A cell pattern generation model based on an extended artificial regulatory network

Cell pattern generation has a fundamental role in both artificial and natural development. This paper presents results from a model in which a genetic algorithm (GA) was used to evolve an artificial regulatory network (ARN) to produce predefined 2D cell patterns through the selective activation and inhibition of genes. The ARN used in this work is an extension of a model previously used to create simple geometrical patterns. The GA worked by evolving the gene regulatory network that was used to control cell reproduction, which took place in a testbed based on cellular automata (CA). After the final chromosomes were produced, a single cell in the middle of the CA lattice was allowed to replicate controlled by the ARN found by the GA, until the desired cell pattern was formed. The model was applied to the problem of generating a French flag pattern.     

软计算在生态模型中的应用

由于生态系统的高度复杂性和非线性以及空间数据采集技术的快速发展,近年来越来越多的软计算方法开始应用到生态模拟中来。软计算是个非常广泛的领域,在模式上主要包括元胞自动机、基于个体和盒式模式等;在方法上代表性的有人工神经网络、模糊数学、遗传算法、混沌理论等。重点介绍元胞自动机和规律方法在生态模型中的应用,具体实例包括种群动态模拟、水华预警和生境栖息地模拟。     

Evolution of robustness to damage in artificial 3-dimensional development

GReaNs is an Artificial Life platform we have built to investigate the general principles that guide evolution of multicellular development and evolution of artificial gene regulatory networks. The embryos develop in GReaNs in a continuous 3-dimensional (3D) space with simple physics. The developmental trajectories are indirectly encoded in linear genomes. The genomes are not limited in size and determine the topology of gene regulatory networks that are not limited in the number of nodes. The expression of the genes is continuous and can be modified by adding environmental noise. In this paper we evolved development of structures with a specific shape (an ellipsoid) and asymmetrical pattering (a 3D pattern inspired by the French flag problem), and investigated emergence of the robustness to damage in development and the emergence of the robustness to noise. Our results indicate that both types of robustness are related, and that including noise during evolution promotes higher robustness to damage. Interestingly, we have observed that some evolved gene regulatory networks rely on noise for proper behaviour.     

Inferring gene regulatory networks with time delays using a genetic algorithm

Recently a state-space model with time delays for inferring gene regulatory networks was proposed. It was assumed that each regulation between two internal state variables had multiple time delays. This assumption caused underestimation of the model with many current gene expression datasets. In biological reality, one regulatory relationship may have just a single time delay, and not multiple time delays. This study employs Boolean variables to capture the existence of the time-delayed regulatory relationships in gene regulatory networks in terms of the state-space model. As the solution space of time delayed relationships is too large for an exhaustive search, a genetic algorithm (GA) is proposed to determine the optimal Boolean variables (the optimal time-delayed regulatory relationships). Coupled with the proposed GA, Bayesian information criterion (BIC) and probabilistic principle component analysis (PPCA) are employed to infer gene regulatory networks with time delays. Computational experiments are performed on two real gene expression datasets. The results show that the GA is effective at finding time-delayed regulatory relationships. Moreover, the inferred gene regulatory networks with time delays from the datasets improve the prediction accuracy and possess more of the expected properties of a real network, compared to a gene regulatory network without time delays.     

The ornithine aminotransferase gene in gyrate,atrophy of the retina: Analysis of expression and gross structure of this gene in cultured fibroblasts

