合成微生物体系研究进展

合成微生物体系作为自下而上构建的人工合成微生物群落,相比于自然微生物群落具有复杂度低及可控性、可操作性强等特点。其作为新兴的生物技术,综合借鉴了合成生物学、系统生物学、生物进化等知识,通过合理的设计、规划与调控,成为研究微生物生态学理论的实验平台,以及验证已知理论的微生物系统。本文首先简单介绍了合成微生物体系的概念及其由来,阐述了其基本构建原则,随后介绍了其生态学理论基础,并总结概括了近年来的实际应用,最后提出合成微生物体系的发展前景,包括需要设计构建更为复杂的人工合成微生物群落,以及优化生态模型。     

Fractal analysis provides new insights into the complexity of marine mammal behavior: A review,two methods,their application to diving and surfacing patterns,and their relevance to marine mammal welfare assessment

Fractals have been applied to describe the complexity of behavioral displays in a range of organisms. Recent work suggests that they may represent a promising tool in the quantification of subtle behavioral responses in marine mammals under chronic exposure to disturbance. This paper aims at introducing the still seldom used fractals to the broader community of marine mammal scientists. We first briefly rehearse some of the fundamental principles behind fractal theory and review the previous uses of fractals in marine mammal science. We subsequently introduce two methods that may be used to assess the complexity of marine mammal diving patterns, and we apply them to the temporal dynamics of the diving patterns of killer whales in the presence and absence of sea kayaks, the sequential behavior of harbor and gray seals in environments with distinct levels of anthropogenic influence, and southern right whales with and without calves. We discuss the ecological relevance of identifying fractal properties in marine mammal behavior, and the potential strength of the fractal behavioral parameters in comparison to more standard behavioral metrics. We finally briefly address the relevance fractal methods may have for the design and implementation of management and conservation strategies.     

Self-organization and complexity in historical landscape patterns

Self‐organization describes the evolution process of complex structures where systems emerge spontaneously, driven internally by variations of the system itself. Self‐organization to the critical state is manifested by scale‐free behavior across many orders of magnitude (Bak et al. 1987, Bak 1996, Solé et al. 1999). Spatial scale‐free behavior implies fractal properties and is quantified by the fractal dimension. Temporal scale‐free behavior is evident in power spectra of fluctuations that obey power laws. Self‐organized criticality is a universal phenomenon that likely produces some of the fractals and power laws observed in nature. We investigated the historical landscape of southern Wisconsin (USA) (60,000 km²) for self‐organization and complexity. The landscape is patterned into prairies, savannas, and open and closed forests, using data from the United States General Land Office Surveys that were conducted during the 19th century, at a time prior to Euro‐American settlement. We applied a two‐dimensional cellular automaton model with one adjustable parameter. Model evolution replaces a cell that dies at random times by a cell chosen randomly from within a circular radius r, where r typically takes values between 1 (local) and 10 units (regional). Cluster probability is used to measure the degree of organization. The model landscape self‐organizes to a realistic critical state if neighborhoods of intermediate size (r=3) are chosen, indicating that (a) no particular time or space scale for the clusters is singled out, i.e. the spatial dependence is fractal, and temporal fluctuations in the cluster probability exhibit power laws; (b) a simple model suffices to replicate the landscape pattern resulting from complex spatial and temporal interactions. Measures of comparison between the observed and the simulated landscape show good agreement: fractal dimensions for simulated (1.6) and observed landscapes (1.64), cluster probabilities for simulated (32.3%) and observed (32.6%) landscapes, and algorithmic complexity for simulated (6792 bytes) and observed (6205 bytes) landscapes. The results are robust towards variation of initial and boundary conditions as well as perturbations.     

The formalism of fractal aggregation phenomena of colloidal drug delivery systems

Classical Newtonian Physics and Euclidean Geometry are currently used to describe biological phenomena and the processes of drug formulation, which are characterized by homogeneity and linearity. On the other hand, at the mesoscopic level, the principles and the laws of physics are quite different from the Classical Newtonian Physics and Euclidean approach especially at nanoscale dimension. The investigation of the aggregation process of liposomes is of paramount importance due to their applications in pharmaceutical nanotechnology as drug delivery systems and as membrane models, in biosciences. The physical stability and the aggregation process of colloidal systems as well as the surface phenomena are described using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. The elucidation of the dimensionality of liposome aggregates obeys the fractal approach because the aggregation phenomena are irreversible. This approach can be correlated with the extended DLVO theory, which includes the hydration energy, too.     

肝脏超声图象局部分形指数研究

为了研究人体组织超声图象的局部特征,并为进行人体组织定征研究提供新的参数,提出了一个分析超声图象局部分形指数的新方法－局部分形指数小波分析法ＬＦＷＡＭ（ＬｏｃａｌＦｒａｃｔａｌＳｃａｌｅＷａｖｅｌｅｔＡｎａｌｙｓｉｓＭｅｔｈｏｄ）。应用此法研究了人体肝脏组织超声图象分形体的构造规则;进行了局部分形指数的分析。验证了ＬＦＷＡＭ法分析肝脏超声图象局部特性的有效性,得出了局部分形指数更能全面、细致地刻画肝脏组织超声图象分形特征的结论,为进而研究局部病变的识别与图象的分割提供了基础。     

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent''s rule, which is related to the dimensionality of the circuit''s interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent''s rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.     

