Astrobiological Perspectives on Consciousness

The Stanley Miller experiment suggests that amino acid-based life is ubiquitous in our universe, although its varieties are not likely to have followed the particular, highly contingent and path-dependent, trajectory found on Earth. Are many of these life forms likely to be conscious in ways that we would recognize? Almost certainly. Will many conscious entities develop high order technology? Less likely. If so, will we be able to communicate with them? Only on a basic level, and only with profound difficulty. The argument is straightforward

Expanding the modern synthesis II: Formal perspectives on the inherent role of niche construction in fitness

Expanding the modern synthesis requires elevating the role of interaction within and across various biological scales to the status of an evolutionary principle. One way to do this is to characterize genes, gene expression, and embedding environment as information sources linked by crosstalk, constrained by the asymptotic limit theorems of information theory (Wallace, 2010a). This produces an inherently interactive structure that escapes the straightjacket of mathematical population genetics or other replicator dynamics. Here, we examine fitness from that larger perspective, finding it intimately intertwined with niche construction. Two complementary models are explored: niche construction as mediating the connection between environmental signals and gene expression, and as a means of tuning the channel for the transmission of genetic information in a noisy environment. These are different views of the same elephant, in a sense, seen as simplified projections down from a larger dynamic system

Metabolic constraints on the evolution of genetic codes: Did multiple 'preaerobic' ecosystem transitions entrain richer dialects via Serial Endosymbiosis?

A mathematical model based on Tlusty's topological deconstruction suggests that multiple punctuated ecosystem shifts in available metabolic free energy, broadly akin to the 'aerobic' transition, enabled a punctuated sequence of increasingly complex genetic codes and protein translators under mechanisms similar to the Serial Endosymbiosis effecting the Eukaryotic transition. These evolved until the ancestor to the present narrow spectrum of nearly maximally robust codes became locked-in by path dependence

Unnatural Selection: A new formal approach to punctuated equilibrium in economic systems

Generalized Darwinian evolutionary theory has emerged as central to the description of economic process (e.g., Aldrich et. al., 2008). Here we demonstrate that, just as Darwinian principles provide necessary, but not sufficient, conditions for understanding the dynamics of social entities, in a similar manner the asymptotic limit theorems of information theory provide another set of necessary conditions that constrain the evolution of socioeconomic process. These latter constraints can, however, easily be formulated as a statistics-like analytic toolbox for the study of empirical data that is consistent with a generalized Darwinism, and this is no small thing

A knowledge representation meta-model for rule-based modelling of signalling networks

The study of cellular signalling pathways and their deregulation in disease states, such as cancer, is a large and extremely complex task. Indeed, these systems involve many parts and processes but are studied piecewise and their literatures and data are consequently fragmented, distributed and sometimes--at least apparently--inconsistent. This makes it extremely difficult to build significant explanatory models with the result that effects in these systems that are brought about by many interacting factors are poorly understood. The rule-based approach to modelling has shown some promise for the representation of the highly combinatorial systems typically found in signalling where many of the proteins are composed of multiple binding domains, capable of simultaneous interactions, and/or peptide motifs controlled by post-translational modifications. However, the rule-based approach requires highly detailed information about the precise conditions for each and every interaction which is rarely available from any one single source. Rather, these conditions must be painstakingly inferred and curated, by hand, from information contained in many papers--each of which contains only part of the story. In this paper, we introduce a graph-based meta-model, attuned to the representation of cellular signalling networks, which aims to ease this massive cognitive burden on the rule-based curation process. This meta-model is a generalization of that used by Kappa and BNGL which allows for the flexible representation of knowledge at various levels of granularity. In particular, it allows us to deal with information which has either too little, or too much, detail with respect to the strict rule-based meta-model. Our approach provides a basis for the gradual aggregation of fragmented biological knowledge extracted from the literature into an instance of the meta-model from which we can define an automated translation into executable Kappa programs.Comment: In Proceedings DCM 2015, arXiv:1603.0053

On the Steady States of Uncertain Genetic Regulatory Networks

This correspondence addresses the analysis of the steady states of uncertain genetic regulatory networks (GRNs). The uncertainty is represented as a vector constrained in a given set that affects the coefficients of the mathematical model of the GRN. It is shown how regions containing all possible steady states can be estimated via an iterative strategy that progressively splits the concentration space into smaller sets, discarding those that are guaranteed not to contain equilibrium points of the considered model. This strategy is based on worst case evaluations of some appropriate functions of the uncertainty via linear matrix inequality optimization.published_or_final_versio

RuleVis: Constructing Patterns and Rules for Rule-Based Models

We introduce RuleVis, a web-based application for defining and editing "correct-by-construction" executable rules that model biochemical functionality, which can be used to simulate the behavior of protein-protein interaction networks and other complex systems. Rule-based models involve emergent effects based on the interactions between rules, which can vary considerably with regard to the scale of a model, requiring the user to inspect and edit individual rules. RuleVis bridges the graph rewriting and systems biology research communities by providing an external visual representation of salient patterns that experts can use to determine the appropriate level of detail for a particular modeling context. We describe the visualization and interaction features available in RuleVisand provide a detailed example demonstrating how RuleVis can be used to reason about intracellular interactions

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres