Exploring Similarities Between Systems: Concepts, Properties and Implications

Correlation of experimental and theoretical data for artificial and natural systems with immobilized biocatalysts

Synthetic molecules and nanodevices, like their more elaborate biological counterparts, have been shown to perform several sophisticated functions, using even fairly simple molecular architectures. One limitation to developing artificial molecular arrays and networks from these miniscule building blocks is the lack of a unifying strategy whereby they can communicate or interact together, which has been successfully developed in natural systems. Understanding and harnessing these efficient biological processes could prove key in the development of future integrated molecule-based nanodevices and networks. Herein, we give a short overview of some manifestations of intra- and intermolecular communication based on chemical messengers in artificial systems, in some ways analogous to natural systems, which are in turn controlled by light, a redox process or a chemical reaction or interaction. Some advantages, limitations, and challenges are highlighted.

Evolvability refers to the organisms ability to create heritable new phenotypes that potentially facilitate the organism's survival and reproduction. In this paper, a general evolvability measure for a computational model of evolutionary development is proposed. The measure is able to quantify individuals' evolvability, including robustness and innovation, independent of the fitness function of the evolutionary system. Empirical studies are performed to check the evolvability of individuals in in silico evolution of oscillatory behavior using the proposed evolvability measure. Our preliminary results suggest that evolvability of the developmental system can evolve without an explicit selection pressure on evolvability, confirming findings revealed in other artificial evolutionary systems.

Systems theory is fundamental to understanding the dynamics of the complex social systems of concern to policy makers. A system is defined as: (1) an assembly of components, connected together in an organised way; (2) the components are affected by being in the system and the behaviour of the systems is changed if they leave it; (3) the organised assembly of components does something; and (4) the assembly has been identified as being of particular interest. Feedback is central to system behaviour at all levels, and can be responsible for systems behaving in complex and unpredictable ways. Systems can be represented by networks and there is a growing literature that shows how the behaviour of individuals is highly dependent on their social networks. This includes copying or following the advice of others when making decisions. Network theory gives insights into social phenomena such as the spread of information and the way people form social groups which then constrain their behaviour. It is emerging as a powerful way of examining the dynamics of social systems. Most systems relevant to policy have many levels, from the individual to local and national and international organisations and institutions. In many social systems the micro, meso and macrolevel dynamics are coupled, meaning that they cannot be studied or modified in isolation. Systems and network science allow computer simulations to be used to investigate possible system behaviour. This science can be made available to policy makers through policy informatics which involves computer-based simulation, data, visualisation, and interactive interfaces. The future of science-based policy making is seen to be through Global Systems Science which combines complex systems science and policy informatics to inform policy makers and facilitate citizen engagement. In this context, systems theory and network science are fundamental for modelling far-from-equilibrium systems for policy purposes.

Biological and artificial evolutionary systems exhibit varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that can potentially improve evolvability or accelerate evolution in artificial systems. Biological notions are discussed to the degree they correspond to notions in Evolutionary Computation. We hope that the findings put forward here can be used to design computational models of evolution that produce significant gains in evolvability and evolutionary speed.

