Identification of gene-gene interactions for Alzheimer's disease using co-operative game theory

Thesis (Ph.D.)--Boston UniversityThe multifactorial nature of Alzheimer's Disease suggests that complex gene-gene interactions are present in AD pathways. Contemporary approaches to detect such interactions in genome-wide data are mathematically and computationally challenging. We investigated gene-gene interactions for AD using a novel algorithm based on cooperative game theory in 15 genome-wide association study (GWAS) datasets comprising of a total of 11,840 AD cases and 10,931 cognitively normal elderly controls from the Alzheimer Disease Genetics Consortium (ADGC). We adapted this approach, which was developed originally for solving multi-dimensional problems in economics and social sciences, to compute a Shapely value statistic to identify genetic markers that contribute most to coalitions of SNPs in predicting AD risk. Treating each GWAS dataset as independent discovery, markers were ranked according to their contribution to coalitions formed with other markers. Using a backward elimination strategy, markers with low Shapley values were eliminated and the statistic was recalculated iteratively. We tested all two-way interactions between top Shapley markers in regression models which included the two SNPs (main effects) and a term for their interaction. Models yielding a p-value<0.05 for the interaction term were evaluated in each of the other datasets and the results from all datasets were combined by meta-analysis. Statistically significant interactions were observed with multiple marker combinations in the APOE regions. My analyses also revealed statistically strong interactions between markers in 6 regions; CTNNA3-ATP11A (p=4.1E-07), CSMD1-PRKCQ (p=3.5E-08), DCC-UNC5CL (p=5.9e-8), CNTNAP2-RFC3 (p=1.16e-07), AACS-TSHZ3 (p=2.64e-07) and CAMK4-MMD (p=3.3e-07). The Shapley value algorithm outperformed Chi-Square and ReliefF in detecting known interactions between APOE and GAB2 in a previously published GWAS dataset. It was also more accurate than competing filtering methods in identifying simulated epistastic SNPs that are additive in nature, but its accuracy was low in identifying non-linear interactions. The game theory algorithm revealed strong interactions between markers in novel genes with weak main effects, which would have been overlooked if only markers with strong marginal association with AD were tested. This method will be a valuable tool for identifying gene-gene interactions for complex diseases and other traits

Algorithms for Computational Genetics Epidemiology

The most intriguing problems in genetics epidemiology are to predict genetic disease susceptibility and to associate single nucleotide polymorphisms (SNPs) with diseases. In such these studies, it is necessary to resolve the ambiguities in genetic data. The primary obstacle for ambiguity resolution is that the physical methods for separating two haplotypes from an individual genotype (phasing) are too expensive. Although computational haplotype inference is a well-explored problem, high error rates continue to deteriorate association accuracy. Secondly, it is essential to use a small subset of informative SNPs (tag SNPs) accurately representing the rest of the SNPs (tagging). Tagging can achieve budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs. Recent successes in high throughput genotyping technologies drastically increase the length of available SNP sequences. This elevates importance of informative SNP selection for compaction of huge genetic data in order to make feasible fine genotype analysis. Finally, even if complete and accurate data is available, it is unclear if common statistical methods can determine the susceptibility of complex diseases. The dissertation explores above computational problems with a variety of methods, including linear algebra, graph theory, linear programming, and greedy methods. The contributions include (1)significant speed-up of popular phasing tools without compromising their quality, (2)stat-of-the-art tagging tools applied to disease association, and (3)graph-based method for disease tagging and predicting disease susceptibility

Abstracts of Papers, 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond VA

Full abstracts of the 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond V

Genomics and spatial surveillance of Chagas disease and American visceral leishmaniasis

