Bayesian methods for proteomics

Alterovitz, Gil; Liu, Jonathan; Afkhami, Ehsan; Ramoni, Marco

doi:10.1002/pmic.200700422

Cited by 20 publications

(11 citation statements)

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“…Causal relations among genes can also be naturally modeled using Bayesian networks which can represent conditional dependencies between expression levels (for a primer on Bayesian network analysis utilizing expression data (see [24]); for a recent review see [25]). Considering the temporal aspects of gene expression profiles, dynamic Bayesian networks have been used to model feedback loops as well as gene regulation patterns [26], [27].…”

Section: Interactomementioning

confidence: 99%

Chapter 5: Network Biology Approach to Complex Diseases

2012

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Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.

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Section: Interactomementioning

confidence: 99%

Chapter 5: Network Biology Approach to Complex Diseases

2012

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“…In a Bayesian network model, probabilities define the relationship between the current node and its predecessor or parent in a graph (Alterovitz et al, 2007). Markov models are another network-based technique that can provide a framework to describe molecular or cellular states and the weighted probability of transitioning between them.…”

Section: Computational Techniques and Advances: Systems Biology Applimentioning

confidence: 99%

Systems approaches for synthetic biology: a pathway toward mammalian design

Rekhi

Qutub

2013

Front. Physiol.

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We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches—stochasticity, complexity, and scale—with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.

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“…1). It also can help in making sense of raw data, allowing researchers to concentrate on other tasks [1,2]. The need for applying automation in proteomics is increasing daily as larg- Figure 1.…”

Section: Proteomics and Automationmentioning

confidence: 99%

“…For example, innovation among drug companies has progressed to the point where automated methods of targeting and screening have replaced traditional, manual techniques [1]. Moreover, in order to make whole-organism proteomebased experiments a reality, machine learning techniques, such as predicting the functional implications of certain posttranslational alteration, are becoming an indispensable part of the research [1].…”

Section: Proteomics and Automationmentioning

confidence: 99%

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Automation, parallelism, and robotics for proteomics

et al. 2006

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The speed of the human genome project (Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C. et al., Nature 2001, 409, 860-921) was made possible, in part, by developments in automation of sequencing technologies. Before these technologies, sequencing was a laborious, expensive, and personnel-intensive task. Similarly, automation and robotics are changing the field of proteomics today. Proteomics is defined as the effort to understand and characterize proteins in the categories of structure, function and interaction (Englbrecht, C. C., Facius, A., Comb. Chem. High Throughput Screen. 2005, 8, 705-715). As such, this field nicely lends itself to automation technologies since these methods often require large economies of scale in order to achieve cost and time-saving benefits. This article describes some of the technologies and methods being applied in proteomics in order to facilitate automation within the field as well as in linking proteomicsbased information with other related research areas.

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Bayesian methods for proteomics

Cited by 20 publications

References 55 publications

Chapter 5: Network Biology Approach to Complex Diseases

Chapter 5: Network Biology Approach to Complex Diseases

Systems approaches for synthetic biology: a pathway toward mammalian design

Automation, parallelism, and robotics for proteomics

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