Towards a Model of Life and Cognition

What should be the ontology of the world such that life and cognition are possible? In this essay, I undertake to outline an alternative ontological foundation which makes biological and cognitive phenomena possible. The foundation is built by defining a model, which is presented in the form of a description of a hypothetical but a logically possible world with a defined ontological base. Biology rests today on quite a few not so well connected foundations: molecular biology based on the genetic dogma; evolutionary biology based on neo-Darwinian model; ecology based on systems view; developmental biology by morphogenetic models; connectionist models for neurophysiology and cognitive biology; pervasive teleonomic explanations for the goal-directed behavior across the discipline; etc. Can there be an underlying connecting theme or a model which could make these seemingly disparate domains interconnected? I shall atempt to answer this question. By following the semantic view of scientific theories, I tend to believe that the models employed by the present physical sciences are not rich enough to capture biological (and some of the non-biological) systems. A richer theory that could capture biological reality could also capture physical and chemical phenomena as limiting cases, but not vice versa

Computational Modeling, Formal Analysis, and Tools for Systems Biology.

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science