Gene Network Model Research Articles

Underexpression of Specific Interferon Genes Is Associated with Poor Prognosis of Melanoma.

Because the prognosis of melanoma is challenging and inaccurate when using current clinical approaches, clinicians are seeking more accurate molecular markers to improve risk models. Accordingly, we performed a survival analysis on 404 samples from The Cancer Genome Atlas (TCGA) cohort of skin cutaneous melanoma. Using our recently developed gene network model, we identified biological signatures that confidently predict the prognosis of melanoma (p-value < 10−5). Our model predicted 38 cases as low–risk and 54 cases as high–risk. The probability of surviving at least 5 years was 64% for low–risk and 14% for high–risk cases. In particular, we found that the overexpression of specific genes in the mitotic cell cycle pathway and the underexpression of specific genes in the interferon pathway are both associated with poor prognosis. We show that our predictive model assesses the risk more accurately than the traditional Clark staging method. Therefore, our model can help clinicians design treatment strategies more effectively. Furthermore, our findings shed light on the biology of melanoma and its prognosis. This is the first in vivo study that demonstrates the association between the interferon pathway and the prognosis of melanoma.

Gen Ağlarının Matematiksel Modellenmesi

Sistem ve hesaplamalı biyoloji alanları, deneysel teknolojinin ilerlemesiyle gelişen ve biyolojik/kimyasal sistem olaylarının anlaşılmasını sağlayacak matematiksel metotları ve modelleri kapsamaktadır. Bu modeller sistem elemanlarının davranışlarının daha iyi anlaşılmasına ve gerektiğinde, bu sistemlerin simülasyonlar yardımıyla yaratılmasına ve birbirleriyle karşılaştırılmasına yardımcı olmaktadır. Ayrıca, matematiksel modellemeler, sistem hakkındaki mevcut bilgilerin doğruluğunun da test edilmesine ve laboratuvar ortamında yapılması maliyetli olabilecek deneyleri yorumlayabilmemize olanak sağlamaktadır. Bu çalışmada, farklı varsayımlara ve veri çeşitlerine göre karmaşık gen ağlarının nasıl modellenebileceği matematiksel ifadelerle açıklanacak ve önerilen alternatif modellerden çıkabilecek ağlar, grafiksel şekillerle anlatılacaktır. Burada sunulan temel yaklaşımların gerçek veri setleri üzerinde uygulanması durumunda, sistemlerden biyolojik olarak önemli olabilecek sonuçların elde edilmesinin mümkün olabileceği düşünülmektedir. Ayrıca, tanıtılan bu modellerin sadece gen ağlarında değil, yüksek boyutlu başka veri analizlerinde de kullanılması mümkündür. Nitekim sistem biyolojinin alt dallarından olan sistem modellemesi ve gen ağlarının modellenmesi, temelde, yüksek boyutlu ve çok değişkenli verilerin analizine imkan veren, karmaşık modellemeler üzerine yoğunlaşmıştır, ve bu çalışma da bunun bir örneğidir.

A novel hardware-efficient gene network model based on asynchronous cellular automaton dynamics

A novel hardware-efficient gene network model based on asynchronous cellular automaton dynamics

Integration of complex data sources to provide biologic insight into pulmonary vascular disease (2015 Grover Conference Series).

The application of complex data sources to pulmonary vascular diseases is an emerging and promising area of investigation. The use of -omics platforms, in silico modeling of gene networks, and linkage of large human cohorts with DNA biobanks are beginning to bear biologic insight into pulmonary hypertension. These approaches to high-throughput molecular phenotyping offer the possibility of discovering new therapeutic targets and identifying variability in response to therapy that can be leveraged to improve clinical care. Optimizing the methods for analyzing complex data sources and accruing large, well-phenotyped human cohorts linked to biologic data remain significant challenges. Here, we discuss two specific types of complex data sources-gene regulatory networks and DNA-linked electronic medical record cohorts-that illustrate the promise, challenges, and current limitations of these approaches to understanding and managing pulmonary vascular disease.

A self-enhanced transport mechanism through long noncoding RNAs for X chromosome inactivation.

X-chromosome inactivation (XCI) is the mammalian dosage compensation strategy for balancing sex chromosome content between females and males. While works exist on initiation of symmetric breaking, the underlying allelic choice mechanisms and dynamic regulation responsible for the asymmetric fate determination of XCI remain elusive. Here we combine mathematical modeling and experimental data to examine the mechanism of XCI fate decision by analyzing the signaling regulatory circuit associated with long noncoding RNAs (lncRNAs) involved in XCI. We describe three plausible gene network models that incorporate features of lncRNAs in their localized actions and rapid transcriptional turnovers. In particular, we show experimentally that Jpx (a lncRNA) is transcribed biallelically, escapes XCI, and is asymmetrically dispersed between two X’s. Subjecting Jpx to our test of model predictions against previous experimental observations, we identify that a self-enhanced transport feedback mechanism is critical to XCI fate decision. In addition, the analysis indicates that an ultrasensitive response of Jpx signal on CTCF is important in this mechanism. Overall, our combined modeling and experimental data suggest that the self-enhanced transport regulation based on allele-specific nature of lncRNAs and their temporal dynamics provides a robust and novel mechanism for bi-directional fate decisions in critical developmental processes.

