Pragmatic Computational Biology: Sequence Analysis and Biological Systems Description

Abstract

In a narrow sense, pragmatic computational biology is today's equivalent of theoretical molecular biology of the 1970s and 1980s combined with genome bioinformatics of 1990s and early 2000s. Similarly, to the latter it aims toward extended interpretation of laboratory and computational experiments in molecular genetics (also known as functional genomics), molecular evolution, and structural biology. The “extended interpretation” consists of several activities, such as the following: Relating the mechanistic and the informational aspects of biological phenomena through appropriate generalization and modeling, for example, postulating the triplet nature of the genetic code before it had been determined experimentally. Devising principles for analyzing inexact data (quantitative representations of qualitative descriptions included), for example, database design, annotation, and searches; logic of heuristic reasoning. Relating principles and laws inferred from studies at the molecular level with those derived from studies of organisms, populations, and ecosystems, for example, molecular evolution, molecular evolutionary genetics. Deriving properties of biological systems from principles of physics and chemistry, for example, biological thermodynamics and kinetics; theories of origin of life; molecular mechanics. Modeling (usually mathematical) properties of laboratory instrumentation and designing experiments, for example, analysis and interpretation of molecular spectra, X-ray data, electrophoretic data, microchips, and so on. Modeling properties of computational tools and designing computational experiments, for example, sequence alignment. These activities often draw on methods that in themselves are established research areas with their own methodological foundations. Some of the methods pertain to physics (for instance, molecular mechanics), some to chemistry (for example, reaction kinetics) and yet others are parts of applied mathematics (combinatorics, for example) or computer science (for instance, theory of algorithms). Pragmatic computational biology differs from chemistry, physics, and even biochemistry by systematically exploring symbolic aspects (besides the material ones) of the biologic role(s) of nucleic acid and protein sequences. In a larger sense, pragmatic computational biology is concerned with cognitive systematization of concepts, theories, models, and observations that could contribute to an explanation of the phenomenon of life itself. In this (larger) sense, the principles of modeling biological systems appear to be more important than design and interpretations of specific experiments. A nonexhaustive list of activities pertaining to the larger aspect of pragmatic computational biology includes the following: Developing a general system theory and research on specific classes of general systems, for example, anticipatory systems and their use for the description of (biochemical) reaction networks. Developing foundations of logic and mathematic such that the resulting mathematical objects would be appropriate for modeling biological systems, for example, theory of categories as a language for formulating models and theories. Developing foundations of general information theory and information science, for example, work on semantic and pragmatic theories of information, knowledge representation, data integration, and so on. Activities 7 to 9 draw on methods from other research programs such as systems theory, cognitive science, logic, and epistemology but do not belong to the current paradigms of these fields. This article is primarily devoted to those aspects of computational biology that pertain to sequence analysis and genomics (activities 1 and 2 above) and thereby are of interest to molecular biologists proper. However, in order to satisfy those readers who would like to predict future research directions in the field, a brief discussion of topics belonging to the larger aspect of computer-assisted systems biology (activities 7–9 above) is given in Sect. 6. Keywords: k-GRAM (k-tuple); Pattern; Motif; Motif Descriptor; Sequence Pattern; Distance Chart; Code