[Show abstract][Hide abstract] ABSTRACT: We undertake an extensive numerical investigation of the graph spectra of
thousands regular graphs, a set of random Erd\"os-R\'enyi graphs, the two most
popular types of complex networks and an evolving genetic network by using
novel conceptual and experimental tools. Our objective in so doing is to
contribute to an understanding of the meaning of the Eigenvalues of a graph
relative to its topological and information-theoretic properties. We introduce
a technique for identifying the most informative Eigenvalues of evolving
networks by comparing graph spectra behavior to their algorithmic complexity.
We suggest that extending techniques can be used to further investigate the
behavior of evolving biological networks. In the extended version of this paper
we apply these techniques to seven tissue specific regulatory networks as
static example and network of a na\"ive pluripotent immune cell in the process
of differentiating towards a Th17 cell as evolving example, finding the most
and least informative Eigenvalues at every stage.
[Show abstract][Hide abstract] ABSTRACT: We survey concepts at the frontier of research connecting artificial, animal
and human cognition to computation and information processing---from the Turing
test to Searle's Chinese Room argument, from Integrated Information Theory to
computational and algorithmic complexity. We start by arguing that passing the
Turing test is a trivial computational problem and that its pragmatic
difficulty sheds light on the computational nature of the human mind more than
it does on the challenge of artificial intelligence. We then review our
proposed algorithmic information-theoretic measures for quantifying and
characterizing cognition in various forms. These are capable of accounting for
known biases in human behavior, thus vindicating a computational algorithmic
view of cognition as first suggested by Turing, but this time rooted in the
concept of algorithmic probability, which in turn is based on computational
universality while being independent of computational model, and which has the
virtue of being predictive and testable as a model theory of cognitive
[Show abstract][Hide abstract] ABSTRACT: ABSTRACT Regular endurance exercise training induces beneficial functional and health effects in human skeletal muscle. The putative contribution to the training response of the epigenome as a mediator between genes and environment has not been clarified. Here we investigated the contribution of DNA methylation and associated transcriptomic changes in a well-controlled human intervention study. Training effects were mirrored by significant alterations in DNA methylation and gene expression in regions with a homogeneous muscle energetics and remodeling ontology. Moreover, a signature of DNA methylation and gene expression separated the samples based on training and gender. Differential DNA methylation was predominantly observed in enhancers, gene bodies and intergenic regions and less in CpG islands or promoters. We identified transcriptional regulator binding motifs of MRF, MEF2 and ETS proteins in the proximity of the changing sites. A transcriptional network analysis revealed modules harboring distinct ontologies and, interestingly, the overall direction of the changes of methylation within each module was inversely correlated to expression changes. In conclusion, we show that highly consistent and associated modifications in methylation and expression, concordant with observed health-enhancing phenotypic adaptations, are induced by a physiological stimulus.
Epigenetics: official journal of the DNA Methylation Society 12/2014; · 5.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.
Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.
In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.
The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.
Journal of Translational Medicine 11/2014; 12. · 3.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background and hypothesis: Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics. Objective and method: To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena. Results: (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering. Conclusions: The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.
Journal of Translational Medicine 11/2014; 12(Suppl 2:S3). · 3.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems.
Journal of Translational Medicine 11/2014; 12 Suppl 2:S2. · 3.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements - data and tools - of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.
Journal of Translational Medicine 11/2014; 12(Suppl 2:S10). · 3.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering.
To assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients.
Underlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them.
(1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems.
The project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes.
Journal of Translational Medicine 11/2014; 12 Suppl 2:S12. · 3.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The production of reactive oxygen species (ROS) from the inner mitochondrial membrane is one of many fundamental processes governing the balance between health and disease. It is well known that ROS are necessary signaling molecules in gene expression, yet when expressed at high levels, ROS may cause oxidative stress and cell damage. Both hypoxia and hyperoxia may alter ROS production by changing mitochondrial Po2 ([Formula: see text]). Because [Formula: see text] depends on the balance between O2 transport and utilization, we formulated an integrative mathematical model of O2 transport and utilization in skeletal muscle to predict conditions to cause abnormally high ROS generation. Simulations using data from healthy subjects during maximal exercise at sea level reveal little mitochondrial ROS production. However, altitude triggers high mitochondrial ROS production in muscle regions with high metabolic capacity but limited O2 delivery. This altitude roughly coincides with the highest location of permanent human habitation. Above 25,000 ft., more than 90% of exercising muscle is predicted to produce abnormally high levels of ROS, corresponding to the "death zone" in mountaineering.
