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Cellular senescence—the permanent arrest of cycling in normally proliferating cells such as fibroblasts—contributes both to age‐related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in‐silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of ‘deep’ cellular senescence. The essential feature of the loop is that long‐term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45‐MAPK14(p38MAPK)‐GRB2‐TGFBR2‐TGFβ. These ROS in turn replenish short‐lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype. Synopsis The phenomenon of cellular ‘senescence’—the permanent arrest of division in normally proliferating mammalian cells such as fibroblasts—is thought to be a central component of the ageing process. Senescence contributes both to age‐related loss of tissue homeostasis, as the loss of division capacity leads to impaired cell renewal, and also to protect against cancer, because it acts to block the uncontrolled proliferation of cells that may give rise to a malignant tumour. Replicative senescence is triggered by uncapped telomeres or by ‘unrepairable’ non‐telomeric DNA damage. Both lesions initiate the same canonical DNA damage response (DDR) (d'Adda di Fagagna, 2008 ). This response is characterized by activation of sensor kinases (ATM/ATR, DNA‐PK), formation of DNA damage foci containing activated H2A.X (γH2A.X) and ultimately induction of cell cycle arrest through activation of checkpoint proteins, notably p53 (TP53) and the CDK inhibitor p21 (CDKN1A). This signalling pathway continues to contribute actively to the stability of the G0 arrest in fully senescent cells long after induction of senescence (d'Adda di Fagagna et al , 2003 ). However, senescence is more complex than mere CDKI‐mediated growth arrest. Senescent cells alter their expression of literally hundreds of genes (Shelton et al , 1999 ), prominent among these being pro‐inflammatory secretory genes (Coppe et al , 2008 ) and marker genes for a retrograde response induced by mitochondrial dysfunction (Passos et al , 2007a ). There is a growing evidence that multiple mechanisms interact to underpin ageing at the cellular level (Kirkwood, 2005 ; Passos et al , 2007b ) necessitating a systems biology approach if the complex mechanisms of ageing are to be understood (Kirkwood, 2008 ). With respect to cell senescence, the two major unanswered questions are (i) How does a DNA lesion that can be repaired, at least in principle, induce and maintain irreversible growth arrest? and (ii) How does a growth arrest trigger a completely different cellular phenotype as soon as it becomes irreversible? To understand those questions, we performed a kinetic analysis of the establishment phase of senescence initiated by DNA damage or telomere dysfunction, focussing on pathways downstream of the classical DDR. Using an approach that combined (i) in‐silico interactome analysis, (ii) functional target gene inhibition, (iii) stochastic modelling, and (iv) live cell microscopy, we identified a positive feedback loop between DDR and mitochondrial production of reactive oxygen species (ROS) as necessary and sufficient for long‐term maintenance of growth arrest. Using pathway log likelihood scores calculated by a quantitative in‐silico interactome analysis to guide siRNA and small molecule inhibition experiments, and using results of sequential and combined inhibition experiments to refine the predictions from the interactome analysis, we found that DDR triggered mitochondrial dysfunction leading to enhanced ROS activation through a linear signal transduction through TP53, CDKN1A, GADD45A, p38 (MAPK14), GRB2, TGFBR2 and TGFβ(Figure 2D ). We hypothesized that these ROS stochastically generate novel DNA damage in the nucleus, thus forming a positive feedback loop contributing to the long‐term maintenance of DDR (Figure 3A ). First confirmation came from static inhibitor experiments as before, showing that nuclear DNA damage foci frequencies in senescent cells were reduced if feedback signalling was suppressed. To formally establish the existence of a feedback loop and its relevance for senescence, we used live cell microscopy in combination with quantitative modelling. We transformed the conceptual model shown in Figure 3A into a stochastic mechanistic model of the DDR feedback loop by extending the previously published model of the TP53/Mdm2 circuit (Proctor and Gray, 2008 ) to include reactions for synthesis/activation and degradation/deactivation/repair of CDKN1A, GADD45, MAPK14, ROS and DNA damage. The model replicated very precisely the kinetic behaviour of activated TP53, CDKN1A, ROS and DNA damage foci after initiation of senescence by irradiation. Having established its concordance with the experimental data, the model was then used to predict the effects of intervening in the feedback loop. The model predicted that any intervention reducing ROS levels by about half would decrease average DNA damage foci frequencies from six to four foci/nucleus within about 15 h. It further predicted that this would be sufficient to reduce CDKN1A to basal levels continuously for at least 6 h in about 20% of the treated cells, thus allowing a significant fraction of cells to escape from growth arrest and to resume proliferation. This should happen even if the intervention into the feedback loop was started at a late time point (e.g. 6 days) after induction of senescence. To analyse DNA damage foci dynamics we used a reporter construct (AcGFP–53BP1c) that quantitatively reports single DNA damage foci kinetics in time‐resolved live cell microscopy (Nelson et al , 2009 ). Foci frequency measurements quantitatively confirmed the prediction from the stochastic model. More importantly, we found that many individual foci in both telomere‐ and stress‐dependent senescence had short lifespans with half‐lives below 15 h. Feedback loop inhibition reduced only the frequencies of short‐lived DNA damage foci in accordance with the hypothesis that ROS production contributed to DDR by constant replenishment of short‐lived DNA damage foci. Finally, we inhibited signalling through the loop at different time points after induction of senescence by ionizing radiation and measured ROS levels, DNA damage foci frequencies and proliferation markers. Treatments with the MAPK14 inhibitor SB203580 or the free radical scavenger PBN were used to block the loop. The results quantitatively confirmed the model prediction and indicated that the feedback loop between DDR and ROS production was both necessary and sufficient to maintain cell cycle arrest for at least 6–10 days after induction of senescence. Interestingly, the loop was still active at later time points and in deep senescence, but proliferation arrest was then stabilized by additional factor(s). This indicated that certain features of the senescent phenotype‐like ROS production that might be responsible for the negative impact of senescent cells into their tissue environment can be successfully inhibited even in deep senescence. This may prove relevant for novel therapeutic studies aiming to modulate intracellular ROS levels in both aging and cancer. The sustained activation of CDKN1A (p21/Waf1/Cip1) by a DNA damage response induces mitochondrial dysfunction and reactive oxygen species (ROS) production via signalling through CDKN1A‐GADD45A‐MAPK14‐ GRB2‐TGFBR2‐TGFbeta in senescing primary human and mouse cells in vitro and in vivo. Enhanced ROS production in senescing cells generates additional DNA damage. Although this damage is repairable and transient, it elevates the average levels of DNA damage response permanently, thus forming a positive feedback loop. This loop is necessary and sufficient to maintain the stability of growth arrest until a ‘point of no return’ is reached during establishment of senescence.