Periodic solutions of travelling-wave type in circular gene networks

We consider circular chains of unidirectionally coupled ordinary differential equations which are mathematical models of artificial gene networks. We study the problems of the existence and stability of special periodic solutions, the so-called travelling waves, in these chains. We establish that the number of such periodic solutions grows unboundedly as the number of links in the chain grows. However, at most one of these travelling waves can be stable. We give an explicit algorithm for choosing the stable cycle.

Hematopoiesis and T-cell specification as a model developmental system.

The pathway to generate T cells from hematopoietic stem cells guides progenitors through a succession of fate choices while balancing differentiation progression against proliferation, stage to stage. Many elements of the regulatory system that controls this process are known, but the requirement for multiple, functionally distinct transcription factors needs clarification in terms of gene network architecture. Here, we compare the features of the T-cell specification system with the rule sets underlying two other influential types of gene network models: first, the combinatorial, hierarchical regulatory systems that generate the orderly, synchronized increases in complexity in most invertebrate embryos; second, the dueling 'master regulator' systems that are commonly used to explain bistability in microbial systems and in many fate choices in terminal differentiation. The T-cell specification process shares certain features with each of these prevalent models but differs from both of them in central respects. The T-cell system is highly combinatorial but also highly dose-sensitive in its use of crucial regulatory factors. The roles of these factors are not always T-lineage-specific, but they balance and modulate each other's activities long before any mutually exclusive silencing occurs. T-cell specification may provide a new hybrid model for gene networks in vertebrate developmental systems.

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient–host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators.

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context, and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the parametric robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence, combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of autoregulation, and degradation of mRNA and protein affects the frequency, amplitude, and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modeling approaches to systems and synthetic biology.

Systems Biology Approach to Model the Life Cycle of Trypanosoma cruzi.

Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of the Trypanosoma cruzi’s life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasite’s life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.

The effects of time-varying temperature on delays in genetic networks.

Delays in gene networks result from the sequential nature of protein assembly. However, it is unclear how models of gene networks that use delays should be modified when considering time-dependent changes in temperature. This is important, as delay is often used in models of genetic oscillators that can be entrained by periodic fluctuations in temperature. Here, we analytically derive the time dependence of delay distributions in response to time-varying temperature changes. We find that the resulting time-varying delay is nonlinearly dependent on parameters of the time-varying temperature such as amplitude and frequency, therefore, applying an Arrhenius scaling may result in erroneous conclusions. We use these results to examine a model of a synthetic gene oscillator with temperature compensation. We show that temperature entrainment follows from the same mechanism that results in temperature compensation. Under a common Arrhenius scaling alone, the frequency of the oscillator is sensitive to changes in the mean temperature but robust to changes in the frequency of a periodically time-varying temperature. When a mechanism for temperature compensation is included in the model, however, we show that the oscillator is entrained by periodically varying temperature even when maintaining insensitivity to the mean temperature.

Analysis of functional importance of binding sites in the Drosophila gap gene network model.

BackgroundThe statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions.ResultsWe elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions.ConclusionsThe assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

A YAP/TAZ-miR-130/301 molecular circuit exerts systems-level control of fibrosis in a network of human diseases and physiologic conditions.

The molecular origins of fibrosis affecting multiple tissue beds remain incompletely defined. Previously, we delineated the critical role of the control of extracellular matrix (ECM) stiffening by the mechanosensitive microRNA-130/301 family, as activated by the YAP/TAZ co-transcription factors, in promoting pulmonary hypertension (PH). We hypothesized that similar mechanisms may dictate fibrosis in other tissue beds beyond the pulmonary vasculature. Employing an in silico combination of microRNA target prediction, transcriptomic analysis of 137 human diseases and physiologic states, and advanced gene network modeling, we predicted the microRNA-130/301 family as a master regulator of fibrotic pathways across a cohort of seemingly disparate diseases and conditions. In two such diseases (pulmonary fibrosis and liver fibrosis), inhibition of microRNA-130/301 prevented the induction of ECM modification, YAP/TAZ, and downstream tissue fibrosis. Thus, mechanical forces act through a central feedback circuit between microRNA-130/301 and YAP/TAZ to sustain a common fibrotic phenotype across a network of human physiologic and pathophysiologic states. Such re-conceptualization of interconnections based on shared systems of disease and non-disease gene networks may have broad implications for future convergent diagnostic and therapeutic strategies.

Effects of Collective Histone State Dynamics on Epigenetic Landscape and Kinetics of Cell Reprogramming.