Summary Gyrate atrophy (GA), a degenerative disease of the human chorioretina, is associated with a deficiency of ornithine aminotransferase (OAT) activity, hyperornithinemia, and ornithinuria. We have characterized a cDNA clone for OAT (HLOAT) that was isolated from a cDNA library constructed from mRNA prepared from Hep G2, cells, a human hepatoma cell line. We have used HLOAT and a nearly full length OAT cDNA clone isolated from, a rat liver library (RLOAT) to examine in cultured fibroblasts from individuals with GA and control individuals, the expression of OAT mRNA and the gross structure of the OAT gene. Northern blot analyses of total cellular RNA indicated that 3 of 3 control cell lines and 5 of 6 GA cell lines are capable of expressing an OAT related mRNA of approximately 2100 bases, the size of OAT mRNA. To date, this is the only case of GA in which a complete lack of OAT mRNA has been observed. Southern blot analyses of DNA isolated from these cell lines indicated that the gross structure of the OAT gene is usually not detectably altered in individuals with GA. However, a unique pattern, of restriction fragments was observed upon digestion with Eco RI or Hind III of DNA from the GA cell line that does not express OAT mRNA. These unique Eco RI and Hind III fragments arise from the OAT structural gene and will serve as useful molecular markers that allow this particular defective OAT allele to be identified. When the cellular DNAs were digested with Hinf I and examined with a probe that corresponds to at least a portion of the active site of the enzyme, i. e., the pyridoxal phosphate binding site, identical patterns of fragments were detected in all samples. Therefore, it appears unlikely that the loss of OAT activity associated with these GA cases, 4 of which are pyridoxal phosphate responders, is the result of insertions or deletions in this region of the OAT gene. This study indicates that the lack of OAT enzyme activity associated with GA is the result of a variety of different molecular defects within the OAT gene. This project was initiated in the laboratory of H. C. P. and was supported by grants CA07175, CA22484, and 5 T32 CA09020 from the National Cancer Institute and Postdoctoral Fellowship PF-2414 from the American Cancer Society. The continuing work in the laboratory of J. D. S. was supported by grants CA36727 and HD24189 from the National, Institutes of Health, grants SIG-16, ACS-IN165A, and a Junior Faculty Research Award (JFRA-227) from the American Cancer Society, and by University of Nebraska Medical Center Seed Research Grant 88-10.     

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.     

A publish-subscribe model of genetic networks

We present a simple model of genetic regulatory networks in which regulatory connections among genes are mediated by a limited number of signaling molecules. Each gene in our model produces (publishes) a single gene product, which regulates the expression of other genes by binding to regulatory regions that correspond (subscribe) to that product. We explore the consequences of this publish-subscribe model of regulation for the properties of single networks and for the evolution of populations of networks. Degree distributions of randomly constructed networks, particularly multimodal in-degree distributions, which depend on the length of the regulatory sequences and the number of possible gene products, differed from simpler Boolean NK models. In simulated evolution of populations of networks, single mutations in regulatory or coding regions resulted in multiple changes in regulatory connections among genes, or alternatively in neutral change that had no effect on phenotype. This resulted in remarkable evolvability in both number and length of attractors, leading to evolved networks far beyond the expectation of these measures based on random distributions. Surprisingly, this rapid evolution was not accompanied by changes in degree distribution; degree distribution in the evolved networks was not substantially different from that of randomly generated networks. The publish-subscribe model also allows exogenous gene products to create an environment, which may be noisy or stable, in which dynamic behavior occurs. In simulations, networks were able to evolve moderate levels of both mutational and environmental robustness.     

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Background

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.

Results

We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.

Conclusions

Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users.     

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.     

3D genome and its disorganization in diseases

The chromosomes in eukaryotic cells are highly folded and organized to form dynamic three-dimensional (3D) structures. In recent years, many technologies including chromosome conformation capture (3C) and 3C-based technologies (Hi-C, ChIA-PET) have been developed to investigate the 3D structure of chromosomes. These technologies are enabling research on how gene regulatory events are affected by the 3D genome structure, which is increasingly implicated in the regulation of gene expression and cellular functions. Importantly, many diseases are associated with genetic variations, most of which are located in non-coding regions. However, it is difficult to determine the mechanisms by which these variations lead to diseases. With 3D genome technologies, we can now better determine the consequences of non-coding genome alterations via their impact on chromatin interactions and structures in cancer and other diseases. In this review, we introduce the various 3D genome technologies, with a focus on their application to cancer and disease research, as well as future developments to extend their utility.     

ECM concentration and cell-mediated traction forces play a role in vascular network assembly in 3D bioprinted tissue

