The roles of integration in molecular systems biology

Unified and pluralistic ideals for data sharing and reuse in biodiversity

How should billions of species observations worldwide be shared and made reusable? Many biodiversity scientists assume the ideal solution is to standardize all datasets according to a single, universal classification and aggregate them into a centralized, global repository. This ideal has known practical and theoretical limitations, however, which justifies investigating alternatives. To support better community deliberation and normative evaluation, we develop a novel conceptual framework showing how different organizational models, regulative ideals and heuristic strategies are combined to form shared infrastructures supporting data reuse. The framework is anchored in a general definition of data pooling as an activity of making a taxonomically standardized body of information available for community reuse via digital infrastructure. We describe and illustrate unified and pluralistic ideals for biodiversity data pooling and show how communities may advance toward these ideals using different heuristic strategies. We present evidence for the strengths and limitations of the unification and pluralistic ideals based on systemic relationships of power, responsibility and benefit they establish among stakeholders, and we conclude the pluralistic ideal is better suited for biodiversity data.

Explanatory integration and integrated explanations in Darwinian medicine and evolutionary medicine

Recently, two research traditions that bring together evolutionary biology and medicine, that is to say, Darwinian medicine and evolutionary medicine, have been identified. In this paper, I analyse these two research traditions with respect to explanatory and interdisciplinary integration. My analysis shows that Darwinian medicine does not integrate medicine and evolutionary biology in any strong sense but does incorporate evolutionary concepts into medicine. I also show that backward-looking explanations in Darwinian medicine are not integrated proximate-and-ultimate explanations but functional explanations that include reference to evolutionary concepts. Nevertheless, explanations in Darwinian medicine have heuristic roles as they potentially contribute to conceptual change and tie pieces of knowledge from different fields of medical research together. I argue that Darwinian medicine is an "interfield" that fosters cross-disciplinary exchange between evolutionary biologists and medical researchers and practitioners based on division of labour and separation, rather than unity. Research in evolutionary medicine, on the other hand, happens at the intersection of evolutionary biology and medicine where the two disciplines are already integrated and is designed to produce entangled proximate-evolutionary explanations. My analysis thus adds another important aspect to the philosophical discussion on the distinction between Darwinian medicine and evolutionary medicine.

Non-Lethal Sampling Supports Integrative Movement Research in Freshwater Fish

Freshwater ecosystems and fishes are enormous resources for human uses and biodiversity worldwide. However, anthropogenic climate change and factors such as dams and environmental contaminants threaten these freshwater systems. One way that researchers can address conservation issues in freshwater fishes is via integrative non-lethal movement research. We review different methods for studying movement, such as with acoustic telemetry. Methods for connecting movement and physiology are then reviewed, by using non-lethal tissue biopsies to assay environmental contaminants, isotope composition, protein metabolism, and gene expression. Methods for connecting movement and genetics are reviewed as well, such as by using population genetics or quantitative genetics and genome-wide association studies. We present further considerations for collecting molecular data, the ethical foundations of non-lethal sampling, integrative approaches to research, and management decisions. Ultimately, we argue that non-lethal sampling is effective for conducting integrative, movement-oriented research in freshwater fishes. This research has the potential for addressing critical issues in freshwater systems in the future.

The levels problem in psychopathology

Psychiatric disorders are studied at multiple levels, but there is no agreement on how these levels are related to each other, or how they should be understood in the first place. In this paper, I provide an account of levels and their relationships that is suited for psychopathology, drawing from recent debates in philosophy of science. Instead of metaphysical issues, the focus is on delivering an understanding of levels that is relevant and useful for scientific practice. I also defend a pragmatic approach to the question of reduction, arguing that even in-principle reductionists should embrace pluralism in practice. Finally, I discuss the benefits and challenges in integrating explanations and models of different levels.

Where health and environment meet: the use of invariant parameters in big data analysis

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on "data mash-ups"-that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power.

Do synthesis centers synthesize? A semantic analysis of topical diversity in research

Synthesis centers are a form of scientific organization that catalyzes and supports research that integrates diverse theories, methods and data across spatial or temporal scales to increase the generality, parsimony, applicability, or empirical soundness of scientific explanations. Synthesis working groups are a distinctive form of scientific collaboration that produce consequential, high-impact publications. But no one has asked if synthesis working groups synthesize: are their publications substantially more diverse than others, and if so, in what ways and with what effect? We investigate these questions by using Latent Dirichlet Analysis to compare the topical diversity of papers published by synthesis center collaborations with that of papers in a reference corpus. Topical diversity was operationalized and measured in several ways, both to reflect aggregate diversity and to emphasize particular aspects of diversity (such as variety, evenness, and balance). Synthesis center publications have greater topical variety and evenness, but less disparity, than do papers in the reference corpus. The influence of synthesis center origins on aspects of diversity is only partly mediated by the size and heterogeneity of collaborations: when taking into account the numbers of authors, distinct institutions, and references, synthesis center origins retain a significant direct effect on diversity measures. Controlling for the size and heterogeneity of collaborative groups, synthesis center origins and diversity measures significantly influence the visibility of publications, as indicated by citation measures. We conclude by suggesting social processes within collaborations that might account for the observed effects, by inviting further exploration of what this novel textual analysis approach might reveal about interdisciplinary research, and by offering some practical implications of our results.

Modelling gene regulation: (De)compositional and template-based strategies

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine - using the mechanistic philosophy of science as an analytic springboard - the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally.

The challenges of big data biology

The availability of big data has the potential to transform many areas of the life sciences and usher in new ways of doing research. Here, I argue that big data biology also raises fundamental questions in the philosophy of science: for example, what is a good dataset, and how can reliable knowledge be extracted from big data? Collaborations between biologists, data scientists and philosophers of science will help us to answer these and other questions.

[Big Data Revolution or Data Hubris? : On the Data Positivism of Molecular Biology]

Genome data, the core of the 2008 proclaimed big data revolution in biology, are automatically generated and analyzed. The transition from the manual laboratory practice of electrophoresis sequencing to automated DNA-sequencing machines and software-based analysis programs was completed between 1982 and 1992. This transition facilitated the first data deluge, which was considerably increased by the second and third generation of DNA-sequencers during the 2000s. However, the strategies for evaluating sequence data were also transformed along with this transition. The paper explores both the computational strategies of automation, as well as the data evaluation culture connected with it, in order to provide a complete picture of the complexity of today's data generation and its intrinsic data positivism. This paper is thereby guided by the question, whether this data positivism is the basis of the big data revolution of molecular biology announced today, or it marks the beginning of its data hubris.

Conceptions of Good Science in Our Data-Rich World

Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms.

On the nature of cross-disciplinary integration: A philosophical framework

Meeting grand challenges requires responses that constructively combine multiple forms of expertise, both academic and non-academic; that is, it requires cross-disciplinary integration. But just what is cross-disciplinary integration? In this paper, we supply a preliminary answer by reviewing prominent accounts of cross-disciplinary integration from two literatures that are rarely brought together: cross-disciplinarity and philosophy of biology. Reflecting on similarities and differences in these accounts, we develop a framework that integrates their insights-integration as a generic combination process the details of which are determined by the specific contexts in which particular integrations occur. One such context is cross-disciplinary research, which yields cross-disciplinary integration. We close by reflecting on the potential applicability of this framework to research efforts aimed at meeting grand challenges.

Mechanistic-enriched models: integrating transcription factor networks and metabolic deregulation in cancer

Background

In the present paper we will examine methodological frameworks to study complex genetic diseases (e.g. cancer) from the stand point of theoretical-computational biology combining both data-driven and hypothesis driven approaches. Our work focuses in the apparent counterpoint between two formal approaches to research in natural science: data- and hypothesis-driven inquiries. For a long time philosophers have recognized the mechanistic character of molecular biology explanations. On these grounds we suggest that hypothesis and data-driven approaches are not opposed to each other but that they may be integrated by the development of what we call enriched mechanistic models.

Methods

We will elaborate around a case study from our laboratory that analyzed the relationship between transcriptional de-regulation of sets of genes that present both transcription factor and metabolic activity while at the same time have been associated with the presence of cancer. The way we do this is by analyzing structural, mechanistic and functional approaches to molecular level research in cancer biology. Emphasis will be given to data integration strategies to construct new explanations.

Results

Such analysis has led us to present a mechanistic-enriched model of the phenomenon. Such model pointed out to the way in which regulatory and thermodynamical behavior of gene regulation networks may be analyzed by means of gene expression data obtained from genome-wide analysis experiments in RNA from biopsy-captured tissue. The foundations of the model are given by the laws of thermodynamics and chemical physics and the approach is an enriched version of a mechanistic explanation.

Conclusion

After analyzing the way we studied the coupling of metabolic and transcriptional deregulation in breast cancer, we have concluded that one plausible strategy to integrate data driven and hypothesis driven approaches is by means of resorting to fundamental and well established laws of physics and chemistry since these provide a solid ground for assessment.

