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The role of epigenetics in the modulation of longevity has not been studied in humans. To this aim, (1) we evaluated the DNA methylation from peripheral leukocytes of 21 female centenarians, their 21 female offspring, 21 offspring of both non-long-lived parents, and 21 young women through ELISA assay, pyrosequencing analysis of Alu sequences, and quantification of methylation in CpG repeats outside CpG islands; (2) we compared the DNA methylation profiles of these populations through Infinium array for genome-wide CpG methylation analysis. We observed an age-related decrease in global DNA methylation and a delay of this process in centenarians’ offspring. Interestingly, literature data suggest a link between the loss of DNA methylation observed during aging and the development of age-associated diseases. Genome-wide methylation analysis evidenced DNA methylation profiles specific for aging and longevity: (1) aging-associated DNA hypermethylation occurs predominantly in genes involved in the development of anatomical structures, organs, and multicellular organisms and in the regulation of transcription; (2) genes involved in nucleotide biosynthesis, metabolism, and control of signal transmission are differently methylated between centenarians’ offspring and offspring of both non-long-lived parents, hypothesizing a role for these genes in human longevity. Our results suggest that a better preservation of DNA methylation status, a slower cell growing/metabolism, and a better control in signal transmission through epigenetic mechanisms may be involved in the process of human longevity. These data fit well with the observations related to the beneficial effects of mild hypothyroidism and insulin-like growth factor I system impairment on the modulation of human lifespan. Electronic supplementary material The online version of this article (doi:10.1007/s11357-012-9463-1) contains supplementary material, which is available to authorized users.

Glioblastoma are the most common and malignant primary brain tumor, and actual treatments consist of surgery (when possible), radiotherapy and chemotherapy. Recent discoveries in biology revealed the important role of radioresistant cancer stem cell in the tumor proliferation and also showed that differentiated tumor cells can revert to a stem-like state because of radiation. These discoveries can be used to create mathematical models to study and plan new optimized radiotherapy schedules. In literature, some models have already been developed on murine population. The aim of this study was to reproduce these models, to perform a sensitivity analysis to find the most sensitive parameters and to adapt them to standard schedules used with human patients. We found that the most sensitive parameters are those involving tumor cell proliferation, radio-sensibility and quiescence times of both stem and tumor cells.

In this paper we study noise-induced bimodality in a specific circuit with many biological implications, namely a single-step enzymatic cycle described by Michaelis–Menten equations. We study the biological feasibility of this phenomenon, which allows for switch-like behavior in response to graded stimuli, considering a small and discrete number of molecules involved in the circuit, and we characterize the conditions necessary for it. We show that intrinsic noise (due to the stochastic character of the Master Equation approach) of a one-dimensional substrate reaction is not sufficient to achieve bimodality, then we characterize analytically the necessary conditions on enzyme number fluctuations. We implement numerically two model circuits that show bimodality over different parameter windows, that depend critically on system size as predicted by our results, providing hints about how such a phenomenon could be exploited in real biological systems.

The concept of a constant reformulation of the canon due to the notion of singularity or irreducibility of the case can be applied in both scientific and literary fields. In this volume, dynamics of interconnections between the case and the canon are analysed by scholars belonging to different disciplines such as physics, medicine, biology, psychoanalysis, and literature. Particular attention has been given to the science of detection since the techniques of investigation are based on the scientific acquisition of evidence and often imply a scientific (abductive) process. Our contribution reviews how physics (stochastic modeling and network theory) contributes to a better understanding of nature and biology, that contain precise rules (the canon) that can be masked by noise and variability (the case)

