细胞信号网络功能鲁棒的简并性拓扑特征

细胞信号网络对于外界环境的干扰表现出优良的鲁棒性,但是其维持功能鲁棒的内在机制尚未明确,本文研究了细胞信号网络功能鲁棒性的拓扑特征。选择布尔网络模型模拟细胞网络的动态行为,利用网络节点状态的扰动模拟外界环境干扰。基于演化策略探寻不同网络拓扑的功能并分析其在干扰环境下的鲁棒性,采用埃德尔曼提出的基于信息论的计算方法评估网络拓扑的简并度、冗余度和复杂度等拓扑属性,对比分析它们与功能鲁棒度的相关性及作用机理。结果显示,在网络模型的演化过程中,其拓扑简并度与功能鲁棒度显著正相关,相关性水平高于拓扑冗余度与鲁棒度的相关性。并且,随着鲁棒度的提升,网络的节点数和复杂度也随之升高,同样简并度与网络的节点数和复杂度的相关性高于拓扑冗余度与网络的节点数和复杂度的相关性。这说明增加的网络节点以简并的方式同时提高了网络拓扑的鲁棒度和复杂度。因此,细胞网络功能鲁棒性的拓扑特征是简并而不是冗余,简并为解决生物系统的复杂问题提供了有效手段,为人工系统的可靠性设计提供有益的借鉴。     

Knowledge-making distinctions in synthetic biology

Synthetic biology is an increasingly high-profile area of research that can be understood as encompassing three broad approaches towards the synthesis of living systems: DNA-based device construction, genome-driven cell engineering and protocell creation. Each approach is characterized by different aims, methods and constructs, in addition to a range of positions on intellectual property and regulatory regimes. We identify subtle but important differences between the schools in relation to their treatments of genetic determinism, cellular context and complexity. These distinctions tie into two broader issues that define synthetic biology: the relationships between biology and engineering, and between synthesis and analysis. These themes also illuminate synthetic biology's connections to genetic and other forms of biological engineering, as well as to systems biology. We suggest that all these knowledge-making distinctions in synthetic biology raise fundamental questions about the nature of biological investigation and its relationship to the construction of biological components and systems.     

Investigating the evolution and development of biological complexity under the framework of epigenetics

Biological complexity is a key component of evolvability, yet its study has been hampered by a focus on evolutionary trends of complexification and inconsistent definitions. Here, we demonstrate the utility of bringing complexity into the framework of epigenetics to better investigate its utility as a concept in evolutionary biology. We first analyze the existing metrics of complexity and explore the link between complexity and adaptation. Although recently developed metrics allow for a unified framework, they omit developmental mechanisms. We argue that a better approach to the empirical study of complexity and its evolution includes developmental mechanisms. We then consider epigenetic mechanisms and their role in shaping developmental and evolutionary trajectories, as well as the development and organization of complexity. We argue that epigenetics itself could have emerged from complexity because of a need to self‐regulate. Finally, we explore hybridization complexes and hybrid organisms as potential models for studying the association between epigenetics and complexity. Our goal is not to explain trends in biological complexity but to help develop and elucidate novel questions in the investigation of biological complexity and its evolution.     

How scale-free are biological networks.

The concept of scale-free network has emerged as a powerful unifying paradigm in the study of complex systems in biology and in physical and social studies. Metabolic, protein, and gene interaction networks have been reported to exhibit scale-free behavior based on the analysis of the distribution of the number of connections of the network nodes. Here we study 10 published datasets of various biological interactions and perform goodness-of-fit tests to determine whether the given data is drawn from the power-law distribution. Our analysis did not identify a single interaction network that has a nonzero probability of being drawn from the power-law distribution.     

From protein interaction networks to novel therapeutic strategies

Cellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.     

Multiscale complexity in the mammalian circadian clock

The field of systems biology studies how the interactions among individual components (e.g. genes and proteins) yield interesting and complex behavior. The circadian (daily) timekeeping system in mammals is an ideal system to study complexity because of its many biological scales (from genes to animal behavior). A wealth of data at each of these scales has recently been discovered. Within each scale, modeling can advance our understanding of challenging problems that arise in studying mammalian timekeeping. However, future work must focus on bridging the multiple spatial and temporal scales in the modeling of SCN network. Here we review recent advances, and then delve into a few areas that are promising research directions. We also discuss the flavor of modeling needed (simple or detailed) as well as new techniques that are needed to meet the challenges in modeling data across scales.     