PLoS ONE 11/2014; 9(11):e111068. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Translational medicine is becoming increasingly dependent upon data generated from health care, clinical research, and molecular investigations. This increasing rate of production and diversity in data has brought about several challenges, including the need to integrate fragmented databases, enable secondary use of patient clinical data from health care in clinical research, and to create information systems that clinicians and biomedical researchers can readily use. Our case study effectively integrates requirements from the clinical and biomedical researcher perspectives in a translational medicine setting. Our three principal achievements are (a) a design of a user-friendly web-based system for management and integration of clinical and molecular databases, while adhering to proper de-identification and security measures; (b) providing a real-world test of the system functionalities using clinical cohorts; and (c) system integration with a clinical decision support system to demonstrate system interoperability. We engaged two active clinical cohorts, 747 psoriasis patients and 2001 rheumatoid arthritis patients, to demonstrate efficient query possibilities across the data sources, enable cohort stratification, extract variation in antibody patterns, study biomarker predictors of treatment response in RA patients, and to explore metabolic profiles of psoriasis patients. Finally, we demonstrated system interoperability by enabling integration with an established clinical decision support system in health care. To assure the usefulness and usability of the system, we followed two approaches. First, we created a graphical user interface supporting all user interactions. Secondly we carried out a system performance evaluation study where we measured the average response time in seconds for active users, http errors, and kilobits per second received and sent. The maximum response time was found to be 0.12 seconds; no server or client errors of any kind were detected. In conclusion, the system can readily be used by clinicians and biomedical researchers in a translational medicine setting.
PLoS ONE 09/2014; 9(9):e104382. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: IntroductionGenetic susceptibility to complex diseases has been intensively studied during the last decade, yet only signals with small effect have been found leaving open the possibility that subgroups within complex traits show stronger association signals. In rheumatoid arthritis (RA), autoantibody production serves as a helpful discriminator in genetic studies and today anti-citrullinated cyclic peptide (anti-CCP) antibody positivity is employed for diagnosis of disease. The HLA-DRB1 locus is known as the most important genetic contributor for the risk of RA, but is not sufficient to drive autoimmunity and additional genetic and environmental factors are involved. Hence, we addressed the association of previously discovered RA loci with disease-specific autoantibody responses in RA patients stratified by HLA-DRB1*04.Methods
We investigated 2178 patients from three RA cohorts from Sweden and Spain for 41 genetic variants and four autoantibodies, including the generic anti-CCP as well as specific responses towards citrullinated peptides from vimentin, alpha-enolase and type II collagen.ResultsOur data demonstrated different genetic associations of autoantibody-positive disease subgroups in relation to the presence of DRB1*04. Two specific subgroups of autoantibody-positive RA were identified. The SNP in PTPN22 was associated with presence of anti-citrullinated enolase peptide antibodies in carriers of HLA-DRB1*04 (Cochran-Mantel-Haenszel test P¿=¿0.0001, P corrected <0.05), whereas SNPs in CDK6 and PADI4 were associated with anti-CCP status in DRB1*04 negative patients (Cochran-Mantel-Haenszel test P¿=¿0.0004, P corrected <0.05 for both markers). Additionally we see allelic correlation with autoantibody titers for PTPN22 SNP rs2476601 and anti-citrullinated enolase peptide antibodies in carriers of HLA-DRB1*04 (Mann Whitney test P¿=¿0.02) and between CDK6 SNP rs42041 and anti-CCP in non-carriers of HLA-DRB1*04 (Mann Whitney test P¿=¿0.02).Conclusion
These data point to alternative pathways for disease development in clinically similar RA subgroups and suggest an approach for study of genetic complexity of disease with strong contribution of HLA.
Arthritis Research & Therapy 08/2014; 16(5):414. · 4.12 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.
