Noise in cellular signaling pathways: causes and effects

Integration and global analysis of isothermal titration calorimetry data for studying macromolecular interactions

Isothermal titration calorimetry (ITC) is a powerful and widely used method to measure the energetics of macromolecular interactions by recording a thermogram of differential heating power during a titration. However, traditional ITC analysis is limited by stochastic thermogram noise and by the limited information content of a single titration experiment. Here we present a protocol for bias-free thermogram integration based on automated shape analysis of the injection peaks, followed by combination of isotherms from different calorimetric titration experiments into a global analysis, statistical analysis of binding parameters and graphical presentation of the results. This is performed using the integrated public-domain software packages NITPIC, SEDPHAT and GUSSI. The recently developed low-noise thermogram integration approach and global analysis allow for more precise parameter estimates and more reliable quantification of multisite and multicomponent cooperative and competitive interactions. Titration experiments typically take 1-2.5 h each, and global analysis usually takes 10-20 min.

From invasion to latency: intracellular noise and cell motility as key controls of the competition between resource-limited cellular populations

In this paper we analyse stochastic models of the competition between two resource-limited cell populations which differ in their response to nutrient availability: the resident population exhibits a switch-like response behaviour while the invading population exhibits a bistable response. We investigate how noise in the intracellular regulatory pathways and cell motility influence the fate of the incumbent and invading populations. We focus initially on a spatially homogeneous system and study in detail the role of intracellular noise. We show that in such well-mixed systems, two distinct regimes exist: In the low (intracellular) noise limit, the invader has the ability to invade the resident population, whereas in the high noise regime competition between the two populations is found to be neutral and, in accordance with neutral evolution theory, invasion is a random event. Careful examination of the system dynamics leads us to conclude that (i) even if the invader is unable to invade, the distribution of survival times, PS(t), has a fat-tail behaviour (PS(t) ∼ t(-1)) which implies that small colonies of mutants can coexist with the resident population for arbitrarily long times, and (ii) the bistable structure of the invading population increases the stability of the latent population, thus increasing their long-term likelihood of survival, by decreasing the intensity of the noise at the population level. We also examine the effects of spatial inhomogeneity. In the low noise limit we find that cell motility is positively correlated with the aggressiveness of the invader as defined by the time the invader takes to invade the resident population: the faster the invasion, the more aggressive the invader.

Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo

Recent progress in microscopy technologies, biological markers, and automated processing methods is making possible the development of gene expression atlases at cellular-level resolution over whole embryos. Raw data on gene expression is usually very noisy. This noise comes from both experimental (technical/methodological) and true biological sources (from stochastic biochemical processes). In addition, the cells or nuclei being imaged are irregularly arranged in 3D space. This makes the processing, extraction, and study of expression signals and intrinsic biological noise a serious challenge for 3D data, requiring new computational approaches. Here, we present a new approach for studying gene expression in nuclei located in a thick layer around a spherical surface. The method includes depth equalization on the sphere, flattening, interpolation to a regular grid, pattern extraction by Shaped 3D singular spectrum analysis (SSA), and interpolation back to original nuclear positions. The approach is demonstrated on several examples of gene expression in the zebrafish egg (a model system in vertebrate development). The method is tested on several different data geometries (e.g., nuclear positions) and different forms of gene expression patterns. Fully 3D datasets for developmental gene expression are becoming increasingly available; we discuss the prospects of applying 3D-SSA to data processing and analysis in this growing field.

Information transfer through a signaling module with feedback: A perturbative approach

Signal transduction in biological cells is effected by signaling pathways that typically include multiple feedback loops. Here we analyze information transfer through a prototypical signaling module with biochemical feedback. The module switches stochastically between an inactive and active state; the input to the module governs the activation rate while the output (i.e., the product concentration) perturbs the inactivation rate. Using a novel perturbative approach, we compute the rate with which information about the input is gained from observation of the output. We obtain an explicit analytical result valid to first order in feedback strength and to second order in the strength of input. The total information gained during an extended time interval is found to depend on the feedback strength only through the total number of activation/inactivation events.

Biological messiness vs. biological genius: Mechanistic aspects and roles of protein promiscuity

In contrast to the traditional biological paradigms focused on 'specificity', recent research and theoretical efforts have focused on functional 'promiscuity' exhibited by proteins and enzymes in many biological settings, including enzymatic detoxication, steroid biochemistry, signal transduction and immune responses. In addition, divergent evolutionary processes are apparently facilitated by random mutations that yield promiscuous enzyme intermediates. The intermediates, in turn, provide opportunities for further evolution to optimize new functions from existing protein scaffolds. In some cases, promiscuity may simply represent the inherent plasticity of proteins resulting from their polymeric nature with distributed conformational ensembles. Enzymes or proteins that bind or metabolize noncognate substrates create 'messiness' or noise in the systems they contribute to. With our increasing awareness of the frequency of these promiscuous behaviors it becomes interesting and important to understand the molecular bases for promiscuous behavior and to distinguish between evolutionarily selected promiscuity and evolutionarily tolerated messiness. This review provides an overview of current understanding of these aspects of protein biochemistry and enzymology.

