Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment

Creation of Three-Dimensional Liver Tissue Models from Experimental Images for Systems Medicine

In this chapter, we illustrate how three-dimensional liver tissue models can be created from experimental image modalities by utilizing a well-established processing chain of experiments, microscopic imaging, image processing, image analysis and model construction. We describe how key features of liver tissue architecture are quantified and translated into model parameterizations, and show how a systematic iteration of experiments and model simulations often leads to a better understanding of biological phenomena in systems biology and systems medicine.

Defining an essential transcription factor program for naïve pluripotency

The gene regulatory circuitry through which pluripotent embryonic stem (ES) cells choose between self-renewal and differentiation appears vast and has yet to be distilled into an executive molecular program. We developed a data-constrained, computational approach to reduce complexity and to derive a set of functionally validated components and interaction combinations sufficient to explain observed ES cell behavior. This minimal set, the simplest version of which comprises only 16 interactions, 12 components, and three inputs, satisfies all prior specifications for self-renewal and furthermore predicts unknown and nonintuitive responses to compound genetic perturbations with an overall accuracy of 70%. We propose that propagation of ES cell identity is not determined by a vast interactome but rather can be explained by a relatively simple process of molecular computation.

Automatic generation of causal networks linking growth factor stimuli to functional cell state changes

Despite the increasing number of growth factor-related signalling networks, their lack of logical and causal connection to factual changes in cell states frequently impairs the functional interpretation of microarray data. We present a novel method enabling the automatic inference of causal multi-layer networks from such data, allowing the functional interpretation of growth factor stimulation experiments using pathway databases. Our environment of evaluation was hepatocyte growth factor-stimulated cell migration and proliferation in a keratinocyte-fibroblast co-culture. The network for this system was obtained by applying the steps: (a) automatic integration of the comprehensive set of all known cellular networks from the Pathway Interaction Database into a master structure; (b) retrieval of an active-network from the master structure, where the network edges that connect nodes with an absent mRNA level were excluded; and (c) reduction of the active-network complexity to a causal subnetwork from a set of seed nodes specific for the microarray experiment. The seed nodes comprised the receptors stimulated in the experiment, the consequently differentially expressed genes, and the expected cell states. The resulting network shows how well-known players, in the context of hepatocyte growth factor stimulation, are mechanistically linked in a pathway triggering functional cell state changes. Using BIOQUALI, we checked and validated the consistency of the network with respect to microarray data by computational simulation. The network has properties that can be classified into different functional layers because it not only shows signal processing down to the transcriptional level, but also the modulation of the network structure by the preceeding stimulation. The software for generating computable objects from the Pathway Interaction Database database, as well as the generated networks, are freely available at: http://www.tiga.uni-hd.de/supplements/inferringFromPID.html.

The use and abuse of -omes

The diverse fields of Omics research share a common logical structure combining a cataloging effort for a particular class of molecules or interactions, the underlying -ome, and a quantitative aspect attempting to record spatiotemporal patterns of concentration, expression, or variation. Consequently, these fields also share a common set of difficulties and limitations. In spite of the great success stories of Omics projects over the last decade, much remains to be understood not only at the technological, but also at the conceptual level. Here, we focus on the dark corners of Omics research, where the problems, limitations, conceptual difficulties, and lack of knowledge are hidden.

A cell-based simulation software for multi-cellular systems

CellSys is a modular software tool for efficient off-lattice simulation of growth and organization processes in multi-cellular systems in 2D and 3D. It implements an agent-based model that approximates cells as isotropic, elastic and adhesive objects. Cell migration is modeled by an equation of motion for each cell. The software includes many modules specifically tailored to support the simulation and analysis of virtual tissues including real-time 3D visualization and VRML 2.0 support. All cell and environment parameters can be independently varied which facilitates species specific simulations and allows for detailed analyses of growth dynamics and links between cellular and multi-cellular phenotypes.

Availability: CellSys is freely available for non-commercial use at http://msysbio.com/software/cellsys. The current version of CellSys permits the simulation of growing monolayer cultures and avascular tumor spheroids in liquid environment. Further functionality will be made available ongoing with published papers.

Contact:hoehme@izbi.uni-leipzig.de; dirk.drasdo@inria.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

Bayesian network analysis of targeting interactions in chromatin

In eukaryotes, many chromatin proteins together regulate gene expression. Chromatin proteins often direct the genomic binding pattern of other chromatin proteins, for example, by recruitment or competition mechanisms. The network of such targeting interactions in chromatin is complex and still poorly understood. Based on genome-wide binding maps, we constructed a Bayesian network model of the targeting interactions among a broad set of 43 chromatin components in Drosophila cells. This model predicts many novel functional relationships. For example, we found that the homologous proteins HP1 and HP1C each target the heterochromatin protein HP3 to distinct sets of genes in a competitive manner. We also discovered a central role for the remodeling factor Brahma in the targeting of several DNA-binding factors, including GAGA factor, JRA, and SU(VAR)3-7. Our network model provides a global view of the targeting interplay among dozens of chromatin components.

Inference of gene regulatory networks using time-series data: a survey

The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.

Methods for the inference of biological pathways and networks

In this chapter, we discuss a number of approaches to network inference from large-scale functional genomics data. Our goal is to describe current methods that can be used to infer predictive networks. At present, one of the most effective methods to produce networks with predictive value is the Bayesian network approach. This approach was initially instantiated by Friedman et al. and further refined by Eric Schadt and his research group. The Bayesian network approach has the virtue of identifying predictive relationships between genes from a combination of expression and eQTL data. However, the approach does not provide a mechanistic bases for predictive relationships and is ultimately hampered by an inability to model feedback. A challenge for the future is to produce networks that are both predictive and provide mechanistic understanding. To do so, the methods described in several chapters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to "annotate" the edges of the Bayesian networks. This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes.

