ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster

Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network

Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries

Team synergies in sport: theory and measures

Individual players act as a coherent unit during team sports performance, forming a team synergy. A synergy is a collective property of a task-specific organization of individuals, such that the degrees of freedom of each individual in the system are coupled, enabling the degrees of freedom of different individuals to co-regulate each other. Here, we present an explanation for the emergence of such collective behaviors, indicating how these can be assessed and understood through the measurement of key system properties that exist, considering the contribution of each individual and beyond These include: to (i) dimensional compression, a process resulting in independent degree of freedom being coupled so that the synergy has fewer degrees of freedom than the set of components from which it arises; (ii) reciprocal compensation, if one element do not produce its function, other elements should display changes in their contributions so that task goals are still attained; (iii) interpersonal linkages, the specific contribution of each element to a group task; and (iv), degeneracy, structurally different components performing a similar, but not necessarily identical, function with respect to context. A primary goal of our analysis is to highlight the principles and tools required to understand coherent and dynamic team behaviors, as well as the performance conditions that make such team synergies possible, through perceptual attunement to shared affordances in individual performers. A key conclusion is that teams can be trained to perceive how to use and share specific affordances, explaining how individual’s behaviours self-organize into a group synergy.Ecological dynamics explanations of team behaviors can transit beyond mere ratification of sport performance, providing a comprehensive conceptual framework to guide the implementation of diagnostic measures by sport scientists, sport psychologists and performance analysts

High content imaging of unbiased chemical perturbations reveals that the phenotypic plasticity of the actin cytoskeleton is constrained

Although F-actin has a large number of binding partners and regulators, the number of phenotypic states available to the actin cytoskeleton is unknown. Here, we quantified 74 features defining filamentous actin (F-actin) and cellular morphology in >25 million cells after treatment with a library of 114,400 structurally diverse compounds. After reducing the dimensionality of these data, only ∼25 recurrent F-actin phenotypes emerged, each defined by distinct quantitative features that could be machine learned. We identified 2,003 unknown compounds as inducers of actin-related phenotypes, including two that directly bind the focal adhesion protein, talin. Moreover, we observed that compounds with distinct molecular mechanisms could induce equivalent phenotypes and that initially divergent cellular responses could converge over time. These findings suggest a conceptual parallel between the actin cytoskeleton and gene regulatory networks, where the theoretical plasticity of interactions is nearly infinite, yet phenotypes in vivo are constrained into a limited subset of practicable configurations

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin