Verification of a Maternal-Fetal Physiologically Based Pharmacokinetic Model for Passive Placental Permeability Drugs.

Drug Metab Dispos. 2017 Jan 03;:

Authors: Zhang Z, Unadkat JD

Abstract
Fetal exposure to drugs cannot be readily estimated from single time point cord blood sampling at the time of delivery. Therefore, we developed a physiologically-based pharmacokinetic (PBPK) model to estimate fetal drug exposure throughout pregnancy. Here we report verification of this novel maternal-fetal physiologically-based pharmacokinetic (m-f-PBPK) model for drugs that passively diffuse across the placenta and are not metabolized/transported there. Our recently built m-f-PBPK model was populated with gestational age-dependent changes in maternal drug disposition and maternal-fetal physiology. Using midazolam as an in vivo calibrator, the transplacental passive diffusion clearance of theophylline and zidovudine was first estimated. Then, for verification, the predicted maternal plasma (MP) and umbilical venous (UV) plasma drug concentrations by our m-f-PBPK were compared against those observed at term. Overall, our m-f-PBPK model well predicted the maternal and fetal exposure to the two verification drugs, theophylline and zidovudine, at term, across a range of dosing regimens, with nearly all observed MP and UV plasma drug concentrations falling within the 90% prediction interval [i.e.5th -95th percentile range of a virtual pregnant population (n=100)]. Prediction precision and bias of theophylline MP and UV were 14.5% and 12.4%, and 9.4% and 7.5%, respectively. Further, for zidovudine, after the exclusion of one unexpectedly low MP concentration, prediction precision and bias for MP and UV were 50.3 % and 30.2, and 28.3% and 15.0%, respectively. This m-f-PBPK should be useful to predict fetal exposure to drugs, throughout pregnancy, for drugs that passively diffuse across the placenta.

PMID: 28049636 [PubMed - as supplied by publisher]

The application of powerful promoters to enhance gene expression in industrial microorganisms.

World J Microbiol Biotechnol. 2017 Feb;33(2):23

Authors: Zhou S, Du G, Kang Z, Li J, Chen J, Li H, Zhou J

Abstract
Production of useful chemicals by industrial microorganisms has been attracting more and more attention. Microorganisms screened from their natural environment usually suffer from low productivity, low stress resistance, and accumulation of by-products. In order to overcome these disadvantages, rational engineering of microorganisms to achieve specific industrial goals has become routine. Rapid development of metabolic engineering and synthetic biology strategies provide novel methods to improve the performance of industrial microorganisms. Rational regulation of gene expression by specific promoters is essential to engineer industrial microorganisms for high-efficiency production of target chemicals. Identification, modification, and application of suitable promoters could provide powerful switches at the transcriptional level for fine-tuning of a single gene or a group of genes, which are essential for the reconstruction of pathways. In this review, the characteristics of promoters from eukaryotic, prokaryotic, and archaea microorganisms are briefly introduced. Identification of promoters based on both traditional biochemical and systems biology routes are summarized. Besides rational modification, de novo design of promoters to achieve gradient, dynamic, and logic gate regulation are also introduced. Furthermore, flexible application of static and dynamic promoters for the rational engineering of industrial microorganisms is highlighted. From the perspective of powerful promoters in industrial microorganisms, this review will provide an extensive description of how to regulate gene expression in industrial microorganisms to achieve more useful goals.

PMID: 28044271 [PubMed - in process]

Molecular noise can minimize the collective sensitivity of a clonal heterogeneous cell population.

J Theor Biol. 2016 Dec 30;:

Authors: Forment M, Rodrigo G

Abstract
It is now widely accepted that molecular noise, rather than be always detrimental, introduces in many circumstances the required boost to reach fundamental cellular activities or strategies otherwise unattainable. In threshold-like genetic systems, molecular noise serves to generate heterogeneous responses in a clonal population, also in a tissue, due to cell-to-cell variability. Here, we derived a mathematical framework from which we could study in detail this effect. We focused on a minimal decision-making gene circuit implemented as a transcriptional positive-feedback loop. We evidenced that when the individual responses of each cell within the population are averaged, a sort of collective behavior, the resulting dose-response curve is linearized. In other words, the population is less sensitive than the individuals, which otherwise enhances the information transfer from signal to response. We found that the distance to the ideal linear response of the cell population is minimized for a particular noise level, and also characterized the interplay between intrinsic and extrinsic noise. Overall, our results highlight how cells could, by acting as a collective, entangle their genetic systems with their environments by adjusting the intracellular noise levels.