""Long-term biological effects of low-dose radiation are little known nowadays and its carcinogenic risk is estimated on the assumption that the risk remains linearly proportional to the radiation dose down to low-dose levels. However in the last 20 years this hypothesis has gradually begun to seem in contrast with a huge collection of experimental evidences, which has shown the presence of a plethora of non-linear phenomena (including hypersensitivity and induced radioresistance, adaptive response, and non-targeted phenomena like bystander effect and genomic instability) occurring after low-dose irradiation. These phenomena might imply a non-linear behaviour of cancer risk curves in the low-dose region and question the validity of the Linear No-Threshold (LNT) model currently used for cancer risk assessment through extrapolation from existing high-dose data. Moreover only few information is available regarding the effects induced on cryopreserved cells by multi-year background radiation exposure, which might induce a radiation-damage accumulation, due to the inhibition of cellular repair mechanisms. In this framework, the multi-year EXCALIBUR (EXposure effeCts At Low doses of Ionizing radiation in Biological CultURes) experiment, funded by INFN-CNS5, has undertaken a multiscale approach investigation on the biological effects induced in in vitro and in vivo biological systems, in culture and cryopreserved conditions, as a function of radiation quality (X\\\/γ-rays, protons, He-4 ions of various energies) and dose, with particular emphasis on the low-dose region and non-linear phenomena, in terms of different biological endpoints. PACS 87.53.-j-Effects of ionizing radiation on biological systems. PACS 87.53.Ay-Biophysical mechanisms of interaction. PACS 87.53.Bn-Dosimetry\\\/exposure assessment. PACS 87.18.Gh-Cell-cell communication; collective behavior of motile cells""

In the last decade, physics has been expanding to new research areas. In particular, life-related sciences (ecology, sociology, economics, and last but not least biology) have been showing striking analogies with complex systems arising from various physical areas. Such an approach has happened from both fronts: on the life sciences side, huge amounts of data have become available for detailed analysis, thanks also to the Internet, through which these data are nowadays easily collectable and queryable (e.g., stock market financial series, tables of social relationships from movie copart- nerships to e-mail fluxes, high-throughput biological data). On the other side, many physical and mathematical tools that had proven useful in explaining complex phenomena like polymer growth or spin glasses began to spread to other research areas like biological and social sciences in a broad sense. The common trait of these research fields can be found in the framework of network theory, such that focusing on the relationships among elements allows us to draw general conclusions even though the details of the system are not completely known or easily tractable from a mathematical point of view. Relaxing attention to the details of the specific interaction or element, network theory aims to provide tools for the characterization of a set of relationships, represented as edges or links, occurring among similar elements, referred to as vertices or nodes.

We faced the basic question on chronological versus biological age at tissue level in the context of organ transplant. By means of a comprehensive approach we analysed nuclear and mitochondrial DNA, mRNAs, microRNAs, proteins, including proteasome and immunoproteasome, obtained from liver biopsies of donors with different age (13-90 years) to identify biomarkers of biological age. Telomere length and specific miRNAs appear to be important biomarkers of liver aging. This research looks for the improvement of the graft management getting insights into donor- recipient age- mismatch effects.

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as realistic and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies., to appear on Mol. BioSyst. 2009

Fundamental conceptualizations such as dose of degeneracy and bow ties have been addressed in immunological literature (degeneracy), or recently proposed (bow ties), but despite their potential capability to explain the functions of complex biological systems, they have not yet been treated as aspects of a unique integrated vision.

We applied a method for characterizing patterns arising from gene expression time series data. This method has been succesfully applied to two datasets coming from different experimental designs and cell types. The common trait of these datasets was the range of the time series in the order of hours (short time interval with respect to cell time scale), and a conditional activation of transcription factor activity (by c-Myc and DFOXO respectively). We show preliminary results that confirm that such method can be applied to a time series spanning the lifetime of a defined species, in which the progression of ageing is considered as the main perturbation. We observe strong analogies between the global features of the short-time datasets and the life-spanning dataset, with groups of genes showing correlated profiling in both cases. Such correlation is increased whenever genes are correctly grouped by a priori biological knowledge (biological pahways or ontology).