The Trypanosomatidae are a family of parasitic protozoa that infect various animals and plants. Several species within the Trypanosoma and Leishmania genera also pose a major threat to human health. Among these are Trypanosoma cruzi and Leishmania infantum, aetiological agents of the highly debilitating and often deadly vector-borne zoonoses Chagas disease and American visceral leishmaniasis. Current treatment options are far from safe, only partially effective and rarely available in the impoverished regions of Latin America where these ‘neglected tropical diseases’ prevail. Wider-reaching, sustainable protection against T. cruzi and L. infantum might best be achieved by intercepting key routes of zoonotic transmission, but this prophylactic approach requires a better understanding of how these parasites disperse and evolve at various spatiotemporal scales. This dissertation addresses key questions around trypanosomatid parasite biology and spatial epidemiology based on high-resolution, geo-referenced DNA sequence datasets constructed from disease foci throughout Latin America: Which forms of genetic exchange occur in T. cruzi, and are exchange events frequent enough to significantly alter the distribution of important epidemiological traits? How do demographic histories, for example, the recent invasive expansion of L. infantum into the Americas, impact parasite population structure, and do structural changes pose a threat to public health? Can environmental variables predict parasite dispersal patterns at the landscape scale? Following the first chapter’s review of population genetic and genomic approaches in the study of trypanosomatid diseases in Latin America, Chapter 2 describes how reproductive polymorphism segregates T. cruzi populations in southern Ecuador. The study is the first to clearly demonstrate meiotic sex in this species, for decades thought to exchange genetic material only very rarely, and only by non-Mendelian means. T. cruzi subpopulations from the Ecuadorian study site exhibit all major hallmarks of sexual reproduction, including genome-wide Hardy-Weinberg allele frequencies, rapid decay of linkage disequilibrium with map distance and genealogies that fluctuate among chromosomes. The presence of sex promotes the transfer and transformation of genotypes underlying important epidemiological traits, posing great challenges to disease surveillance and the development of diagnostics and drugs. Chapter 3 demonstrates that mating events are also pivotal to L. infantum population structure in Brazil, where introduction bottlenecks have led to striking genetic discontinuities between sympatric strains. Genetic hybridization occurs genome-wide, including at a recently identified ‘miltefosine sensitivity locus’ that appears to be deleted from the majority of Brazilian L. infantum genomes. The study combines an array of genomic and phenotypic analyses to determine whether rapid population expansion or strong purifying selection has driven this prominent > 12 kb deletion to high abundance across Brazil. Results expose deletion size differences that covary with phylogenetic structure and suggest that deletion-carrying strains do not form a private monophyletic clade. These observations are inconsistent with the hypothesis that the deletion genotype rose to high prevalence simply as the result of a founder effect. Enzymatic assays show that loss of ecto-3’-nucleotidase gene function within the deleted locus is coupled to increased ecto-ATPase activity, raising the possibility that alternative metabolic strategies enhance L. infantum fitness in its introduced range. The study also uses demographic simulation modelling to determine whether L. infantum populations in the Americas have expanded from just one or multiple introduction events. Comparison of observed vs. simulated summary statistics using random forests suggests a single introduction from the Old World, but better spatial sampling coverage is required to rule out other demographic scenarios in a pattern-process modelling approach. Further sampling is also necessary to substantiate signs of convergent selection introduced above. Chapter 4 therefore develops a ‘genome-wide locus sequence typing’ (GLST) tool to summarize parasite genetic polymorphism at a fraction of genomic sequencing cost. Applied directly to the infection source (e.g., vector or host tissue), the method also avoids bias from cell purification and culturing steps typically involved prior to sequencing of trypanosomatid and other obligate parasite genomes. GLST scans genomic pilot data for hundreds of polymorphic sequence fragments whose thermodynamic properties permit simultaneous PCR amplification in a single reaction tube. For proof of principle, GLST is applied to metagenomic DNA extracts from various Chagas disease vector species collected in Colombia, Venezuela, and Ecuador. Epimastigote DNA from several T. cruzi reference clones is also analyzed. The method distinguishes 387 single-nucleotide polymorphisms (SNPs) in T. cruzi sub-lineage TcI and an additional 393 SNPs in non-TcI clones. Genetic distances calculated from these SNPs correlate with geographic distances among samples but also distinguish parasites from triatomines collected at common collection sites. The method thereby appears suitable for agent-based spatio-genetic (simulation) analyses left wanted by Chapter 3 – and further formulated in Chapter 5. The potential to survey parasite genetic diversity abundantly across landscapes compels deeper, more systematic exploration of how environmental variables influence the spread of disease. As environmental context is only marginally considered in the population genetic analyses of Chapters 2 – 4, Chapter 5 proposes a new, spatially explicit modelling framework to predict vector-borne parasite gene flow through heterogeneous environment. In this framework, remotely sensed environmental raster values are re-coded and merged into a composite ‘resistance surface’ that summarizes hypothesized effects of landscape features on parasite transmission among vectors and hosts. Parasite population genetic differentiation is then simulated on this surface and fitted to observed diversity patterns in order to evaluate original hypotheses on how environmental variables modulate parasite gene flow. The chapter thereby makes a maiden step from standard population genetic to ‘landscape genomic’ approaches in understanding the ecology and evolution of vector-borne disease. In summary, this dissertation first demonstrates the power of population genetics and genomics to understand fundamental biological properties of important protist parasites, then identifies areas where analytical tools are missing and creates new technical and conceptual frameworks to help fill these gaps. The general discussion (Chapter 6) also outlines several follow-up projects on the key finding of meiotic genetic signatures in T. cruzi. Exploiting recently developed T. cruzi genome-editing systems for the detection of meiotic gene expression and heterozygosis will help understand why and in which life cycle stage some parasite populations use sex and others do not. Long-read sequencing of parental and recombinant genomes will help understand the extent to which sex is diversifying T. cruzi phenotypes, especially virulence and drug resistance properties conferred by surface molecules with repetitive genetic bases intractable to short-read analysis. Chapter 6 also provides follow-up plans for all other research chapters. Emphasis is placed on advancing the complementarity, transferability and public health benefit of the many different methods and concepts employed in this work

