Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes.     

Global Versus Local Centrality in Evolution of Yeast Protein Network

It is shown here that in the yeast protein interaction network the global centrality measure (betweenness) depends on the protein evolutionary age (i.e., on historic contingency) more weakly than the local centrality measure (degree). This phenomenon is not observed in mutational duplication-and-divergence models. The network domains responsible for this difference deal with DNA/RNA information processing, regulation, and cell cycle. A selection vector can operate in these domains, which integrates the network activity and thus compensates for the process of mutational divergence. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Gene regulatory networks in plants: learning causality from time and perturbation

The goal of systems biology is to generate models for predicting how a system will react under untested conditions or in response to genetic perturbations. This paper discusses experimental and analytical approaches to deriving causal relationships in gene regulatory networks.     

Piecewise-linear Models of Genetic Regulatory Networks: Equilibria and their Stability

A formalism based on piecewise-linear (PL) differential equations, originally due to Glass and Kauffman, has been shown to be well-suited to modelling genetic regulatory networks. However, the discontinuous vector field inherent in the PL models raises some mathematical problems in defining solutions on the surfaces of discontinuity. To overcome these difficulties we use the approach of Filippov, which extends the vector field to a differential inclusion. We study the stability of equilibria (called singular equilibrium sets) that lie on the surfaces of discontinuity. We prove several theorems that characterize the stability of these singular equilibria directly from the state transition graph, which is a qualitative representation of the dynamics of the system. We also formulate a stronger conjecture on the stability of these singular equilibrium sets.     

酵母基因内含子中二聚体寡核苷酸转录调控位点的统计分析

对高效和低效转录酵母基因内含子序列中寡核苷酸的出现频率进行对照分析, 结果显示高效和低效内含子序列的结构有差异, 而且高效转录内含子序列含有较多潜在的转录因子结合位点. 观察实验获得的转录调控位点, 发现许多调控位点不是相邻接的寡核苷酸,而是由一对保守寡核苷酸构成, 这对寡核苷酸被一段长度固定的非保守区域间隔开. 于是对此形式的二聚体寡核苷酸(dyad)在高效和低效内含子序列中出现的频率进行统计比较分析,抽提出在高效内含子组出现的频率显著高于在低效内含子组出现频率的二聚体寡核苷酸, 分析这些二聚体寡核苷酸在两组内含子序列中的分布特征, 并对照实验结果, 这些二聚体寡核苷酸可能与基因转录的正调控有关.     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

A systems biology-led insight into the role of the proteome in neurodegenerative diseases

Introduction: Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases.

Areas covered: Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer’s disease, Parkinson’s disease and other neurodegenerative disorders, as well as of affected human tissues.

Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.     

The Sequence of Regulatory Events in the Sporulation Control Network of Physarum polycephalum Analysed by Time-resolved Somatic Complementation of Mutants

The developmental decision for sporulation of Physarum polycephalum plasmodia is under sensory control by environmental factors like visible light or heat shock and endogenous signals like glucose starvation. Several hours after perceiving an inductive stimulus, plasmodia become committed to sporulation; thereby, they lose their unlimited replicative potential and execute a developmental program that involves differentiation into various cell types required to form a mature fruiting body. Plasmodia are multinuclear single cells which spontaneously fuse upon physical contact. Fusion of mutant plasmodia and cytoplasmic mixing allows complementation studies to be performed at the functional level. Mutant cells altered in their ability to sporulate in response to phytochrome activation by far-red light were cured by fusion with wild-type or other mutant plasmodia. Phytochrome activation in one plasmodium and subsequent fusion with a non-induced plasmodium revealed that complementation of the two mutations depended on (i) which of two genetically distinct plasmodial cells was stimulated; and (ii) on the delay time elapsed between stimulation and cytoplasmic mixing. Such experiments allow us to determine the kinetics and the causal sequence of the regulatory events tagged by mutation.     

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data isvery important to understand the underlying biological system,and it has been a challenging task in bioinformatics.TheBayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determinethe network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithmwhich integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use ofboth simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of theknown real regulatory relationships from literature and predict the others unknown with high validity and accuracy.     

Algorithm of Regulatory Signal Recognition in DNA Sequences

An algorithm is proposed for extracting regulatory signals from DNA sequences. The algorithm complexity is nearly quadratic. The results of testing the algorithm on artificial and natural sequences are presented.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

An unbiased sensitivity analysis reveals important parameters controlling periodicity of circadian clock

To assess the importance of model parameters in kinetic models, sensitivity analysis is generally employed to provide key measures. However, it is quite often that no information is available for a significant number of parameters in biochemical models. Therefore, the results of sensitivity analysis that heavily rely on the accuracy of parameters are largely ambiguous. In this study, we propose a computational approach to determine the relative importance of parameters controlling the performance of the circadian clock in Drosophila. While previous attempts to sensitivity analysis largely depend on the knowledge of model parameters which are generally unknown, our study depicts a consistent picture of sensitivity assessment for a large number of parameters, even when the values of these parameters are not available in vivo. The resulting parametric sensitivity analysis suggests that PER/TIM negative loop is critical to maintain the stable periodicity of the circadian clock, which is consistent to the previously experimental and computational findings. Furthermore, our analysis generates a rich hypothesis of important parameters in the circadian clock that can be further tested experimentally. This approach can also be extended to assess the sensitivity of parameters in any biochemical system where a large number of parameters have unknown values. Biotechnol. Bioeng. 2010; 105: 250–259. © 2009 Wiley Periodicals, Inc.     

Differential Complex Formation via Paralogs in the Human Sin3 Protein Interaction Network

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Highlights

• •Sin3 paralog identity influences Sin3 complex composition.
• •Chemical cross-linking mass spectrometry identifies domains in SIN3A and SIN3B that mediate complex formation.
• •Complex subunit homology to yeast Sin3 complex components may assist in defining distinct forms of the Sin3 complex in humans.
• •A nuclear import signal within SIN3B is identified via chemical cross-linking mass spectrometry.