System Biology

We report the construction of a genome-wide fish metabolic network model, MetaFishNet, and its application to analyzing high throughput gene expression data. This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at http://metafishnet.appspot.com.

Background: Cells interact with their environment and they have to react adequately to internal and external changes such changes in nutrient composition, physical properties like temperature or osmolarity and other stresses. More specifically, they must be able to evaluate whether the external change is significant or just in the range of noise. Based on multiple external parameters they have to compute an optimal response. Cellular signaling pathways are considered as the major means of information perception and transmission in cells. Scope of Review: Here, we review different attempts to quantify information processing on the level of individual cells. We refer to Shannon entropy, mutual information, and informal measures of signaling pathway cross-talk and specificity. Major Conclusions: Information theory in systems biology has been successfully applied to identification of optimal pathway structures, mutual information and entropy as system response in sensitivity analysis, and quantification of input and output information. General Significance: While the study of information transmission within the framework of information theory in technical systems is an advanced field with high impact in engineering and telecommunication, its application to biological objects and processes is still restricted to specific fields such as neuroscience, structural and molecular biology. However, in systems biology dealing with a holistic understanding of biochemical systems and cellular signaling only recently a number of examples for the application of information theory have emerged. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Unlike traditional biological research that focuses on a small set of components, systems biology studies the complex interactions among a large number of genes, proteins, and other elements of biological networks and systems. Host-pathogen systems biology examines the interactions between the components of two distinct organisms: a microbial or viral pathogen and its animal host. With the availability of complete genomic sequences of a variety of hosts and pathogens, together with breakthroughs in proteomics, metabolomics, and other experimental areas, the investigation of host-pathogen systems on a multitude of levels of detail has come within reach. This chapter introduces methods and approaches for the analysis of host-pathogen systems. We will particularly emphasize the role of biochemical networks for the study of complex relationships across species boundaries. Although the research area of host-pathogen systems biology spans multiple spatial and temporal scales, we will focus on the molecular and cellular aspects of pathogen-host interactions. We will cover the construction of biochemical networks, the identification of functional response sub-networks, and their comparative network analysis. These methods find application in the identification of host markers and drug targets for further drug development and therapeutic interventions. We will also provide a brief review of other modeling techniques and applications within host-pathogen systems biology, including rule-based modeling of signal transduction pathways, immune system models, and physiological top-down approaches.

Motivation: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). Results: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the Micro-Array Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The * To whom correspondence should be addressed. † The authors wish it to be known that, in their opinion, these two authors should be regarded as joint First Authors. open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. Availability: A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/ SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

Inflammation is a complex, multi-scale biologic response to stress that is also required for repair and regeneration after injury. Despite the repository of detailed data about the cellular and molecular processes involved in inflammation, including some understanding of its ...

Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes. Special thanks are due to Brian Junker who gave me very helpful comments, and to Tim Null who made the printed version look as good as it does. Both waited patiently for me to finish, for which I'm forever grateful.

This article introduces the finite state projection ͑FSP͒ method for use in the stochastic analysis of chemically reacting systems. One can describe the chemical populations of such systems with probability density vectors that evolve according to a set of linear ordinary differential equations known as the chemical master equation ͑CME͒. Unlike Monte Carlo methods such as the stochastic simulation algorithm ͑SSA͒ or leaping, the FSP directly solves or approximates the solution of the CME. If the CME describes a system that has a finite number of distinct population vectors, the FSP method provides an exact analytical solution. When an infinite or extremely large number of population variations is possible, the state space can be truncated, and the FSP method provides a certificate of accuracy for how closely the truncated space approximation matches the true solution. The proposed FSP algorithm systematically increases the projection space in order to meet prespecified tolerance in the total probability density error. For any system in which a sufficiently accurate FSP exists, the FSP algorithm is shown to converge in a finite number of steps. The FSP is utilized to solve two examples taken from the field of systems biology, and comparisons are made between the FSP, the SSA, and leaping algorithms. In both examples, the FSP outperforms the SSA in terms of accuracy as well as computational efficiency. Furthermore, due to very small molecular counts in these particular examples, the FSP also performs far more effectively than leaping methods.

We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity and genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway.

Simplified mathematical representation using explicitly computed time delays to maintain instabilities permits the analysis of negative feedback loops like those observed in biological pathways. ABSTRACT | Negative feedback loops are a common cellular motif underlying many important gene regulation pathways, and therefore a clear understanding of their dynamics is important for the study of gene regulation. Here we analyze the linear stability of negative feedback loops with and without explicit time delays. When the degradation rates of each loop component are identical, we derive the analytical solution of Manuscript

The recognition that nutrients have the ability to interact and modulate molecular mechanisms underlying an organism's physiological functions has prompted a revolution in the field of nutrition. Performing population-scaled epidemiological studies in the absence of genetic knowledge may result in erroneous scientific conclusions and misinformed nutritional recommendations. To circumvent such issues and more comprehensively probe the relationship between genes and diet, the field of nutrition has begun to capitalize on both the technologies and supporting analytical software brought forth in the post-genomic era. The creation of nutrigenomics and nutrigenetics, two fields with distinct approaches to elucidate the interaction between diet and genes but with a common ultimate goal to optimize health through the personalization of diet, provide powerful approaches to unravel the complex relationship between nutritional molecules, genetic polymorphisms, and the biological system as a whole. Reluctance to embrace these new fields exists primarily due to the fear that producing overwhelming quantities of biological data within the confines of a single study will submerge the original query; however, the current review aims to position nutrigenomics and nutrigenetics as the emerging faces of nutrition that, when considered with more classical approaches, will provide the necessary stepping stones to achieve the ambitious goal of optimizing an individual's health via nutritional intervention.-Mutch, D. M., Wahli, W., Williamson, G. Nutrigenomics and nutrigenetics: the emerging faces of nutrition. FASEB J. 19, 1602-1616 (2005)

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.

The medical and pharmaceutical communities are facing a dire need for new druggable targets, while, paradoxically, the targets of some drugs that are in clinical use or development remain elusive. Many compounds have been found to be more promiscuous than originally anticipated, which can potentially lead to side effects, but which may also open up additional medical uses. As we move toward systems biology and personalized medicine, comprehensively determining small molecule-target interaction profiles and mapping these on signaling and metabolic pathways will become increasingly necessary. Chemical proteomics is a powerful mass spectrometry-based affinity chromatography approach for identifying proteome-wide small molecule-protein interactions. Here we will provide a critical overview of the basic concepts and recent advances in chemical proteomics and review recent applications, with a particular emphasis on kinase inhibitors and natural products.

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics.

Factor analysis is a general purpose technique for dimension- ality reduction with applications in diverse areas including computer vision, collaborative filtering and computational bi- ology. Sparse factor analysis is a natural extension that can be motivated by the observation that sparse features tend to generalize better, or justified based on a priori beliefs about the underlying generative model of the

Dynamic flux balance analysis (DFBA) provides a platform for detailed design, control and optimization of biochemical process technologies. It is a promising modeling framework that combines genome-scale metabolic network analysis with dynamic simulation of the extracellular environment. Dynamic flux balance analysis assumes that the intracellular species concentrations are in equilibrium with the extracellular environment. The resulting underdetermined stoichiometric model is solved under the assumption of a biochemical objective such as growth rate maximization. The model of the metabolism is coupled with the dynamic mass balance equations of the extracellular environment via expressions for the rates of substrate uptake and product excretion, which imposes additional constraints on the linear program (LP) defined by growth rate maximization of the metabolism. The linear program is embedded into the dynamic model of the bioreactor, and together with the additional constraints this provides an accurate model of the substrate consumption, product secretion, and biomass production during operation. A DFBA model consists of a system of ordinary differential equations for which the evaluation of the right-hand side requires not only function evaluations, but also the solution of one or more linear programs. The numerical tool presented here accurately and efficiently simulates large-scale dynamic flux balance models. The main advantages that this approach has over existing implementation are that the integration scheme has a variable step size, that the linear program only has to be solved when qualitative changes in the optimal flux distribution of the metabolic network occur, and that it can reliably simulate behavior near the boundary of the domain where the model is defined. This is illustrated through large-scale examples taken from the literature. Biotechnol. Bioeng. 2013; 110: 792–802. © 2012 Wiley Periodicals, Inc.

Systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models that may eventually have 10 6 dynamic variables with non-linear interactions. Historically, single biological measurements are made over minutes, suggesting the challenge of specifying 10 6 model parameters. Except for fluorescence and microelectrode recordings, most cellular measurements have inadequate bandwidth to discern the time course of critical intracellular biochemical events. Micro-array expression profiles of thousands of genes cannot determine quantitative dynamic cellular signalling and metabolic variables. Major gaps must be bridged between the computational vision and experimental reality. The analysis of cellular signalling dynamics and control requires, first, micro-and nano-instruments that measure simultaneously multiple extracellular and intracellular variables with sufficient bandwidth; secondly, the ability to open existing internal control and signalling loops; thirdly, external BioMEMS micro-actuators that provide high bandwidth feedback and externally addressable intracellular nano-actuators; and, fourthly, real-time, closed-loop, single-cell control algorithms. The unravelling of the nested and coupled nature of cellular control loops requires simultaneous recording of multiple single-cell signatures. Externally controlled nano-actuators, needed to effect changes in the biochemical, mechanical and electrical environment both outside and inside the cell, will provide a major impetus for nanoscience.

The feasibility of microalgae production for biodiesel was discussed. Although algae are not yet produced at large scale for bulk applications, there are opportunities to develop this process in a sustainable way. It remains unlikely, however, that the process will be developed for biodiesel as the only end product from microalgae. In order to develop a more sustainable and economically feasible process, all biomass components (e.g. proteins, lipids, carbohydrates) should be used and therefore biorefi ning of microalgae is very important for the selective separation and use of the functional biomass components. If biorefi ning of microalgae is applied, lipids should be fractionated into lipids for biodiesel, lipids as a feedstock for the chemical industry and w -3 fatty acids, proteins and carbohydrates for food, feed and bulk chemicals, and the oxygen produced should be recovered also. If, in addition, production of algae is done on residual nutrient feedstocks and CO 2 , and production of microalgae is done on a large scale against low production costs, production of bulk chemicals and fuels from microalgae will become economically feasible.

This article takes on a perhaps impossible task: not only to reconstruct the core argument of Arthur Peacocke's program in science and religion but also to evaluate it in two major areas where it would seem to be vulnerable, namely, more recent developments in systems biology and the philosophy of mind. If his theory of hierarchies is to be successful, it must stand up to developments in these two areas and then be able to apply the results in a productive way to Christian theological reflection. Peacocke recognized that one's model of the mind-body relation is crucial for one's position on the Godworld relation and divine action. Of the three models that he constructed, it turns out that only the third can serve as a viable model for theology if it is to be more than purely deistic or metaphorical.

RNA processing is a tightly regulated and highly complex pathway which includes transcription, splicing, editing, transportation, translation and degradation. It has been well-documented that splicing of RNA polymerase II medicated nascent transcripts occurs co-transcriptionally and is functionally coupled to other RNA processing. Recently, increasing experimental evidence indicated that pre-mRNA splicing influences RNA degradation and vice versa. In this review, we summarized the recent findings demonstrating the coupling of these two processes. In addition, we highlighted the importance of splicing in the production of intronic miRNA and circular RNAs, and hence the discovery of the novel mechanisms in the regulation of gene expression.

Metabolomics being the most recently introduced "omic" analytical platform is currently at its development phase. For the metabolomics to be broadly deployed to biological and clinical research and practice, issues regarding data validation and reproducibility need to be resolved. Gas chromatography-mass spectrometry (GC-MS) will remain integral part of the metabolomics laboratory. In this paper, the sources of biases in GC-MS metabolomics are discussed and experimental evidence for their occurrence and impact on the final results is provided. When available, methods to correct or account for these biases are presented towards the standardization of a systematic methodology for quantitative GC-MS metabolomics.

Extended Fourier amplitude sensitivity test (eFAST) Agent-based model (ABM) Sensitivity index Monte Carlo methods Aleatory uncertainty Epistemic uncertainty a b s t r a c t Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.

The thyroid, the largest gland in the endocrine system, secretes hormones that help promote bodily growth and development. This gland regulates hormonal secretion rate in spite of changes in dietary iodine which is a key ingredient in the hormone's biosynthesis. The thyroid relies on several feedback mechanisms for this regulation, and in this paper we use recent molecular-level and clinical observations to engineer a computational thyroid model. We use simulation and analysis to show that this models captures known aspects of thyroid physiology. We identify features in the model that are responsible for hormonal regulation, and use the model to identify and evaluate competing hypotheses associated with Wolff-Chaikoff escape.

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.

Predictive microbiology Model building Strategic research Enabling technology Value analysis Modelling food and other ecosystems Microbial persistence and recovery This paper considers the future of predictive microbiology by exploring the balance that exists between science, applications and expectations. Attention is drawn to the development of predictive microbiology as a subdiscipline of food microbiology and of technologies that are required for its applications, including a recently developed biological indicator. As we move into the era of systems biology, in which physiological and molecular information will be increasingly available for incorporation into models, predictive microbiologists will be faced with new experimental and data handling challenges. Overcoming these hurdles may be assisted by interacting with microbiologists and mathematicians developing models to describe the microbial role in ecosystems other than food. Coupled with a commitment to maintain strategic research, as well as to develop innovative technologies, the future of predictive microbiology looks set to fulfil "great expectations".

