Application Of Systems Biology Research Articles

Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model.

BackgroundObesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model.MethodsWe selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms.ResultsWGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2.ConclusionsTo our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis.

Systems Biology and immune aging

Many alterations of innate and adaptive immunity are common in the aging population, which reflect a deterioration of the immune system, and have lead to the terms “immune aging” or “immunosenescence”. Systems Biology aims to the comprehensive knowledge of the structure, dynamics, control and design that define a given biological system. Systems Biology benefits from the continuous advances in the omics sciences, based on high-throughput and high-content technologies, as well as on bioinformatic tools for data mining and integration. The Systems Biology approach is becoming gradually used to propose and to test comprehensive models of aging, both at the level of the immune system and the whole organism. In this way, immune aging may be described by a dynamic view of the states and interactions of every individual cell and molecule of the immune system and their role in the context of aging and longevity. This mini-review presents a panoramics of the current strategies, tools and challenges for applying Systems Biology to immune aging.

Leveraging transcription factors to speed cellobiose fermentation by Saccharomyces cerevisiae.

BackgroundSaccharomyces cerevisiae, a key organism used for the manufacture of renewable fuels and chemicals, has been engineered to utilize non-native sugars derived from plant cell walls, such as cellobiose and xylose. However, the rates and efficiencies of these non-native sugar fermentations pale in comparison with those of glucose. Systems biology methods, used to understand biological networks, hold promise for rational microbial strain development in metabolic engineering. Here, we present a systematic strategy for optimizing non-native sugar fermentation by recombinant S. cerevisiae, using cellobiose as a model.ResultsDifferences in gene expression between cellobiose and glucose metabolism revealed by RNA deep sequencing indicated that cellobiose metabolism induces mitochondrial activation and reduces amino acid biosynthesis under fermentation conditions. Furthermore, glucose-sensing and signaling pathways and their target genes, including the cAMP-dependent protein kinase A pathway controlling the majority of glucose-induced changes, the Snf3-Rgt2-Rgt1 pathway regulating hexose transport, and the Snf1-Mig1 glucose repression pathway, were at most only partially activated under cellobiose conditions. To separate correlations from causative effects, the expression levels of 19 transcription factors perturbed under cellobiose conditions were modulated, and the three strongest promoters under cellobiose conditions were applied to fine-tune expression of the heterologous cellobiose-utilizing pathway. Of the changes in these 19 transcription factors, only overexpression of SUT1 or deletion of HAP4 consistently improved cellobiose fermentation. SUT1 overexpression and HAP4 deletion were not synergistic, suggesting that SUT1 and HAP4 may regulate overlapping genes important for improved cellobiose fermentation. Transcription factor modulation coupled with rational tuning of the cellobiose consumption pathway significantly improved cellobiose fermentation.ConclusionsWe used systems-level input to reveal the regulatory mechanisms underlying suboptimal metabolism of the non-glucose sugar cellobiose. By identifying key transcription factors that cause suboptimal cellobiose fermentation in engineered S. cerevisiae, and by fine-tuning the expression of a heterologous cellobiose consumption pathway, we were able to greatly improve cellobiose fermentation by engineered S. cerevisiae. Our results demonstrate a powerful strategy for applying systems biology methods to rapidly identify metabolic engineering targets and overcome bottlenecks in performance of engineered strains.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-014-0126-6) contains supplementary material, which is available to authorized users.

VANESA - a software application for the visualization and analysis of networks in system biology applications.

VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.

VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications

Summary VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.

A Parkinson's disease gene regulatory network identifies the signaling protein RGS2 as a modulator of LRRK2 activity and neuronal toxicity.

Mutations in LRRK2 are one of the primary genetic causes of Parkinson's disease (PD). LRRK2 contains a kinase and a GTPase domain, and familial PD mutations affect both enzymatic activities. However, the signaling mechanisms regulating LRRK2 and the pathogenic effects of familial mutations remain unknown. Identifying the signaling proteins that regulate LRRK2 function and toxicity remains a critical goal for the development of effective therapeutic strategies. In this study, we apply systems biology tools to human PD brain and blood transcriptomes to reverse-engineer a LRRK2-centered gene regulatory network. This network identifies several putative master regulators of LRRK2 function. In particular, the signaling gene RGS2, which encodes for a GTPase-activating protein (GAP), is a key regulatory hub connecting the familial PD-associated genes DJ-1 and PINK1 with LRRK2 in the network. RGS2 expression levels are reduced in the striata of LRRK2 and sporadic PD patients. We identify RGS2 as a novel interacting partner of LRRK2 in vivo. RGS2 regulates both the GTPase and kinase activities of LRRK2. We show in mammalian neurons that RGS2 regulates LRRK2 function in the control of neuronal process length. RGS2 is also protective against neuronal toxicity of the most prevalent mutation in LRRK2, G2019S. We find that RGS2 regulates LRRK2 function and neuronal toxicity through its effects on kinase activity and independently of GTPase activity, which reveals a novel mode of action for GAP proteins. This work identifies RGS2 as a promising target for interfering with neurodegeneration due to LRRK2 mutations in PD patients.

Defining phenotypes in asthma: a step towards personalized medicine.

Asthma is a common disease with a complex pathophysiology. It can present in various clinical forms and with different levels of severity. Unbiased cluster analytic methods have unravelled several phenotypes in cohorts representative of the whole spectrum of severity. Clusters of severe asthma include those on high-dose corticosteroid treatment, often with both inhaled and oral treatment, usually associated with severe airflow obstruction. Phenotypes with concordance between symptoms and sputum eosinophilia have been reported, including an eosinophilic inflammation-predominant group with few symptoms and late-onset disease who have a high prevalence of rhinosinusitis, aspirin sensitivity, and exacerbations. Sputum eosinophilia is also a biomarker that can predict therapeutic responses to antibody-based treatments to block the effects of the T-helper (Th)-2 cytokine, interleukin (IL)-5. Low Th2-expression has been predictive of poor therapeutic response to inhaled corticosteroid therapy. Current asthma schedules emphasise a step-up approach to treating asthma in relation to increasing severity, but, in more severe disease, phenotyping or endotyping of asthma will be necessary to determine new treatment strategies as severe asthma is recognized as being a particularly heterogeneous disease. Much less is known about 'non-eosinophilic' asthma. Phenotypic characterisation of corticosteroid insensitivity and chronic airflow obstruction of severe asthma is also needed. Phenotype-driven treatment of asthma will be further boosted by the advent of transcriptomic and proteomic technologies, with the application of systems biology or medicine approaches to defining phenotypes and biomarkers of disease and therapeutic response. This will pave the way towards personalized medicine and healthcare for asthma.

Multiscale and multi-modality visualization of angiogenesis in a human breast cancer model

Angiogenesis in breast cancer helps fulfill the metabolic demands of the progressing tumor and plays a critical role in tumor metastasis. Therefore, various imaging modalities have been used to characterize tumor angiogenesis. While micro-CT (μCT) is a powerful tool for analyzing the tumor microvascular architecture at micron-scale resolution, magnetic resonance imaging (MRI) with its sub-millimeter resolution is useful for obtaining in vivo vascular data (e.g. tumor blood volume and vessel size index). However, integration of these microscopic and macroscopic angiogenesis data across spatial resolutions remains challenging. Here we demonstrate the feasibility of 'multiscale' angiogenesis imaging in a human breast cancer model, wherein we bridge the resolution gap between ex vivo μCT and in vivo MRI using intermediate resolution ex vivo MR microscopy (μMRI). To achieve this integration, we developed suitable vessel segmentation techniques for the ex vivo imaging data and co-registered the vascular data from all three imaging modalities. We showcase two applications of this multiscale, multi-modality imaging approach: (1) creation of co-registered maps of vascular volume from three independent imaging modalities, and (2) visualization of differences in tumor vasculature between viable and necrotic tumor regions by integrating μCT vascular data with tumor cellularity data obtained using diffusion-weighted MRI. Collectively, these results demonstrate the utility of 'mesoscopic' resolution μMRI for integrating macroscopic in vivo MRI data and microscopic μCT data. Although focused on the breast tumor xenograft vasculature, our imaging platform could be extended to include additional data types for a detailed characterization of the tumor microenvironment and computational systems biology applications.

