Structure Of Regulatory Networks Research Articles

A Regulatory Network Analysis of Phenotypic Plasticity in Yeast

Models for the evolution of phenotypic plasticity suggest when and why plasticity might evolve. However, relatively little is known about the genetic basis of plasticity. Molecular studies have recently demonstrated that gene networks can provide a powerful way to infer phenotype from genotype. Information on the structure of the yeast gene regulatory network was combined with data on variation in gene expression in yeast across multiple environments in order to explore the genetic basis of phenotypic plasticity. The phenotypic plasticity of a gene was positively correlated with the number of transcription factors regulating that gene and was significantly lower for transcription factors than for downstream, nonregulatory genes. Plasticity of a gene was also affected by the local substructure of the network in which it was found and by the gene's function. These results illustrate how network analyses can be used to understand the complex genetic architecture of quantitative traits.

A Regulatory Network Analysis of Phenotypic Plasticity in Yeast

Models for the evolution of phenotypic plasticity suggest when and why plasticity might evolve. However, relatively little is known about the genetic basis of plasticity. Molecular studies have recently demonstrated that gene networks can provide a powerful way to infer phenotype from genotype. Information on the structure of the yeast gene regulatory network was combined with data on variation in gene expression in yeast across multiple environments in order to explore the genetic basis of phenotypic plasticity. The phenotypic plasticity of a gene was positively correlated with the number of transcription factors regulating that gene and was significantly lower for transcription factors than for downstream, nonregulatory genes. Plasticity of a gene was also affected by the local substructure of the network in which it was found and by the gene’s function. These results illustrate how network analyses can be used to understand the complex genetic architecture of quantitative traits.

Seed maturation: developing an intrusive phase to accomplish a quiescent state

Plants, as sessile life forms, have evolved diverse mechanisms to circumvent unfavourable growth conditions, among them interruption of the life cycle is one of the most successful strategies. During seed formation, embryo development can proceed through a maturation phase that allows the entry into a quiescent state and represents an evolutionary advantage, since it facilitates dispersal and resuming of growth under optimal environmental conditions. In the maturation phase different gene expression programmes devoted to the accumulation of storage compounds, acquisition of desiccation tolerance and entry into quiescence occur, which are highly coordinated and under fine regulatory control. This paper is focused on recent findings related to central transcription factors which regulate gene expression during maturation. The structure of established regulatory networks is presented based on extensive gene promoter analyses and the characterisation of mutants affecting seed development and maturation. Different aspects of gene regulation both in the embryo and endosperm are covered and comparisons between monocot and dicot species on the basis of current knowledge of the system are also presented.

Reprogramming the Transcriptional Response of Chronic Lymphocytic Leukemia (CLL) Cells: Influencing the Temporal Gene Regulatory Network.

Inferring a temporal gene network from a crucial signaling pathway in leukemic cells is a leading problem in oncology. We built a temporal transcriptional network of B-cell receptor (BCR) crosslinking in CLL and healthy B-cells, a critical step to understand the dynamics of BCR gene expression and to understand gene regulation at a system level. CLL cells have defects in apoptosis and the BCR pathway appears crucial in this process, leading to differential signaling and cell response according to the Ig gene mutational status and zap70 expression. We built this network by analysis of the gene expression profile after BCR crosslinking in six mutated (M) and six unmutated (UM) CLL cells and six healthy B-cells. After a pilot study examining multiple time points, total RNA was purified at four time points (60 to 390 min) from stimulated (S) and unstimulated (US) cells, for a total of 170 HU133plus2.0 DNA-chips analyzed. The logarithmic ratio data log(S/US) for each time point and each patient and the linear combination for four time points were analyzed to score expression over time. This temporal clustering discriminates healthy B-cells from CLL-cells, but now also distinguishes two groups of patients, one mainly UM with higher zap70 protein levels. BCR engagement induces different gene expression for this group of aggressive CLL. We built a temporal model of gene expression for these three groups using two iterative steps. The first step is a modified K-means clustering approach using log(S/US) of temporal gene expression. This results in groups of genes with common temporal structure whose expression exhibits significant differences after BCR stimulation. The number of considered genes were then reduced, keeping a small number with the largest increase in expression within each group. Most of these genes are important in BCR transcription including JUN, DUSP1 and NFkB and most first wave genes are transcription factors. The second step is to construct predictive models of gene expression, considering only causal linear predictive models. Specifically the expression of an output gene at each time is predicted using a weighted linear combination of the expression of another gene at past time points. The method groups pairs of genes by common predictive model. While paired genes may reside in different initial clusters, upon convergence they are clustered by which predictive models they use. The procedure first assigns random pairings of genes and then we iterate between two steps, computing the best predictive model using a regularized least squares algorithm emphasizing sparse models and optimal gene pairings using a modified Hungarian bipartite graph matching approach. In practice the method converges in a small number of iterations. To refine and test this model we use RNAi to silence genes in the first wave of transcription after BCR stimulation and study the impact on the model. From the global gene regulatory network, we aim to predict the minimal number of gene to silence to influence the global structure of the BCR regulatory network. Influencing the transcriptional structure of aggressive CLL toward that of indolent CLL and healthy B-cells is a first step to reprogram the transcriptional response of leukemic cells.

Motifs in gene transcription regulatory networks

A brief overview is given of the structure and evolution of gene transcription regulatory networks (GTRNs) of simple organisms like Escherichia coli and yeast Saccharomyces cerevisiae. A prominent motif appearing in the GTRNs is the feed forward loop (FFL). The FFLs have essential functions in gene regulatory processes and it is desirable that the operational noise of a FFL be kept at the minimum for reliability of signal transmission. We calculate the variances around the mean protein levels in the steady states of Type-1 and Type-4 coherent FFLs using a stochastic model of gene expression and the Langevin formalism. The Type-1 FFL is found to be less noisy than the Type-4 FFL. Type-1 FFL motif is more abundant than Type-4 FFL motif in GTRNs. This leads to the conjecture that noise is one of the evolvable traits on which natural selection acts.

Predicting genetic regulatory response using classification.

Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions. We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences ('motifs') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment ('parents'). Thus, our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurements to a classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S.cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. The MLJava package is available upon request to the authors. Supplementary: Additional results are available from http://www.cs.columbia.edu/compbio/geneclass

A Boolean algorithm for reconstructing the structure of regulatory networks

Advances in transcriptional analysis offer great opportunities to delineate the structure and hierarchy of regulatory networks in biochemical systems. We present an approach based on Boolean analysis to reconstruct a set of parsimonious networks from gene disruption and over expression data. Our algorithms, Causal Predictor (CP) and Relaxed Causal Predictor (RCP) distinguish the direct and indirect causality relations from the non-causal interactions, thus significantly reducing the number of miss-predicted edges. The algorithms also yield substantially fewer plausible networks. This greatly reduces the number of experiments required to deduce a unique network from the plausible network structures. Computational simulations are presented to substantiate these results. The algorithms are also applied to reconstruct the entire network of galactose utilization pathway in Saccharomyces cerevisiae. These algorithms will greatly facilitate the elucidation of regulatory networks using large scale gene expression profile data.

In silico simulation of inhibitor drug effects on nuclear factor-kappaB pathway dynamics.

NF-kappaB is a transcription factor family that activates numerous genes that are related to cell survival, apoptosis, and cell migration. Its persistent activity is associated with tumor formation, growth, metastasis, and drug resistance in many cancer types, including lymphoma, colon cancer, and breast cancer. Current therapeutic efforts for inhibiting this central "switch" include using small molecules to block a selected target in this pathway. Recognizing the regulatory network structure of the NF-kappaB pathway, we examine in silico the effects of inhibitors targeting various network components, using a kinetic model of the pathway. By simulating the corresponding perturbed system dynamics, we show the resulting time course of inhibition has distinct target-specific profiles. In particular, greater oscillatory potential exists for inhibition of upstream events than for direct inhibition of NF-kappaB, at low drug concentrations. This phenomenon is observed also when we examine the dynamic effects of the recently approved proteasome inhibitor, bortezomib (PS-341), and compare it with other inhibitors, taking its pharmacokinetics into consideration. Such kinetic analyses of the "drugged" molecular system will facilitate optimal drug target selection and the development of treatment protocols for a molecularly targeted therapy.

Integrating Gene Expression and Metabolic Profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.

