Very Simple Models, the Self-Modifying Automata and Chain of Self-Modifying Automata, Can Explain Self-Referential Properties of Living Beings

Very often, living beings seem able to change their functioning when external conditions vary. In order to study this property, we have devised abstract machines whose internal organisation changes whenever the external conditions vary. The internal organisations of these machines (or programs), are as simple as possible, functions of discrete variables. We call such machines self-modifying automata.These machines stabilise after any transient steps when they go indefinitely through a loop called p-cycle or limit cycle of length p. More often than not, the p in the cycle is equal to one and the cycle reduces to a fixed point.In this case the external value (v) can be considered as the index of function f such as: f_v(v)^–v and the machine has the property of self-replication and to be self-referential. Many authors, in computer and natural science, consider that self-referential objects are a main concept in comprehension of perception, behaviour and associations.In the third part, we have studied chains of automata. Only one automaton changes its internal organisation at each step. Chains of automata have better performances than single self-modifying automata: Higher frequency of fixed point occurrence and a shorter transient length. The performances of the chains of automata improve when the value of their internal states increases whereas the performances of single automata decrease.     

Active Learning to Understand Infectious Disease Models and Improve Policy Making

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings.     

Theory of molecular machines. I. Channel capacity of molecular machines

Like macroscopic machines, molecular-sized machines are limited by their material components, their design, and their use of power. One of these limits is the maximum number of states that a machine can choose from. The logarithm to the base 2 of the number of states is defined to be the number of bits of information that the machine could "gain" during its operation. The maximum possible information gain is a function of the energy that a molecular machine dissipates into the surrounding medium (Py), the thermal noise energy which disturbs the machine (Ny) and the number of independently moving parts involved in the operation (dspace): Cy = dspace log2 [( Py + Ny)/Ny] bits per operation. This "machine capacity" is closely related to Shannon's channel capacity for communications systems. An important theorem that Shannon proved for communication channels also applies to molecular machines. With regard to molecular machines, the theorem states that if the amount of information which a machine gains is less than or equal to Cy, then the error rate (frequency of failure) can be made arbitrarily small by using a sufficiently complex coding of the molecular machine's operation. Thus, the capacity of a molecular machine is sharply limited by the dissipation and the thermal noise, but the machine failure rate can be reduced to whatever low level may be required for the organism to survive.     

Biophysics; fifty years after Schrödinger

E. Schrödinger described his mechanistic view on life in his book “What is Life?” published in 1944. H. Yukawa stated that life is like a building of bricks. Is life understandable in this manner?In 1950–1960 the generation of structure and function in living cells was shown to be analyzable, step by step, within the theoretical framework of physics. In the 1970's the concept of a molecular machine or unit machine in living cells was clearly presented and the effort to experimentally define unit machines was promoted. Recently, new techniques to directly observe their behaviors have been developed. The machines are not always rigid. In sliding machines, the influx-efflux coupling has been found to be loose. For loose coupling, intramachine flexibility seems to be useful.Living cells can be regarded as an organized system composed of many unit machines, some of which exhibit deterministic behaviors while others exhibit probabilistic behaviors. The cells do not always show a definite response to a given input. We need new statistical mechanics for the study of unit machines and their systems which have complex spatial and temporal structures. They may have a mechanism beyond a simple building of bricks.     

Complexity in Urban Development and Management

Systems dynamics, cellular automata, agent-based modeling, and network analyses have been used in population, land use, and transport planning models. An overview of complex systems science as applied to urban development is presented, and examples are given of where the problems of housing people and anticipating their movements have been addressed with complex approaches, sometimes in concert with deterministic, large-scale urban models. Planning for cities today has additional environmental and social priorities in common with many topics that concern industrial ecology. The research agenda suggested here is that this, too, can be enriched with complex systems thinking and models to complement the often static assessment of environmental performance and better inform decision processes.     

深度学习在药物活性预测研究中的应用

药物从研发到临床应用需要耗费较长的时间，研发期间的投入成本可高达十几亿元。而随着医药研发与人工智能的结合以及生物信息学的飞速发展，药物活性相关数据急剧增加，传统的实验手段进行药物活性预测已经难以满足药物研发的需求。借助算法来辅助药物研发，解决药物研发中的各种问题能够大大推动药物研发进程。传统机器学习方法尤其是随机森林、支持向量机和人工神经网络在药物活性方面能够达到较高的预测精度。深度学习由于具有多层神经网络，模型可以接收高维的输入变量且不需要人工限定数据输入特征，可以拟合较为复杂的函数模型，应用于药物研发可以进一步提高各个环节的效率。在药物活性预测中应用较为广泛的深度学习模型主要是深度神经网络（deep neural networks，DNN）、循环神经网络（recurrent neural networks，RNN）和自编码器（auto encoder，AE），而生成对抗网络（generative adversarial networks，GAN）由于其生成数据的能力常常被用来和其他模型结合进行数据增强。近年来深度学习在药物分子活性预测方面的研究和应用综述表明，深度学习模型的准确度和效率均高于传统实验方法和传统机器学习方法。因此，深度学习模型有望成为药物研发领域未来十年最重要的辅助计算模型。     

Morphological Evolution of Physical Robots through Model-Free Phenotype Development

Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.     