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

Osteoarthritis (OA) is a degenerative condition caused by dysregulation of multiple molecular signalling pathways. Such dysregulation results in damage to cartilage, a smooth and protective tissue that enables low friction articulation of synovial joints. Matrix metalloproteinases (MMPs), especially MMP-13, are key enzymes in the cleavage of type II collagen which is a vital component for cartilage integrity. Transforming growth factor beta (TGFβ) can protect against pro-inflammatory cytokine-mediated MMP expression. With age there is a change in the ratio of two TGFβ type I receptors (Alk1/Alk5), a shift that results in TGFβ losing its protective role in cartilage homeostasis. Instead, TGFβ promotes cartilage degradation which correlates with the spontaneous development of OA in murine models. However, the mechanism by which TGFβ protects against pro-inflammatory responses and how this changes with age has not been extensively studied. As TGFβ signalling is complex, we used systems biology to combine experimental and computational outputs to examine how the system changes with age. Experiments showed that the repressive effect of TGFβ on chondrocytes treated with a pro-inflammatory stimulus required Alk5. Computational modelling revealed two independent mechanisms were needed to explain the crosstalk between TGFβ and pro-inflammatory signalling pathways. A novel meta-analysis of microarray data from OA patient tissue was used to create a Cytoscape network representative of human OA and revealed the importance of inflammation. Combining the modelled genes with the microarray network provided a global overview into the crosstalk between the different signalling pathways involved in OA development. Our results provide further insights into the mechanisms that cause TGFβ signalling to change from a protective to a detrimental pathway in cartilage with ageing. Moreover, such a systems biology approach may enable restoration of the protective role of TGFβ as a potential therapy to prevent age-related loss of cartilage and the development of OA.

Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis.

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies.

Background Recent evidence suggests that glycogen synthase kinase-3β (GSK3β) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3β and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3β. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model. Results The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3β and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3β and a vicious cycle ensues. Conclusions Since p53 and GSK3β are both involved in the apoptotic pathway, and GSK3β overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration.

ObjectiveTo use a computational approach to investigate the cellular and extracellular matrix changes that occur with age in the knee joints of mice.MethodsKnee joints from an inbred C57/BL1/6 (ICRFa) mouse colony were harvested at 3–30 months of age. Sections were stained with H&E, Safranin-O, Picro-sirius red and antibodies to matrix metalloproteinase-13 (MMP-13), nitrotyrosine, LC-3B, Bcl-2, and cleaved type II collagen used for immunohistochemistry. Based on this and other data from the literature, a computer simulation model was built using the Systems Biology Markup Language using an iterative approach of data analysis and modelling. Individual parameters were subsequently altered to assess their effect on the model.ResultsA progressive loss of cartilage matrix occurred with age. Nitrotyrosine, MMP-13 and activin receptor-like kinase-1 (ALK1) staining in cartilage increased with age with a concomitant decrease in LC-3B and Bcl-2. Stochastic simulations from the computational model showed a good agreement with these data, once transforming growth factor-β signalling via ALK1/ALK5 receptors was included. Oxidative stress and the interleukin 1 pathway were identified as key factors in driving the cartilage breakdown associated with ageing.ConclusionsA progressive loss of cartilage matrix and cellularity occurs with age. This is accompanied with increased levels of oxidative stress, apoptosis and MMP-13 and a decrease in chondrocyte autophagy. These changes explain the marked predisposition of joints to develop osteoarthritis with age. Computational modelling provides useful insights into the underlying mechanisms involved in age-related changes in musculoskeletal tissues.

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis.

Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.