31 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 2018/08/02

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 2018/08/01

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.

15 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 2018/08/01

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.

32 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 2018/07/31

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.

31 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 2018/07/31

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.

mHealth and Aging.

J Am Med Dir Assoc. 2018 Jul 25;:

Authors: Valenzuela PL, Morales JS, Santos-Lozano A, Serra-Rexach JA, Izquierdo M, Lucia A

PMID: 30056007 [PubMed - as supplied by publisher]

Signal Percolation within a Bacterial Community.

Cell Syst. 2018 Jul 14;:

Authors: Larkin JW, Zhai X, Kikuchi K, Redford SE, Prindle A, Liu J, Greenfield S, Walczak AM, Garcia-Ojalvo J, Mugler A, Süel GM

Abstract
Signal transmission among cells enables long-range coordination in biological systems. However, the scarcity of quantitative measurements hinders the development of theories that relate signal propagation to cellular heterogeneity and spatial organization. We address this problem in a bacterial community that employs electrochemical cell-to-cell communication. We developed a model based on percolation theory, which describes how signals propagate through a heterogeneous medium. Our model predicts that signal transmission becomes possible when the community is organized near a critical phase transition between a disconnected and a fully connected conduit of signaling cells. By measuring population-level signal transmission with single-cell resolution in wild-type and genetically modified communities, we confirm that the spatial distribution of signaling cells is organized at the predicted phase transition. Our findings suggest that at this critical point, the population-level benefit of signal transmission outweighs the single-cell level cost. The bacterial community thus appears to be organized according to a theoretically predicted spatial heterogeneity that promotes efficient signal transmission.

PMID: 30056004 [PubMed - as supplied by publisher]

Euphorbia factor L2 alleviates lipopolysaccharide-induced acute lung injury and inflammation in mice through the suppression of NF-κB activation.

Biochem Pharmacol. 2018 Jul 25;:

Authors: Zhang Q, Zhu S, Cheng X, Lu C, Tao W, Zhang Y, Blackledge W, Cao X, Yi S, Liu Y, Zhao Y, Luo Y

Abstract
Acute respiratory distress syndrome threatens public health with high morbidity and mortality due to ineffective intervention whereby lipopolysaccharide (LPS) induced acute lung injury (ALI) in mice provides a research model. The seeds of Euphorbia lathyris L. have a long history of usage in Traditional Chinese Medicine. Euphorbia factors L1-L11, extracted from this herb, have been reported to have anti-inflammation and anti-cancer effects. Here, we report the preventive and therapeutic potential of Euphorbia factor L2 (EFL2) on LPS-induced ALI in mice, where EFL2 attenuated pathological alterations in the lung and improved survival. Significant suppresson of the recruitment and transmigration of inflammatory cells, specifically neutrophils, by 40 mg/kg of EFL2 was observed. EFL2 exerted potent anti-inflammatory effects by decreasing the levels of interleukin-1β (IL-1 β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF- α), interleukin-8 (IL-8) and myeloperoxidase (MPO) in the lung and bronchioalveolar lavage fluid. Consistent with the findings in vivo, EFL2 also showed robust inhibitory effects on the production of IL-1 β, IL-6, TNF- α and IL-8 released from LPS-stimulated RAW264.7 cells in vitro. Interestingly, this effect appeared to be mediated by EFL2's inhibition of NF-κB signaling activation, but not the MAPK pathway. Not only phosphorylation of IKK α / β and IκBα was down-regulated, p65 translocation and its DNA-binding activity were also significantly suppressed by EFL2. Moreover, overexpression of p65 reversed the inhibitory effect of EFL2 in LPS-induced NF-κB activation and cytokines production. The observed anti-inflammatory bioactivity of EFL2 indicates its potential as a drug development candidate, particularly for LPS-mediated disorders of inflammation.

PMID: 30055150 [PubMed - as supplied by publisher]

Multi-model ensembles improve predictions of crop-environment-management interactions.

