Comprehensive Network Model Research Articles

Hd-ANM a Computationally Efficient, Highly Customizable and Comprehensive Elastic Network Model

The global motions of proteins, occurring in microseconds or slower times are a challenge to obtain. The best method to model these motions remains Molecular Dynamics (MD), which can be computationally intensive and prone to the error accumulations that requires multiple replicas. The Elastic Network Models have been used successfully to study proteins’ global motions more efficiently than with MD. However, the classical Elastic Network Model has some limitations. There have been recent improvements to the elastic network models such as curvilinear extrapolation NOLB and, more recently, Elastic Solid Networks. As an alternative we have developed the ‘hinge-domain Anisotropic Network Model’ (hd-ANM), a novel model that aims to be comprehensive, customizable, computationally efficient, and easy to use. This model not only has the capability of fusing classical the ANM, RTB, and NOLB approaches by changing a few parameters but also is a hybrid of these methods, which allows hd-ANM to incorporate the specific predicted hinges from our PACKMAN tool into the model to study the global motions; we know that hinges are the most common type of motion responsible for the global motions of proteins. This feature allows us to build better models while being computationally efficient because of including domain level coarse-graining, without losing agreement with the dynamics evident in multiple similar experimental structures. The demonstrated success of this method in terms of these conformational overlaps and the ability to simulate closed to open transitions in both directions, demonstrates that the that this protein packing based elastic network model is an excellent choice to study the global motions of the proteins more efficiently than MD to learn about protein mechanisms.

An updatable and comprehensive global cargo maritime network and strategic seaborne cargo routing model for global containerized and bulk vessel flow estimation

The global maritime system provides the backbone of logistics operations for global supply chains and international trade. This paper aims to develop a unifying global network representation and strategic, system-wide decision model, the Strategic Cargo Routing Model, incorporating both liner and bulk shipping markets to estimate real-world traffic flows and study traffic patterns at the global scale. Specifically, taking a shipper's perspective, containerized and bulk movements are jointly modelled within a mixed-integer linear program that includes inbound, outbound, and transshipment cargo flows at ports. An iterative approach that combines heuristic Gradient Descent and Relax-and-Fix Decomposition methods is proposed for the calibration and solution of the Strategic Cargo Routing Model over a proposed joint liner and bulk services Global Cargo Shipping Network representation. The Global Cargo Shipping Network contains 161 seaports covering 52 countries. It is created from updatable, publicly available, data sources, and all data needed for the network representation are made available. Sufficient network details, as well as data sources and methods for extracting needed inputs, are given to allow others to use and update the network. Using the developed maritime network, mathematical model and calibration-solution methodology, 2018 global maritime traffic flow patterns were estimated. The estimates were found to achieve a 91% fit overall to real-world average annual port throughputs. This strategic model provides support to evaluate future, real-world, worldwide changes, such as increased seaborne trade demand, new routes, shipping infrastructure expansion, and transport policies.

A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.

Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BackgroundPresently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis.ResultsWe used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level.ConclusionsTranscriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

Contrast response in a comprehensive network model of macaque V1.

The response to contrast is one of the most important functions of the macaque primary visual cortex, V1, but up to now there has not been an adequate theory for it. To fill this gap in our understanding of cortical function, we built and analyzed a new large-scale, biologically constrained model of the input layer, 4Cα, of macaque V1. We called the new model CSY2. We challenged CSY2 with a three-parameter family of visual stimuli that varied in contrast, orientation, and spatial frequency. CSY2 accurately simulated experimental data and made many new predictions. It accounted for 1) the shapes of firing-rate-versus-contrast functions, 2) orientation and spatial frequency tuning versus contrast, and 3) the approximate contrast-invariance of cortical activity maps. Post-analysis revealed that the mechanisms that were needed to produce the successful simulations of contrast response included strong recurrent excitation and inhibition that find dynamic equilibria across the cortical surface, dynamic feedback between L6 and L4, and synaptic dynamics like inhibitory synaptic depression.

Intelligent diagnosis of natural gas pipeline defects using improved flower pollination algorithm and artificial neural network

With the increasing service life of pipelines, natural gas pipelines can gradually age and produce various corrosion defects. Hence, in order to ensure the efficiency and safety of pipeline transportation in the peak period of natural gas consumption, the intelligent diagnosis technology of the pipelines is of vital importance. In this paper, iterative chaotic map with infinite collapses (ICMIC) and comprehensive opposition (CO) learning strategy are used to optimize flower pollination algorithm (FPA) to enhance search abilities of original FPA algorithm. Among them, the ICMIC enhances the diversity of population, and the local CO learning strategy enhances its exploitation ability. Fifteen classical benchmark functions are used to test the optimization performance of the improved flower pollination algorithm (IFPA). Considering the category, complexity and application of test functions, a more reasonable evaluation formula is proposed. The test shows that the performance of IFPA algorithm is obviously better than other classical intelligent algorithms. Based on the excellent performance of IFPA, the IFPA algorithm is used to optimize the initial weights and thresholds of back propagation (BP) neural network. Therefore, a comprehensive IFPA-BP network model is constructed for the intelligent diagnosis of natural gas pipeline defects. The results show that the proposed model can effectively overcome the problem that BP neural network is prone to fall into local optimal value, and it can accurately identify the pipelines defects. This will facilitate intelligent diagnosis of natural gas pipelines defects.

Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation

This article proposes a hierarchical Bayesian model for probabilistic estimation of the electric vehicle (EV) battery capacity fade. Deterministic estimation of the EV battery degradation is not reliable, as its aging factors are mostly external and varies by time, location, and the driver’s usage pattern, while probabilistic models can address the observations uncertainties and provide more accurate estimations. We have developed a comprehensive Bayesian network model that incorporates two main aspects of the EV battery aging: hierarchy and variety of external effectual factors. Multiple levels of hierarchical relationship with intermediate hidden variables connect the external factors, such as driving behavior and habits, charging options, and grid services to the battery capacity fade. The mathematical expression of the model is extracted based on Bayes’ theorem, the probability distributions for all variables are carefully chosen, and the Metropolis–Hastings Markov chain Monte Carlo sampling method is applied to generate the posterior distributions. The model is trained with a subset of experimental data (85%) and tested with the other 15% of data to prove its accuracy. Also, three case studies for different drivers, different grid services’ repetitions, and different climates are explored to show model’s flexibility with different input data.

The use of quality indicators (QIs) to evaluate the oncologic care in CCCN: a systematic review

Abstract Due to the increase of life expectancy for cancer patients, it has become necessary to implement complete and economically sustainable clinical care pathways that integrate different professional competences. The Comprehensive Cancer Care Network (CCCN) model consists of multiple specialized structures that cooperate for cancer patients care. Quality Indicators (QIs) represent valid and reliable tools of evaluation that allow a standardized comparison among different structures. The aim of this systematic review was to highlight a wide and complete description of the implemented QIs within the CCCNs and to identify and analyze methodologies used for the development of QIs. The literature was performed investigating two databases (PubMed and Scopus) and the search identified 7342 studies. After duplicate removal, title and abstract screening, and full text evaluation, 46 studies were included in the study. Most QIs were implemented in USA, Germany and Italy where the CCCN approach seems to be well defined. Eighty -two QIs concerned diagnosis, 260 concerned treatment, 7 concerned prevention, 29 about follow up, 71 about palliative care, 12 concerned rehabilitation and 7 research. The majority of the identified QIs belonged to the process domain, followed by the structure ones. Excluding QIs related to the management of cancer in general, the most represented organs resulted breast, colorectum and lung. Overall, it can be stated that the most represented categories of QIs concerned diagnosis and treatment. Furthermore, also the palliative care domain appeared very represented. Regarding the methodology of the QIs development, a consensus approach among experts and the Delphi method were the most frequently used methodologies. Only a few studies included the participation of patients for the implementation of the QIs. This systematic review provides a synthesis of existing QIs related to the setting of integrated oncological care Key messages The CCCN has been recognized as an ideal model for structuring the process of care that guarantees a complete and integrated approach for the management of oncological patients. Quality Indicators represent valid and reliable tools that should be used to perform a standardized comparison among different healthcare systems and to guarantee a homogeneous quality of care.

Abstract 675: Network modeling of drug resistance mechanisms and drug combinations in breast cancer

