Bulletin of the American Physical Society
Annual Meeting of the APS Four Corners Section
Volume 60, Number 11
Friday–Saturday, October 16–17, 2015; Tempe, Arizona
Session I11: Emergent Phenomena I |
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Chair: Dmitry Matyushov, Arizona State University Room: PSH151 |
Saturday, October 17, 2015 11:00AM - 11:24AM |
I11.00001: The Informational Architecture of Biological Networks Invited Speaker: Sara Walker An important goal in biology is identifying what features of biological organization may be universal to life and potentially distinguish living systems from other classes of physical system. For example, information is increasingly cited as an important property of biological systems, but it is unclear in what sense information can uniquely characterize life. To address this problem, we use as a case study a Boolean network model for the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure. We report patterns in \textit{local} information processing and storage that distinguish biological from random. Conversely, we find that ``emergent'' information processing, which we quantify using integrated information theory, does not differ from random for the case presented. We discuss implications for our understanding of the informational architecture of the fission yeast cell cycle network and for illuminating any distinctive physics operative in life. [Preview Abstract] |
Saturday, October 17, 2015 11:24AM - 11:36AM |
I11.00002: Open-Ended Evolution and Innovation in a Deterministic Cellular Automata Universe Alyssa Adams, Hector Zenil, Paul Davies, Sara Walker One of the most remarkable features of life on Earth is the apparent open-ended evolution and innovation of the biosphere over its > 3.5 billion year history. This is also one of the most perplexing features of biological evolution from the perspective of theoretical and computational modeling. Here we show that state-dependent dynamical rules can generate open-ended evolution for simple cellular automata (CA) “organisms” coupled to an external “environment” in a fully deterministic system. We present formal definitions of open-ended evolution as patterns that are non-repeating within the expected Poincare recurrence time of an isolated organism, and of innovation as trajectories not observed in isolated organisms. We demonstrate that a small subset of CA organisms implementing a state-dependent update rule, which is a function of the organism’s current state and rule and the state of the environment, satisfy these minimal requirements for open-endedness and innovation. Our results demonstrate that an additional requirement for open-ended evolution and innovation is to remove the segregation of states and (fixed) dynamic laws characteristic of the physical sciences in attempts to model biological complexity. [Preview Abstract] |
Saturday, October 17, 2015 11:36AM - 11:48AM |
I11.00003: Order in Chaos: an Algorithmic Approach~to Flocking Behaviour Garett Brown, Manuel Berrondo Clusters of organisms have a tendency to exhibit emergence from seemingly chaotic behaviour. Emergence is the process wherein coherent patterns arise out of the simple, smaller interactions of its chaotic entities that do not exhibit such behaviour themselves. Using a simple, two-dimensional algorithmic approach, we can show that antagonistic forces - consensus and frustration - lead simple, self-driven particles (boids) to group together and exhibit emergent, flocking behaviour reminiscent of starling murmurations. The cohesive, consensus motion of the boids is manifested in three different types of global, dynamic phase transitions. When frustration is introduced in the form of boundary conditions, these transitions go beyond simple movements as local group phases occur, alternating through the three phase transitions found in consensus. We present visuals and animations that were created using Wolfram Mathematica. We also show how we were able to interpret the emergence using order parameters. Thus using these simple, algorithmic techniques, we are able to produce realistic replicas of complex biology-like interactions. [Preview Abstract] |
Saturday, October 17, 2015 11:48AM - 12:00PM |
I11.00004: New Scaling Relation for Information Transfer in Biological Networks Hjunju Kim Life seems distinctive in its ability to process information. However, precisely what distinguishes information handling in living systems from that of their non-living counterparts remains to be rigorously quantified. While useful tools for quantifying information transfer and causal structure exist in complex systems research, they have been little applied to distributed information processing in biological networks, particularly at the most fundamental level of biological organization - biochemistry. Here, we provide a rigorous case study of the informational architecture of two representative biological networks, Boolean models for the cell-cycle regulatory network of the fission yeast (S. Pombe) and that of the budding yeast (S. cerevisiae). We calculate the information transferred between pairs of nodes within each network in the execution of function and contrast the results with the same analysis performed on ensembles of random networks of two different classes: Random and Scale-Free. We show that both biological networks share features in common that are not shared by either ensemble. In particular, the biological networks in our study, on average, process more information than both classes of random networks. They also exhibit a scaling relation in information transfer between nodes that distinguishes them from either ensemble -- even for Scale-Free networks that share important topological properties, such as power-law degree distribution. We show that the most biologically distinct regime of this scaling relation is associated with the dynamics and function of the biological networks. Therefore, our results suggest that previously unidentified information-based organizational principles that go beyond topological considerations, such as a scale-free structure, which may be critical to biological function. Thus, information may be intrinsic to the operation of living systems, where the informational architecture of biologically evolved networks has the potential to distinguish biological networks from other classes of network architecture that do not exhibit these informational properties. [Preview Abstract] |
Saturday, October 17, 2015 12:00PM - 12:12PM |
I11.00005: Relaxation and the Robustness of Cluster Expansions Andrew Nguyen, Gus Hart Cluster expansion (CE) has been used extensively to predict stable structures of metal alloys. Cluster expansions model alloys on a fixed lattice as a purely configuration problem. CE models are built from data taken from first-principles calculations. In these first-principles calculations, individual atoms move away from the ideal lattice position. A perennial question in the CE community is how accurate the expansion is when these relaxations are allowed –- formally, the formalism of CE breaks down when the underlying lattice is not preserved. We compare fits using relaxed and unrelaxed training sets in an attempt to quantify the effects of relaxation on the robustness of CE predictions. [Preview Abstract] |
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