Cytoprotection of human endothelial cells against oxidative stress by 1-[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole (CDDO-Im): Application of systems biology to understand the mechanism of action

Cellular damage from oxidative stress, in particular following ischemic injury, occurs during heart attack, stroke, or traumatic injury, and is potentially reducible with appropriate drug treatment. We previously reported that caffeic acid phenethyl ester (CAPE), a plant-derived polyphenolic compound, protected human umbilical vein endothelial cells (HUVEC) from menadione-induced oxidative stress and that this cytoprotective effect was correlated with the capacity to induce heme oxygenase-1 (HMOX1) and its protein product, a phase II cytoprotective enzyme. To further improve this cytoprotective effect, we studied a synthetic triterpenoid, 1-[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole (CDDO-Im), which is known as a potent phase II enzyme inducer with antitumor and anti-inflammatory activities, and compared it to CAPE. CDDO-Im at 200nM provided more protection to HUVEC against oxidative stress than 20μM CAPE. We explored the mechanism of CDDO-Im cytoprotection with gene expression profiling and pathway analysis and compared to that of CAPE. In addition to potent up-regulation of HMOX1, heat shock proteins (HSP) were also found to be highly induced by CDDO-Im in HUVEC. Pathway analysis results showed that transcription factor Nrf2-mediated oxidative stress response was among the top canonical pathways commonly activated by both CDDO-Im and CAPE. Compared to CAPE, CDDO-Im up-regulated more HSP and some of them to a much higher extent. In addition, CDDO-Im treatment affected Nrf2 pathway more significantly. These findings may provide an explanation why CDDO-Im is a more potent cytoprotectant than CAPE against oxidative stress in HUVEC.

Heterogeneous Protein-Protein Interaction Systems Modeled using a New Integrator for Single-Particle Reaction Diffusion

Proteins perform functions ranging from signal transmission and transcriptional regulation to assembly into structural scaffolds by stochastically binding with one another in the cellular environment. Approaches to modeling these processes at a scale capable of capturing the dynamics of whole populations of proteins vary in the degree of spatial resolution and the rules describing the interactions between proteins. We present here a new algorithm for simulating both the spatial and temporal evolution of protein interactions by rigorously solving reaction-diffusion equations at single-particle resolution. Our algorithm is designed to be both highly accurate and relatively simple to implement, making it applicable to large and heterogeneous systems, including those arising in systems biology applications. In our approach we combine the use of the exact Green's function for a pair of reacting particles with the approximate free diffusion propagator for position updates to particles. Through the use of a trajectory reweighting scheme our method recovers the exact association rates for a pair of interacting particles at all times. As a result, simulations of many-body systems with our method quite accurately reproduce the theoretically known dynamic behavior for a variety of different reaction types. This approach has applications in modeling pathways and networks of protein driven processes where reactions can range widely in strength and thousands of proteins may participate. With a limited amount of bookkeeping necessary to ensure proper association rates for each reactant pair, our approach can adapt to changes to reaction rates or diffusion constants as a result of reaction events. The formalism can also be extended to physical descriptions of protein interactions that incorporate long-range forces or orientational constraints, providing a framework to help bridge the gap between molecular models and particle-based models of protein interactions.

Free-Propagator Reweighting Integrator for Single-Particle Dynamics in Reaction-Diffusion Models of Heterogeneous Protein-Protein Interaction Systems.

