A novel algorithm for alignment of multiple PPI networks based on simulated annealing

Background

To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem.

Results

In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR.

Conclusions

We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

     

Convergent exaptation and adaptation in solitary island lizards

Independent evolutionary lineages often display similar characteristics in comparable environments. Three kinds of historical hypotheses could explain this convergence. The first is adaptive and evolutionary: nonrandom patterns may result from analogous evolutionary responses to shared conditions. The second explanation is exaptive and ecological: species may be filtered by their suitability for a particular type of environment. The third potential explanation is a null hypothesis of random colonization from a historically nonrandom source pool. Here we demonstrate that both exaptation and adaptation have produced convergent similarity in different size-related characters of solitary island lizards. Large sexual size dimorphism results from adaptive response to solitary existence; uniform, intermediate size results from ecological filtering of potential colonizers. These results demonstrate the existence of deterministic exaptive convergence and suggest that convergent phenomena may require historical explanations that are ecological as well as evolutionary.     

Bootstrapping model of the origin of life

The origin of life is analyzed in terms of the self-facilitating aspect of evolution. According to the self-facilitation (or bootstrap) principle the structure of biological systems becomes increasingly suited to effective evolutionary search through the process of evolution. The principle may be extended to primitive collections of polymers with catalytic properties. The origin of the code may be based on a form of bootstrapping evolution. Once a primitive code appeared it could become more sophisticated through a multi-coding mechanism together with classical Darwinian mechanisms. The bootstrapping principle is also formulated in terms of the fluctuation-instability framework of Prigogine, Nicolis, and Babloyantz. Primitive collections of polymers accumulate evolution-enhancing redundancies which tend to reduce the extent to which they change when destabilized by fluctuation, but which increase the chances that these changes will lead to new predominant regimes. We show that the bootstrapping of evolutionary amenability is accompanied by the accumulation of a thermodynamic load, that is, a free energy cost which reduces the mechanistic efficiency. The bootstrapping effect suggests that the origin of life is most fruitfully approached as a long process during which the capacity to evolve facilitates itself in a step by step fashion rather than as a series of low probability events.     

Food quality, nutrient limitation of secondary production, and the strength of trophic cascades

Recent meta‐analyses confirm that the strength of trophic cascades (indirect positive effects of predators on plant biomass through control of herbivores) varies among ecosystem types. In particular, most terrestrial systems show smaller cascades than most aquatic ones. Ecologists still remain challenged to explain this variation. Here, we examine a food quality hypothesis which states that higher quality plants should promote stronger trophic cascades. Food quality involves two components: digestion resistance of plants and magnitude of stoichiometric imbalance between plants and herbivores (where stoichiometry involves ratios of nutrient:carbon ratio of tissues). Both factors vary among ecosystems and could mediate conversion efficiency of plants into new herbivores (and hence control of plants by herbivores). We explored the food quality hypothesis using two models, one assuming that plant stoichiometry is a fixed trait, the other one allowing this trait to vary dynamically (but with a minimal nutrient:carbon ratio of structural mass). Both models produce the same suite of results. First, as expected, systems with more easily digested plants promote stronger cascades. Second, contrary to expectations, higher (fixed or minimal) nutrient:carbon ratio of plants do not promote stronger cascades, largely because of the net result of ecosystem feedbacks. Still, the model with dynamic stoichiometry permits positive correlations of realized plant nutrient:carbon ratio and cascade strength (as predicted), mediated through digestion resistance. Third, lower nutrient:carbon ratio of herbivores promotes stronger cascades. However, this result likely cannot explain variation in cascade strength because nutrient:carbon stoichiometry of herbivores does not vary greatly between terrestrial and aquatic ecosystems. Finally, we found that predation promotes nutrient limitation of herbivores. This finding highlights that food web processes, such as predation, can influence stoichiometry‐mediated interactions of plants and herbivores.     

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression

Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA–RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.     