Glob Chang Biol. 2018 Jul 28;:

Authors: Wallach D, Martre P, Liu B, Asseng S, Ewert F, Thorburn PJ, van Ittersum M, Aggarwal PK, Ahmed M, Basso B, Biernath C, Cammarano D, Challinor AJ, De Sanctis G, Dumont B, Eyshi Rezaei E, Fereres E, Fitzgerald GJ, Gao Y, Garcia-Vila M, Gayler S, Girousse C, Hoogenboom G, Horan H, Izaurralde RC, Jones CD, Kassie BT, Kersebaum KC, Klein C, Koehler AK, Maiorano A, Minoli S, Müller C, Naresh Kumar S, Nendel C, O'Leary GJ, Palosuo T, Priesack E, Ripoche D, Rötter RP, Semenov MA, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Wolf J, Zhang Z

Abstract
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. This article is protected by copyright. All rights reserved.

PMID: 30055118 [PubMed - as supplied by publisher]

Transcriptomic Analysis of Hepatic Cells in Multicellular Organotypic Liver Models.

Sci Rep. 2018 Jul 27;8(1):11306

Authors: Tegge AN, Rodrigues RR, Larkin AL, Vu L, Murali TM, Rajagopalan P

Abstract
Liver homeostasis requires the presence of both parenchymal and non-parenchymal cells (NPCs). However, systems biology studies of the liver have primarily focused on hepatocytes. Using an organotypic three-dimensional (3D) hepatic culture, we report the first transcriptomic study of liver sinusoidal endothelial cells (LSECs) and Kupffer cells (KCs) cultured with hepatocytes. Through computational pathway and interaction network analyses, we demonstrate that hepatocytes, LSECs and KCs have distinct expression profiles and functional characteristics. Our results show that LSECs in the presence of KCs exhibit decreased expression of focal adhesion kinase (FAK) signaling, a pathway linked to LSEC dedifferentiation. We report the novel result that peroxisome proliferator-activated receptor alpha (PPARα) is transcribed in LSECs. The expression of downstream processes corroborates active PPARα signaling in LSECs. We uncover transcriptional evidence in LSECs for a feedback mechanism between PPARα and farnesoid X-activated receptor (FXR) that maintains bile acid homeostasis; previously, this feedback was known occur only in HepG2 cells. We demonstrate that KCs in 3D liver models display expression patterns consistent with an anti-inflammatory phenotype when compared to monocultures. These results highlight the distinct roles of LSECs and KCs in maintaining liver function and emphasize the need for additional mechanistic studies of NPCs in addition to hepatocytes in liver-mimetic microenvironments.

PMID: 30054499 [PubMed - in process]

Pairwise library screen systematically interrogates Staphylococcus aureus Cas9 specificity in human cells.

Nat Commun. 2018 Jul 27;9(1):2962

Authors: Tycko J, Barrera LA, Huston NC, Friedland AE, Wu X, Gootenberg JS, Abudayyeh OO, Myer VE, Wilson CJ, Hsu PD

Abstract
Therapeutic genome editing with Staphylococcus aureus Cas9 (SaCas9) requires a rigorous understanding of its potential off-target activity in the human genome. Here we report a high-throughput screening approach to measure SaCas9 genome editing variation in human cells across a large repertoire of 88,692 single guide RNAs (sgRNAs) paired with matched or mismatched target sites in a synthetic cassette. We incorporate randomized barcodes that enable whitelisting of correctly synthesized molecules for further downstream analysis, in order to circumvent the limitation of oligonucleotide synthesis errors. We find SaCas9 sgRNAs with 21-mer or 22-mer spacer sequences are generally more active, although high efficiency 20-mer spacers are markedly less tolerant of mismatches. Using this dataset, we developed an SaCas9 specificity model that performs robustly in ranking off-target sites. The barcoded pairwise library screen enabled high-fidelity recovery of guide-target relationships, providing a scalable framework for the investigation of CRISPR enzyme properties and general nucleic acid interactions.