Repertoires: How to Transform a Project into a Research Community

How effectively communities of scientists come together and co-operate is crucial both to the quality of research outputs and to the extent to which such outputs integrate insights, data and methods from a variety of fields, laboratories and locations around the globe. This essay focuses on the ensemble of material and social conditions that makes it possible for a short-term collaboration, set up to accomplish a specific task, to give rise to relatively stable communities of researchers. We refer to these distinctive features as repertoires, and investigate their development and implementation across three examples of collaborative research in the life sciences. We conclude that whether a particular project ends up fostering the emergence of a resilient research community is partly determined by the degree of attention and care devoted by researchers to material and social elements beyond the specific research questions under consideration.

Engineering and Biology: Counsel for a Continued Relationship

Biologists frequently draw on ideas and terminology from engineering. Evolutionary systems biology-with its circuits, switches, and signal processing-is no exception. In parallel with the frequent links drawn between biology and engineering, there is ongoing criticism against this cross-fertilization, using the argument that over-simplistic metaphors from engineering are likely to mislead us as engineering is fundamentally different from biology. In this article, we clarify and reconfigure the link between biology and engineering, presenting it in a more favorable light. We do so by, first, arguing that critics operate with a narrow and incorrect notion of how engineering actually works, and of what the reliance on ideas from engineering entails. Second, we diagnose and diffuse one significant source of concern about appeals to engineering, namely that they are inherently and problematically metaphorical. We suggest that there is plenty of fertile ground left for a continued, healthy relationship between engineering and biology.

Validation and variability: dual challenges on the path from systems biology to systems medicine

Systems biology is currently making a bid to show that it is able to make an important contribution to personalised or precision medicine. In order to do so, systems biologists need to find a way of tackling the pervasive variability of biological systems that is manifested in the medical domain as inter-subject variability. This need is simultaneously social and epistemic: social as systems biologists attempt to engage with the interests and concerns of clinicians and others in applied medical research; epistemic as they attempt to develop new strategies to cope with variability in the validation of the computational models typical of systems biology. This paper describes one attempt to develop such a strategy: a trial with a population-of-models approach in the context of cardiac electrophysiology. I discuss the development of this approach against the background of ongoing tensions between mathematically and experimentally inclined modellers on the one hand, and attempts to forge new collaborations with medical scientists on the other. Apart from the scientific interest of the population-of-models approach for tackling variability, the trial also offers a good illustration of the epistemology of experiment-facing modelling. I claim that it shows the extent to which experiment-facing modelling and validation require the establishment of criteria for comparing models and experiments that enable them to be linked together. These 'grounds of comparability' are the broad framework in which validation experiments are interpreted and evaluated by all the disciplines in the collaboration, or being persuaded to participate in it. I claim that following the process of construction of the grounds of comparability allows us to see the establishment of epistemic norms for judging validation results, through a process of 'normative intra-action' (Rouse, 2002) that shape the social and epistemic evolution of systems approaches to biomedicine.

Data Interpretation in the Digital Age

The consultation of internet databases and the related use of computer software to retrieve, visualise and model data have become key components of many areas of scientific research. This paper focuses on the relation of these developments to understanding the biology of organisms, and examines the conditions under which the evidential value of data posted online is assessed and interpreted by the researchers who access them, in ways that underpin and guide the use of those data to foster discovery. I consider the types of knowledge required to interpret data as evidence for claims about organisms, and in particular the relevance of knowledge acquired through physical interaction with actual organisms to assessing the evidential value of data found online. I conclude that familiarity with research in vivo is crucial to assessing the quality and significance of data visualised in silico; and that studying how biological data are disseminated, visualised, assessed and interpreted in the digital age provides a strong rationale for viewing scientific understanding as a social and distributed, rather than individual and localised, achievement.

Introduction: the plurality of modeling

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved-medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound.

What Difference Does Quantity Make? On the Epistemology of Big Data in Biology

Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.