Micro-and nano-electromechanical systems (MEMS and NEMS)-based drug delivery devices have become commercially-feasible due to converging technologies and regulatory accommodation. The FDA Office of Combination Products coordinates review of innovative medical therapies that join elements from multiple established categories: drugs, devices, and biologics. Combination products constructed using MEMS or NEMS technology offer revolutionary opportunities to address unmet medical needs related to dosing. These products have the potential to completely control drug release, meeting requirements for on-demand pulsatile or adjustable continuous administration for extended periods. MEMS or NEMS technologies, materials science, data management, and biological science have all significantly developed in recent years, providing a multidisciplinary foundation for developing integrated therapeutic systems. If small-scale biosensor and drug reservoir units are combined and implanted, a wireless integrated system can regulate drug release, receive sensor feedback, and transmit updates. For example, an Bartificial pancreas^implementation of an integrated therapeutic system would improve diabetes management. The tools of microfabrication technology, information science, and systems biology are being combined to design increasingly sophisticated drug delivery systems that promise to significantly improve medical care.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

To understand biology at the system level, we must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism. Properties of systems, such as robustness, emerge as central issues, and understanding these properties may have an impact on the future of medicine. However, many breakthroughs in experimental devices, advanced software, and analytical methods are required before the achievements of systems biology can live up to their much-touted potential.

Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

The gut microbiome is the term given to describe the vast collection of symbiotic microorganisms in the human gastrointestinal system and their collective interacting genomes. Recent studies have suggested that the gut microbiome performs numerous important biochemical functions for the host, and disorders of the microbiome are associated with many and diverse human disease processes. Systems biology approaches based on

Modelers of molecular signaling networks must cope with the combinatorial explosion of protein states generated by posttranslational modifications and complex formation. Rule-based models provide a powerful alternative to approaches that require explicit enumeration of all possible molecular species of a system. Such models consist of formal rules stipulating the (partial) contexts wherein specific protein-protein interactions occur. These contexts specify molecular patterns that are usually less detailed than molecular species. Yet, the execution of rule-based dynamics requires stochastic simulation, which can be very costly. It thus appears desirable to convert a rule-based model into a reduced system of differential equations by exploiting the granularity at which rules specify interactions. We present a formal (and automated) method for constructing a coarse-grained and selfconsistent dynamical system aimed at molecular patterns that are distinguishable by the dynamics of the original system as posited by the rules. The method is formally sound and never requires the execution of the rule-based model. The coarse-grained variables do not depend on the values of the rate constants appearing in the rules, and typically form a system of greatly reduced dimension that can be amenable to numerical integration and further model reduction techniques.

The availability of easily programmable manycore CPUs and GPUs has motivated investigations into how to best exploit their tremendous computational power for scientific computing. Here we demonstrate how a systems biology application-detection and tracking of white blood cells in video microscopy-can be accelerated by 200x using a CUDA-capable GPU. Because the algorithms and implementation challenges are common to a wide range of applications, we discuss general techniques that allow programmers to make efficient use of a manycore GPU.

a b s t r a c t a r t i c l e i n f o Keywords: Gut microbiota LPS Obesity/type 2 diabetes Gut permeability Inflammation Endocannabinoid system Obesity, type-2 diabetes and low-grade inflammation are becoming worldwide epidemics. In this regard, the literature provides a novel concept that we call "MicrObesity" (Microbes and Obesity), which is devoted to deciphering the specific role of dysbiosis and its impact on host metabolism and energy storage. In the present review, we discuss novel findings that may partly explain how the microbial community participates in the development of the fat mass development, insulin resistance and low-grade inflammation that characterise obesity. In recent years, numerous mechanisms have been proposed and several proteins identified. Amongst the key players involved in the control of fat mass development, Fasting induced adipose factor, AMP-activated protein kinase, G-protein coupled receptor 41 and G-protein coupled receptor 43 have been linked to gut microbiota. In addition, the discovery that low-grade inflammation might be directly linked to the gut microbiota through metabolic endotoxaemia (elevated plasma lipopolysaccharide levels) has led to the identification of novel mechanisms involved in the control of the gut barrier. Amongst these, the impacts of glucagon-like peptide-2, the endocannabinoid system and specific bacteria (e.g., Bifidobacterium spp.) have been investigated. Moreover, the advent of probiotic and prebiotic treatments appears to be a promising "pharmaco-nutritional" approach to reversing the host metabolic alterations linked to the dysbiosis observed in obesity. Although novel powerful molecular system biology approaches have offered great insight into this "small world within", more studies are needed to unravel how specific changes in the gut microbial community might affect or counteract the development of obesity and related disorders.

DNA microarrays are frequently used to study transcriptome regulation in a wide variety of organisms. Although they are an invaluable tool for the acquisition of large scale dataset in plant systems biology, a number of surprising results and unanticipated complications are often encountered that illustrate the limitations and potential pitfalls of this technology. In this chapter we will present examples of real world studies from two classes of microarray experiments that were designed to (i) identify target genes for transcriptional regulators and (ii) to characterize complex expression patterns to reveal unexpected dependencies within transcriptional networks.