Unifying self-organization and evolution principles in material and biological discrete systems

Atomic force microscopy (AFM)-based single molecule force spectroscopy (SMFS) is one of the most useful methods in the investigation of intra- or intermolecular interactions. To simplify the sample system and the data analysis, real biological and material systems have usually been simplified, and the target molecule of interest is normally extracted and bridged between the AFM tip and substrate for study, which is an effective way of understanding the real systems. With the development of technology (including the improvement of sample immobilization method), it is possible now to directly investigate molecular interactions in living system and real materials. The information obtained will be more useful for the better control of relevant biological processes and the design of high performance polymer materials. In this paper, recent progresses in AFM-SMFS study of molecular interactions in living cells and polymer materials are reviewed.

Economics as if written by intelligent aliens

Complex systems evolution shows three trends. The first trend is that isolated system has the spontaneous tendency to evolve from order to disorder under the mechanism of entropy increment. The second trend is that the opened system has initiative tendency to evolve from disorder to order by the principle of evolution and self-organization. The third trend is that the system will enter an uncertain state when it plunges into the chaos zone controlled by chaos theory. According these trends this paper relates them to the business organization management and gives the useful implications for developing organization management strategy. Firstly, the organization should consider overcoming the entropy increment in the organization so that the organization can survive. Secondly, it should initiatively adjust its inner order of structure and function so that it can get sustainable development in the dynamic environment. Thirdly, it should positively deal with uncertainty to explore the development opportunity for organization.

The Origin of Information and Value Selection: Investigate the Laws of the Generation of Living System

How could mankind, knowing the year 2000 would inevitably arrive, manoeuvre into worldwide technical problems because of a little computer bug? Two major parallels can be drawn to biological systems, and both are based on evolutionary principles. First, any new steps in development are founded on building blocks invented earlier. Basic building blocks are hardly changed anymore because further developments depend on their function. Second, imperfections of such building blocks are irrelevant as long as no corresponding selection pressure exists. If a time-coded computer bug occurs sufficiently early during technological development it can become part of innumerous hard-wired or soft-coded programs and devices without ever attracting attention. However, the arrival of a certain data can instantly put a high selection pressure upon it. This behaviour can be understood as a direct consequence of the autonomous dynamics that the development of complex systems implicates.

Learning classifier systems belong to the class of algorithms based on the principle of self-organization and evolution and have frequently been applied to mazes, an important type of reinforcement learning problem. Mazes may contain aliasing cells, i.e. squares in a different location that look identical to an agent with limited perceptive power. Mazes with aliasing squares present a particular difficult learning problem. As a possible approach to the problem, AgentP, a learning classifier system with associative perception, was recently introduced. AgentP is based on the psychological model of associative perception learning and operates explicitly imprinted images of the environment states. Two types of learning mode are described: the first, self-adjusting AgentP, is more flexible and adapts rapidly to changing information; the second, gradual AgentP, is more conservative in drawing conclusions and rigid when it comes to revising strategy. The performance of both systems is tested on existing and new aliasing environments. The results show that AgentP often outperforms (and always at least matches) the performance of other techniques and, on the large majority of mazes used, learns optimal or near optimal solutions with fewer trials and a smaller classifier population.

This book is devoted to mechatronic, chemical, bacteriological, biological, and hybrid systems, utilizing cooperative, networked, swarm, self-organizing, evolutionary and bio-inspired design principles and targeting underwater, ground, air, and space applications. It addresses issues such as open-ended evolution, self-replication, self-development, reliability, scalability, energy foraging, adaptivity, and artificial sociality. The book has been prepared by 52 authors from world-leading research groups in 14 countries. This book covers not only current but also future key technologies and is aimed at anyone who is interested in learning more about collective robotics and how it might affect our society.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Inspired by the highly efficient information processing of the brain, which is based on the chemistry and physics of biological tissue, any material system and its physical properties could in principle be exploited for computation. However, it is not always obvious how to use a material system’s computational potential to the fullest. Here, we operate a dopant network processing unit (DNPU) as a tuneable extreme learning machine (ELM) and combine the principles of artificial evolution and ELM to optimise its computational performance on a non-linear classification benchmark task. We find that, for this task, there is an optimal, hybrid operation mode (“tuneable ELM mode”) in between the traditional ELM computing regime with a fixed DNPU and linearly weighted outputs (“fixed-ELM mode”) and the regime where the outputs of the non-linear system are directly tuned to generate the desired output (“direct-output mode”). We show that the tuneable ELM mode reduces the number of parameters needed to perform a formant-based vowel recognition benchmark task. Our results emphasise the power of analog in-matter computing and underline the importance of designing specialised material systems to optimally utilise their physical properties for computation.

Pop up traffic hotspots i.e. geographically concentrated user pockets are a time persistent reason behind poor user experience in wireless cellular system. Spatio temporal unpredictability of such pop up hotspots renders them difficult to be designed out in the planning phase of the cellular system hence dynamic and adaptive solutions are required to cope with them in an impromptu manner. In this paper we present a novel solution that addresses this problem by dynamically enhancing spectral efficiency in hotspot regions through optimisation of system wide BS antenna tilts in distributed fashion. Unlike most of the existing works that provide solutions for hotspot relief, our solution does not rely on load transferring to neighbour cells; rather it dynamically enhances the overall spectral efficiency and thus capacity of the system by jointly optimising antenna tilts of multiple adjacent cells with respect to hotspot locations in those cells. Furthermore, being designed on the principles of self organization in biological system, our solution is self organising and can improve the user spectral efficiency in a system by upto 1b/s/Hz in presence of hotspots with no significant overheads.