Abstract Durable control of invasive solid tumors is thwarted by the lack of knowledge of effective drug combinations and of the acquired and intrinsic resistance mechanisms of drugs. In an effort to tackle this problem, the SU2C-NSF-TVF Drug Combination Convergence Team is using mechanistic models of cancer cell signaling based on therapeutic and cell line data in order to identify elements within cancer cells that might eventually be exploited through therapeutic combinations. Here we present a comprehensive mechanistic network model of signal transduction in ER+ PIK3CA-mutant breast cancer. Focusing on PI3K inhibitors, the model recapitulates known resistance mechanisms and predicts other possibilities for resistance: loss of RB1, FOXO3, P27, or PRAS40. To test these predictions, we analyzed genome-wide CRISPR screens of two breast cell lines in the presence of PI3K inhibitors (BYL719 and GDC0032) and found that the predicted genes (RB1, FOXO3, P27, and PRAS40) were significantly enriched in the screens. Some of these resistance genes (e.g. loss of RB1) were found to be cell-line specific and follow-up experiments in RB1-KO cells confirmed the cell-line-specific nature of PI3K-inhibitor resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone. For example, the model predicts that the combination of PI3K inhibitors with inhibitors of anti-apoptotic protein MCL1 would be effective. Follow up experiments in cell lines using cell viability assays, cell death analyses, and dynamic BH3 profiling experiments to determine increases in apoptotic priming upon treatment confirmed that MCL1 inhibitors (S63845) enhance the effect of PI3K inhibitors (BYL719) and that this combinatorial effect is cell-line-specific, similarly to what was found in the resistance genes case. In conclusion, the model predicted drug resistance mechanisms and effective drug combinations, some of which were verified experimentally and found to be cell-line-specific. Next iterations of the model will incorporate the identified discrepancies and newly identified resistance mechanisms to drugs of clinical interest. Citation Format: Jorge Gómez Tejeda Zañudo, Pingping Mao, Joan Montero, Guotai Xu, Kailey J. Kowalski, Gabriela N. Johnson, José Baselga, Maurizio Scaltriti, Anthony G. Letai, Nikhil Wagle, Reka Albert. Network modeling of drug resistance mechanisms and drug combinations in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 675.

CD8+ T cells expand stem and progenitor cells in favorable but not adverse risk acute myeloid leukemia

CD8+ T cell immunosurveillance is crucial in solid tumors and T cell dysfunction leads to tumor progression. In contrast, the role of CD8+ T cells in the control of leukemia is less clear. We characterized the molecular signature of leukemia stem/progenitor cells (LSPCs) and paired CD8+ T cells in patients with acute myeloid leukemia (AML). Epigenetic alterations via histone deacetylation reduced the expression of immune-related genes in bone marrow (BM)-infiltrating CD8+ T cells. Surprisingly, a silenced gene expression pattern in CD8+ T cells significantly correlated with an improved prognosis. To define interactions between CD8+ T cells and LSPCs, we performed comprehensive correlative network modeling. This analysis indicated that CD8+ T cells contribute to the maintenance/expansion of LSPCs, particularly in favorable risk AML. Functionally, CD8+ T cells in favorable AML induced the expansion of LSPCs by stimulating the autocrine production of important hematopoietic cytokines such as interleukin (IL)-3. In contrast, LSPCs in aggressive AML were characterized by a higher activation of stemness/proliferation-related pathways and develop independent of BM CD8+ T cells. Overall, our study indicates that CD8+ T cells support and expand LSPCs in favorable risk AML whereas intermediate and adverse risk AML possess the intrinsic molecular abnormalities to develop independently.

Construction of a Suite of Computable Biological Network Models Focused on Mucociliary Clearance in the Respiratory Tract.

Mucociliary clearance (MCC), considered as a collaboration of mucus secreted from goblet cells, the airway surface liquid layer, and the beating of cilia of ciliated cells, is the airways’ defense system against airborne contaminants. Because the process is well described at the molecular level, we gathered the available information into a suite of comprehensive causal biological network (CBN) models. The suite consists of three independent models that represent (1) cilium assembly, (2) ciliary beating, and (3) goblet cell hyperplasia/metaplasia and that were built in the Biological Expression Language, which is both human-readable and computable. The network analysis of highly connected nodes and pathways demonstrated that the relevant biology was captured in the MCC models. We also show the scoring of transcriptomic data onto these network models and demonstrate that the models capture the perturbation in each dataset accurately. This work is a continuation of our approach to use computational biological network models and mathematical algorithms that allow for the interpretation of high-throughput molecular datasets in the context of known biology. The MCC network model suite can be a valuable tool in personalized medicine to further understand heterogeneity and individual drug responses in complex respiratory diseases.

