Results 201 to 210 of about 58,720 (307)

A Bayesian Framework for Parameter Estimation in Dynamical Models with Applications to Forecasting

, 2009
Mathematical models in Biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to
Claudia T. Codeço, Flávio Codeço Coelho   +1 more
core  

Chameleon sequences reveal structural effects in proteins representing micelle‐like distribution of hydrophobicity

FEBS Open Bio, EarlyView.
Amino acids sequence of two different proteins with the same sequence (chameleon sequence—black boxes) represent in 3D structure of the proteins different secondary structures: HHHH—helical and BBB—Beta‐structural. The chains folded in water environment adopt different III‐order structures in which the chameleon fragments appear to adopt similar status
Irena Roterman, Katarzyna Stapor, Leszek Konieczny, Krzysztof Gądek, Piotr Nowakowski   +4 more
wiley   +1 more source

Dynamical modeling and data analysis of HIV infection with infection-age, CTLs immune response and delayed antibody immune response. [PDF]

J Math Biol
Li Y, Zhang L, Zhang J, Liu S, Peng Z.
europepmc   +1 more source

Exon 7 splicing of ERα predicts poor prognosis and increases phenotypic heterogeneity in luminal a subtype breast cancer

FEBS Open Bio, EarlyView.
ERα splice variant ERα∆7 lacks the C‐terminus, and its expression may change phenotypes of breast cancers. Our results showed that ERα∆7 is found in the luminal A subtype, and elevated ERα∆7 levels are linked to improved cell survival with lower proliferation and migration.
Long Wai Tsui, Taobo Hu, Thomson Christian Husein, Wai Hei Shek, Bingrong Chen, Depeng Wang, Shu Wang, Weichuan Yu, Xiaodan Fan, Mengping Long, Toyotaka Ishibashi   +10 more
wiley   +1 more source

Context-Aware Biosensor Design Through Biology-Guided Machine Learning and Dynamical Modeling. [PDF]

ACS Synth Biol
Tellechea-Luzardo J, Martin Lazaro H, Fernandez Perez C, Henriques D, Otero-Muras I, Carbonell P.   +5 more
europepmc   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation. [PDF]

PNAS Nexus
Parino F, Gustani-Buss E, Bedford T, Suchard MA, Trovão NS, Rambaut A, Colizza V, Poletto C, Lemey P.   +8 more
europepmc   +1 more source

Screening and epitope characterization of Nidogen‐2‐specific nanobodies

FEBS Open Bio, EarlyView.
Camel immunization and phage display were employed to generate high‐affinity VHH nanobodies against Nidogen‐2. After library construction, biopanning, ELISA screening, sequencing, and recombinant expression, selected nanobodies were purified and characterized, leading to the preliminary exploration of a nanobody‐based sandwich ELISA for specific ...
Jianchuan Wen, Qianqian Cui, Zhongyun Lan, Yingjun Wang, Shuaiying Zhao, Wenxuan Feng, Yunfeng Liu, Qiting Huang, Dongna Zhang, Jianfeng Xu   +9 more
wiley   +1 more source

Unraveling sleep apnea dynamics: quantifying loop gain using dynamical modeling of ventilatory control. [PDF]

Sleep
Nassi T, Amidi Y, Oppersma E, Donker DW, Redeker NS, Westover MB, Thomas RJ.   +6 more
europepmc   +1 more source

Matrix metalloproteinase‐9 regulates cell adhesion and membrane protrusive activity of ovarian cancer cells

FEBS Open Bio, EarlyView.
Matrix metalloproteinase‐9 (MMP9) drives ovarian cancer progression. Using MMP9‐null cells (M9‐KO) created from ovarian cancer cells, we found MMP9 loss did not block Epidermal Growth Factor (EGF)‐driven E‐cadherin dissolution or EMT but delayed and reduced EGF‐driven membrane protrusions. Transient MMP9 re‐expression drove membrane protrusion.
Claire Strauel, Sam Bennett, Natalie Ehlinger, Nelson Cook, Katelyn Marvin, Caleb L. Lines, Sam Reyes, Brooke Kirby, Shawn M. Ellerbroek   +8 more
wiley   +1 more source