How to awaken your nanomachines: Site-specific activation of focal adhesion kinases through ligand interactions

The focal adhesion kinase (FAK) and the related protein-tyrosine kinase 2-beta (Pyk2) are highly versatile multidomain scaffolds central to cell adhesion, migration, and survival. Due to their key role in cancer metastasis, understanding and inhibiting their functions are important for the development of targeted therapy. Because FAK and Pyk2 are involved in many different cellular functions, designing drugs with partial and function-specific inhibitory effects would be desirable. Here, we summarise recent progress in understanding the structural mechanism of how the tug-of-war between intramolecular and intermolecular interactions allows these protein 'nanomachines' to become activated in a site-specific manner.

Regulation of EGFR signal transduction by analogue-to-digital conversion in endosomes

An outstanding question is how receptor tyrosine kinases (RTKs) determine different cell-fate decisions despite sharing the same signalling cascades. Here, we uncovered an unexpected mechanism of RTK trafficking in this process. By quantitative high-resolution FRET microscopy, we found that phosphorylated epidermal growth factor receptor (p-EGFR) is not randomly distributed but packaged at constant mean amounts in endosomes. Cells respond to higher EGF concentrations by increasing the number of endosomes but keeping the mean p-EGFR content per endosome almost constant. By mathematical modelling, we found that this mechanism confers both robustness and regulation to signalling output. Different growth factors caused specific changes in endosome number and size in various cell systems and changing the distribution of p-EGFR between endosomes was sufficient to reprogram cell-fate decision upon EGF stimulation. We propose that the packaging of p-RTKs in endosomes is a general mechanism to ensure the fidelity and specificity of the signalling response.

Three level signal transduction cascades lead to reliably timed switches

Signaling cascades proliferate signals received on the cell membrane to the nucleus. While noise filtering, ultra-sensitive switches, and signal amplification have all been shown to be features of such signaling cascades, it is not understood why cascades typically show three or four layers. Using singular perturbation theory, Michaelis-Menten type equations are derived for open enzymatic systems. Cascading these equations we demonstrate that the output signal as a function of time becomes sigmoidal with the addition of more layers. Furthermore, it is shown that the activation time will speed up to a point, after which more layers become superfluous. It is shown that three layers create a reliable sigmoidal response progress curve from a wide variety of time-dependent signaling inputs arriving at the cell membrane, suggesting the evolutionary benefit of the observed cascades.

Conformational dynamics of the focal adhesion targeting domain control specific functions of focal adhesion kinase in cells

Focal adhesion (FA) kinase (FAK) regulates cell survival and motility by transducing signals from membrane receptors. The C-terminal FA targeting (FAT) domain of FAK fulfils multiple functions, including recruitment to FAs through paxillin binding. Phosphorylation of FAT on Tyr(925) facilitates FA disassembly and connects to the MAPK pathway through Grb2 association, but requires dissociation of the first helix (H1) of the four-helix bundle of FAT. We investigated the importance of H1 opening in cells by comparing the properties of FAK molecules containing wild-type or mutated FAT with impaired or facilitated H1 openings. These mutations did not alter the activation of FAK, but selectively affected its cellular functions, including self-association, Tyr(925) phosphorylation, paxillin binding, and FA targeting and turnover. Phosphorylation of Tyr(861), located between the kinase and FAT domains, was also enhanced by the mutation that opened the FAT bundle. Similarly phosphorylation of Ser(910) by ERK in response to bombesin was increased by FAT opening. Although FAK molecules with the mutation favoring FAT opening were poorly recruited at FAs, they efficiently restored FA turnover and cell shape in FAK-deficient cells. In contrast, the mutation preventing H1 opening markedly impaired FAK function. Our data support the biological importance of conformational dynamics of the FAT domain and its functional interactions with other parts of the molecule.

Asymmetric mRNA localization contributes to fidelity and sensitivity of spatially localized systems

Although many proteins are localized after translation, asymmetric protein distribution is also achieved by translation after mRNA localization. Why are certain mRNA transported to a distal location and translated on-site? Here we undertake a systematic, genome-scale study of asymmetrically distributed protein and mRNA in mammalian cells. Our findings suggest that asymmetric protein distribution by mRNA localization enhances interaction fidelity and signaling sensitivity. Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies. Our observations are consistent across multiple mammalian species, cell types and developmental stages, suggesting that localized translation is a recurring feature of cell signaling and regulation.

