ON MODELING OF LIVING ORGANISMS USING HIERARCHICAL COARSE-GRAINING ABSTRACTIONS OF KNOWLEDGE

Abstract

High throughput technologies such as gene expression microarray, ChIP-chips, siRNA and protein arrays and high throughput mass spectrometry are enabling an ever increasing amount of data becoming available about DNA, RNA, proteins, metabolites as well as biological pathways and networks. The knowledge embedded in this data deluge needs to be recast in forms that lend themselves to analysis with the expectation of developing analytical instruments to gain insight and answer questions about life and living organisms. The powers of abstraction and model building are fundamental to the quest of making sense of the biological complexity embedded in these biological and clinical datasets. The modeling of living organisms is explored with a proposed framework for model representation of biological complexity. The principal foundational assumption of the proposed modeling philosophy recognizes the symbiotic relationship between information and energy flows, required for the transformation of matter, as a fundamental organizing force underlying the observable nature of living organisms. The use of the concept of regularities to refer to complexity of structure, function and dynamics alike provides a unified approach to the reasoning about the integration of knowledge representations of varying natures and scales of granularities. The application of the proposed modeling approach is illustrated in broad qualitative terms for the human organism.