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today''s important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales.     

Continu versus discontinu,linéaire versus non linéaire dans l’évolution des espèces

Continuity versus discontinuity, linear versus nonlinear in species evolution. Species evolution is a topic where concepts of continuity and discontinuity have been opposed for two centuries. What is the situation today in biological evolution ? The statistical distribution of appearances and disappearances among rodent species follows power laws, suggesting nonlinearity and fractal structures. Thus, the domain of living beings seems to be extended, through critical physical phenomena, to the analysis of linear versus nonlinear biological phenomena. These observations are consistent with the new scale relativity theory of Nottale, which predicts many domains of resolution in nature separated by scale dependence or scale independence. Despite some scale-dependence particular to various levels of biological organization, the fact that they are also characterized by scale-independence suggests that they could be described by new fundamental laws revealing their potentially fractal and irreversible character. In the proximity of the critical time, specific to each system, the system becomes unstable and fractal, and shows precursor events at an accelerated rate leading up to the critical time. After the critical time, the system shows replicate events, in a decelerating manner. The discontinuous appearance of a number of clades follows a log-periodic law with a middle scale report of 1.73. This law, first applied to earthquakes, stock market crashes, demography, and physical turbulences, and now also being applied with success to macroevolutionary patterns, seems to have a certain universality. To cite this article : J. Chaline, C. R. Palevol 2 (2003).     

Exergy‐Based Population Dynamics

This article discusses whether “sustainability” has a physical meaning in applied thermodynamics. If it has, then it should be possible to derive general principles and rules for devising “sustainable systems.” If not, then other sides of the issue retain their relevance, but thermodynamic laws are not appropriate by themselves to decide whether a system or a scenario is sustainable. Here, we make use of a single axiom: that final consumption (material or immaterial) can be quantified solely in terms of equivalent primary exergy flows. On this basis, we develop a system theory that shows that if “simple” systems are based solely on the exploitation of fossil resources, they cannot be thermodynamically “sustainable.” But as renewable resources are brought into the picture and the system complexity grows, there are thresholds below or beyond which the system exhibits an ability to maintain itself (perhaps through fluctuations), in a self‐preserving (i.e., a sustainable) state. It appears that both complexity and the degree of nonlinearity of the transfer functions of the systems play a major role and—even for some of the simplest cases—lead to nontrivial solutions in phase space. Therefore, even if the examples presented in the article can be considered rather crude approximations to real, complex systems at best, the results show a trend that is worth further consideration.     

兴安落叶松分枝格局的分形特征

对于树木分枝格局分形特征的定量描述,可以加深对树木生长过程的理解。本文采用分形几何学对兴安落叶松(Larix gmelini)的分枝格局进行研究,结果表明1)兴安落叶松分枝格局是一种分形结构,存在自相似性。2)兴安落叶松分枝格局的分形维数介于1.4~1.7之间,揭示了它的结构复杂性程度和占据生态空间、利用生态空间的能力。分形维数在树木光合作用及生长发育研究中是一个有用的参数。     

Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics

While geographers and ecologists are well aware of the scale effects of landscape patterns, there is still a need for quantifying these effects. This paper applies the fractal method to measure the scale (grain or cell size) sensitivity of landscape metrics at both landscape and class levels using the Gold Coast City in Southeast Queensland, Australia as a case study. By transforming the original land use polygon data into raster data at eleven aggregate scales, the fractal dimensions of 57 landscape metrics as defined in FRAGSTATS were assessed. A series of linear log–log regression models were constructed based on the power law to obtain the coefficient of determination (COD or R²) of the models and the fractal dimension (FD) of the landscape metrics. The results show that most landscape metrics in the area and edge, shape and the aggregation groups exhibit a fractal law that is consistent over a range of scales. The six variations of several landscape metrics that belong to both the area/edge and shape groups show different scale behaviours and effects. However, the metrics that belong to the diversity group are scale-independent and do not accord to fractal laws. In addition, the scale effects at the class level are more complex than those at the landscape level. The quantitative assessment of the scale effect using the fractal method provides a basis for investigating landscape patterns when upscaling or downscaling as well as creating any scale-free metric to understand landscape patterns.     

Multifractality in intracellular enzymatic reactions

Enzymatic kinetics adjust well to the Michaelis-Menten paradigm in homogeneous media with dilute, perfectly mixed reactants. These conditions are quite different from the highly structured cell plasm, so applications of the classic kinetics theory to this environment are rather limited. Cytoplasmic structure produces molecular crowding and anomalous diffusion of substances, modifying the mass action kinetic laws. The reaction coefficients are no longer constant but time-variant, as stated in the fractal kinetics theory. Fractal kinetics assumes that enzymatic reactions on such heterogeneous media occur within a non-Euclidian space characterized by a certain fractal dimension, this fractal dimension gives the dependence on time of the kinetic coefficients. In this work, stochastic simulations of enzymatic reactions under molecular crowding have been completed, and kinetic coefficients for the reactions, including the Michaelis-Menten parameter KM, were calculated. The simulations results led us to confirm the time dependence of michaelian kinetic parameter for the enzymatic catalysis. Besides, other chaos related phenomena were pointed out from the obtained KM time series, such as the emergence of strange attractors and multifractality.     