Ecological consulting as a career option

What will artificial cognitive systems of the future look like? If we are asked to imagine robots, or intelligent software agents, several features come to our mind such as the capability to adapt to their environments and to satisfy their goals with only limited human intervention, to plan sequences of actions for realizing long-term objectives, to act collectively in view of complex objectives, to interact and cooperate with us, with and without natural language, to take decisions (also in our place), etc.Currently these capabilities are far beyond the possibilities of robots and other artificial systems. In the next years a huge effort will be required for scaling up the potentialities of the artificial systems that we are able to build nowadays. One way to overcome these limitations is to take inspiration form the functioning of living organisms. A large body of evidence, which we review in this chapter, indicates that natural cognitive systems are not reactive but essentially anticipatory systems. We do not think that this is a mere coincidence. On the contrary, we claim that anticipation is a crucial—and foundational—phenomenon in natural cognition. Individual behavior is guided by anticipatory mechanisms that are used for behavioral control, perceptual processing, goal-directed behavior, and learning. And also effective social behavior relies on the anticipation of the behavior of other agents. We argue that anticipation is a key ingredient for the design of autonomous, artificial cognitive agents of the future: Only cognitive systems with anticipation mechanisms can be credible, adaptive, and successful in interaction with both the environment and other autonomous systems and humans. This is the challenge that we anticipate for the future of cognitive systems research: the passage from reactive to anticipatory cognitive embodied systems.KeywordsCognitive SystemForward ModelInternal ModelMirror NeuronMental Time TravelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Li, X.; Ren, Y.X.; Shi, W.H., Ji, J., and Yin, W.D., 2020. Urban development and natural system evolution in coastal areas. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 449-453. Coconut Creek (Florida), ISSN 0749-0208.The southeast coastal area, where several major urban agglomerations are concentrated in, is the main engine of national economic growth. Studying the natural development context of coastal cities will help to improve the urban scale structure, strengthen the relationship between urban space and surrounding areas as well as bring enlightenment for the coordinated development of regional economic belt. Taking Huzhou as an example, this paper studies the evolution of the natural system of coastal cities in the Taihu Lake River Basin. This research focuses on the formation of urban natural spatial pattern based on the city's natural development mechanism and try to sum up the traditional urban development mode through combining the evolution data of urban natural system in historical period, in order to put forward scientific theory and practical basis for the protection, construction and inheritance of traditional urban natural base in China. Through the field survey and graphic analysis, this article makes an in-depth analysis of the urban development and the evolution of natural system in Huzhou from three aspects: (1) the Urban Development of Ancient Huzhou; (2) the natural urban system of Ancient Huzhou; (3) the exploration of the natural urban development model of Huzhou, and the conclusion is that the relationship between the city and the natural system of Huzhou shows a spiral rise in the process of evolution.

In the continuing quest for operational definitions of information, this paper analyzes samples of data and information measurements used in biological systems at the genetic, cellular, and organ levels. The transduction of energy into information in biological systems yields information in the form of genetic code words (condons), bit sequences, analog codes, and paired chemical markers. Pragmatic information measures applicable to many artificial systems (robots, energy information systems, realtime systems) can be expressed in terms of international system (SI) metric units or the economic productivity units of time or money. Natural and artificial systems have in common identifiable sets of subsystems which are dedicated to processing information: input transducers, internal transducers, channels and nets, decoders, associators, memory, deciders, encoders, and output transducers. Transducers convert energy into information, or conversely. Intelligent systems can substitute informationintensive behavior for energy-intensive behavior, or for capital- or time-intensive behavior. Ratios of energy used by systems to process information tend to be very small fractions of the total energy used by these systems. However, controversy exists between proponents of the information-communications paradigm and those who regard information as a highly refined form of system control energy. Fresh approaches to information measurement are needed. New generations of real-time systems should provide opportunities to measure relationships between real-time information flows and energy flows in object systems or processes.

This article presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon earlier work by Banzhaf et al. (2016), three different kinds of open-endedness are identified: exploratory, expansive, and transformational. These are characterized in terms of their relationship to the search space of phenotypic behaviors. A formalism is introduced to describe three key processes required for an evolutionary process: the generation of a phenotype from a genetic description, the evaluation of that phenotype, and the reproduction with variation of individuals according to their evaluation. The formalism makes explicit various influences in each of these processes that can easily be overlooked. The distinction is made between intrinsic and extrinsic implementations of these processes. A discussion then investigates how various interactions between these processes, and their modes of implementation, can lead to open-endedness. However, an important contribution of the article is the demonstration that these considerations relate to exploratory open-endedness only. Conditions for the implementation of the more interesting kinds of open-endedness-expansive and transformational-are also discussed, emphasizing factors such as multiple domains of behavior, transdomain bridges, and non-additive compositional systems. In contrast to a traditional Darwinian analysis, these factors relate not to the generic evolutionary properties of individuals and populations, but rather to the nature of the building blocks out of which individual organisms are constructed, and the laws and properties of the environment in which they exist. The article ends with suggestions of how the framework can be used to categorize and compare the open-ended evolutionary potential of different systems, how it might guide the design of systems with greater capacity for open-ended evolution, and how it might be further improved.