Structure and evolution of transcriptional regulatory networks

The regulatory interactions between transcription factors and their target genes can be conceptualised as a directed graph. At a global level, these regulatory networks display a scale-free topology, indicating the presence of regulatory hubs. At a local level, substructures such as motifs and modules can be discerned in these networks. Despite the general organisational similarity of networks across the phylogenetic spectrum, there are interesting qualitative differences among the network components, such as the transcription factors. Although the DNA-binding domains of the transcription factors encoded by a given organism are drawn from a small set of ancient conserved superfamilies, their relative abundance often shows dramatic variation among different phylogenetic groups. Large portions of these networks appear to have evolved through extensive duplication of transcription factors and targets, often with inheritance of regulatory interactions from the ancestral gene. Interactions are conserved to varying degrees among genomes. Insights from the structure and evolution of these networks can be translated into predictions and used for engineering of the regulatory networks of different organisms.

Design of genetic networks with specified functions by evolution in silico.

Recent studies have provided insights into the modular structure of genetic regulatory networks and emphasized the interest of quantitative functional descriptions. Here, to provide a priori knowledge of the structure of functional modules, we describe an evolutionary procedure in silico that creates small gene networks performing basic tasks. We used it to create networks functioning as bistable switches or oscillators. The obtained circuits provide a variety of functional designs, demonstrate the crucial role of posttranscriptional interactions, and highlight design principles also found in known biological networks. The procedure should prove helpful as a way to understand and create small functional modules with diverse functions as well as to analyze large networks.

Global analysis of the regulatory network structure of gene expression in Saccharomyces cerevisiae.

Gene expression in eukaryotic cells is controlled by the concerted action of various transcription factors. To help clarify these complex mechanisms, we attempted to develop a method for extracting maximal information regarding the transcriptional control pathways. To this end, we first analyzed the expression profiles of numerous transcription factors in yeast cells, under the assumption that the expression levels of these factors would be elevated under conditions in which the factors were active in the cells. Based on the results, we successfully categorized about 400 transcription factors into three groups based on their expression profiles. We then analyzed the effect of the loss of function of various induced transcription factors on the global expression profile to investigate the above-mentioned assumption of a correlation between transcription elevation and functional activity. By comparing the expression profiles of wild-type with those of disruption mutants using microarrays, we were able to detect a substantial number of relations between transcription factors and the genes they regulate. The results of these experiments suggested that our approach is useful for understanding the global transcriptional networks of eukaryotic cells, in which most genes are regulated in a temporal and conditional manner.

Prediction and measurement of an autoregulatory genetic module.

The deduction of phenotypic cellular responses from the structure and behavior of complex gene regulatory networks is one of the defining challenges of systems biology. This goal will require a quantitative understanding of the modular components that constitute such networks. We pursued an integrated approach, combining theory and experiment, to analyze and describe the dynamics of an isolated genetic module, an in vivo autoregulatory gene network. As predicted by the model, temperature-induced protein destabilization led to the existence of two expression states, thus elucidating the trademark bistability of the positive feedback-network architecture. After sweeping the temperature, observed population distributions and coefficients of variation were in quantitative agreement with those predicted by a stochastic version of the model. Because model fluctuations originated from small molecule-number effects, the experimental validation underscores the importance of internal noise in gene expression. This work demonstrates that isolated gene networks, coupled with proper quantitative descriptions, can elucidate key properties of functional genetic modules. Such an approach could lead to the modular dissection of naturally occurring gene regulatory networks, the deduction of cellular processes such as differentiation, and the development of engineered cellular control.

Reconciling gene expression data with known genome-scale regulatory network structures.

The availability of genome-scale gene expression data sets has initiated the development of methods that use this data to infer transcriptional regulatory networks. Alternatively, such regulatory network structures can be reconstructed based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli and Saccharomyces cerevisiae. By decomposing the regulatory network into a set of basic network elements, we can compute the local consistency of each instance of a particular type of network element. We find that the consistency of network elements is influenced by both structural features of the network such as the number of regulators acting on a target gene and by the functional classes of the genes involved in a particular element. Taken together, the approach presented allows us to define regulatory network subcomponents with a high degree of consistency between the network structure and gene expression data. The results suggest that targeted gene expression profiling data can be used to refine and expand particular subcomponents of known regulatory networks that are sufficiently decoupled from the rest of the network.

Suitability and utility of computational analysis tools: characterization of erythrocyte parameter variation.