We present a new algorithm for simulating reaction-diffusion equations at single-particle resolution. Our algorithm is designed to be both accurate and simple to implement, and to be applicable to large and heterogeneous systems, including those arising in systems biology applications. We combine the use of the exact Green's function for a pair of reacting particles with the approximate free-diffusion propagator for position updates to particles. Trajectory reweighting in our free-propagator reweighting (FPR) method recovers the exact association rates for a pair of interacting particles at all times. FPR simulations of many-body systems accurately reproduce the theoretically known dynamic behavior for a variety of different reaction types. FPR does not suffer from the loss of efficiency common to other path-reweighting schemes, first, because corrections apply only in the immediate vicinity of reacting particles and, second, because by construction the average weight factor equals one upon leaving this reaction zone. FPR applications include the modeling of pathways and networks of protein-driven processes where reaction rates can vary widely and thousands of proteins may participate in the formation of large assemblies. With a limited amount of bookkeeping necessary to ensure proper association rates for each reactant pair, FPR can account for changes to reaction rates or diffusion constants as a result of reaction events. Importantly, FPR can also be extended to physical descriptions of protein interactions with long-range forces, as we demonstrate here for Coulombic interactions.

Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization.

The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.

Molecular systems biology models of the post synaptic density.

Event Abstract Back to Event Molecular systems biology models of the post synaptic density. J Douglas Armstrong1* 1 School of Informatics, University of Edinburgh., UK Information processing in the nervous system takes place at the synapses between neurons and in particular is mediated by some of the largest protein complexes described in biology. We have applied systems biology approaches to the glutamatergic post synaptic density (PSD) which is clearly associated with cognitive processes and human brain diseases. We have initially focused on exploiting protein-protein interaction data within static interaction models. These scale well and can capture the organisation of the 1000s of different proteins that can be found in synapses. However, to gain a more realistic understanding of such large complexes and of their influence on biology one must model their dynamics, their interactions with the cellular environment, as well the side effect of activity on their structure, state, and subsequent responses (e.g. through local translational control). We next applied a stochastic calculus of domain binding provided by the rule-based modelling approach (Kappa) to formalize the highly combinatorial signalling pathway in PSD and performed numerical analysis of the relative distribution of protein complexes and their sizes at steady state. We find that this approach allows us to model, in a much more biologically plausible manner, the molecular interactions at synapses. This modelling approach allows us to study the effect of different perturbations (mutated polypeptides, protein splice variants, etc) on structure and relative stability of multi-protein complexes. Analysis of the basic topological properties of the protein networks obtained in simulation with respect to relevant physiological phenotypes provides a direct link between them. For example we can use these models to predict the impact of genetic disruption on the availability of transmitter receptors - in other words we can use this approach to develop predictive models that link from molecular genetics through to physiological properties of synapses. Keywords: Genomics and genetics, molecular systems biology, glutamatergic post synaptic density, modelling, protein networks Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Armstrong J (2014). Molecular systems biology models of the post synaptic density.. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00094 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. J Douglas Armstrong, School of Informatics, University of Edinburgh., Edinburgh, UK, jda@inf.ed.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers J Douglas Armstrong Google J Douglas Armstrong Google Scholar J Douglas Armstrong PubMed J Douglas Armstrong Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Methylation by DNMT1 is more Efficient in Chronic Lymphocytic Lymphoma Cells than in Normal Cells

