Regulatory Network Motifs Research Articles

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs.

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

A comparative study of qualitative and quantitative dynamic models of biological regulatory networks

Mathematical modeling of biological regulatory networks provides valuable insights into the structural and dynamical properties of the underlying systems. While dynamic models based on differential equations provide quantitative information on the biological systems, qualitative models that rely on the logical interactions among the components provide coarse-grained descriptions useful for systems whose mechanistic underpinnings remain incompletely understood. The middle ground class of piecewise affine differential equation models was proven informative for systems with partial knowledge of kinetic parameters. In this work we provide a comparison of the dynamic characteristics of these three approaches applied on several biological regulatory network motifs. Specifically, we compare the attractors and state transitions in asynchronous Boolean, piecewise affine and Hill-type continuous models. Our study shows that while the fixed points of asynchronous Boolean models are observed in continuous Hill-type and piecewise affine models, these models may exhibit different attractors under certain conditions. Overall, qualitative models are suitable for systems with limited knowledge of quantitative information. On the other hand, when practical, using quantitative models can provide detailed information about additional real-valued attractors not present in the qualitative models.

A physical mechanism of cancer heterogeneity.

We studied a core cancer gene regulatory network motif to uncover possible source of cancer heterogeneity from epigenetic sources. When the time scale of the protein regulation to the gene is faster compared to the protein synthesis and degradation (adiabatic regime), normal state, cancer state and an intermediate premalignant state emerge. Due to the epigenetics such as DNA methylation and histone remodification, the time scale of the protein regulation to the gene can be slower or comparable to the protein synthesis and degradation (non-adiabatic regime). In this case, many more states emerge as possible phenotype alternations. This gives the origin of the heterogeneity. The cancer heterogeneity is reflected from the emergence of more phenotypic states, larger protein concentration fluctuations, wider kinetic distributions and multiplicity of kinetic paths from normal to cancer state, higher energy cost per gene switching, and weaker stability.

Exploring Design Principles of Gene Regulatory Networks via Pareto Optimality

One central problem in systems and synthetic biology is to characterize the biological functions of regulatory network motifs. Here we consider recent model-based exploration approaches used to identify motifs capable of performing a specific biological task. In this work, we propose an optimization based strategy where the motivation is twofold: on the one hand, to introduce efficiency and optimality in the search, by using global mixed integer nonlinear optimization methods. On the other hand, to incorporate multiple design objectives (Pareto optimality), in order to cope with realistic trade-offs observed in nature. The potential of this approach is illustrated through an example where we explore the design principles underlying stripe-forming motifs.

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks.

Identification of cancer-related genes and motifs in the human gene regulatory network.

The authors investigated the regulatory network motifs and corresponding motif positions of cancer-related genes. First, they mapped disease-related genes to a transcription factor regulatory network. Next, they calculated statistically significant motifs and subsequently identified positions within these motifs that were enriched in cancer-related genes. Potential mechanisms of these motifs and positions are discussed. These results could be used to identify other disease- and cancer-related genes and could also suggest mechanisms for how these genes relate to co-occurring diseases.

MicroRNA network changes in the brain stem underlie the development of hypertension.

Hypertension is a major chronic disease whose molecular mechanisms remain poorly understood. We compared neuroanatomical patterns of microRNAs in the brain stem of the spontaneous hypertensive rat (SHR) to the Wistar Kyoto rat (WKY, control). We quantified 419 well-annotated microRNAs in the nucleus of the solitary tract (NTS) and rostral ventrolateral medulla (RVLM), from SHR and WKY rats, during three main stages of hypertension development. Changes in microRNA expression were stage- and region-dependent, with a majority of SHR vs. WKY differential expression occurring at the hypertension onset stage in NTS versus at the prehypertension stage in RVLM. Our analysis identified 24 microRNAs showing time-dependent differential expression in SHR compared with WKY in at least one brain region. We predicted potential gene regulatory targets corresponding to catecholaminergic processes, neuroinflammation, and neuromodulation using the miRWALK and RNA22 databases, and we tested those bioinformatics predictions using high-throughput quantitative PCR to evaluate correlations of differential expression between the microRNAs and their predicted gene targets. We found a novel regulatory network motif consisting of microRNAs likely downregulating a negative regulator of prohypertensive processes such as angiotensin II signaling and leukotriene-based inflammation. Our results provide new evidence on the dynamics of microRNA expression in the development of hypertension and predictions of microRNA-mediated regulatory networks playing a region-dependent role in potentially altering brain-stem cardiovascular control circuit function leading to the development of hypertension.

Analysis of substrate competition in regulatory network motifs: Stimulus–response curves, thresholds and ultrasensitivity

In the simplest case, substrate competition arises if two ligands compete for access to a single binding site of a receptor protein (or enzyme). If the two ligands exhibit different binding affinities the competition becomes biased: as long as the receptor concentration remains lower than that of the high-affinity ligand the latter blocks all of the available binding sites so that the concentration of the complex comprising the low-affinity ligand remains low. The latter only rises if the receptor concentration is increased beyond that of the high-affinity ligand. Depending on the binding affinity of the low-affinity ligand this increase may then occur in an ultrasensitive manner. Similar behavior has been observed in a phosphorylation/dephosphorylation cycle involved in cell-cycle regulation. However, a steady state analysis shows that in this case the threshold concentration is modulated by the catalytic rate constants for phosphorylation and dephosphorylation of the high-affinity substrate. As a consequence, there exists a trade-off between the dynamic range of the system as measured by the maximal phosphorylation level of the substrate and the sensitivity of the system as measured by the position of the threshold. Using the ratio of the binding affinities as a small parameter we derive explicit expressions for the stimulus–response curves as a function of the receptor (or enzyme) concentration as well as conditions for the occurrence of ultrasensitivity. Interestingly, the network motifs investigated in this study are described by structurally similar steady state equations indicating that the analysis presented here may be extendable for analyzing substrate competition in more complex regulatory networks.

Self-assembly of microcapsules regulated via the repressilator signaling network.

One of the intriguing challenges in designing active matter is devising systems that not only self-organize, but also exhibit self-regulation. Inspired by biological regulatory networks, we design a collection of self-organizing, self-regulating microcapsules that move in response to self-generated chemical signals. Three microcapsules act as localized sources of distinct chemicals that diffuse through surrounding fluid. Production rates are modulated by the "repressilator" regulatory network motif: each chemical species represses the production of the next in a cycle. Depending on the maximum production rates and capsule separation distances, we show that immobile capsules either exhibit steady or oscillatory chemical production. We then consider movement of the microcapsules over the substrate, induced by gradients in surface energy due to adsorbed chemicals. We numerically simulate this advection-diffusion-reaction system with solid-fluid interactions by combining lattice Boltzmann, immersed boundary and finite difference methods, and thereby, construct systems where the three capsules spontaneously assemble to form a close-packed triad. Chemical oscillations are shown to be critical to this assembly. By adjusting parameters, the triad can either remain stationary or translate as a cohesive group. Stationary triads can also be made to "turn off", producing chemicals at minimal rates after assembly. These findings provide design rules for creating synthetic material systems that encompass biomimetic feedback loops, which enable dynamic collective behavior.

Modelling and analysis of a gene-regulatory feed-forward loop with basal expression of the second regulator

Efficient adaptation strategies to changing environmental conditions are essential for bacteria to survive and grow. Fundamental restructuring of their metabolism is usually mediated by corresponding gene regulation. Here, often several different environmental stimuli have to be integrated into a reasonable, energy-efficient response. Fast fluctuations and overshooting have to be filtered out. The gene regulatory network for the anaerobic adaptation of the pathogenic bacterium Pseudomonas aeruginosa is organized as a feed-forward loop (FFL), which is a three-gene network motif composed of two transcription factors (Anr for oxygen, NarxL for nitrate) and one target (Nar for nitrate reductase). The upstream transcription factor (Anr) induces the downstream transcription factor (NarXL). Both regulators act together positively by inducing the target (Nar) via a direct and indirect regulation path (coherent type-1 FFL). Since full promoter activity is only achieved when both transcription factors are present the target operon is expressed with a delay. Thus, in response to environmental stimuli (oxygen, nitrate), signals are mediated and processed in a way that short pulses are filtered out. In this study we analyze a special kind of FFL called FFLk by means of a family of ordinary differential equation models. The secondary FFL regulator (NarXL) is expressed constitutively but further induced in the presence of the upstream stimuli. This FFL modification has substantial influence on the response time and cost-benefit ratio mediated by environmental fluctuations. In order to find conditions where this regulatory network motif might be beneficial, we analyzed various models and environments. We describe the observed evolutional advantage of FFLk and its role in environmental adaptation and pathogenicity.