Quantifying and tracing information cascades in swarms

We propose a novel, information-theoretic, characterisation of cascades within the spatiotemporal dynamics of swarms, explicitly measuring the extent of collective communications. This is complemented by dynamic tracing of collective memory, as another element of distributed computation, which represents capacity for swarm coherence. The approach deals with both global and local information dynamics, ultimately discovering diverse ways in which an individual's spatial position is related to its information processing role. It also allows us to contrast cascades that propagate conflicting information with waves of coordinated motion. Most importantly, our simulation experiments provide the first direct information-theoretic evidence (verified in a simulation setting) for the long-held conjecture that the information cascades occur in waves rippling through the swarm. Our experiments also exemplify how features of swarm dynamics, such as cascades' wavefronts, can be filtered and predicted. We observed that maximal information transfer tends to follow the stage with maximal collective memory, and principles like this may be generalised in wider biological and social contexts.     

Increasing data transparency and estimating phylogenetic uncertainty in supertrees: Approaches using nonparametric bootstrapping

The estimation of ever larger phylogenies requires consideration of alternative inference strategies, including divide-and-conquer approaches that decompose the global inference problem to a set of smaller, more manageable component problems. A prominent locus of research in this area is the development of supertree methods, which estimate a composite tree by combining a set of partially overlapping component topologies. Although promising, the use of component tree topologies as the primary data dissociates supertrees from complexities within the underling character data and complicates the evaluation of phylogenetic uncertainty. We address these issues by exploring three approaches that variously incorporate nonparametric bootstrapping into a common supertree estimation algorithm (matrix representation with parsimony, although any algorithm might be used), including bootstrap-weighting, source-tree bootstrapping, and hierarchical bootstrapping. We illustrate these procedures by means of hypothetical and empirical examples. Our preliminary experiments suggest that these methods have the potential to improve the correspondence of supertree estimates to those derived from simultaneous analysis of the combined data and to allow uncertainty in supertree topologies to be quantified. The ability to increase the transparency of supertrees to the underlying character data has several practical implications and sheds new light on an old debate. These methods have been implemented in the freely available program, tREeBOOT.     

Parallel tracking of cAMP and PKA signaling dynamics in living cells with FRET-based fluorescent biosensors

Proper regulation of cellular functions relies upon a network of intricately interwoven signaling cascades in which multiple components must be tightly coordinated both spatially and temporally. To better understand how this network operates within the cellular environment, it is important to define the parameters of various signaling activities and to reveal the characteristic activity structure of the signaling cascades. This task calls for molecular tools capable of parallelly tracking multiple activities in cellular time and space with high sensitivity and specificity. Here, we present new biosensors developed based on two conveniently co-imageable FRET pairs consisting of CFP-RFP and YFP-RFP, specifically Cerulean-mCherry and mVenus-mCherry, for parallel monitoring of PKA activity and cAMP dynamics in living cells. These biosensors provide orthogonal readouts in co-imaging experiments and display a comparable dynamic range to their cyan-yellow counterparts. Characterization of signaling responses induced by a panel of pathway activators using this co-imaging approach reveals distinct activity and kinetic patterns of cAMP and PKA dynamics arising from differential signal activation and processing. This technique is therefore useful for parallel monitoring of multiple signaling dynamics in single living cells and represents a promising approach towards a more precise characterization of the activity structure of the dynamic cellular signaling network.     

Supertree bootstrapping methods for assessing phylogenetic variation among genes in genome-scale data sets

Nonparamtric bootstrapping methods may be useful for assessing confidence in a supertree inference. We examined the performance of two supertree bootstrapping methods on four published data sets that each include sequence data from more than 100 genes. In "input tree bootstrapping," input gene trees are sampled with replacement and then combined in replicate supertree analyses; in "stratified bootstrapping," trees from each gene's separate (conventional) bootstrap tree set are sampled randomly with replacement and then combined. Generally, support values from both supertree bootstrap methods were similar or slightly lower than corresponding bootstrap values from a total evidence, or supermatrix, analysis. Yet, supertree bootstrap support also exceeded supermatrix bootstrap support for a number of clades. There was little overall difference in support scores between the input tree and stratified bootstrapping methods. Results from supertree bootstrapping methods, when compared to results from corresponding supermatrix bootstrapping, may provide insights into patterns of variation among genes in genome-scale data sets.     