Network theory is a successful set of methods used in the complex system analysis and description. While the topological structure and the dynamical consequences of this approach is well known, the theory of network generation is still lacking a well rounded set of methods and theorems, except for a small set of models. Most models have boolean and iterative structure, based on the Erdos-Renyi, Barabasi-Alberts or Watts-Strogatz models. We present a differential equation model based on the bistability and hysteresis properties of neural plasticity as described by the BCM model, that we have extended from single neuron to a whole network. This model is capable to generate interesting network topology starting from a three neuron-based, non-local parameters, which describe the interaction between incoming and outgoing links from each neuron. The underlying concept of the model is the idea of symmetric competition or collaboration between individual link. The resulting system shows convergent solutions and non-trivial behaviours for a great range of parameters.

Learning and memory mechanisms are currently studied at molecular level and theoretical approaches need to be extended by precise and detailed biophysical models for the comprehension of the existing results and for design of new experiments. Synaptic plasticity is, at least partially, based on the molecular transition between a synaptic state of permanent (long-term) potentiation (LTP) and depression (long-term depression, LTD). Phosphorylation and dephosphorylation of receptors mediating synaptic transmission is the major mechanisms for LTP/LTD induction and regulation. This knowledge has fuelled new theoretical and experimental investigations, mainly focused on the stability versus meta-stability properties as a function of the involved enzymes.

In this paper, we calculate the effect of an exogenous perturbation (an electromagnetic field [EMF] oscillating in the range of microwave frequencies in the range of 1 GHz) on the flux of two ion species through a cylindrical ion channel, implementing a continuous model, the Poisson-Smoluchowski system of equations, to study the dynamics of charged particle density inside the channel. The method was validated through comparison with Brownian dynamics simulations, supposed to be more accurate but computationally more demanding, obtaining a very good agreement. No EMF effects were observed for low field intensities below the level for thermal effects, as the highly viscous regime and the simplicity of the channel do not exhibit resonance phenomena. For high intensities of the external field (10(5) V/m), we observed slightly different behavior of ion concentration oscillations and ion currents as a function of EMF orientation with respect to the channel axis.

We describe a novel approach for metabolic network reconstruction in order to switch from the full reaction-metabolite scheme to a more synthetic description at a pathway level. The network thus obtained retains much information of the original model, allowing easier graphical visualizations and multiscale modeling. We apply our approach to the state-of-the-art database of human metabolic network (Recon2): our approach allows different ranking of the network elements based on its topology and on Markov dynamics induced by network structure.

We propose a network model in which the communication between its elements (cells, neurons and lymphocytes) can be established in various ways. The system evolution is driven by a set of equations that encodes various degrees of competition between elements. Each element has an “internal plasticity threshold” that, by setting the number of inputs and outputs, determines different network global topologies.

This paper describes characteristic features of networks reconstructed from gene expression time series data. Several null models are considered in order to discriminate between informations embedded in the network that are related to real data, and features that are due to the method used for network reconstruction (time correlation)., Comment: 10 pages, 3 BMP figures, 1 Table. To appear in Int. J. Bif. Chaos, July 2007, Volume 17, Issue 7

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c- myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes.

Immune cells coordinate their efforts for the correct and efficient functioning of the immune system (IS). Each cell type plays a distinct role and communicates with other cell types through mediators such as cytokines, chemokines and hormones, among others, that are crucial for the functioning of the IS and its fine tuning. Nevertheless, a quantitative analysis of the topological properties of an immunological network involving this complex interchange of mediators among immune cells is still lacking. Here we present a method for quantifying the relevance of different mediators in the immune network, which exploits a definition of centrality based on the concept of efficient communication. The analysis, applied to the human immune system, indicates that its mediators significantly differ in their network relevance. We found that cytokines involved in innate immunity and inflammation and some hormones rank highest in the network, revealing that the most prominent mediators of the IS are molecules involved in these ancestral types of defence mechanisms highly integrated with the adaptive immune response, and at the interplay among the nervous, the endocrine and the immune systems., Comment: 10 pages, 3 figures

Da qualche tempo, la matematica si occupa di questioni che non sono soltanto quelle, tradizionali, che nascono in ambito fisico. In particolare, sempre più determinato ed efficace si va facendo il suo intervento a fianco delle scienze della vita.