Systems approaches to the networks of aging

Integrating bioinformatic strategies in spatial life science research

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

Can aging research generate a theory of health?

While aging research and policy aim to promote 'health' at all ages, there remains no convincing explanation of what this 'health' is. In this paper, I investigate whether we can find, implicit within the sciences of aging, a way to know what health is and how to measure it, i.e. a theory of health. To answer this, I start from scientific descriptions of aging and its modulators and then try to develop some generalizations about 'health' implicit within this research. After discussing some of the core aspects of aging and the ways in which certain models describe spatial and temporal features specific to both aging and healthy phenotypes, I then extract, explicate, and evaluate one potential construct of health in these models. This suggests a theory of health based on the landscape of optimized phenotypic trajectories. I conclude by considering why it matters for more candidate theories to be proposed and evaluated by philosophers and scientists alike.

Impact on Longevity of Genetic Cardiovascular Risk and Lifestyle including Red Meat Consumption

Background

Cardiovascular risk (CVR) underlies aging process and longevity. Previous work points to genetic and environmental factors associated with this risk.

Objectives

The aim of this research is to look for any CVR gene-gene and gene-multifactorial/lifestyle interactions that may impact health and disease and underlie exceptional longevity.

Methods

A case-control study involving 521 both gender individuals, 253 centenarians (100.26 ± 1.98 years), and 268 controls (67.51 ± 3.25 years), low (LCR, n = 107) and high (HCR, n = 161) CVR. Hypertension, diabetes, obesity (BMI, kg·m⁻²), and impaired kidney function were defined according to standard criteria. CVR was calculated using Q risk®. DNA was genotyping (ACE-rs4646994, AGT-rs4762, AGR1-rs5182, GRK4-rs2960306, GRK4-rs1024323, NOS3-rs1799983, and SLC12A3-rs13306673) through iPlex-MassARRAY®, read by MALDI-TOF mass spectrometry, and analyzed by EARTDECODE®.

Results

Antilongevity factors consisted (OR 95% CI, p < 0.05) BMI 1.558 (1.445-1.680), hypertension 2.358 (1.565-3.553), smoking habits 4.528 (2.579-7.949), diabetes 5.553 (2.889-10.675), hypercholesterolemia 1.016 (1.010-1.022), and regular consumption of red meat 22.363 (13.987-35.755). Genetic aspects particularly for HCR individuals ACE II (OR: 3.96 (1.83-8.56), p < 0.0001) and NOS3 TT (OR: 3.11 (1.70-5.70), p < 0.0001) genotypes were also risk associate. Obesity, smoking, hypercholesterolemia, and frequent consumption of red meat have an additive action to hypertension in the longevity process. There was a synergistic interaction between the endothelial NOS3 genotypes and the severity of arterial hypertension. An epistatic interaction between functional genetic variants of GRK4 and angiotensinogen was also observed.

Conclusions

Cardiovascular risk-related genetic and multifactorial or predominantly lifestyle aspects and its interactions might influence the aging process and contribute to exceptional longevity in Portuguese centenarians. Besides lifestyle, the activity of nitrite oxide synthase may be one of the main physiologic regulators of cardiovascular protection in the path of longevity.

Network analysis of frailty and aging: Empirical data from the Mexican Health and Aging Study

BACKGROUND

Frailty remains a challenge in the aging research area with a number of gaps in knowledge still to be filled. Frailty seems to behave as a network, and in silico evidence is available on this matter. Having in vivo evidence that frailty behaves as a complex network was the main purpose of our study.

METHODS

Data from the Mexican Health and Aging Study (main data 2012, mortality 2015) was used. Frailty was operationalized with a 35-deficit frailty index (FI). Analyzed nodes were the deficits plus death. The edges, linking those nodes were obtained through structural learning, and an undirected graph associated with a discrete probabilistic graphical model (Markov network) was derived. Two algorithms, hill-climbing (hc) and Peter and Clark (PC), were used to derive the graph structure. Analyses were performed for the whole population and tertiles of the total FI score.

RESULTS

From the total sample of 10,983 adults aged 50 or older, 43.8% were women, and the mean age was 64.6 years (SD = 9.3). The number of connections increased according to the tertile level of the FI score. As the FI score raised, groups of interconnected deficits increased and how the nodes are connected changed.