Modes and cuts in metabolic networks: complexity and algorithms

Constraint-based approaches recently brought new insight into our understanding of metabolism. By making very simple assumptions such as that the system is at steady-state and some reactions are irreversible, and without requiring kinetic parameters, general properties of the system can be derived. A central concept in this methodology is the notion of an elementary mode (EM for short) which represents a minimal functional subsystem. The computation of EMs still forms a limiting step in metabolic studies and several algorithms have been proposed to address this problem leading to increasingly faster methods. However, although a theoretical upper bound on the number of elementary modes that a network may possess has been established, surprisingly, the complexity of this problem has never been systematically studied. In this paper, we give a systematic overview of the complexity of optimisation problems related to modes. We first establish results regarding network consistency. Most consistency problems are easy, i.e., they can be solved in polynomial time. We then establish the complexity of finding and counting elementary modes. We show in particular that finding one elementary mode is easy but that this task becomes hard when a specific EM (i.e. an EM containing some specified reactions) is sought. We then show that counting the number of elementary modes is musical sharpP-complete. We emphasize that the easy problems can be solved using currently existing software packages. We then analyse the complexity of a closely related task which is the computation of so-called minimum reaction cut sets and we show that this problem is hard. We then present two positive results which both allow to avoid computing EMs as a prior to the computation of reaction cuts. The first one is a polynomial approximation algorithm for finding a minimum reaction cut set. The second one is a test for verifying whether a set of reactions constitutes a reaction cut; this test can be readily included in existing algorithms to improve their performance. Finally, we discuss the complexity of other cut-related problems.     

Shadows of complexity: what biological networks reveal about epistasis and pleiotropy

Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene–gene interaction, has also been treated as an exception to the Mendelian one gene–one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena.     

Platelet factor 4 and interleukin-8 CXC chemokine heterodimer formation modulates function at the quaternary structural level

The apparent complexity of biology increases as more biomolecular interactions that mediate function become known. We have used NMR spectroscopy and molecular modeling to provide direct evidence that tetrameric platelet factor-4 (PF4) and dimeric interleukin-8 (IL8), two members of the CXC chemokine family, readily interact by exchanging subunits and forming heterodimers via extension of their antiparallel beta-sheet domains. We further demonstrate using functional assays that PF4/IL8 heterodimerization has a direct and significant consequence on the biological activity of both chemokines. Formation of heterodimers enhances the anti-proliferative effect of PF4 on endothelial cells in culture, as well as the IL8-induced migration of CXCR2 vector-transfected Baf3 cells. These results suggest that CXC chemokine biology, and perhaps cytokine biology in general, may be functionally modulated at the molecular level by formation of heterodimers. This concept, in turn, has implications for designing chemokine/cytokine variants with modified biological properties.     

From proteomics toward systems biology: integration of different types of proteomics data into network models

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.     

Stratus not altocumulus: a new view of the yeast protein interaction network

Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: "party" hubs are co-expressed and co-localized with their partners, whereas "date" hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball-like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub-hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.     

Selection based on the size of the black tie of the great tit may be reversed in urban habitats

A standard approach to model how selection shapes phenotypic traits is the analysis of capture–recapture data relating trait variation to survival. Divergent selection, however, has never been analyzed by the capture–recapture approach. Most reported examples of differences between urban and nonurban animals reflect behavioral plasticity rather than divergent selection. The aim of this paper was to use a capture–recapture approach to test the hypothesis that divergent selection can also drive local adaptation in urban habitats. We focused on the size of the black breast stripe (i.e., tie width) of the great tit (Parus major), a sexual ornament used in mate choice. Urban great tits display smaller tie sizes than forest birds. Because tie size is mostly genetically determined, it could potentially respond to selection. We analyzed capture/recapture data of male great tits in Barcelona city (N = 171) and in a nearby (7 km) forest (N = 324) from 1992 to 2008 using MARK. When modelling recapture rate, we found it to be strongly influenced by tie width, so that both for urban and forest habitats, birds with smaller ties were more trap‐shy and more cautious than their larger tied counterparts. When modelling survival, we found that survival prospects in forest great tits increased the larger their tie width (i.e., directional positive selection), but the reverse was found for urban birds, with individuals displaying smaller ties showing higher survival (i.e., directional negative selection). As melanin‐based tie size seems to be related to personality, and both are heritable, results may be explained by cautious personalities being favored in urban environments. More importantly, our results show that divergent selection can be an important mechanism in local adaptation to urban habitats and that capture–recapture is a powerful tool to test it.     

Unifying concepts of biological function from molecules to ecosystems

The concept of function arises at all levels of biological study and is often loosely and variously defined, especially within ecology. This has led to ambiguity, obscuring the common structure that unites levels of biological organisation, from molecules to ecosystems. Here we build on already successful ideas from molecular biology and complexity theory to create a precise definition of biological function which spans levels of biological organisation and can be quantified in the unifying currency of biomass, enabling comparisons of functional effectiveness (irrespective of the specific function) across the field of ecology. We give precise definitions of ecological and ecosystem function that bring clarity and precision to studies of biodiversity– ecosystem function relationships and questions of ecological redundancy. To illustrate the new concepts and their unifying power, we construct a simple community‐level model with nutrient cycling and animal‐plant mutualism, emphasising the importance of its network structure in determining overall functioning. This type of network structure is that of an autocatalytic set of functional relationships, which also appears at biochemical, cellular and organism levels of organisation, creating a nested hierarchy. This enables a common and unifying concept of function to apply from molecular interaction networks up to the global ecosystem.     