Landscape ecology development supported by geospatial technologies: A review

Numerous technologies have contributed to the recent development of landscape ecology, especially the geospatial technological advances constitute a revolution in landscape ecology. Extensive applications of geospatial technologies, such as fractal theory, geographic information systems (GIS) and remote sensing (RS) in landscape ecology research, suggest the necessity of such a review of research progress in this field. In this study, a brief introduction to fractal theory, GIS and RS and how they were applied in landscape ecology were provided first. Then, the current state-of-the-art was summarized and analyzed as reference for further promoting the development of landscape ecology science and its applications. Finally, opportunities and challenges of landscape ecology applications using these new technologies were discussed and concluded for future research. It was contended that a combination and integration of these technologies can substantially advance the study of landscape ecology for data acquisition, process modeling, scale transformation, result analysis and visualization. However, as no theoretical framework and application cases of applying an integration of fractal theory, GIS and RS techniques to landscape ecology research have been established, further studies are still much needed.     

The cell as the smallest DNA-based molecular computer

The pioneering work of Adleman (1994) demonstrated that DNA molecules in test tubes can be manipulated to perform a certain type of mathematical computation. This has stimulated a theoretical interest in the possibility of constructing DNA-based molecular computers. To gauge the practicality of realizing such microscopic computers, it was thought necessary to learn as much as possible from the biology of the living cell--presently the only known DNA-based molecular computer in existence. Here the recently developed theoretical model of the living cell (the Bhopalator) and its associated theories (e.g. cell language), principles, laws and concepts (e.g. conformons, IDS's) are briefly reviewed and summarized in the form of a set of five laws of 'molecular semiotics' (synonyms include 'microsemiotics', 'cellular semiotics', or 'cytosemiotics') the study of signs mediating measurement, computation, and communication on the cellular and molecular levels. Hopefully, these laws will find practical applications in designing DNA-based computing systems.     

Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.     

Laws of biology: why so few?

Finding fundamental organizing principles is the current intellectual front end of systems biology. From a hydrogen atom to the whole cell level, organisms manage massively parallel and massively interactive processes over several orders of magnitude of size. To manage this scale of informational complexity it is natural to expect organizing principles that determine higher order behavior. Currently, there are only hints of such organizing principles but no absolute evidences. Here, we present an approach as old as Mendel that could help uncover fundamental organizing principles in biology. Our approach essentially consists of identifying constants at various levels and weaving them into a hierarchical chassis. As we identify and organize constants, from pair-wise interactions to networks, our understanding of the fundamental principles in biology will improve, leading to a theory in biology.     

分子进化研究：Ⅱ.5SrRNA序列的分形与分子进化的关系

本文根据分形理论的原理和方法,在对现行的计算核酸序列分维的方法进行修改的基础上,对各类生物的80余种5SrRNA序列的分维进行了计算,并结合耗散结构理论就其分维与分子进化的关系问题进行了研究和探讨。作者认为,5SrRNA序列的分维与其分子进化间的关系是一种复杂的非线性关系,在分子进化的过程中,序列的分维表现为随机涨落。     

Habitat complexity in aquatic systems: fractals and beyond

Despite the intensity with which ecological information involving habitat complexity has been amassed to date, much remains to be revealed for a comprehensive understanding of the mechanics and implications of the structural complexity of habitats and its influences on ecological communities. This review examines the multi-faceted characteristics of habitat complexity, focusing in particular on aquatic ecosystems. Habitat complexity in aquatic systems is characterised by at least five different traits of physical structure: (1) spatial scales, (2) diversity of complexity-generating physical (structural) elements, (3) spatial arrangement of elements, (4) sizes of elements, (5) abundance/density of elements. Of these five traits, the concept of fractal dimension fully encompasses only the last one; in this sense, habitat complexity is more complex than what fractal measures represent. It is therefore important to investigate exactly which traits of habitat structure are exerting influences on organisms/communities. We hypothesise that, where an entire range of possible fractal dimension D is considered, intermediate levels of D are most likely to be associated with the highest level of biodiversity, to which the body size spectra of assemblages would have a close bearing. In most aquatic ecosystems, broadly two-dimensional structures of bottom substrate at the scale of 1–10 m mean that the addition of vertical, three dimensional structures almost always implies an increase in both the ‘diversity’ and ‘abundance’ components of structural elements, resulting in more habitats being made available to organisms of different sizes and functional designs. The conservation and management of aquatic ecosystems would be facilitated by rigorous assessments of linkages between habitat complexity and aquatic communities, for which an integrative approach to habitat complexity seems to offer a useful and versatile framework.