Human Blood Group Systems and Haemoglobinopathies

The past decade has seen remarkable improvements and advances in the fields of blood transfusion and hematology, particularly with regards to advances in science, technology, method development, quality, standardization, and governance. This book provides more evidenced-based insight into the field of blood transfusion and the management of hemoglobinopathies

Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data

Microarrays can capture gene expression activity for thousands of genes simultaneously and thus make it possible to analyze cell physiology and disease processes on molecular level. The interpretation of microarray gene expression experiments profits from knowledge on the analyzed genes and proteins and the biochemical networks in which they play a role. The trend is towards the development of data analysis methods that integrate diverse data types. Currently, the most comprehensive biomedical knowledge source is a large repository of free text articles. Text mining makes it possible to automatically extract and use information from texts. This thesis addresses two key aspects, biomedical text mining and gene expression data analysis, with the focus on providing high-quality methods and data that contribute to the development of integrated analysis approaches. The work is structured in three parts. Each part begins by providing the relevant background, and each chapter describes the developed methods as well as applications and results. Part I deals with biomedical text mining: Chapter 2 summarizes the relevant background of text mining; it describes text mining fundamentals, important text mining tasks, applications and particularities of text mining in the biomedical domain, and evaluation issues. In Chapter 3, a method for generating high-quality gene and protein name dictionaries is described. The analysis of the generated dictionaries revealed important properties of individual nomenclatures and the used databases (Fundel and Zimmer, 2006). The dictionaries are publicly available via a Wiki, a web service, and several client applications (Szugat et al., 2005). In Chapter 4, methods for the dictionary-based recognition of gene and protein names in texts and their mapping onto unique database identifiers are described. These methods make it possible to extract information from texts and to integrate text-derived information with data from other sources. Three named entity identification systems have been set up, two of them building upon the previously existing tool ProMiner (Hanisch et al., 2003). All of them have shown very good performance in the BioCreAtIvE challenges (Fundel et al., 2005a; Hanisch et al., 2005; Fundel and Zimmer, 2007). In Chapter 5, a new method for relation extraction (Fundel et al., 2007) is presented. It was applied on the largest collection of biomedical literature abstracts, and thus a comprehensive network of human gene and protein relations has been generated. A classification approach (Küffner et al., 2006) can be used to specify relation types further; e. g., as activating, direct physical, or gene regulatory relation. Part II deals with gene expression data analysis: Gene expression data needs to be processed so that differentially expressed genes can be identified. Gene expression data processing consists of several sequential steps. Two important steps are normalization, which aims at removing systematic variances between measurements, and quantification of differential expression by p-value and fold change determination. Numerous methods exist for these tasks. Chapter 6 describes the relevant background of gene expression data analysis; it presents the biological and technical principles of microarrays and gives an overview of the most relevant data processing steps. Finally, it provides a short introduction to osteoarthritis, which is in the focus of the analyzed gene expression data sets. In Chapter 7, quality criteria for the selection of normalization methods are described, and a method for the identification of differentially expressed genes is proposed, which is appropriate for data with large intensity variances between