[Paper Review] Correlative-causative structures and the 'pericause': an analysis of causation and a model based on cellular biology

Sepehr Ehsani|arXiv (Cornell University)|Oct 1, 2013

Bioinformatics and Genomic Networks14 references3 citations

TL;DR

This paper introduces the 'pericause'—a contextual framework that mediates causal effects in complex biological systems—by modeling causation through correlative data in cellular biology. It proposes a formal structure for distinguishing correlation from causation, using cellular networks to explain how shared phenotypic outcomes in common diseases may arise from diverse upstream causes within a pericausal environment.

ABSTRACT

The advent of molecular biology has led to the identification of definitive causative factors for a number of diseases, most of which are monogenic. Causes for most common diseases across the population, however, seem elusive and cannot be pinpointed to a limited number of genes or genetic pathways. This realization has led to the idea of personalized medicine and treating each case individually. Nevertheless, since each common disease appears to have the same endpoint and phenotypic features in all diagnosed individuals, the search for a unifying cause will still continue. Given that multivariate scientific data is of a correlative nature and causation is always inferred, a simple formalization of the general structure of cause and correlation is presented herein. Furthermore, the context in which a causal structure could take shape, termed the 'pericause', is proposed as a tractable and uninvestigated concept which could theoretically play a crucial role in determining the effects of a cause.

Motivation & Objective

• To address the challenge of identifying unifying causes in common, multifactorial diseases despite the absence of single genetic triggers.
• To formalize the distinction between correlative and causative relationships in biological data, especially in systems with high-dimensional, multivariate measurements.
• To propose the 'pericause' as a novel theoretical construct that provides context for how causal effects manifest in biological networks.
• To develop a model grounded in cellular biology that explains consistent phenotypic outcomes across individuals despite variable underlying causes.
• To provide a framework for personalized medicine that reconciles individualized etiologies with shared disease endpoints.

Proposed method

• Proposes a formal structure for causation that differentiates between correlative patterns and actual causal mechanisms in biological systems.
• Introduces the 'pericause' as a dynamic, context-dependent environment that enables causal effects to emerge from correlative data.
• Models causation using principles from cellular biology, particularly signaling and regulatory networks, to simulate how diverse upstream inputs can yield identical phenotypic outputs.
• Applies this model to common diseases where monogenic causes are absent, using network-level analysis to infer causal pathways.
• Uses a systems-level approach to map how multiple genetic and environmental factors converge on a shared disease phenotype through the pericausal framework.
• Employs a conceptual framework rather than computational modeling, focusing on theoretical integration of causation and correlation in biological systems.

Experimental results

Research questions

• RQ1 How can causation be formally distinguished from correlation in complex biological systems with multivariate data?
• RQ2 What contextual factors enable a cause to produce a specific effect in a biological network, and how can these be modeled?
• RQ3 Why do common diseases present consistent phenotypes despite lacking identifiable monogenic causes?
• RQ4 Can a unifying framework explain diverse etiologies leading to the same disease outcome in different individuals?
• RQ5 What role does the 'pericause' play in mediating causal effects across heterogeneous biological inputs?

Key findings

• The 'pericause' is proposed as a theoretical construct that provides the necessary context for causal effects to emerge from correlative data in biological systems.
• Common diseases with no single genetic cause may still have a unifying causal mechanism mediated by the pericausal environment, explaining consistent phenotypic outcomes.
• Correlative data alone cannot identify true causes; a formal structure is required to infer causation, especially in complex, multivariate systems.
• Cellular networks serve as a viable biological model for understanding how diverse upstream factors can produce identical downstream phenotypes.
• The pericausal framework offers a theoretical basis for reconciling personalized etiologies with shared disease endpoints, supporting future systems-level disease modeling.
• The model suggests that causation in complex diseases is not determined by individual factors alone, but by their interaction within a pericausal context.