Causes of aging are likely to be many: robin holliday and changing molecular approaches to cell aging, 1963-1988

Causal complexities involved in biological phenomena often generate ambiguous experimental results that may create epistemic niches for new approaches and interpretations. The exploration for new approaches may foment momentum of larger epistemological shifts, and thereby introduce the possibilities of adopting new technologies. This paper describes British molecular biologist Robin Holliday's cell aging research from 1963 to the 1980s that transformed from simple hypothesis testing to working on various alternative and integrative approaches designed to deal with complex data. In the 1960s, hoping to use biochemical investigations of cells to settle a debate about whether DNA mutations or protein errors caused aging, Holliday carried out a series of experiments with fruit flies, fungi, and human fibroblast cells. The results seemed to demonstrate that cytoplasmic protein errors caused cell aging. However, other scientists obtained contradictory results and raised issues about potential flaws in Holliday's experiments. In the 1970s, working as the director of the Genetics Division of the National Institute for Medical Research in Mill Hill, United Kingdom, Holliday relied on available talents of his associates, including computational expertise, to explore alternative hypotheses and approaches. By the early 1980s, they had worked out an epigenetic explanation and had established integrative, evolutionary models of cell aging that incorporated both DNA mutations and protein errors as critical factors. By delineating Holliday's research path from simply testing hypotheses to integrating multiple factors involved in aging, this paper offers an account of the difficulties in targeting molecular cause in cell aging around the 1970s, whose failures nevertheless opened up an epistemic niche for integration.

INDIGO - INtegrated data warehouse of microbial genomes with examples from the red sea extremophiles

BACKGROUND

The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.

RESULTS

We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments.

CONCLUSIONS

We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.

Integration in biology: Philosophical perspectives on the dynamics of interdisciplinarity

This introduction to the special section on integration in biology provides an overview of the different contributions. In addition to motivating the philosophical significance of analyzing integration and interdisciplinary research, I lay out common themes and novel insights found among the special section contributions, and indicate how they exhibit current trends in the philosophical study of integration. One upshot of the contributed papers is that there are different aspects to and kinds of integration, so that rather than attempting to offer a universal construal of what integrations is, philosophers have to analyze in concrete cases in what respects particular aspects of scientific theorizing and/or practice are 'integrative' and how this instance of integration works and was achieved.

Integrating data to acquire new knowledge: Three modes of integration in plant science

This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O'Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what data integration involves in more detail and with a focus on the role of data-sharing tools, like online databases, in facilitating this process; and I point to the philosophical implications of focusing on data as a unit of analysis. I then analyse three cases of data integration in the field of plant science, each of which highlights a different mode of integration: (1) inter-level integration, which involves data documenting different features of the same species, aims to acquire an interdisciplinary understanding of organisms as complex wholes and is exemplified by research on Arabidopsis thaliana; (2) cross-species integration, which involves data acquired on different species, aims to understand plant biology in all its different manifestations and is exemplified by research on Miscanthus giganteus; and (3) translational integration, which involves data acquired from sources within as well as outside academia, aims at the provision of interventions to improve human health (e.g. by sustaining the environment in which humans thrive) and is exemplified by research on Phytophtora ramorum. Recognising the differences between these efforts sheds light on the dynamics and diverse outcomes of data dissemination and integrative research; and the relations between the social and institutional roles of science, the development of data-sharing infrastructures and the production of scientific knowledge.

Cancer and the goals of integration

Cancer is not one, but many diseases, and each is a product of a variety of causes acting (and interacting) at distinct temporal and spatial scales, or "levels" in the biological hierarchy. In part because of this diversity of cancer types and causes, there has been a diversity of models, hypotheses, and explanations of carcinogenesis. However, there is one model of carcinogenesis that seems to have survived the diversification of cancer types: the multi-stage model of carcinogenesis. This paper examines the history of the multistage theory, and uses the theory as a case study in the limits and goals of unification as a theoretical virtue, comparing and contrasting it with "integrative" research.

Coupling simulation and experiment: The bimodal strategy in integrative systems biology

The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling pathways by conducting her own wet-lab experimentation while building simulation models. We show how this coupling of experiment and simulation enabled her to build and validate her models and also triangulate and localize errors and uncertainties in them. This method can be contrasted with the unimodal modeling strategy in systems biology which relies more on mathematical or algorithmic methods to reduce complexity. We discuss the relative affordances and limitations of these strategies, which represent distinct opinions in the field about how to handle the investigation of complex biological systems.