spots representing the same gene (Fundel et al., 2005b). Furthermore, a system is described that selects an appropriate combination of feature selection method and classifier, and thus identifies genes which lead to good classification results and show consistent behavior in different sample subgroups (Davis et al., 2006). The analysis of several gene expression data sets dealing with osteoarthritis is described in Chapter 8. This chapter contains the biomedical analysis of relevant disease processes and distinct disease stages (Aigner et al., 2006a), and a comparison of various microarray platforms and osteoarthritis models. Part III deals with integrated approaches and thus provides the connection between parts I and II: Chapter 9 gives an overview of different types of integrated data analysis approaches, with a focus on approaches that integrate gene expression data with manually compiled data, large-scale networks, or text mining. In Chapter 10, a method for the identification of genes which are consistently regulated and have a coherent literature background (Küffner et al., 2005) is described. This method indicates how gene and protein name identification and gene expression data can be integrated to return clusters which contain genes that are relevant for the respective experiment together with literature information that supports interpretation. Finally, in Chapter 11 ideas on how the described methods can contribute to current research and possible future directions are presented

Going viral : an integrated view on virological data analysis from basic research to clinical applications

Viruses are of considerable interest for several fields of life science research. The genomic richness of these entities, their environmen- tal abundance, as well as their high adaptability and, potentially, pathogenicity make treatment of viral diseases challenging. This thesis proposes three novel contributions to antiviral research that each concern analysis procedures of high-throughput experimen- tal genomics data. First, a sensitive approach for detecting viral genomes and transcripts in sequencing data of human cancers is presented that improves upon prior approaches by allowing de- tection of viral nucleotide sequences that consist of human-viral homologs or are diverged from known reference sequences. Sec- ond, a computational method for inferring physical protein contacts from experimental protein complex purification assays is put for- ward that allows statistically meaningful integration of multiple data sets and is able to infer protein contacts of transiently binding protein classes such as kinases and molecular chaperones. Third, an investigation of minute changes in viral genomic populations upon treatment of patients with the mutagen ribavirin is presented that first characterizes the mutagenic effect of this drug on the hepatitis C virus based on deep sequencing data.Viren sind von beträchtlichem Interesse für die biowissenschaftliche Forschung. Der genetische Reichtum, die hohe Vielfalt, wie auch die Anpassungsfähigkeit und mögliche Pathogenität dieser Organismen erschwert die Behandlung von viralen Erkrankungen. Diese Promotionsschrift enthält drei neuartige Beiträge zur antiviralen Forschung welche die Analyse von experimentellen Hochdurchsatzdaten der Genomik betreffen: erstens, ein sensitiver Ansatz zur Entdeckung viraler Genome und Transkripte in Sequenzdaten humaner Karzinome, der die Identifikation von viralen Nukleotidsequenzen ermöglicht, die von Referenzgenomen ab- weichen oder homolog zu humanen Faktoren sind. Zweitens, eine computergestützte Methode um physische Proteinkontakte von experimentellen Proteinkomplex-Purifikationsdaten abzuleiten welche die statistische Integration von mehreren Datensätzen erlaubt um insbesondere Proteinkontakte von flüchtig interagierenden Proteinklassen wie etwa Kinasen und Chaperonen aus den Daten ableiten zu können. Drittens, eine Untersuchung von kleinsten Änderungen viraler Genompopulationen während der Behandlung von Patienten mit dem Mutagen ribavirin die zum ersten Mal die mutagene Wirkung dieses Medikaments auf das Hepatitis C Virus mittels Tiefensequenzdaten nachweist