Systems engineering can provide insights into multivariate regulatory networks and pooling in complex biological networks that cannot be fully interpreted through experiments alone. Herein, we analyzed the use of phase planes, modal and time-lagged correlation (TLC) analyses of the human erythrocyte to explore the utility of these techniques for understanding the effect of single parameter changes on the behavior of a metabolic network. Specifically, several parameters in key regulatory steps in erythrocyte glycolysis, Rapoport-Leubering bypass, pentose phosphate pathway, adenosine metabolism, and membrane transport were perturbed. The most sensitive parameters were identified based on the steady-state metabolite concentration changes and were explored further. Modal analysis identified relevant time scales for each parameter change. These time scales were further explored using phase plane and TLC analyses. Phase plane and TLC both inferred pooling changes, while TLC also identified changes in the regulatory network structure that resulted from various parameter changes. Each method has strengths and weaknesses for exploring and gaining insight into complex biological networks.

Transcriptional regulatory networks in Saccharomyces cerevisiae.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses

Reverse engineering algorithms (REAs) aim at using gene expression data to reconstruct interactions in regulatory genetic networks. This may help to understand the basis of gene regulation, the core task of functional genomics. Collecting data for a number of environmental conditions is necessary to reengineer even the smallest regulatory networks with reasonable confidence. We systematically tested the requirements for the experimental design necessary for ranking alternative hypotheses about the structure of a given regulatory network. A genetic algorithm (GA) was used to explore the parameter space of a multistage discrete genetic network model with fixed connectivity and number of states per node. Our results show that it is not necessary to determine all parameters of the genetic network in order to rank hypotheses. The ranking process is easier the more experimental environmental conditions are used for the data set. During the ranking, the number of fixed parameters increases with the number of environmental conditions, while some errors in the hypothetical network structure may pass undetected, due to a maintained dynamical behaviour.

Topological and causal structure of the yeast transcriptional regulatory network.

Interpretation of high-throughput biological data requires a knowledge of the design principles underlying the networks that sustain cellular functions. Of particular importance is the genetic network, a set of genes that interact through directed transcriptional regulation. Genes that exert a regulatory role encode dedicated transcription factors (hereafter referred to as regulating proteins) that can bind to specific DNA control regions of regulated genes to activate or inhibit their transcription. Regulated genes may themselves act in a regulatory manner, in which case they participate in a causal pathway. Looping pathways form feedback circuits. Because a gene can have several connections, circuits and pathways may crosslink and thus represent connected components. We have created a graph of 909 genetically or biochemically established interactions among 491 yeast genes. The number of regulating proteins per regulated gene has a narrow distribution with an exponential decay. The number of regulated genes per regulating protein has a broader distribution with a decay resembling a power law. Assuming in computer-generated graphs that gene connections fulfill these distributions but are otherwise random, the local clustering of connections and the number of short feedback circuits are largely underestimated. This deviation from randomness probably reflects functional constraints that include biosynthetic cost, response delay and differentiative and homeostatic regulation.

Homeobox genes in normal and malignant cells

Homeobox genes are transcription factors primarily involved in embryonic development. Several homeobox gene families have so far been identified: Hox, EMX, PAX, MSX as well as many isolated divergent homeobox genes. Among these, Hox genes are most intriguing for having a regulatory network structure organization. Recent indications suggest the involvement of homeobox genes in (i) crucial adult eukariotic cell functions and (ii) human diseases, spanning from diabetes to cancer. In this review we will discuss the mechanisms through which homeobox genes act, and will propose a model for the function of the Hox gene network as decoding system for achieving specific genetic programs. New technologies for whole-genome RNA expression will be crucial to evaluate the clinical relevance of homeobox genes in structural and metabolic diseases.

From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli

Because a large number of molecular mechanisms involved in gene regulation have been described during the last decades, it is now becoming possible to address questions about the global structure of gene regulatory networks, at least in the case of some of the best-characterized organisms. This paper presents a global characterization of the transcriptional regulation in Escherichia coli on the basis of the current data. The connectivity of the corresponding network was evaluated by analyzing the distribution of the number of genes regulated by a given regulatory protein, and the distribution of the number of regulatory genes regulating a given regulated gene. The mean connectivity found (between 2 and 3) shows a rather loosely interconnected structure. Special emphasis is given to circular sequences of interactions ("circuits") because of their critical dynamical properties. Only one-element circuits were found, in which negative autoregulation is the dominant architecture. These global properties are discussed in light of several pertinent theoretical approaches, as well as in terms of physiological and evolutionary considerations.