Most studies on quantum effects in the process of excitation energy transfer in photosynthesis system are based on the single-excitation initial state hypothesis, which can well describe the initial state of some photosynthesis systems that people are concerned about. But for natural and artificial photosynthesis systems that do not meet the above hypothesis, the excitation process has a non-negligible impact on the system dynamics. Based on a multi-pigments model excited by Gaussian pulse, the effects of the excitation pulse width and the excitation interval on system dynamics and excitation energy transfer efficiency are studied. First, the kinetic equations for the overall evolution of the donor system and the acceptor system that can theoretically contain any number of pigments are derived. Afterwards, the relationship between the excitation energy transfer efficiency and the related parameters of the system, as well as the optimal range of the corresponding parameters are demonstrated by numerical simulation. It is found that under the condition of donor pigments being excited by a single Gaussian pulse, there exists optimal pulse width, and the optimal range of the pigment molecule numbers, the coupling strength as well as the dephasing rate can be modulated by the pulse width. The mechanism of the above modulation is also analyzed and presented. Under the condition of donor pigments being excited by two Gaussian pulses sequentially, there exists an optimal combination of pulse width and pulse interval. The kinetic equations obtained in this paper can be extended to other forms of excitation pulses. The numerical results and the related optimal design principles obtained have reference significance for the optimal design of artificial photosynthesis systems under different light conditions.

Research progress of artificial neural systems based on memristors

The concept of probabilistic determinism is proposed to overcome a problem of significant soil variability which complicates finding of adequate models of soil development. Probabilistic and information indicators as characteristics of evolution of complex systems expand borders of use of soil texture results, allowing correct comparison with other natural and artificial systems on the earth and other planets. Probability distributions, statistical entropy and information divergence of granulometric fractions’ contents are investigated. It is suggested to use these functions as statistical standards of granulometric composition for territory. Regularities of change of probability distribution functions and scale-independent information characteristics allowed receiving holistic numerical assessment of influence of processes of sedimentation, weathering and soil development also geological and contemporary deflation and anthropogenic impacts on probability structure of granulometric fractions contents.

The scope of this thesis is the investigation of the multi-scale signal-transduction dynamics of protein-based photosensors. Based on the previous experimental findings of Kottke et al. (2003), Lanzl et al. (2010) and Kutta et al. (2008) on the LOV1-domain from Chlamydomonas reinhardtii (CrLOV1), we first used the popular molecular dynamics (MD) technique to describe the short-time relaxation dynamics of the CrLOV1-system, as well as the LOV2-domain from Avena sativa and the Vivid LOV-domain from Neurospora crassa. In addition, we simulated the early stages of signal-transduction of the artificial photoactivable-Rac1 PA-Rac1 photo-enzyme. These LOV-photosensors, which are a subclass of the PAS-family, were of essential interest in the Graduate College for sensory photoreceptors in natural and artificial systems GRK640 as well as in the Graduate College for chemical photocatalysis GRK1626, supported by the German Research Council (DFG). Their signal-transduction pathway includes relevant structural changes, which occur on time-scales form ns up to several ms. In order to handle this range of timescales, we developed rate-based Kinetic-Monte-Carlo (KMC) simulation algorithms as well as mixed mesoscopic-atomistic coarse-grained (CG) models. With this new simulation-technique, we described the signaling pathways of LOV-based photosensors on multiple timescales from nanoseconds to seconds. As a result of these simulations, we observed the early signal transduction dynamics in the ns-timescale in the vicinity of the FMN-chromophore, which were complementary to our previous MD-results on the same natural and artificial photosensory systems. Additionally to these early timescales we obtained informations about the late signaling stages of the LOV-based photosensors within timescales from microsconds to many seconds. We start with a general introduction on the photosensory systems and the theory on the methods, which we used in our work. In the next sections we present the algorithmic developments, which we designed for the simulation on multiple timescales for the desciption of short-time as well as long-time signal-transduction events. We continue with the description of our results on the simulations of wild-type protein photosensors and finally will present our findings on artificial LOV-based systems.

This chapter outlines a broad theory of sign use in natural and artificial systems that was developed over several decades within the context of theoretical biology, cybernetics, systems theory, biosemiotics, and neuroscience. Different conceptions of semiosis and information in nature are considered. General functional properties of and operations on signs, including measurement, computation, and sign-directed actions are described. A taxonomy of semiotic systems is built up from combinations of these operations. The respective functional organizations and informational capabilities of formal systems and computempiral-predictive scientific models, percept-action systems, purposive goal-seeking systems, and self-constructing systems are discussed. Semiotic relations are considered in terms of Morrisean semiotic triad of syntactics, semantics, and pragmatics. Analysis of statetransition structure is used to demarcate functional boundaries, such as epistemic and control cuts. Capabilities for open-ended behavior, combinatoric and emergent creativity, and umwelt expansion are taken up. Finally, basic problems of neurosemiotics, neural coding, and neurophenomenology are outlined.