Genetic determinants of human health span and life span: progress and new opportunities

We review three approaches to the genetic analysis of the biology and pathobiology of human aging. The first and so far the best-developed is the search for the biochemical genetic basis of varying susceptibilities to major geriatric disorders. These include a range of progeroid syndromes. Collectively, they tell us much about the genetics of health span. Given that the major risk factor for virtually all geriatric disorders is biological aging, they may also serve as markers for the study of intrinsic biological aging. The second approach seeks to identify allelic contributions to exceptionally long life spans. While linkage to a locus on Chromosome 4 has not been confirmed, association studies have revealed a number of significant polymorphisms that impact upon late-life diseases and life span. The third approach remains theoretical. It would require longitudinal studies of large numbers of middle-aged sib-pairs who are extremely discordant or concordant for their rates of decline in various physiological functions. We can conclude that there are great opportunities for research on the genetics of human aging, particularly given the huge fund of information on human biology and pathobiology, and the rapidly developing knowledge of the human genome.     

Multi-agent-based bio-network for systems biology: protein–protein interaction network as an example

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.     

In the laboratory and during free-flight: old honey bees reveal learning and extinction deficits that mirror mammalian functional decline

Loss of brain function is one of the most negative and feared aspects of aging. Studies of invertebrates have taught us much about the physiology of aging and how this progression may be slowed. Yet, how aging affects complex brain functions, e.g., the ability to acquire new memory when previous experience is no longer valid, is an almost exclusive question of studies in humans and mammalian models. In these systems, age related cognitive disorders are assessed through composite paradigms that test different performance tasks in the same individual. Such studies could demonstrate that afflicted individuals show the loss of several and often-diverse memory faculties, and that performance usually varies more between aged individuals, as compared to conspecifics from younger groups. No comparable composite surveying approaches are established yet for invertebrate models in aging research. Here we test whether an insect can share patterns of decline similar to those that are commonly observed during mammalian brain aging. Using honey bees, we combine restrained learning with free-flight assays. We demonstrate that reduced olfactory learning performance correlates with a reduced ability to extinguish the spatial memory of an abandoned nest location (spatial memory extinction). Adding to this, we show that learning performance is more variable in old honey bees. Taken together, our findings point to generic features of brain aging and provide the prerequisites to model individual aspects of learning dysfunction with insect models.     

A network perspective on metabolism and aging

Aging affects a myriad of genetic, biochemical, and metabolic processes, and efforts to understand the underlying molecular basis of aging are often thwarted by the complexity of the aging process. By taking a systems biology approach, network analysis is well-suited to study the decline in function with age. Network analysis has already been utilized in describing other complex processes such as development, evolution, and robustness. Networks of gene expression and protein-protein interaction have provided valuable insight into the loss of connectivity and network structure throughout lifespan. Here, we advocate the use of metabolic networks to expand the work from genomics and proteomics. As metabolism is the final fingerprint of functionality and has been implicated in multiple theories of aging, metabolomic methods combined with metabolite network analyses should pave the way to investigate how relationships of metabolites change with age and how these interactions affect phenotype and function of the aging individual. The metabolomic network approaches highlighted in this review are fundamental for an understanding of systematic declines and of failure to function with age.     

Synthetic tissue biology: tissue engineering meets synthetic biology

We propose the term "synthetic tissue biology" to describe the use of engineered tissues to form biological systems with metazoan-like complexity. The increasing maturity of tissue engineering is beginning to render this goal attainable. As in other synthetic biology approaches, the perspective is bottom-up; here, the premise is that complex functional phenotypes (on par with those in whole metazoan organisms) can be effected by engineering biology at the tissue level. To be successful, current efforts to understand and engineer multicellular systems must continue, and new efforts to integrate different tissues into a coherent structure will need to emerge. The fruits of this research may include improved understanding of how tissue systems can be integrated, as well as useful biomedical technologies not traditionally considered in tissue engineering, such as autonomous devices, sensors, and manufacturing.     

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.     

Proteogenomics and systems biology: quest for the ultimate missing parts

This review describes how intimately proteogenomics and system biology are imbricated. Quantitative cell-wide monitoring of cellular processes and the analysis of this information is the basis for systems biology. Establishing the most comprehensive protein-parts list is an essential prerequisite prior to analysis of the cell-wide dynamics of proteins, their post-translational modifications, their complex network interactions and interpretation of these data as a whole. High-quality genome annotation is, thus, a crucial basis. Proteogenomics consists of high-throughput identification and characterization of proteins by extra-large shotgun MS/MS approaches and the integration of these data with genomic data. Discovery of the remaining unannotated genes, defining translational start sites, listing signal peptide processing events and post-translational modifications, are tasks that can currently be carried out at a full-genomic scale as soon as the genomic sequence is available. Proteomics is increasingly being used at the primary stage of genome annotation and such an approach may become standard in the near future for genome projects. Advantageously, the same experimental proteomic datasets may be used to characterize the specific metabolic traits of the organism under study. Undoubtedly, comparative genomics will experience a renaissance taking into account this new dimension. Synthetic biology aimed at re-engineering living systems will also benefit from these significant progresses.     

Bacteria as a new model system for aging studies: investigations using light microscopy

Aging-the decline in an individual's condition over time-is at the center of an active research field in medicine and biology. Some very basic questions have, however, remained unresolved, the most fundamental being: do all organisms age? Or are there organisms that would continue to live forever if not killed by external forces? For a long time it was believed that aging only affected organisms such as animals, plants, and fungi. Bacteria, in contrast, were assumed to be potentially immortal and until recently this assertion remained untested. We used phase-contrast microscopy (on an Olympus BX61) to follow individual bacterial cells over many divisions to prove that some bacteria show a distinction between an aging mother cell and a rejuvenated daughter, and that these bacteria thus age. This indicates that aging is a more fundamental property of organisms than was previously assumed. Bacteria can now be used as very simple model system for investigating why and how organisms age.     

