Related Experiment Video
Updated: Jun 23, 2026
04:41
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
Published on: January 9, 2020
In silico generation of alternative hypotheses using causal mapping (CMAP).
Gabriel E Weinreb1, Maryna T Kapustina, Ken Jacobson
1Department of Cell and Developmental Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America. weinreb@med.unc.edu
Plos One
|April 30, 2009
Summary
Causal mapping (CMAP) is a systems biology tool that generates and ranks hypotheses for cellular mechanisms. This method aids in understanding biological processes and suggests experiments to validate findings.
Area of Science:
- Systems biology
- Molecular and cellular biology
- Computational biology
Background:
- Causal mapping (CMAP) offers a graphical modeling approach for biological processes.
- It describes interaction details while maintaining qualitative method simplicity, akin to Boolean networks.
Purpose of the Study:
- To utilize CMAP for generating and ranking hypotheses on molecular and cellular system regulation.
- To demonstrate CMAP's utility in suggesting experimental tests for competing hypotheses.
Main Methods:
- Application of CMAP to a three-element signaling module test case.
- Utilizing CMAP for analyzing the complex phenomenon of cortical oscillations in spreading cells.
- Ranking competing hypotheses using a fitness index.
Main Results:
- Two high-fitness hypotheses were generated for the mechanism underlying cortical oscillations.
- The study demonstrated CMAP's capability to suggest experiments for hypothesis differentiation.
Conclusions:
- CMAP serves as a valuable tool for hypothesis generation and comparison in systems biology.
- The methodology is broadly applicable to diverse cellular systems for data-driven hypothesis refinement using simulations.
Related Concept Videos
01:16Null and Alternative Hypotheses
The actual hypothesis testing begins by considering two hypotheses. They are termed the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:19Counterfactual Thinking
Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in human cognition.Types of...
01:09Epistasis Analysis
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
01:21Causality in Epidemiology
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
01:11Types of Hypothesis Testing
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.