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"systems biology"

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"systems biology"

These pubmed results were generated on 2018/01/13

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

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"systems biology"

These pubmed results were generated on 2018/01/13

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

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"systems biology"

These pubmed results were generated on 2018/01/12

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

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"systems biology"

These pubmed results were generated on 2018/01/11

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

45 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/10

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

35 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/09

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Boolean network modeling in systems pharmacology.

J Pharmacokinet Pharmacodyn. 2018 Jan 06;:

Authors: Bloomingdale P, Nguyen VA, Niu J, Mager DE

Abstract
Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

PMID: 29307099 [PubMed - as supplied by publisher]

Peptide retention time prediction in hydrophilic interaction liquid chromatography. Comparison of separation selectivity between bare silica and bonded stationary phases.

J Chromatogr A. 2017 Dec 18;:

Authors: Spicer V, Krokhin OV

Abstract
Peptide separation selectivity of four different HILIC sorbents was compared through the development of sequence-specific retention prediction algorithms for the datasets of 36-40 thousands peptides each. Hydrophilicity of these sorbents at pH 4.5 (peptide retention under acetonitrile:water gradients) increases in the following order: Luna HILIC < XBridge Amide < Atlantis Silica ∼ Luna HILIC Silica. Bare silica phases are characterized by higher retention coefficients for basic residues (Arg, Lys, His), while interactions with neutral HILIC phases is driven by interaction with the charged residues (Asp, Glu, Arg, Lys, His). Such difference is caused by electrostatic repulsion of negatively charged side chains of Asp and Glu from silanol groups on bare silica sorbents. The sequence-specific features characteristic for both bonded and bare-silica supports were established. Variation of retention coefficients at N- and C-termini showed a peptide orientation effect on silica matrices. The negatively charged surface of silica attracts positively charged groups, thus increasing interaction of N-terminal residues. The effect of peptide helicity is also different between two types of sorbents. The contribution of amphipathic helicity was found to be higher for more hydrophilic silica matrices, causing a reduced predictive accuracy of the models: all four Sequence-Specific Retention Calculator HILIC models had R2-values in 0.973-0.98 range. Development of accurate prediction models provides a solid support for retention prediction filtering of false positive identifications in high-throughput proteomics analyses, guided development of 2D LCMS procedures ((HILIC-RP)). Our analysis also shows that all phases possess a high degree of orthogonality to RPLC (formic acid) with Luna HILIC being the best choice in this regard.

PMID: 29306631 [PubMed - as supplied by publisher]

Relative quantitation of neutral and sialylated N-glycans using stable isotopic labeled d0/d5-benzoyl chloride by MALDI-MS.

Anal Chim Acta. 2018 Mar 09;1002:50-61

Authors: Wang C, Wu Y, Zhang L, Liu BF, Lin Y, Liu X

Abstract
Quantitative analysis of glycans is an emerging field in glycomic research. Herein we present a rapid and effective dual-labeling strategy, in the combination of isotopic derivatization of N-glycosylamine-based glycans by d0/d5-benzoyl chloride and methylamidation of sialic acids, to relatively quantify both neutral and sialylated N-glycans simultaneously by MALDI-MS. The derivatization efficiencies were increased by microwave-accelerated deglycosylation which not only largely reduce the time of glycoprotein deglycosylation but also inhibit the hydrolysis of intermediate glycosylamines produced by PNGase F digestion. Three model glycoproteins, including RNase B, bovine fetuin and IgG from human serum, were applied to validate this technique. Results showed that the glycans from microgram level of glycoprotein can be successfully quantified with high reproducibility and the whole time of analytical procedure was shortened to 4 h. Furthermore, this proposed method was applied for the comparative analysis of N-glycans from serum of healthy donors and multiple myeloma patients. It was found that five N-glycans may be as the potential biomarkers for rapid detection of early multiple myeloma, indicating the feasibility of this strategy in monitoring subtle quantitative differences of serum glycomics.

PMID: 29306413 [PubMed - in process]

Fc gamma receptors are expressed in the developing rat brain and activate downstream signaling molecules upon cross-linking with immune complex.