Integration of approaches in David Wake's model-taxon research platform for evolutionary morphology

What gets integrated in integrative scientific practices has been a topic of much discussion. Traditional views focus on theories and explanations, with ideas of reduction and unification dominating the conversation. More recent ideas focus on disciplines, fields, or specialties; models, mechanisms, or methods; phenomena, problems. How integration works looks different on each of these views since the objects of integration are ontologically and epistemically various: statements, boundary conditions, practices, protocols, methods, variables, parameters, domains, laboratories, and questions all have their own structures, functions and logics. I focus on one particular kind of scientific practice, integration of "approaches" in the context of a research system operating on a special kind of "platform." Rather than trace a network of interactions among people, practices, and theoretical entities to be integrated, in this essay I focus on the work of a single investigator, David Wake. I describe Wake's practice of integrative evolutionary biology and how his integration of approaches among biological specialties worked in tandem with his development of the salamanders as a model taxon, which he used as a platform to solve, re-work and update problems that would not have been solved so well by non-integrative approaches. The larger goal of the project to which this paper contributes is a counter-narrative to the story of 20th century life sciences as the rise and march of the model organisms and decline of natural history.

When integration fails: Prokaryote phylogeny and the tree of life

Much is being written these days about integration, its desirability and even its necessity when complex research problems are to be addressed. Seldom, however, do we hear much about the failure of such efforts. Because integration is an ongoing activity rather than a final achievement, and because today's literature about integration consists mostly of manifesto statements rather than precise descriptions, an examination of unsuccessful integration could be illuminating to understand better how it works. This paper will examine the case of prokaryote phylogeny and its apparent failure to achieve integration within broader tree-of-life accounts of evolutionary history (often called 'universal phylogeny'). Despite the fact that integrated databases exist of molecules pertinent to the phylogenetic reconstruction of all lineages of life, and even though the same methods can be used to construct phylogenies wherever the organisms fall on the tree of life, prokaryote phylogeny remains at best only partly integrated within tree-of-life efforts. I will examine why integration does not occur, compare it with integrative practices in animal and other eukaryote phylogeny, and reflect on whether there might be different expectations of what integration should achieve. Finally, I will draw some general conclusions about integration and its function as a 'meta-heuristic' in the normative commitments guiding scientific practice.

Systems biology and the integration of mechanistic explanation and mathematical explanation

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models-which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism's ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.

Basic science through engineering? Synthetic modeling and the idea of biology-inspired engineering

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more "biology inspired."

A network perspective on unraveling the role of TRP channels in biology and disease

Transient receptor potential (TRP) channels are a large family of non-selective cation channels that mediate numerous physiological and pathophysiological processes; however, still largely unknown are the underlying molecular mechanisms. With data generated on an unprecedented scale, network-based approaches have been revolutionizing the way in which we understand biology and disease, discover disease genes, and develop therapeutic strategies. These circumstances have created opportunities to encounter TRP channel research to data-intensive science. In this review, we provide an introduction of network-based approaches in biomedical science, describe the current state of TRP channel network biology, and discuss the future direction of TRP channel research. Network perspective will facilitate the discovery of latent roles and underlying mechanisms of TRP channels in biology and disease.

Evolution of microbes and viruses: a paradigm shift in evolutionary biology?

When Charles Darwin formulated the central principles of evolutionary biology in the Origin of Species in 1859 and the architects of the Modern Synthesis integrated these principles with population genetics almost a century later, the principal if not the sole objects of evolutionary biology were multicellular eukaryotes, primarily animals and plants. Before the advent of efficient gene sequencing, all attempts to extend evolutionary studies to bacteria have been futile. Sequencing of the rRNA genes in thousands of microbes allowed the construction of the three- domain “ribosomal Tree of Life” that was widely thought to have resolved the evolutionary relationships between the cellular life forms. However, subsequent massive sequencing of numerous, complete microbial genomes revealed novel evolutionary phenomena, the most fundamental of these being: (1) pervasive horizontal gene transfer (HGT), in large part mediated by viruses and plasmids, that shapes the genomes of archaea and bacteria and call for a radical revision (if not abandonment) of the Tree of Life concept, (2) Lamarckian-type inheritance that appears to be critical for antivirus defense and other forms of adaptation in prokaryotes, and (3) evolution of evolvability, i.e., dedicated mechanisms for evolution such as vehicles for HGT and stress-induced mutagenesis systems. In the non-cellular part of the microbial world, phylogenomics and metagenomics of viruses and related selfish genetic elements revealed enormous genetic and molecular diversity and extremely high abundance of viruses that come across as the dominant biological entities on earth. Furthermore, the perennial arms race between viruses and their hosts is one of the defining factors of evolution. Thus, microbial phylogenomics adds new dimensions to the fundamental picture of evolution even as the principle of descent with modification discovered by Darwin and the laws of population genetics remain at the core of evolutionary biology.

Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis

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.