maladaptations underlying disease in the allostatic stress response network. Systemic resilience would improve. This model provides a foundation for theory-driven research on the role of nanomaterials in living systems, mechanisms of homeopathic remedy actions and translational uses in nanomedicine.

The Combinatorial Nature of Osmosensing in Fishes

Organisms exposed to altered salinity must be able to perceive osmolality change because metabolism has evolved to function optimally at specific intracellular ionic strength and composition. Such osmosensing comprises a complex physiological process involving many elements at organismal and cellular levels of organization. Input from numerous osmosensors is integrated to encode magnitude, direction, and ionic basis of osmolality change. This combinatorial nature of osmosensing is discussed with emphasis on fishes.

ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality

The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux or Mac OS. We demonstrate its use on protein structure and metabolic networks.

AVAILABILITY

The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php.