Cellular automata belong to a family of discrete, connectionist techniques being used to investigate fundamental principles of dynamics, evolution, and self-organization. In this paper, a cellular automaton is developed to model the spatial structure of urban land use over time. For realistic parameter values, the model produces fractal or bifractal land-use structures for the urbanized area and for each individual land-use type. Data for a set of US cities show that they have very similar fractal dimensions. The cellular approach makes it possible to achieve a high level of spatial detail and realism and to link the results directly to general theories of structural evolution.

Synthetic biology emerged as an engineering discipline to design and construct artificial biological systems. Synthetic biological designs aim to achieve specific biological behavior, which can be exploited for biotechnological, medical, and industrial purposes. In addition, mimicking natural systems using well-characterized biological parts also provides powerful experimental systems to study evolution at the molecular and systems level. A strength of synthetic biology is to go beyond nature’s toolkit, to test alternative versions and to study a particular biological system and its phenotype in isolation and in a quantitative manner. Here, we review recent work that implemented synthetic systems, ranging from simple regulatory circuits, rewired cellular networks to artificial genomes and viruses, to study fundamental evolutionary concepts. In particular, engineering, perturbing or subjecting these synthetic systems to experimental laboratory evolution provides a mechanistic understanding on important evolutionary questions, such as: Why did particular regulatory network topologies evolve and not others? What happens if we rewire regulatory networks? Could an expanded genetic code provide an evolutionary advantage? How important is the structure of genome and number of chromosomes? Although the field of evolutionary synthetic biology is still in its teens, further advances in synthetic biology provide exciting technologies and novel systems that promise to yield fundamental insights into evolutionary principles in the near future.

Development of a digital framework for the computation of complex material and morphological behavior of biological and technological systems

Solid-state NMR spectroscopy is one of the most commonly used techniques to study the atomic-resolution structure and dynamics of various chemical, biological, material, and pharmaceutical systems spanning multiple forms, including crystalline, liquid crystalline, fibrous, and amorphous states. Despite the unique advantages of solid-state NMR spectroscopy, its poor spectral resolution and sensitivity have severely limited the scope of this technique. Fortunately, the recent developments in probe technology that mechanically rotate the sample fast (100 kHz and above) to obtain "solution-like" NMR spectra of solids with higher resolution and sensitivity have opened numerous avenues for the development of novel NMR techniques and their applications to study a plethora of solids including globular and membrane-associated proteins, self-assembled protein aggregates such as amyloid fibers, RNA, viral assemblies, polymorphic pharmaceuticals, metal-organic framework, bone materials, and inorganic materials. While the ultrafast-MAS continues to be developed, the minute sample quantity and radio frequency requirements, shorter recycle delays enabling fast data acquisition, the feasibility of employing proton detection, enhancement in proton spectral resolution and polarization transfer efficiency, and high sensitivity per unit sample are some of the remarkable benefits of the ultrafast-MAS technology as demonstrated by the reported studies in the literature. Although the very low sample volume and very high RF power could be limitations for some of the systems, the advantages have spurred solid-state NMR investigation into increasingly complex biological and material systems. As ultrafast-MAS NMR techniques are increasingly used in multidisciplinary research areas, further development of instrumentation, probes, and advanced methods are pursued in parallel to overcome the limitations and challenges for widespread applications. This review article is focused on providing timely comprehensive coverage of the major developments on instrumentation, theory, techniques, applications, limitations, and future scope of ultrafast-MAS technology.

Chirality-driven self-assembly is crucial in biological systems, but achieving precise control over chiral narcissistic or social self-sorting as well as the distinction between them remains a major challenge in multicomponent supramolecular systems. To investigate chirality-driven self-assembly, two classes of chiral building blocks, photo-responsive (cinnamic glutamide derivative, L/D-CG) and fluorescent molecules (dansyl glutamide derivative, L/D-DNSG), were designed and integrated into homochiral systems (L-CG/L-DNSG or D-CG/D-DNSG) and heterochiral systems (L-CG/D-DNSG or D-CG/L-DNSG). Either homo- or heterochiral systems could form organogels with fibrous structures. Upon photoirradiation, the homochiral systems remained stable, whereas the heterochiral systems underwent a significant transformation from uniform organogels to suspensions, in which nanokebab structures appeared. The results demonstrate that social self-sorting dominates in the homochiral systems while narcissistic self-sorting prevails in the heterochiral systems. Remarkably, the difference between homochiral and heterochiral systems is magnified through photodimerization. Various characterizations and simulations unveil the underlying homo- and heterochiral interactions that drive narcissistic self-sorting from the molecular to hierarchical nanoscale levels. This work decrypts the narcissistic or social self-sorting behavior in chirality-driven supramolecular systems through photoirradiation and provides a new perspective for understanding the regulation of chiral-driven functions in biological or material systems.