PMID: 28043818 [PubMed - as supplied by publisher]

Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells.

Cell J. 2017;18(4):514-531

Authors: Rabieian R, Abedi M, Gheisari Y

Abstract
OBJECTIVE: Despite the huge efforts, chronic kidney disease (CKD) remains as an unsolved problem in medicine. Many studies have shown a central role for transforming growth factor beta-1 (TGFβ-1) and its downstream signaling cascades in the pathogenesis of CKD. In this study, we have reanalyzed a microarray dataset to recognize critical signaling pathways controlled by TGFβ-1.
MATERIALS AND METHODS: This study is a bioinformatics reanalysis for a microarray data. The GSE23338 dataset was downloaded from the gene expression omnibus (GEO) database which assesses the mRNA expression profile of TGFβ-1 treated human kidney cells after 24 and 48 hours incubation. The protein interaction networks for differentially expressed (DE) genes in both time points were constructed and enriched. In addition, by network topology analysis, genes with high centrality were identified and then pathway enrichment analysis was performed with either the total network genes or with the central nodes.
RESULTS: We found 110 and 170 genes differentially expressed in the time points 24 and 48 hours, respectively. As the genes in each time point had few interactions, the networks were enriched by adding previously known genes interacting with the differentially expressed ones. In terms of degree, betweenness, and closeness centrality parameters 62 and 60 nodes were considered to be central in the enriched networks of 24 hours and 48 hours treatment, respectively. Pathway enrichment analysis with the central nodes was more informative than those with all network nodes or even initial DE genes, revealing key signaling pathways.
CONCLUSION: We here introduced a method for the analysis of microarray data that integrates the expression pattern of genes with their topological properties in protein interaction networks. This holistic novel approach allows extracting knowledge from raw bulk omics data.

PMID: 28042536 [PubMed]

Parameter estimation for dynamical systems with discrete events and logical operations.

Bioinformatics. 2016 Dec 31;:

Authors: Fröhlich F, Theis FJ, Rädler JO, Hasenauer J

Abstract
MOTIVATION: Ordinary differential equation (ODE) models are frequently used to describe the dynamic behaviour of biochemical processes. Such ODE models are often extended by events to describe the effect of fast latent processes on the process dynamics. To exploit the predictive power of ODE models, their parameters have to be inferred from experimental data. For models without events, gradient based optimization schemes perform well for parameter estimation, when sensitivity equations are used for gradient computation. Yet, sensitivity equations for models with parameter- and state-dependent events and event-triggered observations are not supported by existing toolboxes.
RESULTS: In this manuscript, we describe the sensitivity equations for differential equation models with events and demonstrate how to estimate parameters from event-resolved data using event-triggered observations in parameter estimation. We consider a model for GFP expression after transfection and a model for spiking neurons and demonstrate that we can improve computational efficiency and robustness of parameter estimation by using sensitivity equations for systems with events. Moreover, we demonstrate that, by using event-outputs, it is possible to consider event-resolved data, such as time-to-event data, for parameter estimation with ODE models. By providing a user-friendly, modular implementation in the toolbox AMICI, the developed methods are made publicly available and can be integrated in other systems biology toolboxes.
AVAILABILITY AND IMPLEMENTATION: We implement the methods in the open-source toolbox Advanced MATLAB Interface for CVODES and IDAS (AMICI, https://github.com/ICB-DCM/AMICI).
CONTACT: jan.hasenauer@helmholtz-muenchen.deSupplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28040696 [PubMed - as supplied by publisher]

Sex-comparative study of mouse cerebellum physiology under adult-onset hypothyroidism: The significance of GC-MS metabolomic data normalization in meta-analysis.