Molecular approaches to the study of evolution and phylogeny of the Nemertina

Molecular biological tools currently available to us are revolutionizing the way in which we can address questions in evolutionary biology. The purpose of this article is to provide an overview of molecular techniques and applications available to biologists who are interested in evolutionary studies but who have little acquaintance with molecular biology. In evolutionary biology, techniques designed to determine degree of nucleic acid similarity are in common use and will be dealt with first. Another approach, namely gene expression studies, has strong implications for evolutionary biology but generally requires substantial familiarity with molecular biological tools. Expression studies provide powerful tools for discerning processes of speciation, as in the selection of genetic variants, as well as discerning lineages, e.g., expression of specific homeobox genes during segment formation. For investigations where either nucleic acid identity or gene expression are the ultimate goal, detailed information, protocols and appropriate controls are beyond the scope of this work but, where possible, recent review articles are cited.     

Applications of integrative OMICs approaches to gene regulation studies

Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms. Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.     

The Cell Biology of the Endocytic System from an Evolutionary Perspective

Evolutionary cell biology can afford an interdisciplinary comparative view that gives insights into both the functioning of modern cells and the origins of cellular systems, including the endocytic organelles. Here, we explore several recent evolutionary cell biology studies, highlighting investigations into the origin and diversity of endocytic systems in eukaryotes. Beginning with a brief overview of the eukaryote tree of life, we show how understanding the endocytic machinery in a select, but diverse, array of organisms provides insights into endocytic system origins and predicts the likely configuration in the last eukaryotic common ancestor (LECA). Next, we consider three examples in which a comparative approach yielded insight into the function of modern cellular systems. First, using ESCRT-0 as an example, we show how comparative cell biology can discover both lineage-specific novelties (ESCRT-0) as well as previously ignored ancient proteins (Tom1), likely of both evolutionary and functional importance. Second, we highlight the power of comparative cell biology for discovery of previously ignored but potentially ancient complexes (AP5). Finally, using examples from ciliates and trypanosomes, we show that not all organisms possess canonical endocytic pathways, but instead likely evolved lineage-specific mechanisms. Drawing from these case studies, we conclude that a comparative approach is a powerful strategy for advancing knowledge about the general mechanisms and functions of endocytic systems.The endomembrane system mediates transport of lipids, proteins, and other molecules to the various locations in the eukaryotic cell. It also underlies the interactions with the extracellular environment, presenting material at the cell surface as well as secreting and internalizing material. In modern cells, these latter aspects are important for signal transduction, surface remodeling, and nutrient acquisition. Just as these abilities are crucial to modern cells, they were likely equally important for the very first eukaryotes as they underwent speciation from prokaryotic-like ancestors via niche competition in the ancient world (Cavalier-Smith 2002). Understanding the events and biological processes involved in the evolution of the membrane-trafficking system in general, and the endocytic system in particular, gives us insights into landmark events in our cellular past.Evolutionary insight about cellular phenomenon is derived from two basic types of comparative study: from molecular cell biological analyses of increasingly tractable model organisms across the diversity of eukaryotes, and by computational analyses of genomic information (i.e., the genes encoding the membrane-trafficking machinery). Whereas the information gathered from taking this comparative, or evolutionary cell biology, approach (Brodsky et al. 2012) is valuable for evolutionary content, these same analyses are potentially highly valuable in understanding basic cell biology, a benefit that is perhaps less obvious and hence less appreciated. In this article, we frame what has been learned about the evolution of the endocytic system, in the dual context of what it tells us about ancient cells together with what it can tell us about modern ones. We begin with a brief introduction to eukaryotic diversity and the evolution of the membrane-trafficking system. We then delve into the evolution of specific endocytic factors to illustrate the ways in which cell biologists of all stripes can benefit from the emerging field of evolutionary cell biology.     

Theoretical and empirical perspectives on the scaling of supply and demand in social insect colonies

One of the central questions in physiological ecology is how energetic constraints affect organismal performance and the dynamics of ecological systems. Social insect colonies integrate the balance of supply and demand across levels of biological organization such that the individual components are simultaneously serving as the supply transport network and also the source of energetic demand. An increasing number of studies have demonstrated that the per‐capita metabolic rates of individuals within social insect colonies decrease with increasing colony size, a metabolic hypometry much like the pattern exhibited by individual organisms. An important question is thus, whether this scaling pattern is a result of an energetic supply constraint or evidence for an emergent economy of scale. This review synthesizes theoretical models and results from empirical studies on the scaling of resource supply and demand in social insect colonies. Scaling in biology is a powerful tool to unify the study of diverse concepts and organisms; increased integration of mechanistic realism into metabolic models will improve our understanding of the evolution of complex biological systems.