The model organism as a system: integrating 'omics' data sets

Joyce, Andrew; Palsson, Bernhard Ø.

doi:10.1038/nrm1857

Cited by 689 publications

(449 citation statements)

References 127 publications

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“…These 'systems approaches' are used to investigate processes as a whole and enable models to be built to predict the behaviour of a system in response to various external cues, disturbances or modifications of its composition [1]. After ground-breaking work on the properties of small networks that consisted of a few genes, the wiring of complete cells and microbial organisms is now being investigated and modelled [2,3]. However, as free-living organisms constantly interact with each other and the environment, systems biologists are already looking towards the next big challengeunravelling the complexity of complete ecosystems.…”

mentioning

confidence: 99%

“…In single-organism systems biology, the parts list is generally established; almost 700 complete bacterial and archaeal genomes are available and some functional knowledge is available for approximately 70-80% of the encoded genes [8,9]. For several model organisms, large-scale efforts have determined the connectivity among the parts (the physical and genetic interactions between genes) [2]. This, together with an ever increasing amount of temporal, spatial and structural data, means that model microorganism systems biology is ready to enter the third phase and progress towards its final goal -the modelling and manipulation of complete organisms.…”

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confidence: 99%

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Molecular eco-systems biology: towards an understanding of community function

Raes¹,

Bork²

2008

Nat Rev Microbiol

356

274

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| Systems-biology approaches, which are driven by genome sequencing and high-throughput functional genomics data, are revolutionizing single-cell-organism biology. With the advent of various high-throughput techniques that aim to characterize complete microbial ecosystems (metagenomics, meta-transcriptomics and meta-metabolomics), we propose that the time is ripe to consider molecular systems biology at the ecosystem level (eco-systems biology). Here, we discuss the necessary data types that are required to unite molecular microbiology and ecology to develop an understanding of community function and discuss the potential shortcomings of these approaches.Over the past decade, the advent of robotics has enabled a paradigm shift in molecular biology: a change of emphasis from reductionistic approaches and 'singleprotein' studies to global investigations of increasingly more complex systems of molecules and their interrelationships. These 'systems approaches' are used to investigate processes as a whole and enable models to be built to predict the behaviour of a system in response to various external cues, disturbances or modifications of its composition [1]. After ground-breaking work on the properties of small networks that consisted of a few genes, the wiring of complete cells and microbial organisms is now being investigated and modelled [2,3]. However, as free-living organisms constantly interact with each other and the environment, systems biologists are already looking towards the next big challengeunravelling the complexity of complete ecosystems.A microbial ecosystem can be defined as a system that consists of all the microorganisms that live in a certain area or niche and that function together in the context of the other biotic (plants and animals) and abiotic (temperature, chemical composition and structure of the surroundings) factors of that niche. Communities range from being simple (for example, one-or two-speciesdominated bioreactors and biofilms that are growing on ore-mine effluents or medical implants) to complex (for example, symbiotic human gut flora, plant rhizospheres, soil communities and ocean dwelling or even airborne microorganisms, such as those present in clouds). The complexity of the interactions in ecosystems depends on the number of species and the population structure, variation in food and energy supply and the geography of the habitat [4]. Eco-systems biology seeks to understand, as a whole, the immensely complex set of molecular processes and interactions that contribute to ecosystem functioning -the total sum of ecosystem-level processes, such as matter, nutrient and energy cycling [5]. This understanding should ultimately lead to predictive modelling of ecosystems, allowing the in silico investigation of ecosystem properties. Important issues that could be addressed by an ecosystems approach include estimating the relative importance of ecosystem members in ecosystem functioning and productivity, the effect of nutrient availability on species composition or the resil...

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mentioning

confidence: 99%

mentioning

confidence: 99%

Molecular eco-systems biology: towards an understanding of community function

Raes¹,

Bork²

2008

Nat Rev Microbiol

356

274

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“…Various methods are available for measuring the entire repertoire of biological activities, and new methods are constantly being developed (25). It is evident that gene expression (mRNA levels) has many advantages in the first round of systems biological approaches to complex disorders.…”

Section: Whole-genome Activity Measurementsmentioning

confidence: 99%

Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Tegnér

Skogsberg

Björkegren

2007

Journal of Lipid Research

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Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to wholegenome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.-Tegnér, J., J. Skogsberg, and J. Bjö rkegren. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits. J. Lipid Res. 2007. 48: 267-277. Candidate gene approaches, such as positional cloning (1), inherited from studies of single-gene disorders, have thus far generated fragmented knowledge of complex traits. Attention is now being redirected toward systems biological approaches. The general belief is that such approaches, unlike those based on candidate genes, can better take into account the inherent complexity of these disorders. Although systems theory has been around for quite some time (2), its applications in biology are flourishing because of the availability of whole-genome measurement technologies such as genomics (3) in combination with computational analysis and modeling (4). With an increasing number of research communities embracing systems biology, it is important to be clear about what this term means.It is tempting to define systems biology as physiology or pathology-that is, the biological functions of an entire system rather than those of its molecular components. A stricter, and in our view more correct, definition of systems biology is research that focuses not on the molecular parts themselves (i.e., genes, proteins, metabolites) but on their interactions within networks. For such studies, the four Ms-manipulation, measurement, mining, and modeling-are key ingredients (4). Reverse engineering (the process of identifying gene networks from wholegenome data using an underlying computational model) of biological networks requires perturbations (i.e., manipulations) of the biological system followed by measurements of the system response using whole-genome measurement tools (5, 6). Then, mining (data interpretation, network identification) and modeling (model systems based on network architecture) are crucial for guiding the next round of experimental measurements

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“…Various high throughput technologies enable biologists to access important classes of cellular bio-molecules in order to gain insight into the corresponding biological processes [1]. DNA microarray chips for measuring gene expressions are one popular example of such high throughput technologies.…”

Section: Introductionmentioning

confidence: 99%

A Likelihood Ratio Test for Differential Metabolic Profiles in Multiple Intensity Measurements

Klawonn

Choi

Benkert

et al. 2007

Lecture Notes in Computer Science

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Abstract. High throughput technologies like transcriptomics using DNA arrays or metabolomics employing a combination of gas chromatography with mass spectrometry provide valuable information about cellular processes. However, the measurements are often highly corrupted with noise of the experimental data which makes it sometimes difficult to draw reliable conclusions. Therefore, suitable statistical methods are needed for the evaluation of the experimental data to distinguish changes caused by biological phenomena from random variations due to noise. This paper introduces a likelihood ratio test to multiple metabolome measurements. The method was tested to differentiate differential metabolite compositions obtained from the pathogenic bacterium Pseudomonas aeruginosa grown under various environmental conditions.

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The model organism as a system: integrating 'omics' data sets

Cited by 689 publications

References 127 publications

Molecular eco-systems biology: towards an understanding of community function

Molecular eco-systems biology: towards an understanding of community function

Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

A Likelihood Ratio Test for Differential Metabolic Profiles in Multiple Intensity Measurements

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