Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

Emerging Links between Lipid Droplets and Motor Neuron Diseases

Lipid droplets (LDs) are ubiquitous fat storage organelles and play key roles in lipid metabolism and energy homeostasis; in addition, they contribute to protein storage, folding, and degradation. However, a role for LDs in the nervous system remains largely unexplored. We discuss evidence supporting an intimate functional connection between LDs and motor neuron disease (MND) pathophysiology, examining how LD functions in systemic energy homeostasis, in neuron-glia metabolic coupling, and in protein folding and clearance may affect or contribute to disease pathology. An integrated understanding of LD biology and neurodegeneration may open the way for new therapeutic interventions.

Genetic analysis of the Candida albicans biofilm transcription factor network using simple and complex haploinsufficiency

Biofilm formation by Candida albicans is a key aspect of its pathobiology and is regulated by an integrated network of transcription factors (Bcr1, Brg1, Efg1, Ndt80, Rob1, and Tec1). To understand the details of how the transcription factors function together to regulate biofilm formation, we used a systematic genetic interaction approach based on generating all possible double heterozygous mutants of the network genes and quantitatively analyzing the genetic interactions between them. Overall, the network is highly susceptible to genetic perturbation with the six network heterozygous mutants all showing alterations in biofilm formation (haploinsufficiency). In addition, many double heterozygous mutants are as severely affected as homozygous deletions. As a result, the network shows properties of a highly interdependent ‘small-world’ network that is highly efficient but not robust. In addition, these genetic interaction data indicate that TEC1 represents a network component whose expression is highly sensitive to small perturbations in the function of other networks TFs. We have also found that expression of ROB1 is dependent on both auto-regulation and cooperative interactions with other network TFs. Finally, the heterozygous NDT80 deletion mutant is hyperfilamentous under both biofilm and hyphae-inducing conditions in a TEC1-dependent manner. Taken together, genetic interaction analysis of this network has provided new insights into the functions of individual TFs as well as into the role of the overall network topology in its function.

Biofilm formation is part and parcel of the ability of Candida albicans, a normal component of the human gastrointestinal microbial community, to cause disease. Recent work by many investigators has provided detailed information regarding how C. albicans converts to the biofilm stage of growth. The vast majority of these studies have involved the study of strains lacking a single gene that affects biofilm formation. Using this genetic approach in combination with other genome-wide techniques, a network of transcription factors was identified that play a crucial role in regulating biofilm-related processes. Here, we have systematically generated and characterized strains with mutations in two biofilm network transcription factors in order to determine how these genes interact. This represents the first systematic quantitative genetic interaction study in C. albicans and, by combining our results with that of previous labs, provides a new level of detail regarding the integrated functions of these transcription factors. In addition, our data indicate that the biofilm transcription factor network is very sensitive to genetic perturbation and propose that this fragility is a function of its small-world topography which, in turn, promotes network efficiency at the expense of robustness.

Modeling and analysis of modular structure in diverse biological networks

Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful smaller modules. While engineered networks are designed and refined by humans with particular goals in mind, biological networks are created by the selective pressures of evolution. In this paper, we seek to define aspects of network architecture that are shared among different types of evolved biological networks. First, we developed a new mathematical model, the Stochastic Block Model with Path Selection (SBM-PS) that simulates biological network formation based on the selection of edges that increase clustering. SBM-PS can produce modular networks whose properties resemble those of real networks. Second, we analyzed three real networks of very different types, and showed that all three can be fit well by the SBM-PS model. Third, we showed that modular elements within the three networks correspond to meaningful biological structures. The networks chosen for analysis were a proteomic network composed of all proteins required for mitochondrial function in budding yeast, a mesoscale anatomical network composed of axonal connections among regions of the mouse brain, and the connectome of individual neurons in the nematode C. elegans. We find that the three networks have common architectural features, and each can be divided into subnetworks with characteristic topologies that control specific phenotypic outputs.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.