CONCLUSIONS

Frailty phenomenon can be modeled using a Bayesian network. Using the full sample, the most central nodes were self-report of health (most connected node) and difficulty walking a block, and all deficits related to mobility were very interconnected. When frailty levels are considered, the most connected nodes differ, but are related with vitality, mainly at lower frailty levels. We derived that not all deficits are equally related since clusters of very related deficits and non-connected deficits were obtained, which might be considered in the construction of the FI score. Further research should aim to identify the nature of all observed interactions, which might allow the development of specific interventions to mitigate the consequences of frailty in older adults.

Health and Disease-Emergent States Resulting From Adaptive Social and Biological Network Interactions

Health is an adaptive state unique to each person. This subjective state must be distinguished from the objective state of disease. The experience of health and illness (or poor health) can occur both in the absence and presence of objective disease. Given that the subjective experience of health, as well as the finding of objective disease in the community, follow a Pareto distribution, the following questions arise: What are the processes that allow the emergence of four observable states-(1) subjective health in the absence of objective disease, (2) subjective health in the presence of objective disease, (3) illness in the absence of objective disease, and (4) illness in the presence of objective disease? If we consider each individual as a unique biological system, these four health states must emerge from physiological network structures and personal behaviors. The underlying physiological mechanisms primarily arise from the dynamics of external environmental and internal patho/physiological stimuli, which activate regulatory systems including the hypothalamic-pituitary-adrenal axis and autonomic nervous system. Together with other systems, they enable feedback interactions between all of the person's system domains and impact on his system's entropy. These interactions affect individual behaviors, emotional, and cognitive responses, as well as molecular, cellular, and organ system level functions. This paper explores the hypothesis that health is an emergent state that arises from hierarchical network interactions between a person's external environment and internal physiology. As a result, the concept of health synthesizes available qualitative and quantitative evidence of interdependencies and constraints that indicate its top-down and bottom-up causative mechanisms. Thus, to provide effective care, we must use strategies that combine person-centeredness with the scientific approaches that address the molecular network physiology, which together underpin health and disease. Moreover, we propose that good health can also be promoted by strengthening resilience and self-efficacy at the personal and social level, and via cohesion at the population level. Understanding health as a state that is both individualized and that emerges from multi-scale interdependencies between microlevel physiological mechanisms of health and disease and macrolevel societal domains may provide the basis for a new public discourse for health service and health system redesign.

An integrative modeling approach to the age-performance relationship in mammals at the cellular scale

Physical and cognitive performances change across lifespan. Studying cohorts of individuals in specific age ranges and athletic abilities remains essential in assessing the underlying physiological mechanisms that result in such a drop in performance. This decline is now viewed as a unique phenotypic biomarker and a hallmark of the aging process. The rates of decline are well documented for sets of traits such as running or swimming but only a limited number of studies have examined the developmental and senescent phases together. Moreover, the few attempts to do so are merely descriptive and do not include any meaningful biological features. Here we propose an averaged and deterministic model, based on cell population dynamics, replicative senescence and functionality loss. It describes the age-related change of performance in 17 time-series phenotypic traits, including human physical and cognitive skills, mouse lemur strength, greyhound and thoroughbred speed, and mouse activity. We demonstrate that the estimated age of peak performance occurs in the early part of life (20.5% ± 6.6% of the estimated lifespan) thus emphasizing the asymmetrical nature of the relationship. This model is an initial attempt to relate performance dynamics to cellular dynamics and will lead to more sophisticated models in the future.

Recent Advances in the Systems Biology of Aging

SIGNIFICANCE

Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging.

CRITICAL ISSUES

Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology.

FUTURE DIRECTIONS

Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.

How regularities of mortality statistics explain why we age despite having potentially ageless somatic stem cells

Researchers working in the area of ageing have found numerous manifestations of this process at the molecular biological level, including DNA and protein damage, accumulation of metabolic by-products, lipids peroxidation, macromolecular cross-linking, non-enzymatic glycosylation, anti-oxidant/pro-oxidant misbalance, rising of pro-inflammatory cytokines, etc. This results in an increase in the proportion of cells in growth arrest, reduction of the rate of information processing, metabolic rate decrease, and decrease in rates of other processes characterizing dynamic aspects of the organism's interaction with its environment. Such staggering multilevel diversity in manifestation of senescence precludes (without methodology of systems biology) development of a correct understanding of its primary causes and does not allow for developing approaches capable of postponing ageing or reducing organisms' ageing rate to attain health preservation. Moreover, it turns out that damage production and damage elimination processes, the misbalance of which results in the ageing process, can to a large extent be regulated by external signals. The purpose of this report is to provide evidence supporting this view and its compatibility with the regularities of mortality statistics, because the main idea is very simple. Even potentially a non-senescent but certainly not immortal body must start to age under inadequate conditions (like a non-melting piece of ice taken out from the deepfreeze inevitably start to melt at the temperatures above zero Celsius). This conclusion is totally consistent with existing patterns of mortality and with agelessness potential of somatic stem cells. Therefore, there is no need to build up and explore too complicated, computational and sophisticated systems models of intrinsic ageing to understand the origin of this mainly extrinsic root cause of natural ageing, which is controlled by environmental signals. In our case, a simple phenomenological black-box approach with Input-Output analysis is ample. Here Input refers to the environmentally dependent initial force of mortality, whereas Output is a rate of age-related increase of mortality force.

A synopsis on aging-Theories, mechanisms and future prospects

Answering the question as to why we age is tantamount to answering the question of what is life itself. There are countless theories as to why and how we age, but, until recently, the very definition of aging - senescence - was still uncertain. Here, we summarize the main views of the different models of senescence, with a special emphasis on the biochemical processes that accompany aging. Though inherently complex, aging is characterized by numerous changes that take place at different levels of the biological hierarchy. We therefore explore some of the most relevant changes that take place during aging and, finally, we overview the current status of emergent aging therapies and what the future holds for this field of research. From this multi-dimensional approach, it becomes clear that an integrative approach that couples aging research with systems biology, capable of providing novel insights into how and why we age, is necessary.

Vision from next generation sequencing: multi-dimensional genome-wide analysis for producing gene regulatory networks underlying retinal development, aging and disease

Genomics and genetics have invaded all aspects of biology and medicine, opening uncharted territory for scientific exploration. The definition of "gene" itself has become ambiguous, and the central dogma is continuously being revised and expanded. Computational biology and computational medicine are no longer intellectual domains of the chosen few. Next generation sequencing (NGS) technology, together with novel methods of pattern recognition and network analyses, has revolutionized the way we think about fundamental biological mechanisms and cellular pathways. In this review, we discuss NGS-based genome-wide approaches that can provide deeper insights into retinal development, aging and disease pathogenesis. We first focus on gene regulatory networks (GRNs) that govern the differentiation of retinal photoreceptors and modulate adaptive response during aging. Then, we discuss NGS technology in the context of retinal disease and develop a vision for therapies based on network biology. We should emphasize that basic strategies for network construction and analyses can be transported to any tissue or cell type. We believe that specific and uniform guidelines are required for generation of genome, transcriptome and epigenome data to facilitate comparative analysis and integration of multi-dimensional data sets, and for constructing networks underlying complex biological processes. As cellular homeostasis and organismal survival are dependent on gene-gene and gene-environment interactions, we believe that network-based biology will provide the foundation for deciphering disease mechanisms and discovering novel drug targets for retinal neurodegenerative diseases.

Distinctive topology of age-associated epigenetic drift in the human interactome

Recently, it has been demonstrated that DNA methylation, a covalent modification of DNA that can regulate gene expression, is modified as a function of age. However, the biological and clinical significance of this age-associated epigenetic drift is unclear. To shed light on the potential biological significance, we here adopt a systems approach and study the genes undergoing age-associated changes in DNA methylation in the context of a protein interaction network, focusing on their topological properties. In contrast to what has been observed for other age-related gene classes, including longevity- and disease-associated genes, as well as genes undergoing age-associated changes in gene expression, we here demonstrate that age-associated epigenetic drift occurs preferentially in genes that occupy peripheral network positions of exceptionally low connectivity. In addition, we show that these genes synergize topologically with disease and longevity genes, forming unexpectedly large network communities. Thus, these results point toward a potentially distinct mechanistic and biological role of DNA methylation in dictating the complex aging and disease phenotypes.

Robustness and aging--a systems-level perspective

The theory of robustness describes a system level property of evolutionary systems, which predicts tradeoffs of great interest for the systems biology of aging, such as accumulation of non-heritable damage, occurrence of fragilities and limitations in performance, optimized allocation of restricted resources and confined redundancies. According to the robustness paradigm cells and organisms evolved into a state of highly optimized tolerance (HOT), which provides robustness to common perturbations, but causes tradeoffs generally characterized as "robust yet fragile". This raises the question whether the ultimate cause of aging is more than a lack of adaptation, but an inherent fragility of complex evolutionary systems. Since robustness connects to evolutionary designs, consideration of this theory provides a deeper connection between evolutionary aspects of aging, mathematical models and experimental data. In this review several mechanisms influential for aging are re-evaluated in support of robustness tradeoffs. This includes asymmetric cell division improving performance and specialization with limited capacities to prevent and repair age-related damage, as well as feedback control mechanisms optimized to respond to acute stressors, but unable to halt nor revert aging. Improvement in robustness by increasing efficiencies through cellular redundancies in larger organisms alleviates some of the damaging effects of cellular specialization, which can be expressed in allometric relationships. The introduction of the robustness paradigm offers unique insights for aging research and provides novel opportunities for systems biology endeavors.

Lithocholic acid extends longevity of chronologically aging yeast only if added at certain critical periods of their lifespan

Our studies revealed that LCA (lithocholic bile acid) extends yeast chronological lifespan if added to growth medium at the time of cell inoculation. We also demonstrated that longevity in chronologically aging yeast is programmed by the level of metabolic capacity and organelle organization that they developed before entering a quiescent state and, thus, that chronological aging in yeast is likely to be the final step of a developmental program progressing through at least one checkpoint prior to entry into quiescence. Here, we investigate how LCA influences longevity and several longevity-defining cellular processes in chronologically aging yeast if added to growth medium at different periods of the lifespan. We found that LCA can extend longevity of yeast under CR (caloric restriction) conditions only if added at either of two lifespan periods. One of them includes logarithmic and diauxic growth phases, whereas the other period exists in early stationary phase. Our findings suggest a mechanism linking the ability of LCA to increase the lifespan of CR yeast only if added at either of the two periods to its differential effects on various longevity-defining processes. In this mechanism, LCA controls these processes at three checkpoints that exist in logarithmic/diauxic, post-diauxic and early stationary phases. We therefore hypothesize that a biomolecular longevity network progresses through a series of checkpoints, at each of which (1) genetic, dietary and pharmacological anti-aging interventions modulate a distinct set of longevity-defining processes comprising the network; and (2) checkpoint-specific master regulators monitor and govern the functional states of these processes.

Dementia: Alzheimer pathology and vascular factors: from mutually exclusive to interaction

Alzheimer's disease (AD) is the most common type of dementia. Both its incidence and prevalence are expected to increase exponentially as populations' age worldwide. Despite impressive efforts of research worldwide, neither cure nor effective preventive strategy is available for this devastating disease. Currently there are several hypotheses on what causes AD, with the amyloid hypothesis being the most investigated and accepted hypothesis over the past 20 years. However the exact role of amyloid-β in the onset and progression of AD is not yet fully understood, and even the validity of the amyloid hypothesis itself is still being discussed. This debate is fuelled by the vascular hypothesis, as increasing epidemiological, neuroimaging, pathological, pharmacotherapeutic and clinical studies suggest that vascular pathology plays a key role in the onset and progression of AD. We here will discuss arguments in favor and limitations of both hypotheses within the framework of available literature, but also provide arguments for convergence of both hypotheses. Finally we propose approaches that may aid in unraveling the etiology and treatment of AD. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Fractal immunology and immune patterning: potential tools for immune protection and optimization