Cell commitment motif composed of progenitor-specific transcription factors and mutual-inhibition regulation.

Simple mutual-inhibition networks are frequently occurring motifs in transcriptional regulatory networks for cell lineage commitment. Stable attractors represent cell commitment states. However, how progenitor-specific transcription factors stabilise progenitor cells and commit them to different cell fates remains unexplained. In this study, the authors represent the cell commitment motifs composed of mutual-inhibition regulation and progenitor-specific transcription factors, and develop associated mathematical model to understand how specific cell fate decisions are made. Bifurcation analysis and numerical simulation show that the model could exhibit multiple stable steady states corresponding to progenitor and committed cell states. The transitions between different cell states correspond to different commitment processes. Furthermore, the authors demonstrate that different commitment patterns, for example, haematopoietic and neural fate decisions fall within the scope of proposed framework.

Coherent feedforward transcriptional regulatory motifs enhance drug resistance.

Fluctuations in gene expression give identical cells access to a spectrum of phenotypes that can serve as a transient, nongenetic basis for natural selection by temporarily increasing drug resistance. In this study, we demonstrate using mathematical modeling and simulation that certain gene regulatory network motifs, specifically coherent feedforward loop motifs, can facilitate the development of nongenetic resistance by increasing cell-to-cell variability and the time scale at which beneficial phenotypic states can be maintained. Our results highlight how regulatory network motifs enabling transient, nongenetic inheritance play an important role in defining reproductive fitness in adverse environments and provide a selective advantage subject to evolutionary pressure.

Combinatorial regulation of lipoprotein lipase by microRNAs during mouse adipogenesis

MicroRNAs (miRNAs) regulate gene expression directly through base pairing to their targets or indirectly through participating in multi-scale regulatory networks. Often miRNAs take part in feed-forward motifs where a miRNA and a transcription factor act on shared targets to achieve accurate regulation of processes such as cell differentiation. Here we show that the expression levels of miR-27a and miR-29a inversely correlate with the mRNA levels of lipoprotein lipase (Lpl), their predicted combinatorial target, and its key transcriptional regulator peroxisome proliferator-activated receptor gamma (Pparg) during 3T3-L1 adipocyte differentiation. More importantly, we show that Lpl, a key lipogenic enzyme, can be negatively regulated by the two miRNA families in a combinatorial fashion on the mRNA and functional level in maturing adipocytes. This regulation is mediated through the Lpl 3′UTR as confirmed by reporter gene assays. In addition, a small mathematical model captures the dynamics of this feed-forward motif and predicts the changes in Lpl mRNA levels upon network perturbations. The obtained results might offer an explanation to the dysregulation of LPL in diabetic conditions and could be extended to quantitative modeling of regulation of other metabolic genes under similar regulatory network motifs.

Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases

Transcription factors (TFs) and microRNAs (miRNAs) can jointly regulate target gene expression in the forms of feed-forward loops (FFLs) or feedback loops (FBLs). These regulatory loops serve as important motifs in gene regulatory networks and play critical roles in multiple biological processes and different diseases. Major progress has been made in bioinformatics and experimental study for the TF and miRNA co-regulation in recent years. To further speed up its identification and functional study, it is indispensable to make a comprehensive review. In this article, we summarize the types of FFLs and FBLs and their identified methods. Then, we review the behaviors and functions for the experimentally identified loops according to biological processes and diseases. Future improvements and challenges are also discussed, which includes more powerful bioinformatics approaches and high-throughput technologies in TF and miRNA target prediction, and the integration of networks of multiple levels.