Tissue vascularization is critical to enable oxygen and nutrient supply. Therefore, establishing expedient vasculature is necessary for the survival of tissue after transplantation. The use of biomechanical forces, such as cell-induced traction forces, may be a promising method to encourage growth of the vascular network. Three-dimensional (3D) bioprinting, which offers unprecedented versatility through precise control over spatial distribution and structure of tissue constructs, can be used to generate capillary-like structures in vitro that would mimic microvessels. This study aimed to develop an in vitro, 3D bioprinted tissue model to study the effect of cellular forces on the spatial organization of vascular structures and tissue maturation. The developed in vitro model consists of a 3D bioprinted polycaprolactone (PCL) frame with a gelatin spacer hydrogel layer and a gelatin–fibrin–hyaluronic acid hydrogel layer containing normal human dermal fibroblasts and human umbilical vein endothelial cells printed as vessel lines on top. The formation of vessel-like networks and vessel lumens in the 3D bioprinted in vitro model was assessed at different fibrinogen concentrations with and without inhibitors of cell-mediated traction forces. Constructs containing 5 mg/ml fibrinogen had longer vessels compared to the other concentrations of fibrinogen used. Also, for all concentrations of fibrinogen used, most of the vessel-like structures grew parallel to the direction the PCL frame-mediated tensile forces, with very few branching structures observed. Treatment of the 3D bioprinted constructs with traction inhibitors resulted in a significant reduction in length of vessel-like networks. The 3D bioprinted constructs also had better lumen formation, increased collagen deposition, more elaborate actin networks, and well-aligned matrix fibers due to the increased cell-mediated traction forces present compared to the non-anchored, floating control constructs. This study showed that cell traction forces from the actomyosin complex are critical for vascular network assembly in 3D bioprinted tissue. Strategies involving the use of cell-mediated traction forces may be promising for the development of bioprinting approaches for fabrication of vascularized tissue constructs.     

Artificial mammalian gene regulation networks-novel approaches for gene therapy and bioengineering

Recently developed strategies for targeted molecular interventions in mammalian cells have created novel opportunities in biotechnological and biomedical research with huge economic and therapeutic impact: the design of mammalian cells with desired phenotypes for biopharmaceutical manufacturing, tissue engineering and gene therapy. These advances have been enabled by constructing artificial gene regulation systems with control modalities similar to those evolved in key regulatory networks of mammalian cells. This review highlights recurring cellular regulation strategies and artificial gene regulation technology currently in use for rational reprogramming of cellular key events including metabolism, growth, differentiation and cell death to achieve sophisticated bioprocess and therapeutic goals.     

Detection of coregulation in differential gene expression profiles

Genomics and proteomics approaches generate distinct gene expression and protein profiles, listing individual genes embedded in broad functional terms as gene ontologies. However, interpretation of gene profiles in a regulatory and functional context remains a major issue. Elucidation of regulatory mechanisms at the gene expression level via analysis of promoter regions is a prominent procedure to decipher such gene regulatory networks. We propose a novel genetic algorithm (GA) to extract joint promoter modules in a set of coexpressed genes as resulting from differential gene expression experiments. Algorithm design has focused on the following constraints: (I) identification of the major promoter modules, which are (II) characterized by a maximum number of joint motifs and (III) are found in a maximum number of coexpressed genes. The capability of the GA in detecting multiple modules was evaluated on various test data sets, analyzing the impact of the number of motifs per promoter module, the number of genes associated with a module, as well as the total number of distinct promoter modules encoded in a sequence set. In addition to the test data sets, the GA was evaluated on two biological examples, namely a muscle-specific data set and the upstream sequences of the beta-actin gene (ACTB) derived from different species, complemented by a comparison to alternative promoter module identification routines.     

Sp family of transcription factors is involved in iron-induced collagen alpha1(I) gene expression

The purpose of this study was to identify the cis-acting elements and the trans-acting factors involved in the iron-induced expression of the collagen alpha1(I) (COL1aI) gene. Rat hepatic stellate cells were cultured in the presence of 50 microM ferric chloride, 50 microM ascorbic acid, and 250 microM citric acid (Fe/AA/CA), and the effects on collagen gene expression and the binding of nuclear proteins to the COL1aI promoter were measured. The Fe/AA/CA treatment induced a time- and dose-dependent increase in the cellular levels of COL1aI mRNA that was abrogate by pretreating cells with cycloheximide, antioxidants, and inhibitors of aldehyde-protein adduct formation. Transient transfection experiments showed that Fe/AA/CA exerted its effect through regulatory elements located between -220 and -110 bp of the COL1aI promoter. Gel retardation assays showed that Fe/AA/CA increased the binding of nuclear proteins to two elements located between -161 and -110 bp of the COL1aI promoter. These bindings were blocked by unlabeled consensus Sp1 oligonucleotide and supershifted with Sp1 and Sp3 antibodies. Finally, Fe/AA/CA increased cellular levels of the Sp1 and Sp3 proteins and Sp1 mRNA. Treatment with Fe/AA/CA stimulates COL1aI gene expression by inducing the synthesis of Sp1 and Sp3 and their binding to two regulatory elements located between -161 and -110 bp of the COL1aI promoter.