General consideration of four-layer series-coupled machines with learning mechanisms

In general, four-layer series-coupled machines can be divided into two types according to learning methods. One is the machine in which the change of variable connecting coefficients depends upon the state of association units in both layers A ^I and A ^II. The other type is the machine in which the change depends upon the state of association units in only layer A ^I. In this paper, four-layer series-coupled machines of the latter type are discussed. They can be classified into six types according to the properties of the units and the learning algorithm. Some mathematical models of the machines are developed in which both excitory and inhibitory stimuli are used. The performance of these models compare favourably with machines in which only excitory stimuli are used. Learning procedure in each machine is analyzed and the convergence conditions are derived. Furthermore, some applications of the fourlayer machines to multi-category classification are discussed.The author is with the Faculty of Engineering Science, Osaka University, Osaka, Japan and presently a visiting staff of the Department of Cybernetics, Brunel University, Middlesex, England.The author is with the Department of Cybernetics, Brunel University, Uxbridge, Middlesex, England.     

Comment on "Female reproductive development: a hazards model analysis," by Andrew Foster, Jane Menken, Aladdin Chowdhury and James Trussell

This article summarizes the differences between 2 similar studies. Endocrinological studies of the time to the 1st ovulatory cycle in early and late maturing girls in Finland (Apter and Vihko, 1983) are contrary to the Bangladeshi results reported by Foster in 1986. Apter found that the frequency of ovulation depended significantly on both the time since menarche and the age at menarche. Early menarche was associated with early onset of ovulatory cycles. It would be interesting to know why the Bangladesh data differ. They are puzzling, and their use in fertility models is problematic. This article also makes 2 corrections in the use of the fatness index in the Foster paper.     

Towards a systems biology approach to study type II/IV secretion systems

Many gram-negative bacteria produce thin protein filaments, named pili, which extend beyond the confines of the outer membrane. The importance of these pili is illustrated by the fact that highly complex, multi-protein pilus-assembly machines have evolved, not once, but several times. Their many functions include motility, adhesion, secretion, and DNA transfer, all of which can contribute to the virulence of bacterial pathogens or to the spread of virulence factors by horizontal gene transfer. The medical importance has stimulated extensive biochemical and genetic studies but the assembly and function of pili remains an enigma. It is clear that progress in this field requires a more holistic approach where the entire molecular apparatus that forms the pilus is studied as a system. In recent years systems biology approaches have started to complement classical studies of pili and their assembly. Moreover, continued progress in structural biology is building a picture of the components that make up the assembly machine. However, the complexity and multiple-membrane spanning nature of these secretion systems pose formidable technical challenges, and it will require a concerted effort before we can create comprehensive and predictive models of these remarkable molecular machines.     

Towards a systems biology approach to study type II/IV secretion systems

Many gram-negative bacteria produce thin protein filaments, named pili, which extend beyond the confines of the outer membrane. The importance of these pili is illustrated by the fact that highly complex, multi-protein pilus-assembly machines have evolved, not once, but several times. Their many functions include motility, adhesion, secretion, and DNA transfer, all of which can contribute to the virulence of bacterial pathogens or to the spread of virulence factors by horizontal gene transfer. The medical importance has stimulated extensive biochemical and genetic studies but the assembly and function of pili remains an enigma. It is clear that progress in this field requires a more holistic approach where the entire molecular apparatus that forms the pilus is studied as a system. In recent years systems biology approaches have started to complement classical studies of pili and their assembly. Moreover, continued progress in structural biology is building a picture of the components that make up the assembly machine. However, the complexity and multiple-membrane spanning nature of these secretion systems pose formidable technical challenges, and it will require a concerted effort before we can create comprehensive and predictive models of these remarkable molecular machines.     

Adaptive properties of living beings: proposal for a generic mechanism. (Self-programming machines III)

Living systems are capable to have appropriate responses to unpredictable environment. This kind of self-organization seems to operate as a self-programming machine, i.e. an organization able to modify itself. Until now the models of self-organization of living beings proposed are functions solutions of differential systems or transition functions of automata. These functions are fixed and these models are therefore unable to modify their organization. On the other hand, computer science propose a lot of models having the properties of adaptive systems of living beings, but all these models depend on the comparison between a goal and the results and ingenious choices of parameters by programmers, whereas there are no programmer's intention nor choice in the living systems. From two best known examples of adaptive systems of living beings, nervous system and immune system that have in common that the external signals modify the rewriting of their organization and therefore work as self-organizing machines, we devised machines with a finite set of inputs, based upon a recurrence, are able to rewrite their organization (Self-programming machines or m(sp)) whenever external conditions vary and have striking properties of adaptation. M(sp) have similar properties whatever the operation defining the recurrence maybe. These results bring us to make the following statement: adaptive properties of living systems can be explained by their ability to rewrite their organization whenever external conditions vary under the only assumption that the rewriting mechanism be a deterministic constant recurrence in a finite state set.     

