Article

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks.

Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 02/2012; 109(8):2825-30. DOI: 10.1073/pnas.1106612109
Source: PubMed

ABSTRACT The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.

0 Bookmarks
 · 
103 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Mycobacterium bovis is an intracellular pathogen that causes tuberculosis in cattle. Following infection, the pathogen resides and persists inside host macrophages by subverting host immune responses via a diverse range of mechanisms. Here, a high-density bovine microarray platform was used to examine the bovine monocyte-derived macrophage transcriptome response to M. bovis infection relative to infection with the attenuated vaccine strain, M. bovis Bacille Calmette-Guérin. Differentially expressed genes were identified (adjusted P-value ≤0.01) and interaction networks generated across an infection time course of 2, 6, and 24 h. The largest number of biological interactions was observed in the 24-h network, which exhibited scale-free network properties. The 24-h network featured a small number of key hub and bottleneck gene nodes, including IKBKE, MYC, NFKB1, and EGR1 that differentiated the macrophage response to virulent and attenuated M. bovis strains, possibly via the modulation of host cell death mechanisms. These hub and bottleneck genes represent possible targets for immuno-modulation of host macrophages by virulent mycobacterial species that enable their survival within a hostile environment.
    Frontiers in Immunology 01/2014; 5:422.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.
    PLoS Computational Biology 10/2014; 10(10):e1003881. · 4.87 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Artificial, neurobiological, and social networks are three distinct complex adaptive systems (CASs), each containing discrete processing units (nodes, neurons, and humans, respectively). Despite the apparent differences, these three networks are bound by common underlying principles which describe the behavior of the system in terms of the connections of its components, and its emergent properties. The longevity (long-term retention and functionality) of the components of each of these systems is also defined by common principles. Here, I will examine some properties of the longevity and function of the components of artificial and neurobiological systems, and generalize these to the longevity and function of the components of social CAS. In other words, I will show that principles governing the long-term functionality of computer nodes and of neurons, may be extrapolated to the study of the long-term functionality of humans (or more precisely, of the noemes, an abstract combination of “existence” and “digital fame”). The study of these phenomena can provide useful insights regarding practical ways that can be used to maximize human longevity. The basic law governing these behaviors is the “Law of Requisite Usefulness,” which states that the length of retention of an agent within a CAS is proportional to the agent's contribution to the overall adaptability of the system. © 2014 Wiley Periodicals, Inc. Complexity, 2014
    Complexity 10/2014; · 1.33 Impact Factor

Full-text (2 Sources)

Download
52 Downloads
Available from
May 17, 2014