J Neuroinflammation. 2018 Jan 06;15(1):7

Authors: Stamou M, Grodzki AC, van Oostrum M, Wollscheid B, Lein PJ

Abstract
BACKGROUND: Exposure of the developing brain to immune mediators, including antibodies, is postulated to increase risk for neurodevelopmental disorders and neurodegenerative disease. It has been suggested that immunoglobulin G-immune complexes (IgG-IC) activate Fc gamma receptors (FcγR) expressed on neurons to modify signaling events in these cells. However, testing this hypothesis is hindered by a paucity of data regarding neuronal FcγR expression and function.
METHODS: FcγR transcript expression in the hippocampus, cortex, and cerebellum of neonatal male and female rats was investigated ex vivo and in mixed cultures of primary hippocampal and cortical neurons and astrocytes using quantitative PCR analyses. Expression at the protein level in mixed cultures of primary hippocampal and cortical neurons and astrocytes was determined by immunocytochemistry, western blotting, proteotype analysis, and flow cytometry. The functionality of these receptors was assessed by measuring changes in intracellular calcium levels, Erk phosphorylation, and IgG internalization following stimulation with IgG-immune complexes.
RESULTS: FcgrIa, FcgrIIa, FcgrIIb, FcgrIIIa, and Fcgrt transcripts were detectable in the cortex, hippocampus, and cerebellum at postnatal days 1 and 7. These transcripts were also present in primary hippocampal and cortical cell cultures, where their expression was modulated by IFNγ. Expression of FcγRIa, FcγRIIb, and FcγRIIIa, but not FcγRIIa or FcRn proteins, was confirmed in cultured hippocampal and cortical neurons and astrocytes at the single cell level. A subpopulation of these cells co-expressed the activating FcγRIa and the inhibitory FcγRIIb. Functional analyses demonstrated that exposure of hippocampal and cortical cell cultures to IgG-IC increases intracellular calcium and Erk phosphorylation and triggers FcγR-mediated internalization of IgG.
CONCLUSIONS: Our data demonstrate that developing neurons and astrocytes in the hippocampus and the cortex express signaling competent FcγR. These findings suggest that IgG antibodies may influence normal neurodevelopment or function via direct interactions with FcγR on non-immune cells in the brain.

PMID: 29306331 [PubMed - in process]

Papain-like cysteine proteases in Carica papaya: lineage-specific gene duplication and expansion.

BMC Genomics. 2018 Jan 06;19(1):26

Authors: Liu J, Sharma A, Niewiara MJ, Singh R, Ming R, Yu Q

Abstract
BACKGROUND: Papain-like cysteine proteases (PLCPs), a large group of cysteine proteases structurally related to papain, play important roles in plant development, senescence, and defense responses. Papain, the first cysteine protease whose structure was determined by X-ray crystallography, plays a crucial role in protecting papaya from herbivorous insects. Except the four major PLCPs purified and characterized in papaya latex, the rest of the PLCPs in papaya genome are largely unknown.
RESULTS: We identified 33 PLCP genes in papaya genome. Phylogenetic analysis clearly separated plant PLCP genes into nine subfamilies. PLCP genes are not equally distributed among the nine subfamilies and the number of PLCPs in each subfamily does not increase or decrease proportionally among the seven selected plant species. Papaya showed clear lineage-specific gene expansion in the subfamily III. Interestingly, all four major PLCPs purified from papaya latex, including papain, chymopapain, glycyl endopeptidase and caricain, were grouped into the lineage-specific expansion branch in the subfamily III. Mapping PLCP genes on chromosomes of five plant species revealed that lineage-specific expansions of PLCP genes were mostly derived from tandem duplications. We estimated divergence time of papaya PLCP genes of subfamily III. The major duplication events leading to lineage-specific expansion of papaya PLCP genes in subfamily III were estimated at 48 MYA, 34 MYA, and 16 MYA. The gene expression patterns of the papaya PLCP genes in different tissues were assessed by transcriptome sequencing and qRT-PCR. Most of the papaya PLCP genes of subfamily III expressed at high levels in leaf and green fruit tissues.
CONCLUSIONS: Tandem duplications played the dominant role in affecting copy number of PLCPs in plants. Significant variations in size of the PLCP subfamilies among species may reflect genetic adaptation of plant species to different environments. The lineage-specific expansion of papaya PLCPs of subfamily III might have been promoted by the continuous reciprocal selective effects of herbivore attack and plant defense.

