Narrow Results

This paper proposes to model protein regulation networks associated with severe acute respiratory syndrome (SARS) for early prediction of SARS. In the approach, specific to a patient group, a regulatory network is simulated using a fully-connected neural network and is optimized towards minimizing a novel energy function that is defined as a measure of disagreement between the input and output of the network. The nonlinear version of the network is achieved by applying a sigmoid function. Experimental results show that the proposed approaches can capture regulatory patterns associated with SARS and efficiently implement early prediction of SARS.

In the development of Drosophila wing disc, morphogen Dpp, which is a signaling molecule from a local region and disperses into anterior and posterior compartments, builds up a gradient with precise pattern information. Experiments have demonstrated that the key genes (brk, dad, omb and sal) and phosphorylated protein (pMad), which are activated by Dpp signaling molecules and form the gradients of the corresponding proteins of these genes, direct and control the spatial pattern of the wing disc. However, the regulatory network of these genes are in complex and nonlinear interaction with upstream regulators and downstream targets. In this paper, the mathematical model is built according to the regulatory relationships of these key genes. The stabilities of the gradients of these corresponding proteins are investigated. Furthermore, numerical simulations show that these gradients are robust with respect to some major reaction rates in this regulatory network.

In this paper, we study equilibria of differential equation models for networks. When interactions between nodes are taken to be piecewise constant, an efficient combinatorial analysis can be used to characterize the equilibria. When the piecewise constant functions are replaced with piecewise linear functions, the equilibria are preserved as long as the piecewise linear functions are sufficiently steep. Therefore the combinatorial analysis can be leveraged to understand a broader class of interactions. To better understand how broad this class is, we explicitly characterize how steep the piecewise linear functions must be for the correspondence between equilibria to hold. To do so, we analyze the steady state and Hopf bifurcations which cause a change in the number or stability of equilibria as slopes are decreased. Additionally, we show how to choose a subset of parameters so that the correspondence between equilibria holds for the smallest possible slopes when the remaining parameters are fixed.

In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein–protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-κB, and c-Myc) for HeLa cells.

A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed.

We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.

The evolutionary history and origin of the regulatory function of animal non-coding RNAs are not well understood. Lack of conservation of long non-coding RNAs and small sizes of microRNAs has been major obstacles in their phylogenetic analysis. In this study, we tried to shed more light on the evolution of ncRNA regulatory networks by changing our phylogenetic strategy to focus on the evolutionary pattern of their protein coding targets. We used available target databases of miRNAs and lncRNAs to find their protein coding targets in human. We were able to recognize evolutionary hallmarks of ncRNA targets by phylostratigraphic analysis. We found the conventional 3′-UTR and lesser known 5′-UTR targets of miRNAs to be enriched at three consecutive phylostrata. Firstly, in eukaryata phylostratum corresponding to the emergence of miRNAs, our study revealed that miRNA targets function primarily in cell cycle processes. Moreover, the same overrepresentation of the targets observed in the next two consecutive phylostrata, opisthokonta and eumetazoa, corresponded to the expansion periods of miRNAs in animals evolution. Coding sequence targets of miRNAs showed a delayed rise at opisthokonta phylostratum, compared to the 3′ and 5′ UTR targets of miRNAs. LncRNA regulatory network was the latest to evolve at eumetazoa.

C₄ photosynthesis is a system that uses resources present in C₃ plants. C₄ photosynthesis has evolved numerous times, in widely separated phylogenetic groups. All existing species that are able to fix carbon dioxide through one of the variety of C₄ schemes appear to rely on enzymatic activities and other factors present in most or all plant species, but regulated to exhibit an extreme intercellular or intracellular compartmentalization that supports the delivery of CO₂-derived carbon to Rubisco in an environment that disfavors competition from oxygen. C₄ species are particularly numerous in certain subfamilies of grasses, suggesting that the resources required for C₄ physiology are present and predisposed to this re-regulation. Rice lacks the dense leaf venation, bundle sheath (BS) differentiation, high BS plasmodesmatal density, and compartmentalization of photosynthetic activities that characterize nearly all C₄ grass species. To what extent are these C₄ resources already networked together in C₃ grasses such as rice and how might we find the targets and means for engineering the re-regulation of this network? A systems biology approach that compares the development of cell types in rice leaves to those in C₄ grasses could provide these targets and means. Emerging techniques such as laser microdissection of cell types and microarray profiling can provide the comprehensive data needed for a systems approach.