Use of microbiome data to explain the expression of productive traits in domestic species

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Veterinaria, leída el 11-03-2022El descubrimiento de comunidades microbianas asociadas simbióticamente a organismos eucariotas ha llevado a un cambio de paradigma en la definición de individuo biológico, que ahora se ve como una combinación codependiente del hospedador y su microbioma, u holobionte. Por tanto, el estudio de los microbiomas se ha convertido en algo fundamental para comprender la biología de los organismos vivos complejos. De hecho, se ha observado que las comunidades microbianas poseen un papel crucial en la salud, supervivencia, desarrollo y metabolismo del hospedador. Los recientes avances en secuenciación genética han supuesto un importante impulso para la investigación en microbiología, al permitir la obtención de bases de datos de secuenciación masiva que abarcan una gran parte de la diversidad presente dentro de los microbiomas. La era del next-generation sequencing ha aportado nuevos conocimientos sobre el efecto de las comunidades microbianas sobre el fenotipo del hospedador, con especial relevancia del microbioma intestinal. Para la industria ganadera este hecho ha dado lugar a importantes avances en la comprensión de los mecanismos biológicos que influyen en productividad, sostenibilidad y bienestar animal, lo que podría ser útil para afrontar los desafíos existentes en este sector...The discovery of microbial communities symbiotically associated with eukaryotic organisms has led to a paradigm shift in the definition of the biological individual, which is now seen as a co-dependent combination of the host and its microbiome, or holobiont. Thus, the study of microbiomes has become essential to understand the biology of complex living organisms. Indeed, current research points to a crucial role of microbial communities in host health, survivability, development and metabolism. Recent advances in DNA sequencing have entailed a significant boost to microbial research, allowing the generation of massive sequencing databases encompassing a large proportion of the diversity inside microbiomes. The era of next-generation sequencing has brought new knowledge about the role of microbial communities, with special significance for gut microbiomes, in host phenotype. For livestock industry, this has led to important advances in the understanding of biological mechanisms influencing animal welfare, productivity and sustainability, which could be useful to face existing challenges in animal production...Fac. de VeterinariaTRUEunpu