Cell reprogramming is a process of transitions from differentiated to pluripotent cell states via transient intermediate states. Within the epigenetic landscape framework, such a process is regarded as a sequence of transitions among basins on the landscape; therefore, theoretical construction of a model landscape which exhibits experimentally consistent dynamics can provide clues to understanding epigenetic mechanism of reprogramming. We propose a minimal gene-network model of the landscape, in which each gene is regulated by an integrated mechanism of transcription-factor binding/unbinding and the collective chemical modification of histones. We show that the slow collective variation of many histones around each gene locus alters topology of the landscape and significantly affects transition dynamics between basins. Differentiation and reprogramming follow different transition pathways on the calculated landscape, which should be verified experimentally via single-cell pursuit of the reprogramming process. Effects of modulation in collective histone state kinetics on transition dynamics and pathway are examined in search for an efficient protocol of reprogramming.

Time-Dependent Gene Network Modelling by Sequential Monte Carlo.

Most existing methods used for gene regulatory network modeling are dedicated to inference of steady state networks, which are prevalent over all time instants. However, gene interactions evolve over time. Information about the gene interactions in different stages of the life cycle of a cell or an organism is of high importance for biology. In the statistical graphical models literature, one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method, namely particle filtering (PF), provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression data tracking are demonstrated. The data used for validation are synthetic time series data available from the DREAM4 challenge, generated from known network topologies and obtained from transcriptional regulatory networks of S. cerevisiae. We model the gene interactions over the course of time with multivariate linear regressions where the parameters of the regressive process are changing over time.

Mosaic gene network modelling identified new regulatory mechanisms in HCV infection

Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus, the mosaic model integrates the model of HCV subgenomic replicon replication with the model of TNF-induced apoptosis and NF-κB induction. Analysis of the mosaic model revealed that the regulation of TNF-induced signaling by the HCV network is crucially dependent on the RIP1, TRADD, TRAF2, FADD, IKK, IκBα, c-FLIP, and BAR genes. Overall, the developed mosaic gene network modelling approach demonstrated good predictive power and allowed the prediction of new regulatory nodes in HCV action on apoptosis and the NF-κB pathway. Those theoretical predictions could be a basis for further experimental verification.

Inferring Time-Delayed Causal Gene Network Using Time-Series Expression Data.

Inferring gene regulatory network (GRN) from the microarray expression data is an important problem in Bioinformatics, because knowing the GRN is an essential first step in understanding the inner workings of the cell and the related diseases. Time delays exist in the regulatory effects from one gene to another due to the time needed for transcription, translation, and to accumulate a sufficient number of needed proteins. Also, it is known that the delays are important for oscillatory phenomenon. Therefore, it is crucial to develop a causal gene network model, preferably as a function of time. In this paper, we propose an algorithm CLINDE to infer causal directed links in GRN with time delays and regulatory effects in the links from time-series microarray gene expression data. It is one of the most comprehensive in terms of features compared to the state-of-the-art discrete gene network models. We have tested CLINDE on synthetic data, the in vivo IRMA (On and Off) datasets and the [1] yeast expression data validated using KEGG pathways. Results show that CLINDE can effectively recover the links, the time delays and the regulatory effects in the synthetic data, and outperforms other algorithms in the IRMA in vivo datasets.

Dimension Reduction of Large Sparse AND-NOT Network Models

In this manuscript we propose and implement a dimension reduction algorithm of AND-NOT networks for the purpose of steady state computation. Our method of network reduction consists in using “steady state approximations” that do not change the number of steady states. The algorithm is designed to work at the wiring diagram level without the need to evaluate or simplify Boolean functions. Also, our implementation of the algorithm takes advantage of the sparsity typical of discrete models of biological systems.The main features of our reduction algorithm are that it works at the wiring diagram level and it preserves the number of steady states. Furthermore, the steady states of the original network can be recovered from the steady states of the reduced network; thus, all steady states are found. Also, heuristic analysis and simulations show that it runs in polynomial time. We used our results to study AND-NOT network models of gene networks and showed that our algorithm greatly simplifies steady state analysis. Furthermore, our algorithm can handle sparse AND-NOT networks with up to 1,000,000 nodes.

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks.

Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here, we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single-gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased robustness. These results chart the constraints and possibilities of adaptation to reduce expression noise and demonstrate the potential of a novel modeling framework for gene networks.

Potential role of lncRNA cyp2c91-protein interactions on diseases of the immune system.

With unprecedented increase in next generation sequencing technologies, there has been a persistent interest on transcript profiles of long non-coding RNAs (lncRNAs) and protein-coding genes forming an interaction network. Apart from protein–protein interaction (PPI), gene network models such as Weighted Gene Co-expression Network Analysis (WGCNA) are used to functionally annotate lncRNAs in identifying their potential disease associations. To address this, studies have led to characterizing transcript structures and understanding expression profiles mediating regulatory roles. In the current exploratory analysis, we show how a lncRNA – cyp2c91 contributes to the transcriptional regulation localized to cytoplasm thereby making refractory environment for transcription. By applying network methods and pathway analyses on genes related to a disease such as obesity and systemic lupus erythematosus, we show that we can gain deeper insight in biological processes such as the perturbances in immune system, and get a better understanding of the systems biology of diseases.