Network analysis of microRNAs, genes and their regulation in human bladder cancer

Bladder cancer (BC) is the fifth most common malignancy occurring worldwide and a significant cause of cancer-related morbidity and mortality. Although BC is a serious health issue, studies available concerning the relationship of genes, microRNAs (miRNAs) and their host genes has been lacking. In the present study, we assessed experimentally validated data from various sources that reported the effect of miRNA on various diseases, miRNA targeting of mRNAs, and combined these data with initial transcription factor (TF) binding site predictions within miRNA promoter regions. Topology networks obtained in this study included the differentially expressed, BC-associated and global networks. The three networks may be used to assess the effect of miRNAs and their regulation in human BC. By comparing and analyzing the similarities and differences among the three networks, key nodes with the largest potential of affecting the behavior of a particular network were identified. The results also showed potentially substantially influential miRNAs and TFs, which revealed subnetworks demonstrating the mechanisms involved as well as regulatory miRNA network motifs in human BC. Regulatory pathways regarding differentially expressed elements, such as genes and miRNAs, demonstrate self-adapting associations including, self-adapting associations and feedback loops in genes MYC, TP53, PTEN and 10 differentially expressed miRNAs. The differentially expressed network partially identified the BC mechanism. miRNA-targeted human BC genes were also enriched in highly relevant pathways, cell cycle regulation and apoptosis. The present study systematically delineated the pathogenesis of BC and provided theoretical foundations for gene therapy investigators to focu attention on key genes and miRNAs in future studies.

Counting motifs in the human interactome

Small over-represented motifs in biological networks often form essential functional units of biological processes. A natural question is to gauge whether a motif occurs abundantly or rarely in a biological network. Here we develop an accurate method to estimate the occurrences of a motif in the entire network from noisy and incomplete data, and apply it to eukaryotic interactomes and cell-specific transcription factor regulatory networks. The number of triangles in the human interactome is about 194 times that in the Saccharomyces cerevisiae interactome. A strong positive linear correlation exists between the numbers of occurrences of triad and quadriad motifs in human cell-specific transcription factor regulatory networks. Our findings show that the proposed method is general and powerful for counting motifs and can be applied to any network regardless of its topological structure.

Oscillatory and anti-oscillatory motifs in genetic regulatory networks

Recently, self-sustained oscillatory genetic regulatory networks (GRNs) have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate oscillation with proper parameters, and what the key ingredients for the oscillation are. In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered. In particular, some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed. Moreover, there are some anti-oscillatory motifs which have a strong oscillation repressing effect. Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.

Modeling gene regulatory network motifs using statecharts

BackgroundGene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks.For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs.ResultsWe present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal.We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation.ConclusionsWe present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed.

New Tools for Discovering the Role sRNA Plays in Cell Regulation

Small RNA (sRNA, [1]) is a recently discovered class of small molecules recognized as an important regulator of cellular response. Direct study of sRNA dynamics within living cells faces two important challenges. First, the functional role of sRNA can be difficult to discern, as a given cell response or observable phenotype could have produced from a variety of possible regulatory network motifs. Second, the small size of sRNA makes it difficult to attach enough fluorescent probes to achieve a measurable signal without perturbing the system dynamics. To address these challenges, we have used single molecule fluorescence in situ hybridization (smFISH, [2]) to study cell-to-cell heterogeneity of mRNA copy numbers for human host cells in the presence and absence of bacterial sRNA. These experimental mRNA distributions are used to refine and down-select regulatory model that are evaluated by the Finite State Approach [3] along with other theoretical techniques. A large number a cells are subjected to varying conditions such as nutrient concentration, salt content, and pathogen infection to eliminate incorrect regulatory models. The models, in turn, provide feedback and guidance to our experimental work to better understand the roles that sRNA plays in the cellular response.[1] Fire et al., “Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans”, Nature (1998)[2] Raj et al., “Imaging individual mRNA molecules using multiple singly labeled probes”, Nature Methods (2008).[3] Munsky et al., “Listening to the noise: random fluctuations reveal gene network parameters”, Molecular System Biology (2009).