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The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.

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This paper examines some methodological and epistemological issues underlying the ongoing "artificial" divide between pragmatic-systems biology and systems-theoretical biology. The pragmatic systems view of biology has encountered problems and constraints on its explanatory power because pragmatic systems biologists still tend to view systems as mere collections of parts, not as "emergent realities" produced by adaptive interactions between the constituting components. As such, they are incapable of characterizing the higher-level biological phenomena adequately. The attempts of systems-theoretical biologists to explain these "emergent realities" using mathematics also fail to produce satisfactory results. Given the increasing strategic importance of systems biology, both from theoretical and research perspectives, we suggest that additional epistemological and methodological insights into the possibility of further integration between traditional experimental studies and complex modeling are required. This integration will help to improve the currently underdeveloped pragmatic-systems biology and system-theoretical biology. The "epistemology of complexity," I contend, acts as a glue that connects and integrates different and sometimes opposing viewpoints, perspectives, streams, and practices, thus maintaining intellectual and research coherence of systems research of life. It allows scientists to shift the focus from traditional experimental research to integrated, modeling-based holistic practices capable of providing a comprehensive knowledge of organizing principles of living systems. It also opens the possibility of the development of new practical and theoretical foundations of systems biology to build a better understanding of complex organismic functions.

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El estudio de los procesos biogeoquímicos implica entender cómo los macro y micro nutrientes que componen los seres vivos se mueven de un componente a otro del ecosistema (incluyendo la atmósfera, organismos, suelo, cuerpos de agua, etc.). Usualmente, una mayor diversidad biótica y una mayor complejidad de las interacciones bióticas y abióticas, resultan en una mayor estabilidad ecosistémica. El rol de los hongos en los ciclos biogeoquímicos se suele estudiar superficialmente, no mucho más allá de sus funciones ecosistémicas generales: descomposición, simbiosis mutualista, y parasitismo. Esta revisión tiene por objetivo ilustrar los conceptos base de los roles ecológicos de los hongos del suelo, que debieran enseñarse en tres públicos objetivo: universitario, tomadores de decisiones, y estudiantes de educación secundaria/público general. En estos públicos, se propone abordar cuatro áreas temáticas: introducción al suelo, ecología de comunidades, interacciones de hongos con otros organismos, y biogeoquímica. Aunque los roles ecosistémicos de los hongos del suelo están bien documentados, su estudio debería partir de la base de que estos afectan y son afectados tanto por variables climáticas, como por características físico-químicas del suelo, y por flujos biogeoquímicos. Los roles ecológicos de los hongos del suelo debieran entenderse en un contexto holístico de integración multidisciplinar, y el nivel de especialización del conocimiento debiera darse hacia niveles superiores de la jerarquía biológica, es decir, conocer más en detalle la ecología de ecosistemas y comunidades de hongos que la de poblaciones y organismos, o que sus procesos bioquímicos y edáficos específicos.

The study of biogeochemical processes involves understanding how the macro and micro nutrients that make up living things move from one ecosystem component to another (including the atmosphere, organisms, soil, waterbodies, etc.). Usually, a greater diversity of biotic diversity and a greater complexity of biotic and abiotic interactions, result in a greater ecosystemic stability. The role of fungi in biogeochemical cycles is usually studied superficially, not much beyond their general ecosystem functions: decomposition, mutualistic symbiosis, and parasitism. The objective of this review is to illustrate the basic concepts of the ecological roles of soil fungi, which should be taught in three target audiences: university students, decision makers, and secondary school students / general public. In these audiences, it is proposed to address four thematic areas: introduction to soil, community ecology, interactions of fungi with other organisms, and biogeochemistry. Although the ecosystemic roles of soil fungi are well documented, their study should be based on the fact that they affect and are affected by climatic variables, physical-chemical soil characteristics, and biogeochemical flows. The ecological roles of soil fungi should be understood in an holistic context of multidisciplinary integration, and the level of specialization of knowledge should be given to higher levels of the biological hierarchy, that is, to know more in detail the ecology of ecosystems and communities of fungi than that of populations and organisms, or than that of their specific biochemical and edaphic processes.

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Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.

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The traditional classification of signalling in biological systems is insufficient and outdated and novel efforts must take into account advances in systems theory, information theory and linguistics. We present some of the classification systems currently used both within and outside of the biological field and discuss some specific aspects of the nature of signalling in tissue development. The analytical methods used in understanding non-biological networks provide a valuable vocabulary, which requires integration and a system of classification to further facilitate development.

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BACKGROUND: An old debate has undergone a resurgence in systems biology: that of reductionism versus holism. At least 35 articles in the systems biology literature since 2003 have touched on this issue. The histories of holism and reductionism in the philosophy of biology are reviewed, and the current debate in systems biology is placed in context. RESULTS: Inter-theoretic reductionism in the strict sense envisaged by its creators from the 1930s to the 1960s is largely impractical in biology, and was effectively abandoned by the early 1970s in favour of a more piecemeal approach using individual reductive explanations. Classical holism was a stillborn theory of the 1920s, but the term survived in several fields as a loose umbrella designation for various kinds of anti-reductionism which often differ markedly. Several of these different anti-reductionisms are on display in the holistic rhetoric of the recent systems biology literature. This debate also coincides with a time when interesting arguments are being proposed within the philosophy of biology for a new kind of reductionism. CONCLUSIONS: Engaging more deeply with these issues should sharpen our ideas concerning the philosophy of systems biology and its future best methodology. As with previous decisive moments in the history of biology, only those theories that immediately suggest relatively easy experiments will be winners.

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BACKGROUND: Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R. RESULTS: A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML() which maps this R model structure to SBML level 2, and read.SBML() which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML. CONCLUSIONS: List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted.

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