Two faces of entropy and information in biological systems

An Approximate Method of System Entropy in Discrete-Time Nonlinear Biological Networks

This study discusses the calculation of entropy of discrete-time stochastic biological systems. First, measurement methods of the system entropy of discrete-time linear stochastic networks are introduced. The system entropy is found to be characterized by system matrices of the discrete-time biological systems. Secondly, the system entropy of nonlinear discrete-time stochastic biological systems is discussed and is calculated based on a global linearization method. The approximation of the values of system entropy of nonlinear stochastic systems needs to solve an optimization problem that is constrained by a kind of linear matrix inequality (LMI). Finally, a practical biochemical system is provided to verify the effectiveness of the proposed calculation method.

Entropy and institutional theory

Purpose

Once introduced and conceptualized as a factor that causes erosion and decay of social institutions and subsequent deinstitutionalization, the notion of entropy is at odds with predictions of institutional isomorphism and seems to directly contradict the tendency toward ever-increasing institutionalization. The purpose of this paper is to offer a resolution of this theoretical inconsistency by revisiting the meaning of entropy and reconceptualizing institutionalization from an information-theoretic point of view.

Design/methodology/approach

It is a theoretical paper that offers an information perspective on institutionalization.

Findings

A mistaken understanding of the nature and role of entropy in the institutional theory is caused by conceptualizing it as a force that counteracts institutional tendencies and acts in opposite direction. Once institutionalization and homogeneity are seen as a product of natural tendencies in the organizational field, the role of entropy becomes clear. Entropy manifests itself at the level of information processing and corresponds with increasing uncertainty and the decrease of the value of information. Institutionalization thus can be seen as a special case of an increase in entropy and a decrease of knowledge. Institutionalization is a state of maximum entropy.

Originality/value

It is explained why institutionalization and institutional persistence are what to be expected in the long run and why information entropy contributes to this tendency. Contrary to the tenets of the institutional work perspective, no intentional efforts of individuals and collective actors are needed to maintain institutions. In this respect, the paper contributes to the view of institutional theory as a theory of self-organization.

On the nature and origin of biological information: The curious case of RNA

Biological information is most commonly thought of in terms of biology's Central Dogma where DNA is viewed as a linearized code used to synthesize proteins. Using DNA's chemical cousin, RNA, as a case study we consider how biological information operates outside the linear arrangement of its polymeric subunits. Much like individual pieces of a jigsaw puzzle, particular structures enable biomolecules to undergo precise molecular interactions with one another based on their respective shapes. By exploring the relationship between sequence and structure in RNA we argue that biological information finds its ultimate functional fulfillment in the three-dimensional structural arrangement of its atoms. We show how recurrent structural RNA motifs-operating at the tertiary level of a molecule-provide robust building blocks for the formation of new structural configurations and thereby convey the information required for emergent biological functions. We posit that these same RNA structures, guided by their respective thermodynamic stabilities, experience selective pressure to maintain particular three-dimensional architectures over and above pressures to maintain a particular sequence of nucleotides. Ultimately, this framework for understanding the nature of biological information provides a useful paradigm for understanding its origins and how biological information can result from chaotic prebiotic conditions.