DNA-based assembly lines and nanofactories

With the invention of the DNA origami technique, DNA self-assembly has reached a new level of sophistication. DNA can now be used to arrange molecules and other nanoscale components into almost arbitrary geometries-in two and even three dimensions and with nanometer precision. One exciting prospect is the realization of dynamic systems based on DNA, in which chemical reactions are precisely controlled by the spatial arrangement of components, ultimately resulting in nanoscale analogs of molecular assembly lines or 'nanofactories'. This review will discuss recent progress toward this goal, ranging from DNA-templated synthesis over artificial DNA-based enzyme cascades to first examples of 'molecular robots'.     

Reliability of Bayesian posterior probabilities and bootstrap frequencies in phylogenetics

Many empirical studies have revealed considerable differences between nonparametric bootstrapping and Bayesian posterior probabilities in terms of the support values for branches, despite claimed predictions about their approximate equivalence. We investigated this problem by simulating data, which were then analyzed by maximum likelihood bootstrapping and Bayesian phylogenetic analysis using identical models and reoptimization of parameter values. We show that Bayesian posterior probabilities are significantly higher than corresponding nonparametric bootstrap frequencies for true clades, but also that erroneous conclusions will be made more often. These errors are strongly accentuated when the models used for analyses are underparameterized. When data are analyzed under the correct model, nonparametric bootstrapping is conservative. Bayesian posterior probabilities are also conservative in this respect, but less so.     

Minimizing model fitting objectives that contain spurious local minima by bootstrap restarting

Objective functions that arise when fitting nonlinear models often contain local minima that are of little significance except for their propensity to trap minimization algorithms. The standard methods for attempting to deal with this problem treat the objective function as fixed and employ stochastic minimization approaches in the hope of randomly jumping out of local minima. This article suggests a simple trick for performing such minimizations that can be employed in conjunction with most conventional nonstochastic fitting methods. The trick is to stochastically perturb the objective function by bootstrapping the data to be fit. Each bootstrap objective shares the large-scale structure of the original objective but has different small-scale structure. Minimizations of bootstrap objective functions are alternated with minimizations of the original objective function starting from the parameter values with which minimization of the previous bootstrap objective terminated. An example is presented, fitting a nonlinear population dynamic model to population dynamic data and including a comparison of the suggested method with simulated annealing. Convergence diagnostics are discussed.     

Clinical proteomics: revolutionizing disease detection and patient tailoring therapy

The evolving discipline of Clinical Proteomics is more than simply describing and enumerating the systematic changes in the protein constituency of a cell, or just generating lists of proteins that increase or decrease in expression as a cause or consequence of disease. Clinical applications of proteomics involve the use of proteomic technologies at the bedside with the ultimate goal to characterize the information flow through the intra- and extracellular molecular protein networks that interconnect organ and circulatory systems together. These networks are both new targets for therapeutics themselves as well as underpin the dynamic changes that give rise to cascades of new diagnostic biomarkers. The analysis of human cancer can be used as a model for how clinical proteomics is having an impact at the bedside for early detection, rational therapeutic targeting, and patient-tailored therapy.     

A Critical Meta-Analysis of Lens Model Studies in Human Judgment and Decision-Making

Achieving accurate judgment (‘judgmental achievement’) is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping.     

Computational modeling of cellular signaling processes embedded into dynamic spatial contexts

Cellular signaling processes depend on spatiotemporal distributions of molecular components. Multicolor, high-resolution microscopy permits detailed assessment of such distributions, providing input for fine-grained computational models that explore mechanisms governing dynamic assembly of multimolecular complexes and their role in shaping cellular behavior. However, it is challenging to incorporate into such models both complex molecular reaction cascades and the spatial localization of signaling components in dynamic cellular morphologies. Here we introduce an approach to address these challenges by automatically generating computational representations of complex reaction networks based on simple bimolecular interaction rules embedded into detailed, adaptive models of cellular morphology. Using examples of receptor-mediated cellular adhesion and signal-induced localized mitogen-activated protein kinase (MAPK) activation in yeast, we illustrate the capacity of this simulation technique to provide insights into cell biological processes. The modeling algorithms, implemented in a new version of the Simmune toolset, are accessible through intuitive graphical interfaces and programming libraries.     

