TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach

Background  

One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory.     

Reverse engineering large-scale genetic networks: synthetic versus real data

Development of microarray technology has resulted in an exponential rise in gene expression data. Linear computational methods are of great assistance in identifying molecular interactions, and elucidating the functional properties of gene networks. It overcomes the weaknesses of in vivo experiments including high cost, large noise, and unrepeatable process. In this paper, we propose an easily applied system, Stepwise Network Inference (SWNI), which integrates deterministic linear model with statistical analysis, and has been tested effectively on both simulated experiments and real gene expression data sets. The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements. In particular, our algorithm shows efficiency and outperforms the existing ones presented in this paper when dealing with large-scale sparse networks without any prior knowledge.     

Mining parasite data using genetic programming

Genetic programming is a technique that can be used to tackle the hugely demanding data-processing problems encountered in the natural sciences. Application of genetic programming to a problem using parasites as biological tags demonstrates its potential for developing explanatory models using data that are both complex and noisy.     

Extracting binary signals from microarray time-course data

This article presents a new method for analyzing microarray time courses by identifying genes that undergo abrupt transitions in expression level, and the time at which the transitions occur. The algorithm matches the sequence of expression levels for each gene against temporal patterns having one or two transitions between two expression levels. The algorithm reports a P-value for the matching pattern of each gene, and a global false discovery rate can also be computed. After matching, genes can be sorted by the direction and time of transitions. Genes can be partitioned into sets based on the direction and time of change for further analysis, such as comparison with Gene Ontology annotations or binding site motifs. The method is evaluated on simulated and actual time-course data. On microarray data for budding yeast, it is shown that the groups of genes that change in similar ways and at similar times have significant and relevant Gene Ontology annotations.     

Improvement of downstream processing of recombinant proteins by means of genetic engineering methods

The rapid advancement of genetic engineering has allowed to produce an impressive number of proteins on a scale which would not have been achieved by classical biotechnology. At the beginning of this development research was focussed on elucidating the mechanisms of protein overexpression. The appearance of inclusion bodies may illustrate the success. In the meantime, genetic engineering is not only expected to achieve overexpression, but to improve the whole process of protein production. For downstream processing of recombinant proteins, the synthesis of fusion proteins is of primary importance. Fusion with certain proteins or peptides may protect the target protein from proteolytic degradation and may alter its solubility. Intracellular proteins may be translocated by means of fusions with signal peptides. Affinity tags as fusion complements may render protein separation and purification highly selective. These methods as well as similar ones for improving the downstream processing of proteins will be discussed on the basis of recent literature.     

Reverse engineering discrete dynamical systems from data sets with random input vectors.

Recently a new algorithm for reverse engineering of biochemical networks was developed by Laubenbacher and Stigler. It is based on methods from computational algebra and finds most parsimonious models for a given data set. We derive mathematically rigorous estimates for the expected amount of data needed by this algorithm to find the correct model. In particular, we demonstrate that for one type of input parameter (graded term orders), the expected data requirements scale polynomially with the number n of chemicals in the network, while for another type of input parameters (randomly chosen lex orders) this number scales exponentially in n. We also show that, for a modification of the algorithm, the expected data requirements scale as the logarithm of n.     

Adapting genetic regulatory models by genetic programming

In this paper, we focus on the task of adapting genetic regulatory models based on gene expression data from microarrays. Our approach aims at automatic revision of qualitative regulatory models to improve their fit to expression data. We describe a type of regulatory model designed for this purpose, a method for predicting the quality of such models, and a method for adapting the models by means of genetic programming. We also report experimental results highlighting the ability of the methods to infer models on a number of artificial data sets. In closing, we contrast our results with those of alternative methods, after which we give some suggestions for future work.     

Reverse engineering of protein secretion by uncoupling of cell cycle phases from growth

The demand for recombinant proteins both for biopharmaceutical and technical applications is rapidly growing, and therefore the need to establish highly productive expression systems is steadily increasing. Yeasts, such as Pichia pastoris, are among the widely used production platforms with a strong emphasis on secreted proteins. Protein secretion is a limiting factor of productivity. There is strong evidence that secretion is coupled to specific growth rate (µ) in yeast, being higher at higher µ. For maximum productivity and product titer, high specific secretion rates at low µ would be desired. At high secretion rates cultures contain a large fraction of cells in the G2 and M phases of cell cycle. Consequently, the cell design target of a high fraction of cells in G2 + M phase was achieved by constitutive overexpression of the cyclin gene CLB2. Together with predictive process modeling this reverse engineered production strain improved the space time yield (STY) of an antibody Fab fragment by 18% and the product titer by 53%. This concept was verified with another secreted protein, human trypsinogen. Biotechnol. Bioeng. 2011;108: 2403–2412. © 2011 Wiley Periodicals, Inc.     

Progress in cellular engineering of plants: biochemical and genetic assessment of selectable markers from cultured cells

Summary Recent availability of stable and well characterized selectable markers and ability to combine alien genomes parasexually have contributed to the development of molecular biology in higher plants, including gene expression and genetic manipulation.Several types of biochemical mutants (resistant to inhibitory concentrations of aminoacid(s) or aminoacid analogs as well as deficient for enzyme activity) have recently been isolated and characterized biochemically and genetically. Among them, mutants with alterations in the nitrogen and aminoacid metabolism, or in the activity of alcohol dehydrogenases are being used in the development of more efficient techniques of gene transfer.The manipulation of whole genomes by sexual or somatic cell fusion offers new potential in this field, but refinement of transfer techniques is desirable. The new set of selectable markers obtained through advanced cellular technology, as well as our ability to regenerate plants from manipulated cell lines are expected to play a major role in cellular engineering.     

Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data

Background

Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.

Results

We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.

Conclusions

Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users.     

长春花吲哚生物碱合成途径的基因工程研究进展

对长春花吲哚生物碱合成途径的基因工程研究进行了综述。研究人员为探索利用长春花大量生产抗肿瘤药物长春碱和长春新碱等,对长春花吲哚生物碱的合成途径展开了深入的研究,克隆和鉴定了多个编码合成途径关键酶的基因,并研究了相关转录因子对该合成途径基因表达的调控作用;另外,一些关键基因和转录因子已被用于长春花吲哚生物碱代谢途径的遗传改造,相关研究显示利用基因工程手段提高长春花药用成分含量的可行性。     

Noise-robust soft clustering of gene expression time-course data

Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.     

Uncovering signal transduction networks from high-throughput data by integer linear programming

Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system.