Additive functions in boolean models of gene regulatory network modules

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.     

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.     

Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules

Background

Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as ‘a form of biological systems’, consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic.

Methodology/Principal Findings

In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways.

Conclusions/Significance

Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases.     

Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

Background  

Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks.     

Questioning the Ubiquity of Neofunctionalization

Gene duplication provides much of the raw material from which functional diversity evolves. Two evolutionary mechanisms have been proposed that generate functional diversity: neofunctionalization, the de novo acquisition of function by one duplicate, and subfunctionalization, the partitioning of ancestral functions between gene duplicates. With protein interactions as a surrogate for protein functions, evidence of prodigious neofunctionalization and subfunctionalization has been identified in analyses of empirical protein interactions and evolutionary models of protein interactions. However, we have identified three phenomena that have contributed to neofunctionalization being erroneously identified as a significant factor in protein interaction network evolution. First, self-interacting proteins are underreported in interaction data due to biological artifacts and design limitations in the two most common high-throughput protein interaction assays. Second, evolutionary inferences have been drawn from paralog analysis without consideration for concurrent and subsequent duplication events. Third, the theoretical model of prodigious neofunctionalization is unable to reproduce empirical network clustering and relies on untenable parameter requirements. In light of these findings, we believe that protein interaction evolution is more persuasively characterized by subfunctionalization and self-interactions.     

Menin, a gene product responsible for multiple endocrine neoplasia type 1, interacts with the putative tumor metastasis suppressor nm23

Although the gene responsible for multiple endocrine neoplasia type 1 (MEN1) has been identified, the function of its gene product, menin, is unknown. To examine the biological role of the MEN1 gene, we searched for associated proteins with a yeast two-hybrid system using the MEN1 cDNA fragment as bait. On screening a rat fetal brain embryonic day 17 library, in which a high level of MEN1 expression was detected, we identified a putative tumor metastasis suppressor nm23/nucleoside diphosphate (NDP) kinase as an associated protein. This finding was confirmed by in vitro interaction assays based on glutathione S-transferase pull down experiments. The association required almost the entire menin protein, and several missense MEN1 mutations reported in MEN1 patients caused a loss of the binding activity for nm23. This result suggests that this interaction may play important roles in the biological functions of the menin protein, including tumor suppressor activity.     

Circadian clocks - the fall and rise of physiology

Circadian clocks control the daily life of most light-sensitive organisms - from cyanobacteria to humans. Molecular processes generate cellular rhythmicity, and cellular clocks in animals coordinate rhythms through interaction (known as coupling). This hierarchy of clocks generates a complex, approximately 24-hour temporal programme that is synchronized with the rotation of the Earth. The circadian system ensures anticipation and adaptation to daily environmental changes, and functions on different levels - from gene expression to behaviour. Circadian research is a remarkable example of interdisciplinarity, unravelling the complex mechanisms that underlie a ubiquitous biological programme. Insights from this research will help to optimize medical diagnostics and therapy, as well as adjust social and biological timing on the individual level.     

Identification of a plum pox virus CI-interacting protein from chloroplast that has a negative effect in virus infection

The cylindrical inclusion (CI) protein of potyviruses is involved in virus replication and cell-to-cell movement. These two processes should rely on multiple plant-virus interactions; however, little is known about the host factors that are involved in, or that may interfere with, CI functions. By using a yeast two-hybrid system, the CI protein from Plum pox virus (PPV) was found to interact with the photosystem I PSI-K protein, the product of the gene psaK, of Nicotiana benthamiana. Coexpression of PPV CI was shown to cause a decrease in the accumulation level of PSI-K transiently expressed in N. benthamiana leaves. To test the biological relevance of this interaction, we have analyzed the infection of PPV in N. benthamiana plants in which psaK gene expression has been silenced by RNA interference, as well as in Arabidopsis thaliana psaK knockout plants. Our results show that downregulation of the psaK gene leads to higher PPV accumulation, suggesting a role for the CI-PSI-K interaction in PPV infection.     

The effects of network neighbours on protein evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.