Fractals are self-similar geometric patterns that are inherently embedded throughout nature. Their discovery and application have produced significant benefits across a wide variety of biomedical applications. Recently, complex physiological systems (e.g., neurological, respiratory, cardiovascular) have been shown to exhibit fractal dimensions that are capable of distinguishing among physiologic function versus dysfunction and, in turn, health versus disease. Additionally, fractal data suggest that the immune system operates under similar patterned relationships, and this is in keeping with the recent findings that immune-based diseases are organized according to specific patterns. This review considers the potential benefits of using fractal analysis along with considerations of nonlinearity, scaling, and chaos as calibration tools to obtain holistic information on immune-environment interactions. The potential uses of both synthetic and artificial immune systems for improved protection of the biological immune system are also discussed. The addition of holistic measures of immune status to currently collected biomarkers of immunotoxicity has the potential to increase the effectiveness of health risk assessment. The objective of extending fractal physiology analyses to the immune system would be to promote immune optimization as a public health benefit, which would include improved: (1) immunotoxicity testing and effective health risk reduction and (2) measures of effective immune management for children, adults, and aged individuals.

Computational systems biology of aging

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

Rule-based cell systems model of aging using feedback loop motifs mediated by stress responses

Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-κB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype.

The global process of aging disturbs a broad range of cellular mechanisms in a complex fashion and is not well understood. One important goal of computational approaches in aging is to develop integrated models in terms of a unifying aging theory, predicting progression of aging phenotypes grounded on molecular mechanisms. However, current experimental data incoherently reflects many isolated processes from a large diversity of approaches, biological model systems, and species, which makes such integration a challenging task. In an attempt to close this gap, we iteratively develop a fuzzy-logic cell systems model considering the interplay of damage, metabolism, and signaling by positive and negative feedback-loop motifs using relationships drawn from literature data. Because cellular biodynamics may be considered a complex control system, this approach seems particularly suitable. Here, we demonstrate that rule-based fuzzy-logic models provide semi-quantitative predictions that enhance our understanding of complex and interlocked molecular mechanisms and their implications on the aging physiome.

Disease-aging network reveals significant roles of aging genes in connecting genetic diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system-based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.

Toward a systems biology framework for understanding aging and health span

It is argued that aging research is at a stage where it could benefit greatly from a more intense engagement with the perspectives emphasized by systems biology and complexity science. A more integrated, systematic approach is needed if we are ever to have a fully developed, fundamental understanding of aging, longevity, and their relationship to health. A broader, deeper, more quantitative, and predictive conceptual framework can lead to theoretical approaches and realistic models that can be quantitatively confronted with data and, perhaps more importantly, stimulate novel questions and novel experiments. Integral to this is the search for underlying causal multilevel mechanisms and principles that can be quantified and developed into a serious predictive theoretical framework, providing a point of departure for framing a more integrated research agenda.

Gender-dependent progression of systemic metabolic states in early childhood

Little is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state-based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

Application of advanced technologies in ageing research

Several technologies that emerged in the post-genomic era have been particularly useful in dissecting the molecular mechanisms of complex biological processes through the systems approach. Here, we review how three of these technologies, namely transcriptional profiling, large-scale RNA interference (RNAi) and genome-wide location analysis of protein-DNA interactions, have been used in the study of ageing in metazoans. We also highlight recent developments of these three technologies and how these developments are applicable to ageing research.

Integrative database management for mouse development: Systems and concepts

Cells in the developing embryo must integrate complex signals from the genome and environment to make decisions about their behavior or fate. The ability to understand the fundamental biology of the decision-making process, and how these decisions may go awry during abnormal development, requires a systems biology paradigm. Presently, the ability to build models with predictive capability in birth defects research is constrained by an incomplete understanding of the fundamental parameters underlying embryonic susceptibility, sensitivity, and vulnerability. Key developmental milestones must be parameterized in terms of system structure and dynamics, the relevant control methods, and the overall design logic of metabolic and regulatory networks. High-content data from genome-based studies provide some comprehensive coverage of these operational processes but a key research challenge is data integration. Analysis can be facilitated by data management resources and software to reveal the structure and function of bionetwork motifs potentially associated with an altered developmental phenotype. Borrowing from applied mathematics and artificial intelligence, we conceptualize a system that can help address the new challenges posed by the transformation of birth defects research into a data-driven science.