EMC2 Human

What is the EMC2 protein and what is its role in human cells?

EMC2 is a subunit of the Endoplasmic Reticulum Membrane Protein Complex (EMC), which plays a crucial role in membrane protein biogenesis. EMC2 contains conserved tetratricopeptide repeat (TPR) domains that form a cup-like structure beneath the hydrophilic vestibule in the membrane between transmembrane domains of EMC3 and EMC6 . This arrangement allows EMC2 to bridge the cytosolic ends of transmembrane domains of EMC1, EMC3, and EMC5, serving as a structural stabilizer of the complex . The EMC as a whole functions in the insertion and folding of transmembrane proteins, which is essential for maintaining cellular homeostasis.

How is EMC2 structurally organized within the endoplasmic reticulum membrane protein complex?

According to high-resolution cryo-EM studies at 3.4 Å, the human EMC consists of nine proteins (EMC1-8 and EMC10), with EMC2 serving as a critical cytosolic component. EMC2 forms a tetratricopeptide repeat (TPR) spiral that creates a cup-shaped structure positioned beneath a partially hydrophilic vestibule in the membrane . This vestibule forms between the transmembrane domains of EMC3 and EMC6. Structurally, EMC2 plays a crucial role by bridging the cytosolic ends of transmembrane domains of EMC1, EMC3, and EMC5 . Additionally, cytosolic EMC8 (or alternatively EMC9, which is structurally similar and mutually exclusive with EMC8) binds to the opposite face of EMC2, further stabilizing the complex's architecture .

How does ncRNA-mediated regulation impact EMC2 expression in cancer?

Non-coding RNAs (ncRNAs) significantly influence EMC2 expression in cancer through complex regulatory mechanisms. Research has identified specific miRNAs that target EMC2, with particular emphasis on miR-410-3p, which shows a significant negative correlation with EMC2 expression in breast cancer . Analysis reveals that miR-410-3p is downregulated in breast cancer tissues, which may contribute to the observed upregulation of EMC2 in these malignancies .

Beyond miRNAs, long non-coding RNAs (lncRNAs) also participate in EMC2 regulation. Researchers have constructed regulatory networks to map these interactions, revealing complex interplays between different classes of ncRNAs and EMC2 expression . The ncRNA-mediated upregulation of EMC2 appears to be associated with poor prognosis and reduced tumor immune infiltration in breast invasive carcinoma (BRCA) . Methodologically, these relationships are established through correlation analyses, survival analyses using Kaplan-Meier estimations, and multivariate Cox proportional hazards models.

What are the implications of EMC2 mutations for cancer prognosis?

EMC2 mutations exhibit variable frequencies and prognostic implications across different cancer types, as summarized in the table below:

How does EMC2 CpG site methylation affect gene expression across different cancer types?

EMC2 CpG site methylation exhibits distinct patterns across various cancer types with significant implications for gene expression and patient outcomes. Analysis of tumor and normal samples reveals that CpG sites in the EMC2 promoter region display significantly increased methylation in tumor samples compared to normal tissues across most cancer types .

These methylation patterns significantly impact patient survival across different tumor types, suggesting EMC2 methylation status may serve as a potential biomarker for prognosis assessment . Methodologically, researchers typically employ bisulfite sequencing or methylation array data from paired tumor and normal samples to quantify these changes, followed by correlation analyses with expression data.

What is the EMC2 framework in computational psychology?

EMC2 (Evidence Accumulation Modeling in Cognitive Computation) is a computational framework designed for specifying and fitting evidence accumulation models (EAMs) to behavioral data in psychological research. The framework provides a formula-based method for model specification, similar to how regression models are specified in R packages like lme4 .

EMC2 facilitates the implementation of various evidence accumulation models, with particular emphasis on the Drift Diffusion Model (DDM), which is widely used to account for decision-making behaviors across multiple cognitive domains including recognition memory, working memory, speed-accuracy trade-offs, word recognition, and cognitive control .

The primary advantage of this framework is its ability to convert model formulas into design matrices that map model parameters to likelihood functions, creating a systematic way to relate parameters to experimental conditions .

What types of psychological data can be analyzed using the EMC2 framework?

The EMC2 framework is specifically designed to analyze behavioral data from experiments involving decision-making processes, with particular emphasis on reaction time and choice data. This framework is especially suited for implementing Evidence Accumulation Models (EAMs), which account for both the speed and accuracy of decisions .

EMC2 can be applied to data from various psychological experiments, including:

• Recognition memory tasks

• Working memory experiments

• Perceptual decision-making tasks

• Speed-accuracy trade-off paradigms

• Word recognition studies

• Cognitive control experiments

• Response inhibition tasks

The framework accommodates experimental designs with various factors, including within-subject and between-subject manipulations. For example, in the case study demonstrated in the search results, EMC2 was used to analyze data from Forstmann et al. (2008), which included a 3×2 design with 19 subjects .

What are best practices for parameter specification in EMC2 models?