J Chromatogr B Analyt Technol Biomed Life Sci. 2016 Dec 12;1041-1042:158-166

Authors: Maga-Nteve C, Vasilopoulou CG, Constantinou C, Margarity M, Klapa MI

Abstract
A systematic data quality validation and normalization strategy is an important component of the omic profile meta-analysis, ensuring comparability of the profiles and exclusion of experimental biases from the derived biological conclusions. In this study, we present the normalization methodology applied on the sets of cerebellum gas chromatography-mass spectrometry metabolic profiles of 124days old male and female animals in an adult-onset-hypothyroidism (AOH) mouse model before combining them into a sex-comparative analysis. The employed AOH model concerns the monitoring of the brain physiology of Balb/cJ mice after eight-week administration of 1%w/v KClO4 in the drinking water, initiated on the 60th day of their life. While originating from the same animal study, the tissues of the two sexes were processed and their profiles acquired and analyzed at different time periods. Hence, the previously published profile set of male mice was first re-annotated based on the presently available resources. Then, after being validated as acquired under the same analytical conditions, both profiles sets were corrected for derivatization biases and filtered for low-confidence measurements based on the same criteria. The final normalized 73-metabolite profiles contribute to the currently few available omic datasets of the AOH effect on brain molecular physiology, especially with respect to sex differentiation. Multivariate statistical analysis indicated one (unknown) and three (succinate, benzoate, myristate) metabolites with significantly higher and lower, respectively, cerebellum concentration in the hypothyroid compared to the euthyroid female mice. The respective numbers for the males were two and 24. Comparison of the euthyroid cerebellum metabolic profiles between the two sexes indicated 36 metabolites, including glucose, myo- and scyllo-inositol, with significantly lower concentration in the females versus the males. This implies that the female mouse cerebellum has been conditioned to smaller changes in its metabolic activity with respect to the pathways involving these metabolites compared to the male animals. In conclusion, our study indicated a much subtler AOH effect on the cerebellum metabolic activity of the female compared to the male mice. The leaner metabolic profile of the female mouse cerebellum was suggested as a potential factor contributing to this phenomenon.

PMID: 28040659 [PubMed - as supplied by publisher]

(Epi)genetic Inheritance in Schistosoma mansoni: A Systems Approach.

Trends Parasitol. 2016 Dec 28;:

Authors: Cosseau C, Wolkenhauer O, Padalino G, Geyer KK, Hoffmann KF, Grunau C

Abstract
The G×E concept, in which genotype × environment interactions bring about the phenotype, is widely used to describe biological phenomena. We propose to extend the initial notion of the concept, replacing G by 'inheritance system'. This system, comprised of both genome and epigenome components, collectively interacts with the environment to shape the development of a phenotype. In the case of the human blood fluke Schistosoma mansoni, responsible for intestinal bilharzia, the phenotypic trait that is most relevant to global health is infection success. Taking a systems biology view we show how genetic and epigenetic interactions result in ephemeral, but also heritable, phenotypic variations that are important for infection success.

PMID: 28040375 [PubMed - as supplied by publisher]

Data Integration in Physiology Using Bayes' Rule and Minimum Bayes' Factors: Deubiquitylating Enzymes in the Renal Collecting Duct.

Physiol Genomics. 2016 Dec 30;:physiolgenomics.00120.2016

Authors: Xue Z, Chen JX, Zhao Y, Medvar B, Knepper MA

Abstract
A major challenge in physiology is to exploit the many large-scale datasets available from "-omic" studies in order to seek answers to key physiological questions. In previous studies, Bayes' Theorem has been used for this purpose. This approach requires a means to map continuously distributed experimental data to probabilities (likelihood values) in order to derive posterior probabilities from the combination of prior probabilities and new data. Here, we introduce the use of Minimum Bayes' Factors for this purpose and illustrate the approach by addressing a physiological question, "Which deubiquitylating enzymes (DUBs) encoded by mammalian genomes are most likely to regulate plasma membrane transport processes in renal cortical collecting duct principal cells?" To do this, we have created a comprehensive online database of 110 DUBs present in the mammalian genome (https://hpcwebapps.cit.nih.gov/ESBL/Database/DUBs/). We used Bayes' theorem to integrate available information from large-scale data sets derived from proteomic and transcriptomic studies of renal collecting duct cells to rank the 110 known DUBs. The top-ranked DUBs were OTUB1, USP14, PSMD7, PSMD14, USP7, USP9X, OTUD4, USP10, and UCHL5. Among these USP7, USP9X, OTUD4 and USP10 are known to be involved in endosomal trafficking and have potential roles in endosomal recycling of plasma membrane proteins in the mammalian cortical collecting duct.

PMID: 28039431 [PubMed - as supplied by publisher]

Correction: Suppression of transcriptional drift extends C. elegans lifespan by postponing the onset of mortality.