PMID: 30054474 [PubMed - in process]

Does the primary site really matter? Profiling mucinous ovarian cancers of uncertain primary origin (MO-CUP) to personalise treatment and inform the design of clinical trials.

Gynecol Oncol. 2018 Jul 24;:

Authors: Meagher NS, Schuster K, Voss A, Budden T, Pang CNI, deFazio A, Ramus SJ, Friedlander ML

Abstract
OBJECTIVE: Advanced stage mucinous ovarian cancers are diagnostically and therapeutically challenging. Histotype specific trials have failed due to low recruitment after excluding non-ovarian primaries. Mucinous ovarian cancers are commonly metastatic from other sites however lack definitive diagnostic markers. We suggest a classification of mucinous ovarian cancers of uncertain primary origin 'MO-CUPs' in clinical trials. This study aims to identify drug targets to guide treatment and future trials.
METHODS: We analyzed a large de-identified, multi-platform tumor profiling dataset of MO-CUPs enriched for advanced stage and recurrent cases submitted to Caris Life Sciences. Available data included a 45-gene next-generation sequencing (NGS) panel, gene amplification of HER2 and cMET and 18 immunohistochemical (IHC) markers of drug sensitivity/resistance.
RESULTS: Mucinous tumors from 333 patients were analyzed, including 38 borderline tumors and 295 invasive cancers. The most common mutations in a subset (n = 128) of invasive cancers were KRAS (60%), TP53 (38%), PIK3CA (13%) and PTEN (9%). Borderline tumors had higher rates of BRAF mutations, and PGP and TOP2A overexpression than invasive cases. KRAS mutant invasive cancers had lower expression of thymidylate synthase (p = 0.01) and higher expression of TUBB3 (p = 0.01) than KRAS wildtype tumors.
CONCLUSIONS: To our knowledge, this is the largest series profiling mucinous ovarian cancers and almost certainly includes cases of ovarian and non-ovarian origin. Given the difficulty recruiting patients to histotype-specific trials in rare subsets of ovarian cancer, it may be more important to focus on identifying potential treatment targets and to personalise treatment and design clinical trials in MO-CUPS agnostic of primary site to overcome these issues.

PMID: 30054102 [PubMed - as supplied by publisher]

Evaluating model reduction under parameter uncertainty.

BMC Syst Biol. 2018 Jul 27;12(1):79

Authors: Frøysa HG, Fallahi S, Blaser N

Abstract
BACKGROUND: The dynamics of biochemical networks can be modelled by systems of ordinary differential equations. However, these networks are typically large and contain many parameters. Therefore model reduction procedures, such as lumping, sensitivity analysis and time-scale separation, are used to simplify models. Although there are many different model reduction procedures, the evaluation of reduced models is difficult and depends on the parameter values of the full model. There is a lack of a criteria for evaluating reduced models when the model parameters are uncertain.
RESULTS: We developed a method to compare reduced models and select the model that results in similar dynamics and uncertainty as the original model. We simulated different parameter sets from the assumed parameter distributions. Then, we compared all reduced models for all parameter sets using cluster analysis. The clusters revealed which of the reduced models that were similar to the original model in dynamics and variability. This allowed us to select the smallest reduced model that best approximated the full model. Through examples we showed that when parameter uncertainty was large, the model should be reduced further and when parameter uncertainty was small, models should not be reduced much.
CONCLUSIONS: A method to compare different models under parameter uncertainty is developed. It can be applied to any model reduction method. We also showed that the amount of parameter uncertainty influences the choice of reduced models.

PMID: 30053887 [PubMed - in process]

32 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 2018/07/28

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.

28 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 2018/07/28

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.

14 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 2018/07/27

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.

14 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 2018/07/27

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.

26 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 2018/07/26

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.

24 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 2018/07/26

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.

31 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 2018/07/25

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.