How to find a leucine in a haystack? Structure, ligand recognition and regulation of leucine-aspartic acid (LD) motifs

LD motifs (leucine-aspartic acid motifs) are short helical protein-protein interaction motifs that have emerged as key players in connecting cell adhesion with cell motility and survival. LD motifs are required for embryogenesis, wound healing and the evolution of multicellularity. LD motifs also play roles in disease, such as in cancer metastasis or viral infection. First described in the paxillin family of scaffolding proteins, LD motifs and similar acidic LXXLL interaction motifs have been discovered in several other proteins, whereas 16 proteins have been reported to contain LDBDs (LD motif-binding domains). Collectively, structural and functional analyses have revealed a surprising multivalency in LD motif interactions and a wide diversity in LDBD architectures. In the present review, we summarize the molecular basis for function, regulation and selectivity of LD motif interactions that has emerged from more than a decade of research. This overview highlights the intricate multi-level regulation and the inherently noisy and heterogeneous nature of signalling through short protein-protein interaction motifs.

Prioritizing protein complexes implicated in human diseases by network optimization

Background

The detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes.

Results

We propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer.

Conclusions

MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins.

Information transfer by leaky, heterogeneous, protein kinase signaling systems

Cells must sense extracellular signals and transfer the information contained about their environment reliably to make appropriate decisions. To perform these tasks, cells use signal transduction networks that are subject to various sources of noise. Here, we study the effects on information transfer of two particular types of noise: basal (leaky) network activity and cell-to-cell variability in the componentry of the network. Basal activity is the propensity for activation of the network output in the absence of the signal of interest. We show, using theoretical models of protein kinase signaling, that the combined effect of the two types of noise makes information transfer by such networks highly vulnerable to the loss of negative feedback. In an experimental study of ERK signaling by single cells with heterogeneous ERK expression levels, we verify our theoretical prediction: In the presence of basal network activity, negative feedback substantially increases information transfer to the nucleus by both preventing a near-flat average response curve and reducing sensitivity to variation in substrate expression levels. The interplay between basal network activity, heterogeneity in network componentry, and feedback is thus critical for the effectiveness of protein kinase signaling. Basal activity is widespread in signaling systems under physiological conditions, has phenotypic consequences, and is often raised in disease. Our results reveal an important role for negative feedback mechanisms in protecting the information transfer function of saturable, heterogeneous cell signaling systems from basal activity.

The Stress-response protein prostate-associated gene 4, interacts with c-Jun and potentiates its transactivation

The Cancer/Testis Antigen (CTA), Prostate-associated Gene 4 (PAGE4), is a stress-response protein that is upregulated in prostate cancer (PCa) especially in precursor lesions that result from inflammatory stress. In cells under stress, translocation of PAGE4 to mitochondria increases while production of reactive oxygen species decreases. Furthermore, PAGE4 is also upregulated in human fetal prostate, underscoring its potential role in development. However, the proteins that interact with PAGE4 and the mechanisms underlying its pleiotropic functions in prostatic development and disease remain unknown. Here, we identified c-Jun as a PAGE4 interacting partner. We show that both PAGE4 and c-Jun are overexpressed in the human fetal prostate; and in cell-based assays, PAGE4 robustly potentiates c-Jun transactivation. Single-molecule Förster resonance energy transfer experiments indicate that upon binding to c-Jun, PAGE4 undergoes conformational changes. However, no interaction is observed in presence of BSA or unilamellar vesicles containing the mitochondrial inner membrane diphosphatidylglycerol lipid marker cardiolipin. Together, our data indicate that PAGE4 specifically interacts with c-Jun and that, conformational dynamics may account for its observed pleiotropic functions. To our knowledge, this is the first report demonstrating crosstalk between a CTA and a proto-oncogene. Disrupting PAGE4/c-Jun interactions using small molecules may represent a novel therapeutic strategy for PCa.