Visualization of dynamic simulations of muscle thin filaments

Following a novel computational formalism, the thin filament of muscle can be modeled by a computational machine containing a large number of finite automata that have one-to-one correspondence with the constituent protein molecules.¹ Computer graphics can be used to visualize the correspondence between the states of finite automata and the configurations of protein molecules according to the structural data. The dynamic simulation of the muscle filament that corresponds to the concurrent state transitions of finite automata can be represented as a sequence of video images. The kinetic and structural knowledge of individual protein molecules is, therefore, integrated into a coherent and functional system. This type of computation and visualization can also be useful for the investigation of molecular structure, function, and interaction in various complex biological systems.     

Gaussian processes for machine learning

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.     

基于智能体模型的土地利用动态模拟研究进展

土地利用动态变化是全球变化和可持续发展研究的基础,对区域水循环、大气循环、环境质量、气候变化及陆地生态系统生产力等具有重要影响,也是造成生物多样性衰减的最主要原因.目前,建立于复杂性科学基础上的的智能体模型(ABM)成为土地利用动态模拟的重要方法.智能体模型能模拟个体或群体的行为及决策模式,从而能将政府、城市规划、房地产开发商、住户等社会群体及个人对土地利用产生的影响进行模拟,同时能对不同社会经济政策对土地动态影响进行模拟.智能体模型在元胞自动机基础上,加入了人为因素的智能体概念,从而能更好地模拟土地动态.在分析总结了智能体模型的相关概念和组织结构,并分析了其在土地利用动态、城市动态模拟及生态过程模拟等方面的应用与元胞自动机的关系,比较了常用的智能体模型的主要软件,最后概括了智能体模型优点、发展趋势及存在的主要问题.     

In vitro assembly of semi-artificial molecular machine and its use for detection of DNA damage

Naturally occurring bio-molecular machines work in every living cell and display a variety of designs. Yet the development of artificial molecular machines centers on devices capable of directional motion, i.e. molecular motors, and on their scaled-down mechanical parts (wheels, axels, pendants etc). This imitates the macro-machines, even though the physical properties essential for these devices, such as inertia and momentum conservation, are not usable in the nanoworld environments. Alternative designs, which do not follow the mechanical macromachines schemes and use mechanisms developed in the evolution of biological molecules, can take advantage of the specific conditions of the nanoworld. Besides, adapting actual biological molecules for the purposes of nano-design reduces potential dangers the nanotechnology products may pose. Here we demonstrate the assembly and application of one such bio-enabled construct, a semi-artificial molecular device which combines a naturally-occurring molecular machine with artificial components. From the enzymology point of view, our construct is a designer fluorescent enzyme-substrate complex put together to perform a specific useful function. This assembly is by definition a molecular machine, as it contains one. Yet, its integration with the engineered part - fluorescent dual hairpin - re-directs it to a new task of labeling DNA damage. Our construct assembles out of a 32-mer DNA and an enzyme vaccinia topoisomerase I (VACC TOPO). The machine then uses its own material to fabricate two fluorescently labeled detector units (Figure 1). One of the units (green fluorescence) carries VACC TOPO covalently attached to its 3'end and another unit (red fluorescence) is a free hairpin with a terminal 3'OH. The units are short-lived and quickly reassemble back into the original construct, which subsequently recleaves. In the absence of DNA breaks these two units continuously separate and religate in a cyclic manner. In tissue sections with DNA damage, the topoisomerase-carrying detector unit selectively attaches to blunt-ended DNA breaks with 5'OH (DNase II-type breaks), fluorescently labeling them. The second, enzyme-free hairpin formed after oligonucleotide cleavage, will ligate to a 5'PO(4) blunt-ended break (DNase I-type breaks), if T4 DNA ligase is present in the solution. When T4 DNA ligase is added to a tissue section or a solution containing DNA with 5'PO(4) blunt-ended breaks, the ligase reacts with 5'PO(4) DNA ends, forming semi-stable enzyme-DNA complexes. The blunt ended hairpins will interact with these complexes releasing ligase and covalently linking hairpins to DNA, thus labeling 5'PO(4) blunt-ended DNA breaks. This development exemplifies a new practical approach to the design of molecular machines and provides a useful sensor for detection of apoptosis and DNA damage in fixed cells and tissues.     

Study of the dynamic behavior of the chemostat system

This paper deals with a theoretical study on the dynamic, character of the chemostat system. It. is primarily based on the Monod model for growth limitation, although certain more complex models are considered. Since the Monod model is described in terms of two variables, an analysis by use of a phase plane plot will show the various possible types of behavior theoretically expected for transient conditions of the system. In this paper it will be shown that the chemostat system might show an overshoot (or an underswing) with respect to changes in cell and substrate concentrations, depending on the extent to which the system might be disturbed from steady-slate conditions. Other types of transient behavior ran also be expected when one of the system parameters such as dilution rate or input substrate concentration is disturbed in a stepwise manner. The simple Monod chemostat model was found never to oscillate in either a damped or a sustained manner as has been experimentally reported. Discussion is included about the transient behavior of other chemostat models such as that involving a variable yield coefficient, i.e., including the effect of cell maintenance requirements.