Elife. 2016 Dec 30;5:

Authors: Rangaraju S, Solis GM, Thompson RC, Gomez-Amaro RL, Kurian L, Encalada SE, Niculescu AB, Salomon DR, Petrascheck M

PMID: 28036253 [PubMed - in process]

Adequate immune response ensured by binary IL-2 and graded CD25 expression in a murine transfer model.

Elife. 2016 Dec 30;5:

Authors: Fuhrmann F, Lischke T, Gross F, Scheel T, Bauer L, Kalim KW, Radbruch A, Herzel H, Hutloff A, Baumgrass R

Abstract
The IL-2/IL-2Ralpha (CD25) axis is of central importance for the interplay of effector and regulatory T cells. Nevertheless, the question how different antigen loads are translated into appropriate IL-2 production to ensure adequate responses against pathogens remains largely unexplored. Here we find that at single cell level, IL-2 is binary (digital) and CD25 is graded expressed whereas at population level both parameters show graded expression correlating with the antigen amount. Combining in vivo data with a mathematical model we demonstrate that only this binary IL-2 expression ensures a wide linear antigen response range for Teff and Treg cells under real spatiotemporal conditions. Furthermore, at low antigen concentrations binary IL-2 expression safeguards by its spatial distribution selective STAT5 activation only of closely adjacent Treg cells regardless of their antigen specificity. These data show that the mode of IL-2 secretion is critical to tailor the adaptive immune response to the antigen amount.

PMID: 28035902 [PubMed - in process]

Human gut microbiota and healthy aging: Recent developments and future prospective.

Nutr Healthy Aging. 2016 Oct 27;4(1):3-16

Authors: Kumar M, Babaei P, Ji B, Nielsen J

Abstract
The human gut microbiota alters with the aging process. In the first 2-3 years of life, the gut microbiota varies extensively in composition and metabolic functions. After this period, the gut microbiota demonstrates adult-like more stable and diverse microbial species. However, at old age, deterioration of physiological functions of the human body enforces the decrement in count of beneficial species (e.g. Bifidobacteria) in the gut microbiota, which promotes various gut-related diseases (e.g. inflammatory bowel disease). Use of plant-based diets and probiotics/prebiotics may elevate the abundance of beneficial species and prevent gut-related diseases. Still, the connections between diet, microbes, and host are only partially known. To this end, genome-scale metabolic modeling can help to explore these connections as well as to expand the understanding of the metabolic capability of each species in the gut microbiota. This systems biology approach can also predict metabolic variations in the gut microbiota during ageing, and hereby help to design more effective probiotics/prebiotics.

PMID: 28035338 [PubMed]

Sensitivity analysis of Repast computational ecology models with R/Repast.

Ecol Evol. 2016 Dec;6(24):8811-8831

Authors: Prestes García A, Rodríguez-Patón A

Abstract
Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom-up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in-silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.

PMID: 28035271 [PubMed]

Dendritic trafficking faces physiologically critical speed-precision tradeoffs.

Elife. 2016 Dec 30;5:

Authors: Williams AH, O'Donnell C, Sejnowski TJ, O'Leary T

Abstract
Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the 'sushi-belt model' (Doyle and Kiebler, 2011). Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimates of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. These findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons.

PMID: 28034367 [PubMed - in process]

Clinical, Immune, and Microbiome Traits of Gingivitis and Peri-implant Mucositis.

J Dent Res. 2017 Jan;96(1):47-55

Authors: Schincaglia GP, Hong BY, Rosania A, Barasz J, Thompson A, Sobue T, Panagakos F, Burleson JA, Dongari-Bagtzoglou A, Diaz PI

Abstract
Tissues surrounding dental implants and teeth develop clinical inflammation in response to microbial stimuli. However, the literature suggests that differences exist in the microbial insult and inflammatory responses leading to gingivitis and peri-implant mucositis. In this pilot study, the authors use for the first time a systems biology approach to comprehensively evaluate clinical parameters, selected inflammatory markers, and the microbiome of subject-matched tooth and implant sites during native inflammation and in response to experimental plaque accumulation. Fifteen subjects with 2 posterior implants and corresponding contralateral teeth were examined at enrollment; at day 0, after reinstitution of gingival/mucosal health; at days 7, 14, and 21, during stent-mediated oral hygiene (OH) abstention; and at day 42, after resumption of OH. The subgingival microbiome was evaluated via 16S rRNA gene sequencing and 8 selected inflammatory markers measured in crevicular fluid. Comparison of teeth and implants via general linear models based on orthogonal polynomials showed similar responses in clinical parameters, inflammatory mediators, and proportions of individual microbial taxa during OH abstention. Implants, however, accumulated less plaque and underwent more heterogeneous shifts in microbiome structure. A multilevel, within-group, sparse partial least squares analysis of covariation of microbial, inflammatory, and clinical parameters throughout all study visits found inflammation around teeth and implants positively correlated with IL-1 alpha and IL-1 beta and with the proportions of Selenomonas, Prevotella, and 5 species-level phylotypes. Gingivitis, however, showed a stronger positive correlation with lactoferrin and IL-1ra and a stronger negative correlation with Rothia. Peri-implant mucositis, on the contrary, correlated positively with certain microbial taxa not associated with gingivitis by a previous study or the current one. In summary, differences existed between implants and tooth sites in microbiome evolution during OH abstention and in the correlation of specific inflammatory mediators and microbial taxa with clinical inflammation. Common biological features, however, were also identified for gingivitis and mucositis.

PMID: 28033066 [PubMed - in process]

A unique large-scale undergraduate research experience in molecular systems biology for non-mathematics majors.

Biochem Mol Biol Educ. 2016 Dec 28;:

Authors: Kappler U, Rowland SL, Pedwell RK

Abstract
Systems biology is frequently taught with an emphasis on mathematical modeling approaches. This focus effectively excludes most biology, biochemistry, and molecular biology students, who are not mathematics majors. The mathematical focus can also present a misleading picture of systems biology, which is a multi-disciplinary pursuit requiring collaboration between biochemists, bioinformaticians, and mathematicians. This article describes an authentic large-scale undergraduate research experience (ALURE) in systems biology that incorporates proteomics, bacterial genomics, and bioinformatics in the one exercise. This project is designed to engage students who have a basic grounding in protein chemistry and metabolism and no mathematical modeling skills. The pedagogy around the research experience is designed to help students attack complex datasets and use their emergent metabolic knowledge to make meaning from large amounts of raw data. On completing the ALURE, participants reported a significant increase in their confidence around analyzing large datasets, while the majority of the cohort reported good or great gains in a variety of skills including "analysing data for patterns" and "conducting database or internet searches." An environmental scan shows that this ALURE is the only undergraduate-level system-biology research project offered on a large-scale in Australia; this speaks to the perceived difficulty of implementing such an opportunity for students. We argue however, that based on the student feedback, allowing undergraduate students to complete a systems-biology project is both feasible and desirable, even if the students are not maths and computing majors. © 2016 by The International Union of Biochemistry and Molecular Biology, 2016.

PMID: 28032403 [PubMed - as supplied by publisher]

BluePen Biomarkers LLC: integrated biomarker solutions.

Future Sci OA. 2016 Jun;2(2):FSO124

Authors: Blair IA, Mesaros C, Lilley P, Nunez M

Abstract
BluePen Biomarkers provides a unique comprehensive multi-omics biomarker discovery and validation platform. We can quantify, integrate and analyze genomics, proteomics, metabolomics and lipidomics biomarkers, alongside clinical data, demographics and other phenotypic data. A unique bio-inspired signal processing analytic approach is used that has the proven ability to identify biomarkers in a wide variety of diseases. The resulting biomarkers can be used for diagnosis, prognosis, mechanistic studies and predicting treatment response, in contexts from core research through clinical trials. BluePen Biomarkers provides an additional groundbreaking research goal: identifying surrogate biomarkers from different modalities. This not only provides new biological insights, but enables least invasive, least-cost tests that meet or exceed the predictive quality of current tests.

PMID: 28031971 [PubMed]

Metabolic systems biology: a brief primer.

J Physiol. 2016 Dec 28;:

Authors: Edwards LM

Abstract
In the early to mid 20(th) Century, reductionism as a concept in biology was challenged by key thinkers, including Ludwig Von Bertalanffy. He proposed that living organisms were specific examples of complex systems and, as such, they should display characteristics including hierarchical organisation and emergent behaviour. Yet the true study of complete biological systems (for example, metabolism) was not possible until technological advances that occurred 60 years later. Technology now exists that permits the measurement of complete levels of the biological hierarchy, for example the genome and transcriptome. The complexity and scale of these data require computational models for their interpretation. The combination of these - systems thinking, high-dimensional data and computation - defines systems biology, typically accompanied by some notion of iterative model refinement. Only sequencing-based technologies, however, offer full coverage. Other 'omics' platforms trade coverage for sensitivity, although the densely-connected nature of biological networks suggest that full coverage may not be necessary. Systems biology models are often characterised as either 'bottom-up' (mechanistic) or 'top-down' (statistical). This distinction can mislead, as all models rely on data and all are, to some degree, 'middle-out'. Systems biology has matured as a discipline, and its methods are commonplace in many laboratories. However, many challenges remain, especially those related to large-scale data integration. This article is protected by copyright. All rights reserved.