Integrated Energy System Configuration Optimization for Multi-Zone Heat-Supply Network Interaction

The integrated energy system effectively improves the comprehensive utilization of energy through cascade utilization and coordinated scheduling of various types of energy. Based on the independent integrated energy system, the thermal network interaction between different load characteristic regions is introduced, requiring a minimum thermal grid construction cost, CCHP investment operation cost and carbon emission tax as the comprehensive optimization targets, and making overall optimization to the configuration and operation of the multi-region integrated energy systems. This paper focuses on the planning of equipment capacity of multi-region integrated energy system based on a CCHP system and heat network. Combined with the above comprehensive target and heat network model, a mixed integer linear programming model for a multi-region CCHP system capacity collaborative optimization configuration is established. The integrated energy system, just a numerical model solved with the LINGO software, is presented. Taking a typical urban area in Shanghai as an example, the simulation results show that the integrated energy system with multi-zone heat-suply network interaction compared to the single area CCHP model improved the clean energy utilization of the system, rationally allocates equipment capacity, promotes the local consumption of distributed energy, and provides better overall system benefits.

Metabolic network reconstruction and phenome analysis of the industrial microbe, Escherichia coli BL21(DE3).

Escherichia coli BL21(DE3) is an industrial model microbe for the mass-production of bioproducts such as biofuels, biorefineries, and recombinant proteins. However, despite its important role in scientific research and biotechnological applications, a high-quality metabolic network model for metabolic engineering is yet to be developed. Here, we present the comprehensive metabolic network model of E. coli BL21(DE3), named iHK1487, based on the latest genome reannotation and phenome analysis. The metabolic model consists of 1,164 unique metabolites, 2,701 metabolic reactions, and 1,487 genes. The model was validated and improved by comparing the simulation results with phenome data from phenotype microarray tests. Previous transcriptome profile data was incorporated during model reconstruction, and flux prediction was simulated using the model. iHK1487 was simulated to explore the metabolic features of BL21(DE3) such as broad spectrum amino acid utilization and enhanced flux through the upper glycolytic pathway and TCA cycle. iHK1487 will contribute to systematic understanding of cellular physiology and metabolism of E. coli BL21(DE3) and highlight its biotechnological applications.

Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells.

Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when feedback is present.

Innovative means of cargo transport: A scalable method for estimating regional impacts

Abstract The integration of innovative means of transport, like the Hyperloop concept, cargo drones and cargo zeppelins, into existing transport networks presents a challenge not only from a technological point of view, but also has various economic, social, and ecological impacts. Therefore, this work estimates regional impacts of a connection utilizing innovative means of transport. The presented scalable method is not limited to a specific type or number of regions, but capable to estimate the impacts of large-scale transport networks covering a great number of diverse regions. The work contributes to quantitative transport network modelling, as it provides a method for the quantification of essential regional impacts, which can be incorporated into comprehensive transport network models. The applicability of the method is demonstrated with a real-life example. As a result, values of an impact indicator for selected international regions are available for a set of innovative means of transport.

Modeling the response of a tumor-suppressive network to mitogenic and oncogenic signals

Intrinsic tumor-suppressive mechanisms protect normal cells against aberrant proliferation. Although cellular signaling pathways engaged in tumor repression have been largely identified, how they are orchestrated to fulfill their function still remains elusive. Here, we built a tumor-suppressive network model composed of three modules responsible for the regulation of cell proliferation, activation of p53, and induction of apoptosis. Numerical simulations show a rich repertoire of network dynamics when normal cells are subject to serum stimulation and adenovirus E1A overexpression. We showed that oncogenic signaling induces ARF and that ARF further promotes p53 activation to inhibit proliferation. Mitogenic signaling activates E2F activators and promotes Akt activation. p53 and E2F1 cooperate to induce apoptosis, whereas Akt phosphorylates p21 to repress caspase activation. These prosurvival and proapoptotic signals compete to dictate the cell fate of proliferation, cell-cycle arrest, or apoptosis. The cellular outcome is also impacted by the kinetic mode (ultrasensitivity or bistability) of p53. When cells are exposed to serum deprivation and recovery under fixed E1A, the shortest starvation time required for apoptosis induction depends on the terminal serum concentration, which was interpreted in terms of the dynamics of caspase-3 activation and cytochrome c release. We discovered that caspase-3 can be maintained active at high serum concentrations and that E1A overexpression sensitizes serum-starved cells to apoptosis. This work elucidates the roles of tumor repressors and prosurvival factors in tumor repression based on a dynamic network analysis and provides a framework for quantitatively exploring tumor-suppressive mechanisms.

Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma

Glioblastomas are characterized by transcriptionally distinct subtypes, but despite possible clinical relevance, their regulation remains poorly understood. The commonly used molecular classification systems for GBM all identify a subtype with high expression of mesenchymal marker transcripts, strongly associated with invasive growth. We used a comprehensive data-driven network modeling technique (augmented sparse inverse covariance selection, aSICS) to define separate genomic, epigenetic, and transcriptional regulators of glioblastoma subtypes. Our model identified Annexin A2 (ANXA2) as a novel methylation-controlled positive regulator of the mesenchymal subtype. Subsequent evaluation in two independent cohorts established ANXA2 expression as a prognostic factor that is dependent on ANXA2 promoter methylation. ANXA2 knockdown in primary glioblastoma stem cell-like cultures suppressed known mesenchymal master regulators, and abrogated cell proliferation and invasion. Our results place ANXA2 at the apex of a regulatory cascade that determines glioblastoma mesenchymal transformation and validate aSICS as a general methodology to uncover regulators of cancer subtypes.

Genome-wide profiling of 24 hr diel rhythmicity in the water flea, Daphnia pulex: network analysis reveals rhythmic gene expression and enhances functional gene annotation

BackgroundMarine and freshwater zooplankton exhibit daily rhythmic patterns of behavior and physiology which may be regulated directly by the light:dark (LD) cycle and/or a molecular circadian clock. One of the best-studied zooplankton taxa, the freshwater crustacean Daphnia, has a 24 h diel vertical migration (DVM) behavior whereby the organism travels up and down through the water column daily. DVM plays a critical role in resource tracking and the behavioral avoidance of predators and damaging ultraviolet radiation. However, there is little information at the transcriptional level linking the expression patterns of genes to the rhythmic physiology/behavior of Daphnia.ResultsHere we analyzed genome-wide temporal transcriptional patterns from Daphnia pulex collected over a 44 h time period under a 12:12 LD cycle (diel) conditions using a cosine-fitting algorithm. We used a comprehensive network modeling and analysis approach to identify novel co-regulated rhythmic genes that have similar network topological properties and functional annotations as rhythmic genes identified by the cosine-fitting analyses. Furthermore, we used the network approach to predict with high accuracy novel gene-function associations, thus enhancing current functional annotations available for genes in this ecologically relevant model species. Our results reveal that genes in many functional groupings exhibit 24 h rhythms in their expression patterns under diel conditions. We highlight the rhythmic expression of immunity, oxidative detoxification, and sensory process genes. We discuss differences in the chronobiology of D. pulex from other well-characterized terrestrial arthropods.ConclusionsThis research adds to a growing body of literature suggesting the genetic mechanisms governing rhythmicity in crustaceans may be divergent from other arthropod lineages including insects. Lastly, these results highlight the power of using a network analysis approach to identify differential gene expression and provide novel functional annotation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2998-2) contains supplementary material, which is available to authorized users.

Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts.

In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using 13C-metabolic flux analysis (13C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems.

Estimation of asphaltene precipitation in light, medium and heavy oils: experimental study and neural network modeling

Asphaltene can precipitate in oil reservoirs as a result of natural depletion and/or gas injection crippling the oil production performance. Most of the conventional models for asphaltene precipitation cannot precisely capture the asphaltene precipitation at a wide pressure range and for different oil types. To have a precise model that can be used for various oil types at a wide range of pressure conditions, a comprehensive artificial neural network (ANN) model was proposed to estimate the weight percent of precipitated asphaltene in different oil types (three oil types, namely light, medium and heavy). The dilution ratio, pressure, molecular weight of solvent, API gravity and resin-to-asphaltene ratio were considered as the model input parameters. The oil samples were thus categorized based on the differences in their API gravity and resin-to-asphaltene ratio. Five hundred and fifty experimental precipitation datapoints were obtained from our experimental apparatus in a wide range of pressure, dilution ratio and injected fluid molecular weight, and used to make a comprehensive databank for model calibration and verification. At the test stage, the coefficient of correlation (R2) was higher than 0.98 and mean square error was less than 0.04 indicating the good performance of the proposed model. Furthermore, a comparison between the prediction of ANN model and two types of alternative approaches, namely the thermodynamic and the fractal/aggregation approaches, was performed. For this purpose, the prediction of two of the widely used solubility models, Flory---Huggins and Modified Flory---Huggins and also a polydisperse thermodynamic model was compared to the prediction of the proposed ANN model. In addition to those, as a fractal/aggregation model, a scaling model was also selected and employed to compare its performance against that of the proposed ANN model. The ANN model showed a better performance as compared to the other conventional models. The results demonstrated that the proposed model provides acceptable prediction for different oil types over a wide range of pressure which is a difficult task for most of the conventional techniques.