Effective parameter specification in EMC2 models requires careful consideration of several methodological principles:

• Theoretical motivation: Prioritize theoretical justification when deciding which parameters to vary across experimental conditions. Each additional parameter variation increases model complexity and may lead to overfitting .

• Formula-based constraints: Leverage EMC2's formula-based specification to implement principled parameter constraints. Rather than allowing parameters to vary freely across all conditions, use main effects and interaction terms that reflect theoretical predictions .

• Appropriate parameterization: Consider the nature of each parameter type. EMC2's "TZD" parameterization for the DDM ensures parameters remain within theoretically plausible ranges by applying appropriate transformations (natural scale for drift rate, log scale for positive-constrained parameters, and probit scale for parameters bounded between 0 and 1) .

• Strategic contrast coding: Utilize contrast coding to test specific hypotheses. EMC2 allows direct estimation of effects of interest through contrast coding, which can provide more interpretable parameters than post-hoc comparisons .

• Hierarchical modeling: Implement hierarchical approaches when analyzing multi-subject data to account for individual differences while achieving more stable parameter estimates. This allows parameters to vary between subjects while maintaining partial pooling across the sample.

What are the primary objectives of the EMC2 project in economic research?

The EMC2 (Emergence of Money with Cognitive Computational Agents) project aims to address fundamental questions in social sciences, particularly economics, through innovative computational methodologies. The primary objective is to use empirically-guided cognitive agent-based modeling to understand the emergence of money in decentralized trading systems .

This research is particularly timely given the rise of cryptocurrencies like Bitcoin and Ethereum, which has renewed interest in how monetary systems emerge endogenously in decentralized environments. The project seeks to develop theoretical models where money emerges spontaneously within simulated economic systems, rather than being imposed exogenously .

By creating computational frameworks that accurately model the emergence of monetary systems, the EMC2 project aims to provide insights into the fundamental mechanisms underlying currency adoption. Additionally, the project focuses on understanding speculative behavior, which is considered necessary for money emergence in decentralized markets .

How does the EMC2 project approach the study of money emergence?

The EMC2 project employs a multi-stage, interdisciplinary approach to studying money emergence:

• Human-subject experiments: Researchers conduct online experiments using the Mechanical Turk service, where human participants interact with automated agents in controlled economic environments. These automated agents are designed to make speculation the most profitable strategy for human participants .

• Behavioral pattern analysis: During these experiments, researchers closely monitor human subject behavior to identify and extract characteristic patterns that distinguish between fundamentalist and speculative economic behaviors .

• Computational modeling: The researchers incorporate the behavioral traits identified from human experiments into computational agent-based models. These models simulate decentralized trading systems populated by cognitive agents whose decision-making processes reflect empirically observed patterns of human economic behavior .

This methodology represents a significant advance by grounding computational models in empirical observations of human decision-making. By combining expertise from computer science with economic theory, the project creates more realistic models of how money emerges endogenously in trading systems .

What computational methods does EMC2 use to model cognitive agents in economic systems?

The EMC2 project employs sophisticated computational methods to model cognitive agents in economic systems, focusing specifically on empirically-guided cognitive agent-based modeling. Unlike traditional economic models that often assume perfectly rational agents, EMC2 incorporates cognitive limitations and behavioral patterns observed in human subjects .

The computational approach follows this methodology:

• Data collection: Human-subject online experiments via Mechanical Turk, where researchers design environments that incentivize speculative behavior .

• Behavioral pattern extraction: Researchers identify key discriminating characteristics between fundamentalist and speculative behaviors, including decision heuristics, risk attitudes, and adaptation strategies .

• Computational implementation: The identified behavioral patterns are formalized into computational rules and algorithms that govern agent behavior in simulations .

• System-level emergence analysis: The agent-based models allow for the emergence of macro-level phenomena (such as the adoption of a specific item as currency) from micro-level interactions between cognitive agents .

This approach creates a more realistic simulation of economic systems by incorporating actual human behavioral patterns rather than theoretical assumptions about rationality, potentially revealing mechanisms of monetary emergence that traditional economic models might miss .

How does the EMC2 project integrate findings about cryptocurrency systems with traditional economic theory?

The EMC2 project represents a significant attempt to bridge the gap between emerging cryptocurrency phenomena and traditional economic theories of money emergence. By studying "the potential effect of such crypto-currencies on economic and social systems" through the production of "theoretical models where money emerges endogenously," the project directly addresses one of the most pressing questions in contemporary economics .

The project's methodology provides a unique framework for understanding how the decentralized nature of cryptocurrencies might lead to different emergence patterns compared to traditional currencies. By using empirically-guided cognitive agent-based modeling, researchers can simulate various conditions that might affect cryptocurrency adoption and stability, potentially informing both regulatory approaches and economic theory .

The focus on speculative behavior is particularly relevant to cryptocurrency markets, which have shown pronounced speculative dynamics. By extracting "guiding characteristics for discrimination across fundamentalist and speculative behavior" from human subjects, the project can identify patterns that might predict cryptocurrency market behavior under different conditions .