Computational prediction and analysis of impact of the cross‐talks between JNK and P38 kinase cascades

Signal transduction is a complex protein signaling process with a rich network of multifunctional interactions that occur in a non‐linear fashion. Mitogen‐activated protein kinase (MAPK) signal transduction pathways regulate diverse cellular processes ranging from proliferation and differentiation to apoptosis. In mammals, out of five, there are three well characterized subfamilies of MAPKs ‐ ERKs (Extracellular signal‐regulated kinases), JNKs (c‐Jun N‐terminal kinases), and P38 kinases, and their activators, are implicated in human diseases and are targets for drug development. Kinase cascades in MAPK pathways mediate the sensing and processing of stimuli. To understand how cells makes decisions, the dynamic interactions of components of signaling cascades are important rather than just creating static maps. Based on enzyme kinetic reactions, we have developed a mathematical model to analyze the impact of the cross‐talks between JNK and P38 kinase cascades. Cross‐talks between JNK and P38 kinase cascades influence the activities of P38 kinases. Responses of the signals should be studied for network of kinase cascades by considering cross‐talks.     

NMDA受体信号复合体中蛋白质的相互作用

谷氨酸能兴奋性突触的突触后密集区(postsynaptic density,PSD)包含多种受体蛋白、骨架蛋白和信号蛋白,它们通过分子中特定的结构域相互识别并动态地结合,形成多个信号复合体,参与突触后受体功能的调节及其下游特异性信号转导通路的激活。其中,NMDA受体信号复合体中蛋白质-蛋白质的相互作用及其调控机制的阐明,对于深入了解神经发育、突触可塑性、兴奋性毒性等生理病理的分子机制有重要意义。     

Genomes were forged by massive bombardments with retroelements and retrosequences

Retroposition is an efficient route to move coding regions around the genome ‘in search’ of novel regulatory elements and to shotgun regulatory elements into the genome ‘in search’ of new target genes. The templates for such retrogenes are mRNAs, and for regulatory retronuons (nuon=any definable nucleic acid sequence) usually small non-mRNAs. An example in support of the ‘master gene’ model for SINEs (short interspersed repetive elements) is provided with neuronal BC1 RNA. Furthermore, an alternative explanation of LINE (long interspersed repetive elements) involvement in the generation of SINEs is given. I will also argue that the status of transposable elements with respect to the host resembles more symbiosis than parasitiasis and that host defense is often lenient as if even to ‘tolerate or support’ retronuons. Finally the paradox of evolution's lack of foresight and the future exaptive use of retronuons is being dealt with by referring to W.F. Doolittle's ‘Hierarchical Approaches to Genome Evolution’. This revised version was published online in July 2006 with corrections to the Cover Date.     

Integrin-associated molecules and signalling cross talking in osteoclast cytoskeleton regulation

In the ageing skeleton, the balance of bone reconstruction could commonly be broken by the increasing of bone resorption and decreasing of bone formation. Consequently, the bone resorption gradually occupies a dominant status. During this imbalance process, osteoclast is unique cell linage act the bone resorptive biological activity, which is a highly differentiated ultimate cell derived from monocyte/macrophage. The erosive function of osteoclasts is that they have to adhere the bone matrix and migrate along it, in which adhesive cytoskeleton recombination of osteoclast is essential. In that, the podosome is a membrane binding microdomain organelle, based on dynamic actin, which forms a cytoskeleton superstructure connected with the plasma membrane. Otherwise, as the main adhesive protein, integrin regulates the formation of podosome and cytoskeleton, which collaborates with the various molecules including: c-Cbl, p130^Cas, c-Src and Pyk2, through several signalling cascades cross talking, including: M-CSF and RANKL. In our current study, we discuss the role of integrin and associated molecules in osteoclastogenesis cytoskeletal, especially podosomes, regulation and relevant signalling cascades cross talking.