PMID: 29306330 [PubMed - in process]

Perspective: Maximum caliber is a general variational principle for dynamical systems.

J Chem Phys. 2018 Jan 07;148(1):010901

Authors: Dixit PD, Wagoner J, Weistuch C, Pressé S, Ghosh K, Dill KA

Abstract
We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics-such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production-are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

PMID: 29306272 [PubMed - in process]

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

JMIR Med Inform. 2018 Jan 05;6(1):e2

Authors: Kim S, Park D, Choi Y, Lee K, Kim B, Jeon M, Kim J, Tan AC, Kang J

Abstract
BACKGROUND: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain.
OBJECTIVE: This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset.
METHODS: We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models.
RESULTS: The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average.
CONCLUSIONS: In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge.

PMID: 29305341 [PubMed]

Comparative analysis of gene expression in maternal peripheral blood and monocytes during spontaneous preterm labor.

Am J Obstet Gynecol. 2018 Jan 02;:

Authors: Paquette AG, Shynlova O, Kibschull M, Price ND, Lye SJ, GAPPS Systems Biology of Preterm Birth Team

Abstract
BACKGROUND: Preterm birth is the leading cause of newborn death worldwide, and is associated with significant cognitive and physiological challenges in later life. There is a pressing need to define the mechanisms that initiate spontaneous preterm labor , and for development of novel clinical biomarkers to identify high-risk pregnancies. Most preterm birth studies utilize fetal tissues, and there is limited understanding of the transcriptional changes that occur in mothers undergoing spontaneous preterm labor. Earlier work revealed that a specific population of maternal peripheral leukocytes (macrophages/monocytes) play an active role in the initiation of labor. Thus, we hypothesized that there are dynamic gene expression changes in maternal blood leukocytes during PTL.
OBJECTIVE: Using next generation sequencing we aim to characterize the transcriptome in whole blood leukocytes and peripheral monocytes of women undergoing sPTL compared to healthy pregnant women that subsequently delivered at full term.
STUDY DESIGN: RNA sequencing was performed in both whole blood and peripheral monocytes from women who underwent preterm labor (24-34 weeks of gestation, N=20) matched for gestational age to healthy pregnant controls (N=30). All participants were a part of the Ontario Birth Study cohort (Toronto, Canada).
RESULTS: We identified significant differences in expression of 262 genes in peripheral monocytes and 184 genes in whole blood of women who were in active sPTL compared to pregnant women of the same gestational age not undergoing labor, with 43 of these genes differentially expressed in both whole blood and peripheral monocytes. ADAMTS2 expression was significantly increased in women actively undergoing sPTL, which we validated through digital droplet RT-PCR. Intriguingly, we have also identified a number of gene sets including signaling by SCF-Kit, nucleotide metabolism, and Trans-Golgi network vesicle budding, which exhibited changes in relative gene expression that was predictive of preterm labor status in both maternal whole blood and peripheral monocytes.
CONCLUSION: This study is the first to investigate changes in both whole blood leukocytes and peripheral monocytes of women actively undergoing spontaneous preterm labor through robust transcript measurements from RNA-sequencing. Our unique study design overcame confounding based on gestational age by collecting blood samples from women matched by gestational age, allowing us to study transcriptomics changes directly related to the active preterm parturition. We performed RNA profiling using whole genome sequencing; which is highly sensitive and allowed us to identify subtle changes in specific genes. ADAMTS2 expression emerged as a marker of prematurity within peripheral blood leukocytes; an accessible tissue which plays a functional role in signaling during the onset of labor. We identified changes in relative gene expression in a number of gene sets related to signaling in monocytes and whole blood of women undergoing sPTL compared to controls. These genes and pathways may help identify potential targets for the development of novel drugs for preterm birth prevention.

PMID: 29305255 [PubMed - as supplied by publisher]

38 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/06

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

38 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/06

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

19 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/05

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

19 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/05

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

21 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"systems biology"

These pubmed results were generated on 2018/01/04

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.