Biological Information Processing for State Prediction
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Solution Overview
Problem
Current clinical practices face challenges in predicting and controlling future changes in human biological states due to a gap between basic biology's focus on micro-scale issues and clinical research's handling of macro-scale outcomes, leading to symptomatic therapies that fail to recover the original health state and ineffective prevention and treatment schemes.
Innovation Solution
A biological information processing method that measures molecule expression levels over time, divides time-series data into periodic, environmental stimulus response, and baseline components, identifies constant regions, and infers causal relations to predict and control future changes in the organism's state, incorporating cell memory information, environmental data, and gene information to personalize health management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic biology focuses on micro-scale molecular parameters, then molecular-level understanding is improved, but clinical applicability deteriorates
Solution Approach 1:
The patent segments biological information into multiple hierarchical levels (molecular, cellular, tissue, organ, organism) and processes each level separately through dedicated measurement and analysis modules, allowing precise molecular measurement while maintaining clinical relevance through hierarchical integration
Solution Approach 2:
The patent implements a nested hierarchical structure where molecular parameters are nested within cellular contexts, which are nested within tissue systems, and so forth up to the organism level. This nested approach allows micro-scale molecular data to be integrated into macro-scale clinical outcomes through multiple nested analysis layers
2Adaptability or versatility
If clinical research handles macro-scale outcomes, then clinical relevance is improved, but understanding of underlying mechanisms deteriorates
Solution Approach 1:
The patent segments clinical outcomes into traceable hierarchical components, breaking down macro-scale clinical observations into meso-scale tissue patterns and micro-scale molecular mechanisms through separate but connected analysis modules, preserving mechanism understanding while maintaining clinical relevance
Solution Approach 2:
The patent adds a temporal dimension to the hierarchical analysis by measuring parameters across multiple time points and decomposing them into periodic, stimulus response, and baseline components, allowing macro-scale clinical outcomes to be traced back to micro-scale mechanisms through time-resolved hierarchical analysis
3Adaptability or versatility
If treatment schemes are developed statistically for large groups, then general applicability is improved, but individualized effectiveness deteriorates
Solution Approach 1:
The patent applies local quality by measuring and analyzing multiple parameters at different hierarchical levels simultaneously for each individual, creating a unique multidimensional biological profile that captures individual variability while allowing statistical comparison across groups through standardized hierarchical frameworks
Solution Approach 2:
The patent performs preliminary measurement and analysis of multiple hierarchical parameters before treatment decision-making, decomposing time-series data into periodic, stimulus response, and baseline components to establish individual baseline patterns and predictions in advance, enabling personalized treatment planning before clinical intervention
4Loss of information
If time-series data is measured continuously, then dynamic changes are captured, but data complexity and processing burden increase
Solution Approach 1:
The patent segments continuous time-series data into distinct decomposed components (periodic, stimulus response, baseline) through separate processing modules, allowing dynamic biological information to be captured and analyzed in organized, manageable segments rather than as undifferentiated continuous data
Solution Approach 2:
The patent applies periodic action by decomposing time-series data into periodic components that capture rhythmic biological patterns, allowing continuous dynamic information to be represented through discrete periodic patterns that are easier to analyze and interpret while preserving essential dynamic characteristics
Data Source
AI summary
Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.