Microcirculation and inflammation in a numerical simulation approach

Inflammation is the response of the organism to eradicate the agent of lesion or infection in order to achieve hemostasis. This response requires the migration of specific leukocyte populations from the blood circulation towards the inflamed area. Leukocyte recruitment constitutes a complex cellular process by which leukocytes are first recruited to the endothelial vascular wall of post-capillary venules across which they further extravasate into the interstitial tissue. Recruitment is mediated via cell-cell interactions between the leukocyte and the endothelium and occurs through a multi-step cascade: tethering, rolling, slow rolling, arrest, crawling, adhesion and transmigration. However, whether or not the leukocytes adhere to the endothelium depends not only on the chemical forces generated by adhesion molecules on leukocytes and endothelial cells, but also on the physical forces that act on those cells. It has been suggested that fluid shear stress resulting from blood flow also regulates leukocyte activity which makes the fluid dynamic environment of the circulation to be considered an important aspect for leukocyte recruitment and migration during the inflammatory response. Most of the studies on the inflammatory response and in particular on leukocyte recruitment are based on animal models and involve, among others, the quantification of inflammatory mediators and cellular players, and/or the analysis of the leukocyte-endothelial cell interactions by intravital microscopy. However, the contribution of hemodynamics for leukocyte recruitment has been seldom addressed in those studies. This is mostly due to the fact that the study of hemodynamics in in vivo animal models is not straightforward and moreover, that several hemodynamic parameters cannot be experimentally determined due to technical constraints. In this work, we reasoned that these limitations could be circumvented by the development and use of numerical simulations to describe leukocyte recruitment. Many of the processes, which take place in living organisms, can be expressed as mathematical equations. This applies to leukocyte recruitment, for which scarce numerical models existed before the beginning of this work. Importantly, these mathematical simulations were performed without considering simultaneously all the players in the process, namely the vessel, the blood flow and the leukocytes. Moreover, most of these studies were two dimensional, assumed blood as a Newtonian fluid with constant viscosity and did not take into account in vivo experimental data. Taken this, our major goal with this work was to understand the contribution of hemodynamics to leukocyte recruitment in inflammation. For such purpose, we aimed here at developing numerical simulations that more adequately reproduced this process. For such, we set up animal models of inflammation to obtain the experimental data required for the development of those numerical simulations. Finally, we used these models to investigate the role of hemodynamics in leukocyte recruitment in inflammation. First, we considered the simpler case of a numerical simulation that assumed leukocytes to be rigid spheres and blood, a non-Newtonian fluid. For such, we initially developed an animal model of inflammation in Wistar rats using a lipopolysaccharide (LPS) as an inflammatory agent. Blood samples were collected for determination of TNF-α levels to ensure the triggering of the inflammatory process. Importantly, the number of rolling and adherent leukocytes in post-capillary venules was monitored using an intravital microscopy approach. As expected, our results showed that there is an increase in TNF-α concentrations after 15 minutes of LPS administration and a significant increase in the number of rolling and adherent leukocytes. The recorded intravital microcopy images, along with other recorded parameters, were then used, in collaboration with a group of mathematicians, to develop a numerical model capable of describing leukocyte recruitment in the microcirculation. To evaluate the contribution of hemodynamics, the localized velocity fields and shear stresses on the surface of leukocytes and near the vessel wall contact points have been computed in two discrete situations, namely as a single leukocyte or when a cluster of them are recruited towards the vessel wall. In the first situation, our numerical results showed the presence of one region of maximum shear stress on the surface of the leuko- cyte close to the endothelial wall and of two regions of minimum shear stress on the op- posite side of the cell. The different areas of shear stress observed in the surface of the leukocyte may be important in directing it towards the endothelial wall during an inflammatory response. The identification of a region of maximum shear stress is consistent with the molecular mechanisms that govern leukocyte rolling because it may actually cor- respond to the area that supports the interaction with the endothelium. On the other hand, the relatively lower shear stress regions may correlate with leukocyte surface areas where binding to the endothelium is not occurring at the moment, thus enabling the roll- ing of the cell along the endothelium. It was also observed that the shear stress at the endothelium gets higher as a cluster of leukocytes moves in the main stream. This sug- gests that the presence of a cluster of leukocytes may potentiate leukocyte rolling, as the increase in the shear stress promoted by the recruited leukocytes may support the migra- tion and recruitment of additional cells. Despite closely simulating leukocyte recruitment, our initial numerical simulation consid- ered the simple case of leukocytes as rigid spheres. However, while circulating leukocytes maintain an approximately spherical shape, rolling leukocytes are known to deform. In order to account for the leukocyte deformability changes that occur during its recruit- ment in inflammation, we needed to assess the deformability profile of the leukocytes under flow and therefore, to “directly” observe them regardless of the other blood cells. For such, intravital microscopy was performed in