Allostery mediates ligand binding to Grb2 adaptor in a mutually exclusive manner

Allostery plays a key role in dictating the stoichiometry and thermodynamics of multi-protein complexes driving a plethora of cellular processes central to health and disease. Herein, using various biophysical tools, we demonstrate that although Sos1 nucleotide exchange factor and Gab1 docking protein recognize two non-overlapping sites within the Grb2 adaptor, allostery promotes the formation of two distinct pools of Grb2-Sos1 and Grb2-Gab1 binary signaling complexes in concert in lieu of a composite Sos1-Grb2-Gab1 ternary complex. Of particular interest is the observation that the binding of Sos1 to the nSH3 domain within Grb2 sterically blocks the binding of Gab1 to the cSH3 domain and vice versa in a mutually exclusive manner. Importantly, the formation of both the Grb2-Sos1 and Grb2-Gab1 binary complexes is governed by a stoichiometry of 2:1, whereby the respective SH3 domains within Grb2 homodimer bind to Sos1 and Gab1 via multivalent interactions. Collectively, our study sheds new light on the role of allostery in mediating cellular signaling machinery.

Cargo recognition in clathrin-mediated endocytosis

The endosomal system is expansive and complex, characterized by swift morphological transitions, dynamic remodeling of membrane constituents, and intracellular positioning changes. To properly navigate this ever-altering membrane labyrinth, transmembrane protein cargoes typically require specific sorting signals that are decoded by components of protein coats. The best-characterized sorting process within the endosomal system is the rapid internalization of select transmembrane proteins within clathrin-coated vesicles. Endocytic signals consist of linear motifs, conformational determinants, or covalent modifications in the cytosolic domains of transmembrane cargo. These signals are interpreted by a diverse set of clathrin-associated sorting proteins (CLASPs) that translocate from the cytosol to the inner face of the plasma membrane. Signal recognition by CLASPs is highly cooperative, involving additional interactions with phospholipids, Arf GTPases, other CLASPs, and clathrin, and is regulated by large conformational changes and covalent modifications. Related sorting events occur at other endosomal sorting stations.

Competitive interactions of ligands and macromolecular crowders with maltose binding protein

Cellular signaling involves a cascade of recognition events occurring in a complex environment with high concentrations of proteins, polysaccharides, and other macromolecules. The influence of macromolecular crowders on protein binding affinity through hard-core repulsion is well studied, and possible contributions of protein-crowder soft attraction have been implicated recently. Here we present direct evidence for weak association of maltose binding protein (MBP) with a polysaccharide crowder Ficoll, and that this association effectively competes with the binding of the natural ligand, maltose. Titration data over wide ranges of maltose and Ficoll concentrations fit well with a three-state competitive binding model. Broadening of MBP ¹H­¹⁵N TROSY spectra by the addition of Ficoll indicates weak protein-crowder association, and subsequent recovery of sharp NMR peaks upon addition of maltose indicates that the interactions of the crowder and the ligand with MBP are competitive. We hypothesize that, in the Escherichia coli periplasm, the competitive interactions of polysaccharides and maltose with MBP could allow MBP to shuttle between the peptidoglycan attached to the outer membrane and the ATP-binding cassette transporter in the inner membrane.

Cancer: tilting at windmills?

One of the striking characteristics of cancer cells is their phenotypic diversity and ability to switch phenotypes in response to environmental fluctuations. Such phenotypic changes (e.g. from drug-sensitive to drug-resistant), which are critical for survival and proliferation, are widely believed to arise due to mutations in the cancer cell's genome. However, there is growing concern that such a deterministic view is not entirely consistent with multiple lines of evidence which indicate that cancer can arise in the absence of mutations and can even be reversed to normalcy despite the mutations. In this Commentary, we wish to present an alternate view that highlights how stochasticity in protein interaction networks (PINs) may play a key role in cancer initiation and progression. We highlight the potential role of intrinsically disordered proteins (IDPs) and submit that targeting IDPs can lead to new insights and treatment protocols for cancer.

Stochastic effects as a force to increase the complexity of signaling networks

Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects—called deviant effects—in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components.

Ultrafast cell switching for recording cell surface transitions: new insights into epidermal growth factor receptor signalling

A pinched-flow deflection technology was developed for rapid single cell switching between biochemical microenvironments. Millisecond switching was used to stimulate and preserve epidermal growth factor receptor (EGFR) autophosphorylation transitions. Intramolecular phosphorylation initiates signal transduction, is silenced by phosphatase activity until EGFR dimerization enables intermolecular phosphorylation to initiate downstream signalling.