A copula method for modeling directional dependence of genes

Background

Genes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. With the deposition of huge microarray data sets in public domains, study on gene networking is now possible. In recent years, there has been an increasing interest in the reconstruction of gene networks from gene expression data. Recent work includes linear models, Boolean network models, and Bayesian networks. Among them, Bayesian networks seem to be the most effective in constructing gene networks. A major problem with the Bayesian network approach is the excessive computational time. This problem is due to the interactive feature of the method that requires large search space. Since fitting a model by using the copulas does not require iterations, elicitation of the priors, and complicated calculations of posterior distributions, the need for reference to extensive search spaces can be eliminated leading to manageable computational affords. Bayesian network approach produces a discretely expression of conditional probabilities. Discreteness of the characteristics is not required in the copula approach which involves use of uniform representation of the continuous random variables. Our method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation.

Results

We analyzed the gene interactions for two gene data sets (one group is eight histone genes and the other group is 19 genes which include DNA polymerases, DNA helicase, type B cyclin genes, DNA primases, radiation sensitive genes, repaire related genes, replication protein A encoding gene, DNA replication initiation factor, securin gene, nucleosome assembly factor, and a subunit of the cohesin complex) by adopting a measure of directional dependence based on a copula function. We have compared our results with those from other methods in the literature. Although microarray results show a transcriptional co-regulation pattern and do not imply that the gene products are physically interactive, this tight genetic connection may suggest that each gene product has either direct or indirect connections between the other gene products. Indeed, recent comprehensive analysis of a protein interaction map revealed that those histone genes are physically connected with each other, supporting the results obtained by our method.

Conclusion

The results illustrate that our method can be an alternative to Bayesian networks in modeling gene interactions. One advantage of our approach is that dependence between genes is not assumed to be linear. Another advantage is that our approach can detect directional dependence. We expect that our study may help to design artificial drug candidates, which can block or activate biologically meaningful pathways. Moreover, our copula approach can be extended to investigate the effects of local environments on protein-protein interactions. The copula mutual information approach will help to propose the new variant of ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks): an algorithm for the reconstruction of gene regulatory networks.

Inferring connectivity of genetic regulatory networks using information-theoretic criteria

Recently, the concept of mutual information has been proposed for inferring the structure of genetic regulatory networks from gene expression profiling. After analyzing the limitations of mutual information in inferring the gene-to-gene interactions, this paper introduces the concept of conditional mutual information and based on it proposes two novel algorithms to infer the connectivity structure of genetic regulatory networks. One of the proposed algorithms exhibits a better accuracy while the other algorithm excels in simplicity and flexibility. By exploiting the mutual information and conditional mutual information, a practical metric is also proposed to assess the likeliness of direct connectivity between genes. This novel metric resolves a common limitation associated with the current inference algorithms, namely the situations where the gene connectivity is established in terms of the dichotomy of being either connected or disconnected. Based on the data sets generated by synthetic networks, the performance of the proposed algorithms is compared favorably relative to existing state-of-the-art schemes. The proposed algorithms are also applied on realistic biological measurements, such as the cutaneous melanoma data set, and biological meaningful results are inferred.

Inferring biomolecular interaction networks based on convex optimization

We present an optimization-based inference scheme to unravel the functional interaction structure of biomolecular components within a cell. The regulatory network of a cell is inferred from the data obtained by perturbation of adjustable parameters or initial concentrations of specific components. It turns out that the identification procedure leads to a convex optimization problem with regularization as we have to achieve the sparsity of a network and also reflect any a priori information on the network structure. Since the convex optimization has been well studied for a long time, a variety of efficient algorithms were developed and many numerical solvers are freely available. In order to estimate time derivatives from discrete-time samples, a cubic spline fitting is incorporated into the proposed optimization procedure. Throughout simulation studies on several examples, it is shown that the proposed convex optimization scheme can effectively uncover the functional interaction structure of a biomolecular regulatory network with reasonable accuracy.

Global genetic regulatory networks controlling hematopoietic cell fates

PURPOSE OF REVIEW

The gene expression profile of a cell is a consequence of transcription factor activities, which, in turn, are controlled by extra-cellular signals. The relationships between all these regulators constitute a genetic regulatory network, which can be used to predict the behavior of the cell in changing environments. We outline the progress being made to identify Genetic Regulatory Networks for hematopoiesis, using gene-by-gene approaches or emerging genomic technologies.

RECENT FINDINGS

The construction of genetic regulatory networks for single and multicellular organisms has inspired the building of genetic regulatory networks for erythropoiesis and B-cell differentiation. genetic regulatory networks are 'scale-free', whereby some genes have many connections while others have very few. The well connected genes, or hubs, correspond to master regulators of the networks, acting to integrate signals and control the sequential passage of the cells through the differentiation process. Lineage decisions are governed by cross-antagonism between two hubs. Large datasets from genome-wide analyses support the concept of multilineage priming and will increasingly refine the network topologies.

SUMMARY

As the underlying genetic regulatory networks for hematopoiesis continue to emerge, the program for lineage choice and differentiation will be revealed. More large-scale datasets identifying network components are needed alongside continued gene-by-gene analyses.