The feasibility of genome-scale biological network inference using Graphics Processing Units

Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.     

Molecular genetics of aging in the fly: Is this the end of the beginning?

How we age and what we can do about it have been uppermost in human thought since antiquity. The many false starts have frustrated experimentalists and theoretical arguments pronouncing the inevitability of the process have created a nihilistic climate among scientists and the public. The identification of single gene alterations that substantially extend life span in nematodes and flies however, have begun to reinvigorate the field. Drosophila's long history of contributions to aging research, rich storehouse of genetic information, and powerful molecular techniques make it an excellent system for studying the molecular mechanisms underlying the process of aging. In recent years, Drosophila has been used to test current theories on aging and explore new directions of potential importance to the biology of aging. One such example is the surprising finding that, as opposed to the commonly held assumption that adult life is a period of random passive decline in which all things are thought to fall apart, the molecular life of the adult fly appears to be a state of dynamic well-regulated change. In the fly, the level of expression of many different genes changes in an invariant, often age-dependent, manner. These as well as other molecular genetic studies and demographic analyses using the fly have begun to challenge widely held ideas about aging providing evidence that aging may be a much more dynamic and malleable process than anticipated. With the enormous success that Drosophila molecular genetics has demonstrated in helping understand complex biological phenomena such as development there is much optimism that similar approaches can be adapted to assist in understanding the process of aging.     

Predicting complex phenotype–genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both “red” and “white” biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype–phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.     

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.     

Holding Multiple Items in Short Term Memory: A Neural Mechanism

Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging.     

Ecosystems biology of microbial metabolism

The metabolic capabilities of many environmentally and medically important microbes can be quantitatively explored using systems biology approaches to metabolic networks. Yet, as we learn more about the complex microbe-microbe and microbe-environment interactions in microbial communities, it is important to understand whether and how system-level approaches can be extended to the ecosystem level. Here we summarize recent work that addresses these challenges at multiple scales, starting from two-species natural and synthetic ecology models, up to biosphere-level approaches. Among the many fascinating open challenges in this field is whether the integration of high throughput sequencing methods and mathematical models will help us capture emerging principles of ecosystem-level metabolic organization and evolution.     

Plant Molecular and Genomic Responses to Stresses in Projected Future CO2 Environment

The Earth has been undergoing climatic changes for centuries, driven by increasing concentration of atmospheric carbon dioxide (CO₂). The atmospheric CO₂ concentration has been predicted to reach 550–750 μmol mol^?1 by 2050, or twice as high as the current level. Much of the research in the last 20–30 years concerning elevated CO₂ (eCO₂) has been about how plants would respond to the eCO₂ at physiological levels. As eCO₂ can lead to more frequent drought or extreme high or low temperature, increasingly more research has focused on the interactions between eCO₂ and other abiotic stresses. How stresses may affect plant growth and development and productivity, as well as how agricultural practices may be altered to cope with these changes must be determined. These concerns have been the subject of numerous reviews. However, it is only in the last several years that data at the “omics” levels has been available to explore how necessary physiological changes may be brought about in a future complex environment. The systems biology approaches provide us an insight into the mechanism of plant responses to climatic changes at the genomics level. In this review, we present an overview of physiological effects of eCO₂ on plants, but focus on the interactions of eCO₂ with drought, high temperature, O₃, and multiple abiotic stresses, with particular emphasis at the molecular and genomics levels. We also provide perspectives on future research and emphasize the importance of integrated research on eCO₂ and multiple environmental stresses using systems biology approaches.     

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

Systems biology of cellular rhythms

Rhythms abound in biological systems, particularly at the cellular level where they originate from the feedback loops present in regulatory networks. Cellular rhythms can be investigated both by experimental and modeling approaches, and thus represent a prototypic field of research for systems biology. They have also become a major topic in synthetic biology. We review advances in the study of cellular rhythms of biochemical rather than electrical origin by considering a variety of oscillatory processes such as Ca++ oscillations, circadian rhythms, the segmentation clock, oscillations in p53 and NF-κB, synthetic oscillators, and the oscillatory dynamics of cyclin-dependent kinases driving the cell cycle. Finally we discuss the coupling between cellular rhythms and their robustness with respect to molecular noise.     

Cats, "rats," and bats: the comparative biology of aging in the 21st century

Laboratory models have suggested a link between metabolism and life span in vertebrates, and it is well known that the evolution of specific life histories can be driven by metabolic factors. However, little is known regarding how the adoption of specific life-history strategies can shape aging and life span in populations facing different energetic demands from either a theoretical or a mechanistic viewpoint but significant insight can be gained by using a comparative approach. Comparative biology plays several roles in our understanding of the virtually ubiquitous phenomenon of aging in animals. First, it provides a critical evaluation of broad hypotheses concerning the evolutionary forces underlying the modulation of aging rate. Second, it suggests mechanistic hypotheses about processes of aging. Third, it illuminates particularly informative species because of their exceptionally slow or rapid aging rates to be interrogated about potentially novel mechanisms of aging. Although comparative biology has played a significant role in research on aging for more than a century, the new comparative biology of aging is poised to dwarf those earlier contributions, because: (1) new cellular and molecular techniques for investigating novel species are in place and more are being continually generated, (2) molecular systematics has resolved the phylogenetic relationships among a wide range of species, which allow for the implementation of analytic tools specialized for comparative biology, and (3) in addition to facilitating the construction of accurate phylogenies, the dramatic acceleration in DNA-sequencing technology is providing us with new tools for a comparative genomic approach to understanding aging.