Intrinsically disordered proteins and conformational noise: implications in cancer

Intrinsically disordered proteins, IDPs, are proteins that lack a rigid 3D structure under physiological conditions, at least in vitro. Despite the lack of structure, IDPs play important roles in biological processes and transition from disorder to order upon binding to their targets. With multiple conformational states and rapid conformational dynamics, they engage in myriad and often "promiscuous" interactions. These stochastic interactions between IDPs and their partners, defined here as conformational noise, is an inherent characteristic of IDP interactions. The collective effect of conformational noise is an ensemble of protein network configurations, from which the most suitable can be explored in response to perturbations, conferring protein networks with remarkable flexibility and resilience. Moreover, the ubiquitous presence of IDPs as transcriptional factors and, more generally, as hubs in protein networks, is indicative of their role in propagation of transcriptional (genetic) noise. As effectors of transcriptional and conformational noise, IDPs rewire protein networks and unmask latent interactions in response to perturbations. Thus, noise-driven activation of latent pathways could underlie state-switching events such as cellular transformation in cancer. To test this hypothesis, we created a model of a protein network with the topological characteristics of a cancer protein network and tested its response to a perturbation in presence of IDP hubs and conformational noise. Because numerous IDPs are found to be epigenetic modifiers and chromatin remodelers, we hypothesize that they could further channel noise into stable, heritable genotypic changes.

Global multi-method analysis of affinities and cooperativity in complex systems of macromolecular interactions

Cooperativity, multisite, and multicomponent interactions are hallmarks of biological systems of interacting macromolecules. Their thermodynamic characterization is often very challenging due to the notoriously low information content of binding isotherms. We introduce a strategy for the global multimethod analysis of data from multiple techniques (GMMA) that exploits enhanced information content emerging from the mutual constraints of the simultaneous modeling of orthogonal observables from calorimetric, spectroscopic, hydrodynamic, biosensing, or other thermodynamic binding experiments. We describe new approaches to address statistical problems that arise in the analysis of dissimilar data sets. The GMMA approach can significantly increase the complexity of interacting systems that can be accurately thermodynamically characterized.

Dynamic hubs show competitive and static hubs non-competitive regulation of their interaction partners

Date hub proteins have 1 or 2 interaction interfaces but many interaction partners. This raises the question of whether all partner proteins compete for the interaction interface of the hub or if the cell carefully regulates aspects of this process? Here, we have used real-time rendering of protein interaction networks to analyse the interactions of all the 1 or 2 interface hubs of Saccharomyces cerevisiae during the cell cycle. By integrating previously determined structural and gene expression data, and visually hiding the nodes (proteins) and their edges (interactions) during their troughs of expression, we predict when interactions of hubs and their partners are likely to exist. This revealed that 20 out of all 36 one- or two- interface hubs in the yeast interactome fell within two main groups. The first was dynamic hubs with static partners, which can be considered as ‘competitive hubs’. Their interaction partners will compete for the interaction interface of the hub and the success of any interaction will be dictated by the kinetics of interaction (abundance and affinity) and subcellular localisation. The second was static hubs with dynamic partners, which we term ‘non-competitive hubs’. Regulatory mechanisms are finely tuned to lessen the presence and/or effects of competition between the interaction partners of the hub. It is possible that these regulatory processes may also be used by the cell for the regulation of other, non-cell cycle processes.

The divergence-convergence model of acquired neuroprotection

It is commonly known that mental activity helps to maintain a healthy brain. Recent research has unraveled the underlying molecular mechanisms that explain why an active brain lives longer. These mechanisms involve the activation of a comprehensive transcriptional program that is triggered by enhanced synaptic activity and renders neurons resistant to harmful conditions. Functionally, this state of acquired neuroprotection may be achieved mainly via one mechanism, which is the stabilization of mitochondria. In this review we propose a model that describes the signaling network that links synaptic activity to neuroprotection. We suggest that the divergent-convergent architecture of this signaling network ensures both robust and reliable as well as persistent activation of the neuroprotective machinery.

A multidimensional matrix for systems biology research and its application to interaction networks

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.

A network-oriented perspective on cardiac calcium signaling

The normal contractile, electrical, and energetic function of the heart depends on the synchronization of biological oscillators and signal integrators that make up cellular signaling networks. In this review we interpret experimental data from molecular, cellular, and transgenic models of cardiac signaling behavior in the context of established concepts in cell network architecture and organization. Focusing on the cellular Ca(2+) handling machinery, we describe how the plasticity and adaptability of normal Ca(2+) signaling is dependent on dynamic network configurations that operate across a wide range of functional states. We consider how (mal)adaptive changes in signaling pathways restrict the dynamic range of the network such that it cannot respond appropriately to physiologic stimuli or perturbation. Based on these concepts, a model is proposed in which pathologic abnormalities in cardiac rhythm and contractility (e.g., arrhythmias and heart failure) arise as a consequence of progressive desynchronization and reduction in the dynamic range of the Ca(2+) signaling network. We discuss how a systems-level understanding of the network organization, cellular noise, and chaotic behavior may inform the design of new therapeutic modalities that prevent or reverse the disease-linked unraveling of the Ca(2+) signaling network.