Complexity, Self-Organization and Selection

Recent work on self organization promises an explanation of complex order which is independent of adaptation. Self-organizing systems are complex systems of simple units, projecting order as a consequence of localized and generally nonlinear interactions between these units. Stuart Kauffman offers one variation on the theme of self-organization, offering what he calls a ``statistical mechanics' for complex systems. This paper explores the explanatory strategies deployed in this ``statistical mechanics,' initially focusing on the autonomy of statistical explanation as it applies in evolutionary settings and then turning to Kauffman's analysis. Two primary morals emerge as a consequence of this examination: first, the view that adaptation and self-organization should be seen as competing theories or models is misleading and simplistic; and second, while we need a synthesis treating self-organization and adaptation as geared toward different problems, at different levels of organization, and deploying different methods, we do not yet have such a synthesis.     

Modulated Self-Organization in Complex Amphiphilic Systems

Abstract

We discuss novel simulation methods for 3D pattern formation in complex amphiphilic systems. The focus is on the supra-molecular or mesoscopic level. The building blocks consist of sequences of dissimilar monomers, connected in copolymer chain molecules. Internal factors such as composition and architecture of the polymers, but also external factors such as applied shear, embedded reactions and level of confinement control the self-organization phenomena. Specific examples include dynamical pattern formation in polymer surfactant solution, reactive polymer blends and surface directed structure formation in block copolymer liquids. The approach lives in a twilight zone between scientific disciplines. The ambitious goal is the invention of methods for the rational design of truly complex bio-mimicking materials, in which we combine principles from chemical engineering, physics, chemistry and biology. The keyword is self-organization, of course. But do not be mistaken: autonomous self-organization leads to trouble, modulated self-organization leads to beauty.     

Nonlocal Mechanism of Self-Organization and Centering of Microtubule Asters

Fragments of fish melanophore cells can form and center aggregates of pigment granules by dynein-motor-driven transport along a self-organized radial array of microtubules (MTs). We present a quantitative model that describes pigment aggregation and MT-aster self-organization and the subsequent centering of both structures. The model is based on the observations that MTs are immobile and treadmill, while dynein-motor-covered granules have the ability to nucleate MTs. From assumptions based on experimental observations, we derive partial integro-differential equations describing the coupled granule–MT interaction. We use scaling arguments and perturbation theory to study the model in two limiting cases. The model analysis explains the mechanism of aster self-organization as a positive feedback loop between motor aggregation at the MT minus ends and MT nucleation by motors. Furthermore, the centering mechanism is explained by the spontaneous nucleation of MTs throughout the cytosol which acts as a volume sensing tool. Numerical simulations lend additional support to the analysis. The model sheds light on role of polymer dynamics and polymer–motor interactions in cytoskeletal organization.     

Chirality As a Symmetric Basis of Self-Organization of Biomacromolecules

Biophysics - A review of materials within the concept of chirality as a symmetric basis of self-organization in biomacromolecules is presented. The following topics are considered: methods for...     

On the Question of Self-Organization of Population Dynamics on Earth

Biophysics - A new mathematical model based on the predator–prey interactions has been proposed. Strictly analytical solution has been found for a system of nonlinear differential equations...     

Self-Organization and Evolutionin a Simulated Cross Catalyzed Network

Motivated by an alternative to the concept of a prebiotic soup inthe form of interacting crystal growth close to hot vents, we investigatea model system in which the growth rate of a particular entity is modified (enhanced or reduced) by other entities present, thus forming aweb of cross catalysis. Initially random interactions are imposed, butthe entities compete for a common source, and some entities may thusvanish in the competition. New entities, or mutations (error copies),with randomly selected interactions to the web are then introduced,and the concentrations of the entities are followed as solutions to stiffordinary differential equations. Entities with positive growth maycreate new related entities with slightly randomly modified interactions to the web. Extinctions, wild-type survival and replacement,and self-organization to sustain periodic external variations, are studied. It is shown that even systems with mostly cross-inhibition and noinitial autocatalysis may eventually create highly stable self-organizedsystems. We find that an already established cross catalyzed systemoften wins over a selfreplicating invader (or mutant).     

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.     

Deracemization of Amino Acids by Partial Sublimation and via Homochiral Self-Organization

Deracemization of a 50/50 mixture of enantiomers of aliphatic amino acids (Ala, Leu, Pro, Val) can be achieved by a simple sublimation of a pre-solubilized solid mixture of the racemates with a huge amount of a less-volatile optically active amino acid (Asn, Asp, Glu, Ser, Thr). The choice of chirality correlates with the handedness of the enantiopure amino acids—Asn, Asp, Glu, Ser, and Thr. The deracemization, enantioenrichment and enantiodepletion observed in these experiments clearly demonstrate the preferential homochiral interactions and a tendency of natural amino acids to homochiral self-organization. These data may contribute toward an ultimate understanding of the pathways by which prebiological homochirality might have emerged.     

Active Transport of Membrane Components by Self-Organization of the Min Proteins

Heterogeneous distribution of components in the biological membrane is critical in the process of cell polarization. However, little is known about the mechanisms that can generate and maintain the heterogeneous distribution of the membrane components. Here, we report that the propagating wave patterns of the bacterial Min proteins can impose steric pressure on the membrane, resulting in transport and directional accumulation of the component in the membrane. Therefore, the membrane component waves represent transport of the component in the membrane that is caused by the steric pressure gradient induced by the differential levels of binding and dissociation of the Min proteins in the propagating waves on the membrane surface. The diffusivity, majorly influenced by the membrane anchor of the component, and the repulsed ability, majorly influenced by the steric property of the membrane component, determine the differential spatial distribution of the membrane component. Thus, transportation of the membrane component by the Min proteins follows a simple physical principle, which resembles a linear peristaltic pumping process, to selectively segregate and maintain heterogeneous distribution of materials in the membrane.

Self-Organization on Social Media: Endo-Exo Bursts and Baseline Fluctuations

A salient dynamic property of social media is bursting behavior. In this paper, we study bursting behavior in terms of the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter. We found that there is a fluctuation threshold up to which the burst size increases as the fluctuation increases and that above the threshold, there appears a variety of burst sizes. We call this threshold the critical threshold. Investigating this threshold in relation to endogenous bursts and exogenous bursts based on peak ratio and burst size reveals that the bursts below this threshold are endogenously caused and above this threshold, exogenous bursts emerge. Analysis of the 3,000 keywords shows that all the nouns have both endogenous and exogenous origins of bursts and that each keyword has a critical threshold in the baseline fluctuation value to distinguish between the two. Having a threshold for an input value for activating the system implies that Twitter is an excitable medium. These findings are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, to predict the response to particular information on social media.