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WIREs Syst Biol Med
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Computational systems biology of aging

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Computational systems biology is expected to make major contributions to unravel the complex molecular mechanisms underlying the progression of aging in cells, tissues, and organisms. The development of computational approaches is, however, challenged by a wide spectrum of aging mechanisms participating on different levels of biological organization. The tight connectivity between the molecular constituents, functions, and cell states requires frameworks and strategies that extend beyond current practice to model, simulate, and predict the progression of aging and the emerging aging phenotypes. We provide a general overview of the specific computational tasks and opportunities in aging research, and discuss some illustrative systems level concepts in more detail. One example provided here is the assembly of a conceptual whole cell model that considers the temporal dynamics of the aging process grounded on molecular mechanisms. Another application is the assembly of interactomes, such as protein networks that allow us to analyze changes in network topology and interaction of proteins that have been implicated in aging with other cellular constituents and processes. We introduce the necessary key steps to build these applications and discuss their merits and future extensions for aging research. WIREs Syst Biol Med 2011 3 414–428 DOI: 10.1002/wsbm.126

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  • Physiology > Mammalian Physiology in Health and Disease

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Figure 1.

Landscape of biological data modeling in aging research. Aging research provides diverse data from different experimental protocols and biological model systems. Some of the data are amenable to established analytical tools deciphering networks from omics profiles, or mechanistic modeling of biochemical processes in signaling pathways. However, aging ‘works’ on a cell systems level, and relevant data have to be derived from different sources and brought together and assembled with respect to underlying relationships. For instance, oxidative damage changes the function of organelles, second messengers modulate signaling pathway activities, which in return are influential for transcription factors shaping gene expression profiles. Such complex interactions can be implemented by a hybrid, multiscale, or rule‐based framework to provide informative model predictions for cell systems behavior. As aging is global and progresses across biological states, phenotypic descriptions of cells are expected to provide a foundation to develop comprehensive models across the aging physiome.

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Figure 2.

Whole cell model. Conceptual mammalian cell model assembled with CellDesigner suitable to study fundamental processes related to aging. This whole cell representations is structured into subcellular entities for mitochondria, the endoplasmatic reticulum, proteosomes/lysosomes, and nucleus and uses symbols for proteins (squares), ions (circles), metabolites (ovals), mediating processes (diamonds), and model sinks (crossed circles). Interactions are shown as arrows (state transitions), with inhibiting (bar‐headed), catalyzing (circle‐headed) or modulating (diamond‐headed) processes. Metabolic fluxes between ATP producing mitochondria and ATP consuming biosynthesis color coded in blue are initially in equilibrium. Reactive oxygen species (ROS) from mitochondrial respiration cause oxidative damage, here shown for mitochondrial proteins (mOXPROT), and impair mitochondrial respiration (MMP), functioning as an amplifying positive feedback loop shown by red lines. Implemented are two stress pathways, the pro‐survival transcription factor NF‐κB and the energy sensor mTOR, involved in both transcriptional and translational regulation (green lines). These negative feedback loops modulate mitochondrial respiration, biosynthesis, and free radical scavenging (MnSOD) adaptively (see text for details).

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Figure 3.

Analysis of mTOR. Alteration in mTOR activity (p‐mTOR) introduced in steps of ±5% has a significant effect on lifespan, as indicated by the length of simulation runs carried out until oxidized protein concentration reaches a critical level. Similarly, ROS levels reach critical high levels when mTOR activity is high. Stronger inhibition of mTOR slows accumulation of oxidized proteins, but reduces other fitness parameters such as ATP consumption (dotted magenta line) that may negatively impact physiological functions. (Courtesy of PLOs21).

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Figure 4.

Protein interactomes and aging. Phenotypical data from gene expression studies can be used to investigate the network topology of organelle or pathway specific protein–protein interactomes (PPIs) in aging. Candidate proteins may reveal connections to other organelles. Knowledge of networks and pathways aids the construction of genotype‐to‐phenotype maps.

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Figure 5.

Organization of multiple complexes in HNI. The connections between complexes were detected with a node score cutoff in MCODE of 0.33. Panel (a) contains the top scoring connected cluster of proteins involved in transcription by RNA polymerase II and mRNA processing; 5′ cap binding, splicing, and polyadenylation. Panel (b) contains complexes involved in transcription by RNA polymerases I and III, the nuclear pore complex, DNA replication, and the mediator complex involved in activating transcription. Panel (c) contains complexes linked to the regulation of transcription (SAGA, NuRD, and SMARC proteins), the cell cycle and nucleotide excision repair (NER).

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Figure 6.

Regulatory network of human aging. Visualization of the regulatory network associated with human aging using 10 nuclear proteins as seed nodes to select a subnetwork from HNI. There are 588 proteins and 4861 interactions within this aging subnetwork. Red nodes are nuclear proteins and yellow nodes are non‐nuclear proteins demonstrating a high connectivity of proteins throughout the cell. Green diamonds are proteins known to be involved in aging, which were used to determine this network as an aging‐related subnetwork of the larger human nuclear interactome (HNI). The aging protein TP53 represents a major hub located at the lower center of this network.

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Jens Nielsen

Jens Nielsen
is a Professor in the Department of Biology and Biological Engineering at Chalmers University of Technology in Göteborg, Sweden. His research focus is on systems biology of metabolism. The yeast Saccharomyces cerevisiae is the lab’s key organism for experimental research, but they also work with Aspergilli oryzae.

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