From molecular to modular cell biology

Hartwell, Leland H.; Hopfield, J. J.; Leibler, Stanislas; Murray, Andrew W.

doi:10.1038/35011540

Cited by 3,287 publications

(2,494 citation statements)

References 31 publications

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“…3,6,33 This complexity 6 underlies the phenomena now often referred to as protein homeostasis or ''proteostasis'' 7 perhaps in a similar manner that, for example, individual organisms interact 34 in an ecosystem. The investigation of this particular class of biological molecules could therefore potentially shed a great deal of light on more general questions of the design and evolution of biological molecules and the environments in which they function.…”

Section: A Conceptual Framework For Understanding Protein Homeostasismentioning

confidence: 99%

“…10 Information can then be transmitted rapidly, largely by molecular diffusion, between different components enabling them to function efficiently. 3 Molecules such as proteins remain soluble and able to avoid interaction with all but a relatively small and specific selection of other molecules, yet are composed of chemical species that are often extremely hydrophobic and prone to self-assemble. We are beginning to think that the ability to maintain the solubility of its component molecules is of much more general significance in biology than previously imagined.…”

Section: Protein Solubility and Biological Complexitymentioning

confidence: 99%

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Quantitative approaches to defining normal and aberrant protein homeostasis

Vendruscolo

2009

Faraday Discuss.

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Protein homeostasis refers to the ability of cells to generate and regulate the levels of their constituent proteins in terms of conformations, interactions, concentrations and cellular localisation. We discuss here an approach in which physico-chemical properties of proteins and their environments are used to understand the underlying principles governing this process, which is crucial in all living systems. By adopting the strategy of characterising the origins of specific diseases to inform us about normal biology, we are bringing together methods and concepts from chemistry, physics, engineering, genetics and medicine. In particular, we are using a combination of in vitro, in silico and in vivo approaches to study protein homeostasis through the analysis of the effects that result from its perturbation in a select group of specific proteins, from either amino acid mutations, or changes in concentration and solubility, or interactions with other molecules. By developing a coherent and quantitative description of such phenomena, we are finding that it is possible to shed new light on how the physical and chemical properties of the cellular components can provide an understanding of the normal and aberrant behaviour of living systems. Through such an approach it is possible to provide new insights into the origin and consequences of the failure to maintain homeostasis that is associated with neurodegenerative diseases, in particular, and the phenomenon of ageing, in general, and hence provide a framework for the rational design of therapeutic approaches.

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Section: A Conceptual Framework For Understanding Protein Homeostasismentioning

confidence: 99%

Section: Protein Solubility and Biological Complexitymentioning

confidence: 99%

Quantitative approaches to defining normal and aberrant protein homeostasis

Vendruscolo

2009

Faraday Discuss.

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“…Motifs are analyzed using recognizable engineering concepts to define subfunctions such as positive and negative feedback loops, autoregulation, coincidence detection, and feed-forward control, among others (7,48). The feed-forward control motif, for example, can be as simple as a three-node triangle (35,43,44).…”

Section: Motifsmentioning

confidence: 99%

“…1). While less well defined, modules are another level of abstraction helpful in understanding the biologic function of cells to the point where modular cell biology is a term being used (48). Modules have distinct, homogeneous functions.…”

Section: Modulesmentioning

confidence: 99%

Exploring the cell's network with molecular imaging

Enzmann

2006

Magnetic Resonance Imaging

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Molecular imaging is already a powerful tool for investigating molecular interactions within the cell. Interpreting molecular imaging findings will, however, take us into the more unfamiliar, nonlinear realm of networks. The network class of interest is the "scale-free" network, which characterizes not only the cell, but also surprisingly, other real work networks such as the world wide web. This network topology yields insights in how the cell is functionally organized via motifs, modules, and different types of hubs. Additional organizational information is gained from the cell's evolutionary history. Interpretation of molecular images will be deepened by a both qualitative and quantitative knowledge of the cell's network. Importantly, cell network behavior can be independent of molecular detail. For this reason, the same molecule can serve different functions in different cells or even within the same cell. Since a scalefree network's behavior is likely to be nonlinear and exhibit emergent behavior, a degree of caution is prudent in assigning cause and effect to molecular imaging findings in our effort to reengineer some of the cell's functions. Molecular imagers will need to be cognizant of the level of organization in the cell's network they are interrogating.

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“…47 These molecularly encoded functional components can be assembled during evolution to create simple circuits, 48 which can grow into pathways, 49,50 and ultimately the genetic modules corresponding to distinct cellular programs. 51 Such modules might include transcription networks, 52,53 certain axis specification systems in developmental biology, 54 or any number of metabolic and/or signaling pathway components. [55][56][57] In order to better understand the evolutionary mechanisms contributing to the complexity of the eukaryotic cell, consider the optimal features of a fundamental signaling component.…”

Section: Incremental Evolution Of Cellular Systemsmentioning

confidence: 99%

The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies

et al. 2004

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The size complexity of the human genome has been traditionally viewed as an obstacle that frustrates efforts aimed at identifying the genetic correlates of complex human phenotypes. As such complex phenotypes are attributed to the combined action of numerous genomic loci, attempts to identify the underlying multi-locus interactions may produce a combinatorial sum of false positives that drown out the real signal. Faced with such grim prospects for successfully identifying the genetic basis of complex phenotypes, many geneticists simply disregard epistatic interactions altogether. However, the emerging picture from systems biology is that the cellular programs encoded by the genome utilize nested signaling hierarchies to integrate a number of loosely coupled, semiautonomous, and functionally distinct genetic networks. The current view of these modules is that connections encoding inter-module signaling are relatively sparse, while the gene-to-gene (protein-to-protein) interactions within a particular module are typically denser. We believe that each of these modules is encoded by a finite set of discontinuous, sequence-specific, genomic intervals that are functionally linked to association rules, which correlate directly to features in the environment. Furthermore, because these environmental association rules have evolved incrementally over time, we explore theoretical models of cellular evolution to better understand the role of evolution in genomic complexity. Specifically, we present a conceptual framework for (1) reducing genomic complexity by partitioning the genome into subsets composed of functionally distinct genetic modules and (2) improving the selection of coding region SNPs, which results in an increased probability of identifying functionally relevant SNPs. Additionally, we introduce the notion of 'genomic closure,' which provides a quantitative measure of how functionally insulated a specific genetic module might be from the influence of the rest of the genome. We suggest that the development and use of theoretical models can provide insight into the nature of biological systems and may lead to significant improvements in computational algorithms designed to reduce the complexity of the human genome.

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From molecular to modular cell biology

Cited by 3,287 publications

References 31 publications

Quantitative approaches to defining normal and aberrant protein homeostasis

Quantitative approaches to defining normal and aberrant protein homeostasis

Exploring the cell's network with molecular imaging

The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies

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