PMID: 28028815 [PubMed - as supplied by publisher]

A Review of Analytical Techniques and Their Application in Disease Diagnosis in Breathomics and Salivaomics Research.

Int J Mol Sci. 2016 Dec 23;18(1):

Authors: Beale DJ, Jones OA, Karpe AV, Dayalan S, Oh DY, Kouremenos KA, Ahmed W, Palombo EA

Abstract
The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions.

PMID: 28025547 [PubMed - in process]

A Network-based Systems Biology Platform for Predicting Disease-Metabolite Links.

Comb Chem High Throughput Screen. 2016 Dec 14;

Authors: Wathieu H, Issa NT, Mohandoss M, Byers SW, Dakshanamurthy S

Abstract
Metabolites constitute phenotypic end products of gene expression, and are key players in biological networks. For this reason, the field of metabolomics has been useful in predicting, explaining, and affecting the mechanisms of disease phenotypes. MSD-MAP (Multi Scale Disease-Metabolite Association Platform) is a powerful computational tool for hypothesizing new links between diseases and metabolites, and characterizing the functional basis of those links in a systems biology context. Upon integrating both predicted and known metabolite-protein associations, MSD-MAP takes a two-pronged approach to associating metabolites to a disease, relying on network-based characterization of disease perturbation at multiple levels of biological activity as well as statistical matching of metabolite- and disease-associated biological profiles. MSD-MAP successfully recapitulated cross-disease links of cancer-associated metabolites, and predicted key metabolites associated with colorectal, esophageal, and prostate cancers after the integration of patient-based gene expression analysis. For example, the catecholamine dopamine was correctly predicted to be strongly associated with colorectal cancer based on statistical coincidence with its disease perturbation network.

PMID: 28024464 [PubMed - as supplied by publisher]

Optimizing personalized treatment of oral mucositis secondary to cancer therapy through systems biology.

J Clin Oncol. 2011 May 20;29(15_suppl):e19690

Authors: Srivastava R, White JR, Lalla RV, Loew LM, Peterson DE

Abstract
e19690 Background: Oral mucositis (OM) can result in clinically significant adverse outcomes that require high resource utilization. Clinical trials for mucositis drug development have typically incorporated a cohort-based methodology. However, patients vary considerably in risk and severity of OM. In addition, the modeling for OM pathobiology is complex and includes up-regulation of NF-kB and TNF-a. We therefore tested the hypothesis that an individual patient-oriented systems biology approach to mucositis management will maximize treatment efficacy, while reducing drug dosage.
METHODS: A mathematical model of the biomolecular reaction network describing the dynamics of NF-kB and TNF-a up-regulation was developed using data derived from published studies. To represent inter-subject variation, 10,000 in silico subjects were generated, with the reaction rate constants of each being randomly distributed by up to ± 30% relative to a pre-specified base-case. OM treatment was optimized using a genetic algorithm designed to down-regulate TNF-a levels to a pre-determined level using the minimum possible drug dosage. Two in silico subjects who deviated from average cohort behavior were randomly selected for comparison.
RESULTS: When optimized for the individual, required drug dosage for the first in silico subject was 20% of the cohort-optimized dosage on average. However, TNF-a levels for the individual were maintained within 5% of the average case. The optimal drug treatment for the second subject consisted of the maximum allowable drug dosage, representing a 10-fold increase versus the cohort-optimized case. Even with maximum drug dosage, TNF-a levels were at least 7.5-fold higher than the average case. In all cases, NF-kB dynamic behavior reflected that seen for TNF-a.
CONCLUSIONS: Individually-optimized OM treatment may improve therapeutic efficacy in selected patients, while limiting drug dosage and associated side-effects. Supported by NIH Career Development Award K23DE016946 and NIH grant P41RR013186.

PMID: 28021950 [PubMed - in process]