[Show abstract][Hide abstract] ABSTRACT: High-throughput sequencing assays are now routinely used to study different aspects of genome organization. As decreasing costs and widespread availability of sequencing enable more laboratories to use sequencing assays in their research projects, the number of samples and replicates in these experiments can quickly grow to several dozens of samples and thus require standardized annotation, storage and management of preprocessing steps. As a part of the STATegra project, we have developed an Experiment Management System (EMS) for high throughput omics data that supports different types of sequencing-based assays such as RNA-seq, ChIP-seq, Methyl-seq, etc, as well as proteomics and metabolomics data. The STATegra EMS provides metadata annotation of experimental design, samples and processing pipelines, as well as storage of different types of data files, from raw data to ready-to-use measurements. The system has been developed to provide research laboratories with a freely-available, integrated system that offers a simple and effective way for experiment annotation and tracking of analysis procedures.
BMC Systems Biology 03/2014; 8(Suppl 2):S9. · 2.85 Impact Factor
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[Show abstract][Hide abstract] ABSTRACT: Network theory is today a central topic in computational systems biology as a
framework to understand and reconstruct relations among biological components.
For example, constructing networks from a gene expression dataset provides a
set of possible hypotheses explaining connections among genes, vital knowledge
to advancing our understanding of living organisms as systems. Here we briefly
survey aspects at the intersection of information theory and network biology.
We show that Shannon's information entropy, Kolmogorov complexity and
algorithmic probability quantify different aspects of biological networks at
the interplay of local and global pattern detection. We provide approximations
to the algorithmic probability and Kolmogorov complexity of motifs connected to
the asymptotic topological properties of networks.
[Show abstract][Hide abstract] ABSTRACT: To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.
BMC Systems Biology 01/2014; 8. · 2.85 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Network theory is today a central topic in computational systems biology as a framework to understand and reconstruct relations among biological components. For example, constructing networks from a gene expression dataset provides a set of possible hypotheses explaining connections among genes, vital knowledge to advancing our understanding of living organisms as systems. Here we briefly survey aspects at the intersection of information theory and network biology. We show that Shannon's information entropy, Kolmogorov complexity and algorithmic probability quantify different aspects of biological networks at the interplay of local and global pattern detection. We provide approximations to the algorithmic probability and Kolmogorov complexity of motifs connected to the asymptotic topological properties of networks.
[Show abstract][Hide abstract] ABSTRACT: Representing biological systems as networks has proved to be very powerful. For example, local graph analysis of substructures such as subgraph over representation (or motifs) has elucidated different sub-types of networks. Here we report that using numerical approximations of Kolmogorov complexity, by means of algorithmic probability, clusters different classes of networks. For this, we numerically estimate the algorithmic probability of the sub-matrices from the adjacency matrix of the original network (hence including motifs). We conclude that algorithmic information theory is a powerful tool supplementing other network analysis techniques.
2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 12/2013
[Show abstract][Hide abstract] ABSTRACT: In previous calculations of how the O2 transport system limits [Formula: see text] , it was reasonably assumed that mitochondrial [Formula: see text] ( [Formula: see text] ) could be neglected (set to zero). However, in reality, [Formula: see text] must exceed zero and the red cell to mitochondrion diffusion gradient may therefore be reduced, impairing diffusive transport of O2 and [Formula: see text] . Accordingly, we investigated the influence of [Formula: see text] on these calculations by coupling previously used equations for O2 transport to one for mitochondrial respiration relating mitochondrial [Formula: see text] to [Formula: see text] . This hyperbolic function, characterized by its P50 and V˙MAX, allowed [Formula: see text] to become a model output (rather than set to zero as previously). Simulations using data from exercising normal subjects showed that at [Formula: see text] , [Formula: see text] was usually<1mm Hg, and that the effects on [Formula: see text] were minimal. However, when O2 transport capacity exceeded mitochondrial V˙MAX, or if P50 were elevated, [Formula: see text] often reached double digit values, thereby reducing the diffusion gradient and significantly decreasing [Formula: see text] .
[Show abstract][Hide abstract] ABSTRACT: The new discipline of computational biomedicine is concerned with the application of computer-based techniques and particularl modelling and simulation to human health. Since 2007, this discipline has been synonymous, in Europe, with the name give to the European Union's ambitious investment in integrating these techniques with the eventual aim of modelling the huma body as a whole: the virtual physiological human. This programme and its successors are expected, over the next decades, t transform the study and practice of healthcare, moving it towards the priorities known as ‘4P's’: predictive, preventative personalized and participatory medicine.
Interface focus: a theme supplement of Journal of the Royal Society interface 04/2013; 3(2). · 3.12 Impact Factor