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Record W4404640864 · doi:10.1002/ange.202418431

Synthetic Biomolecular Condensates: Phase‐Separation Control, Cytomimetic Modelling and Emerging Biomedical Potential

2024· article· en· W4404640864 on OpenAlexaff
Siyu Song, Tsvetomir Ivanov, Lucas Caire da Silva, Jing Xie, Katharina Landfester, Shoupeng Cao

Bibliographic record

VenueAngewandte Chemie · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsMcGill University
FundersSichuan Province Science and Technology Support ProgramNational Natural Science Foundation of China
KeywordsCoacervateNanotechnologySynthetic biologyBiomimeticsBiomimetic materialsBiomedicineBiochemical engineeringComputer scienceMaterials scienceEngineeringBiologyBioinformaticsChemical engineering

Abstract

fetched live from OpenAlex

Abstract Liquid‐liquid phase separation towards the formation of synthetic coacervate droplets represents a rapidly advancing frontier in the fields of synthetic biology, material science, and biomedicine. These artificial constructures mimic the biophysical principles and dynamic features of natural biomolecular condensates that are pivotal for cellular regulation and organization. Via adapting biological concepts, synthetic condensates with dynamic phase‐separation control provide crucial insights into the fundamental cell processes and regulation of complex biological pathways. They are increasingly designed with the ability to display more complex and ambitious cell‐like features and behaviors, which offer innovative solutions for cytomimetic modeling and engineering active materials with sophisticated functions. In this minireview, we highlight recent advancements in the design and construction of synthetic coacervate droplets; including their biomimicry structure and organization to replicate life‐like properties and behaviors, and the dynamic control towards engineering active coacervates. Moreover, we highlight the unique applications of synthetic coacervates as catalytic centers and promising delivery vehicles, so that these biomimicry assemblies can be translated into practical applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.293
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

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