Binding of activators and repressors plays a significant role in transcriptional regulation of gene expression. The systems biology approach has been applied to predict and determine the efficiency of this process; however, most of the work is done in prokaryotic systems. Epigenetic regulation provides another dimension of transcriptional regulation, which does not involve changes in the coding region of genes. Methylation of specific CpG residues in the CpG islands of DNA by the enzyme DNA methyl transferase 1 (DNMT1) causes gene silencing. In cancer cells, the enzyme DNMT1 is highly expressed, and the methylation level at specific sites of genes, including tumor suppressor genes, is elevated. This results in the silencing of tumor suppressor genes for carcinogenesis. However, it is not known whether the increase in DNMT1 expression is proportional to the increase in the level of methylation. This paper developed a systems biology approach to determine whether DNMT1 acts more efficiently in cancer cells than in normal cells. The cooperativity of DNMT1 binding to eight selected genes was determined in chronic lymphocytic leukemia (CLL) cells using a modified Hill equation and compared these values with those obtained from normal lymphocytes and granulocytes. The cooperativity of DNMT1 was found to be positive in four cancer-associated genes, whereas their values were negative in both normal lymphocytes and granulocytes. These results show for the first time how to apply systems biology in the analysis of transcriptional gene regulation. This approach will be beneficial in determining the efficiency of genome-wide methylation in development and in diverse types of diseases.

Systems Biology and Systems Medicine: The Technological Tools of the System Approaches to Complexity

Progresses made in “omics” research have fueled the collection and storage of large quantities of data. Now these data can be used for knowledge gaining. Systems biology aims at studying biological organisms as interconnected systems. Systems Medicine, by applying systems biology approaches, tries to understand illness in the context of altered regulatory circuitry. Studies in a Systems Medicine framework will enable a better understanding of diseases, and the delivery of a personalized healthcare, tailored on the specific phenotype of each single patient. In this relatively new discipline, the review describes those tools useful in this approach. The paradigm: “disease → therapy → recovery” is not still suitable and superseded by the new one: “person → definition of the problems → quality of life”.

Leveraging transcription factors to speed cellobiose fermentation by

Saccharomyces cerevisiae, a key organism used for the manufacture of renewable fuels and chemicals, has been engineered to utilize non-native sugars derived from plant cell walls, such as cellobiose and xylose. However, the rates and efficiencies of these non-native sugar fermentations pale in comparison with those of glucose. Systems biology methods, used to understand biological networks, hold promise for rational microbial strain development in metabolic engineering. Here, we present a systematic strategy for optimizing non-native sugar fermentation by recombinant S. cerevisiae, using cellobiose as a model. Differences in gene expression between cellobiose and glucose metabolism revealed by RNA deep sequencing indicated that cellobiose metabolism induces mitochondrial activation and reduces amino acid biosynthesis under fermentation conditions. Furthermore, glucose-sensing and signaling pathways and their target genes, including the cAMP-dependent protein kinase A pathway controlling the majority of glucose-induced changes, the Snf3-Rgt2-Rgt1 pathway regulating hexose transport, and the Snf1-Mig1 glucose repression pathway, were at most only partially activated under cellobiose conditions. To separate correlations from causative effects, the expression levels of 19 transcription factors perturbed under cellobiose conditions were modulated, and the three strongest promoters under cellobiose conditions were applied to fine-tune expression of the heterologous cellobiose-utilizing pathway. Of the changes in these 19 transcription factors, only overexpression of SUT1 or deletion of HAP4 consistently improved cellobiose fermentation. SUT1 overexpression and HAP4 deletion were not synergistic, suggesting that SUT1 and HAP4 may regulate overlapping genes important for improved cellobiose fermentation. Transcription factor modulation coupled with rational tuning of the cellobiose consumption pathway significantly improved cellobiose fermentation. We used systems-level input to reveal the regulatory mechanisms underlying suboptimal metabolism of the non-glucose sugar cellobiose. By identifying key transcription factors that cause suboptimal cellobiose fermentation in engineered S. cerevisiae, and by fine-tuning the expression of a heterologous cellobiose consumption pathway, we were able to greatly improve cellobiose fermentation by engineered S. cerevisiae. Our results demonstrate a powerful strategy for applying systems biology methods to rapidly identify metabolic engineering targets and overcome bottlenecks in performance of engineered strains.

Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks.

In this survey paper, we will present a number of core algorithmic questions concerning several transitive reduction problems on network that have applications in network synthesis and analysis involving cellular processes. Our starting point will be the so-called minimum equivalent digraph problem, a classic computational problem in combinatorial algorithms. We will subsequently consider a few non-trivial extensions or generalizations of this problem motivated by applications in systems biology. We will then discuss the applications of these algorithmic methodologies in the context of three major biological research questions: synthesizing and simplifying signal transduction networks, analyzing disease networks, and measuring redundancy of biological networks.

MINING POSETS FROM LINEAR ORDERS

There has been much research on the combinatorial problem of generating the linear extensions of a given poset. This paper focuses on the reverse of that problem, where the input is a set of linear orders, and the goal is to construct a poset or set of posets that generates the input. Such a problem finds applications in computational neuroscience, systems biology, paleontology, and physical plant engineering. In this paper, two algorithms are presented for efficiently finding a single poset, if, such a poset exists whose linear extensions are exactly the same as the input set of linear orders. The variation of the problem where a minimum set of posets that cover the input is also explored. This variation is shown to be polynomially solvable for one class of simple posets (kite(2) posets) but NP-complete for a related class (hammock(2,2,2) posets).

GPU code generation for ODE-based applications with phased shared-data access patterns

We present a novel code generation scheme for GPUs. Its key feature is the platform-aware generation of a heterogeneous pool of threads. This exposes more data-sharing opportunities among the concurrent threads and reduces the memory requirements that would otherwise exceed the capacity of the on-chip memory. Instead of the conventional strategy of focusing on exposing as much parallelism as possible, our scheme leverages on the phased nature of memory access patterns found in many applications that exhibit massive parallelism. We demonstrate the effectiveness of our code generation strategy on a computational systems biology application. This application consists of computing a Dynamic Bayesian Network (DBN) approximation of the dynamics of signalling pathways described as a system of Ordinary Differential Equations (ODEs). The approximation algorithm involves (i) sampling many (of the order of a few million) times from the set of initial states, (ii) generating trajectories through numerical integration, and (iii) storing the statistical properties of this set of trajectories in Conditional Probability Tables (CPTs) of a DBN via a prespecified discretization of the time and value domains. The trajectories can be computed in parallel. However, the intermediate data needed for computing them, as well as the entries for the CPTs, are too large to be stored locally. Our experiments show that the proposed code generation scheme scales well, achieving significant performance improvements on three realistic signalling pathways models. These results suggest how our scheme could be extended to deal with other applications involving systems of ODEs.

GPU code generation for ODE-based applications with phased shared-data access patterns

We present a novel code generation scheme for GPUs. Its key feature is the platform-aware generation of a heterogeneous pool of threads. This exposes more data-sharing opportunities among the concurrent threads and reduces the memory requirements that would otherwise exceed the capacity of the on-chip memory. Instead of the conventional strategy of focusing on exposing as much parallelism as possible, our scheme leverages on the phased nature of memory access patterns found in many applications that exhibit massive parallelism. We demonstrate the effectiveness of our code generation strategy on a computational systems biology application. This application consists of computing a Dynamic Bayesian Network (DBN) approximation of the dynamics of signalling pathways described as a system of Ordinary Differential Equations (ODEs). The approximation algorithm involves (i) sampling many (of the order of a few million) times from the set of initial states, (ii) generating trajectories through numerical integration, and (iii) storing the statistical properties of this set of trajectories in Conditional Probability Tables (CPTs) of a DBN via a prespecified discretization of the time and value domains. The trajectories can be computed in parallel. However, the intermediate data needed for computing them, as well as the entries for the CPTs, are too large to be stored locally. Our experiments show that the proposed code generation scheme scales well, achieving significant performance improvements on three realistic signalling pathways models. These results suggest how our scheme could be extended to deal with other applications involving systems of ODEs.