the mouse cremaster of a transgenic mice strain (Lys-EGFP-ki) in which fluorescent neutrophils can be individually tracked. By using PAF as an inflammatory agent, the analysis of the leukocyte-endothelial cell interac- tions showed a continuous increase in the number of rolling and adherent neutrophils up to 4 hours after the introduction of the inflammatory stimuli, thus confirming the devel- opment of an inflammatory response. As the properties of the red blood cells modulate blood flow properties, erythrocyte deformability was also addressed in this model. A con- tinuous decrease of this parameter was observed throughout time. The decrease in the erythrocyte deformability will most probably lead to an increase in the blood viscosity and to the decrease of the blood flow velocity. These conditions should facilitate the mi- gration of leukocytes from the mainstream to the endothelial wall and promote leukocyte slow rolling and adhesion during the inflammatory response. Importantly, in the intravital microcopy images obtained with this latter model, we clearly observed the deformation of neutrophils along the endothelial wall during rolling, as well as the formation of tethers. As such, in these images, leukocyte trajectories were tracked and their velocities and diameters were measured and further applied to the numerical simulations. Using a recent validated mathematical model describing the coupled defor- mation-flow of an individual leukocyte and the respective experimental results, numerical simulations of the recruitment of an individual leukocyte and of two leukocytes under different velocities were performed, considering a constant blood viscosity. The mathe- matical models obtained showed that under conditions of increased velocity the cell movement is accelerated along the endothelial layer, favouring the dissociation of leuko- cyte-endothelium interactions at designated attraction points. These observations lead us to propose that, in order to attain an efficient inflammatory response, the blood flow ve- locity needs so as to decrease to facilitate slow rolling and subsequent adhesion. These results are corroborated by the decrease in the erythrocyte deformability observed in our animal model, which will ultimately have an impact on the blood flow velocity. Our results further showed that in the vicinity of an adherent leukocyte there is an early slight decel- eration of the rolling leukocyte when compared with the case of an individual leukocyte. As such, these observations strongly suggest that the presence of an adherent cell in the vicinity should decrease the velocity of another leukocyte that is being recruited, thus promoting its slow rolling, and contributing to its capture by the endothelial cells. Altogether, our experimental data and numerical simulations support our working hy- pothesis that the hemodynamic properties of the flow and of the cells in circulation should play an essential role in the margination and rolling of the leukocytes to the endo- thelial wall, which in turn will impact the success of the inflammatory response. In partic- ular, our results strongly suggest that changes in hemodynamic conditions, such as de- creased flow velocities and the increase of the shear stress, will contribute to target leu- kocytes to the endothelial wall. Given our results, we propose that any change in the he- modynamic properties will certainly influence the outcome of the inflammatory response. As such, the adherence of the leukocytes to the endothelium should depend not only on the relative magnitude of the chemical forces generated by the interaction of adhesion molecules between leukocytes and endothelial cells, but also on the physical forces that act on the leukocytes. In this respect, our results suggest that alterations in the blood flow, for example in the flow velocity, will occur during an inflammatory process, thus potentiating the recruitment of more leukocytes towards the inflamed area and contrib- uting to a successful inflammatory response. Overall, the numerical simulations allowed us to better understand the contribution of the hemodynamic properties of the flow to the progression of an inflammatory response and to deepen our knowledge on leukocyte recruitment in inflammation. Importantly, our work provided numerical tools that can be used for the subsequent study and modulation of the hemodynamic parameters involved in an inflammatory response. In particular, these numerical simulations will surely enable us, in the near future, to determine or es- timate a large set of parameters which are unlikely to be recoverable by in vivo experi- ments. Moreover, our methods will allow us to analyze how the parameters evolve over time. Altogether our results further reinforce the notion that the improvement and de- velopment of animal models and numerical tools will certainly provide the medical and biological community with useful tools to study leukocyte recruitment in inflammation. By closely reproducing the microcirculation and the inflammatory process, these tools will be critical for a better comprehension of the inflammatory process and of the mecha- nisms underlying a multitude of inflammatory pathological conditions

Aging and Health

Aging is a major risk factor for chronic diseases, which in turn can provide information about the aging of a biological system. This publication serves as an introduction to systems biology and its application to biological aging. Key pathways and processes that impinge on aging are reviewed, and how they contribute to health and disease during aging is discussed. The evolution of this situation is analyzed, and the consequences for the study of genetic effects on aging are presented. Epigenetic programming of aging, as a continuation of development, creates an interface between the genome and the environment. New research into the gut microbiome describes how this interface may operate in practice with marked consequences for a variety of disorders. This analysis is bolstered by a view of the aging organism as a whole, with conclusions about the mechanisms underlying resilience of the organism to change, and is expanded with a discussion of circadian rhythms in aging