Calculating life? Duelling discourses in interdisciplinary systems biology

Tissue culture and biological time: Alexis Carrel, Henri Bergson and the plasticity of living matter

Taking the early tissue culture experiments of Alexis Carrel in the 1910s–1930s as its example, the article explores the relationship between advances in biotechnological control over living matter and a holistic ontology of life, which stresses the temporal specificity of living things. With reference to Henri Bergson, Carrel argued that physiological time depends on an organism’s relationship to its milieu. By developing a laboratory apparatus and culture media, new objects of investigation could be made to live outside the organism and be brought to behave in novel temporal ways. In difference to recent biotechnological advances, like for example genome editing, which seek to ‘engineer’ living organisms by rebuilding them from their DNA up, then, early twentieth century interventionist laboratory practices were often linked to an understanding that biological plasticity results from organismic complexity and interactions between organism and milieu. These notions contributed to shaping laboratory apparatuses and techniques; they also helped to establish an understanding of environmental control that would allow for the production of novel ‘living things’.

In risk we trust/Editing embryos and mirroring future risks and uncertainties

Tendencies and efforts have shifted from genome description, DNA mapping, and DNA sequencing to active and profound re-programming, repairing life on genetic and molecular levels in some parts of contemporary life science research. Mirroring and materializing this atmosphere, various life engineering technologies have been used and established in many areas of life sciences in the last decades. A contemporary progressive example of one such technology is DNA editing. Novel developments related to reproductive technologies, particularly embryo editing, prenatal human life engineering, and germline engineering need to be analyzed against the broader social and structural background. The crucial analytical scope for this paper is a specific field: the life-editing technologies used in reproductive medicine and performed experimentally on viable human embryos, particularly CRISPR/Cas9 technology. This text argues that germline editing technologies, as a representative part of contemporary biomedicine, are merging ideas of treatment and enhancement to avoid future risks. Using this specific life manipulation of embryos and gametes, the text analyzes these processes within the concept of power over life-biopower and the specific governing rationality that imagines, classifies, and governs contemporary societies. The text specifically focuses on the potential to create, define, and manage future risks and uncertainties related to prenatal life.

Pluralization through epistemic competition: scientific change in times of data-intensive biology

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call "epistemic competition" that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe-contrary to many other accounts of scientific change-that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape.

From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013

Systems biology is a discipline that studies biological systems from a holistic and interdisciplinary perspective. It brings together biologists, mathematicians, computer scientists, physicists, and engineers, so it has both biology-oriented components and systems-oriented components. We applied several computational tools to analyze the bibliographic information of published articles in systems biology to answer the question: Did the research topics of systems biology become more biology-oriented or more systems-oriented from 1992 to 2013? We analyzed the metadata of 9923 articles on systems biology from the Web of Science database. We identified the most highly cited 330 references using computational tools and through close reading we divided them into nine categories of research types in systems biology. Interestingly, we found that articles in one category, namely, systems biology’s applications in medical research, increased tremendously. This finding was corroborated by computational analysis of the abstracts, which also suggested that the percentages of topics on vaccines, diseases, drugs and cancers increased over time. In addition, we analyzed the institutional backgrounds of the corresponding authors of those 9923 articles and identified the most highly cited 330 authors over time. We found that before the mid-1990s, systems-oriented scientists had made the most referenced contributions. However, in recent years, researchers from biology-oriented institutions not only represented a huge percentage of the total number of researchers, but also had made the most referenced contributions. Notably, interdisciplinary institutions only produced a small percentage of researchers, but had made disproportionate contributions to this field.

Bioinformatics: indispensable, yet hidden in plain sight?

Background

Bioinformatics has multitudinous identities, organisational alignments and disciplinary links. This variety allows bioinformaticians and bioinformatic work to contribute to much (if not most) of life science research in profound ways. The multitude of bioinformatic work also translates into a multitude of credit-distribution arrangements, apparently dismissing that work.

Results

We report on the epistemic and social arrangements that characterise the relationship between bioinformatics and life science. We describe, in sociological terms, the character, power and future of bioinformatic work. The character of bioinformatic work is such that its cultural, institutional and technical structures allow for it to be black-boxed easily. The result is that bioinformatic expertise and contributions travel easily and quickly, yet remain largely uncredited. The power of bioinformatic work is shaped by its dependency on life science work, which combined with the black-boxed character of bioinformatic expertise further contributes to situating bioinformatics on the periphery of the life sciences. Finally, the imagined futures of bioinformatic work suggest that bioinformatics will become ever more indispensable without necessarily becoming more visible, forcing bioinformaticians into difficult professional and career choices.

Conclusions

Bioinformatic expertise and labour is epistemically central but often institutionally peripheral. In part, this is a result of the ways in which the character, power distribution and potential futures of bioinformatics are constituted. However, alternative paths can be imagined.

Prospects and problems for standardizing model validation in systems biology

There are currently no widely shared criteria by which to assess the validity of computational models in systems biology. Here we discuss the feasibility and desirability of implementing validation standards for modeling. Having such a standard would facilitate journal review, interdisciplinary collaboration, model exchange, and be especially relevant for applications close to medical practice. However, even though the production of predictively valid models is considered a central goal, in practice modeling in systems biology employs a variety of model structures and model-building practices. These serve a variety of purposes, many of which are heuristic and do not seem to require strict validation criteria and may even be restricted by them. Moreover, given the current situation in systems biology, implementing a validation standard would face serious technical obstacles mostly due to the quality of available empirical data. We advocate a cautious approach to standardization. However even though rigorous standardization seems premature at this point, raising the issue helps us develop better insights into the practices of systems biology and the technical problems modelers face validating models. Further it allows us to identify certain technical validation issues which hold regardless of modeling context and purpose. Informal guidelines could in fact play a role in the field by helping modelers handle these.

Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology

In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding."

Varieties of noise: analogical reasoning in synthetic biology

The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized by synthetic biologists by drawing analogies between the different fields of inquiry. We will study analogical reasoning in synthetic biology through the emergence of the functional meaning of noise, which marks an important shift in how engineering concepts are employed in this field. The notion of noise serves also to highlight the differences between the two branches of synthetic biology: the basic science-oriented branch and the engineering-oriented branch, which differ from each other in the way they draw analogies to various other fields of study. Moreover, we show that fixing the mapping between a source domain and the target domain seems not to be the goal of analogical reasoning in actual scientific practice.

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.

Challenges and obstacles related to solving the codon bias riddles

Dozens of papers have been written about the relationship between codon bias, transcript features and gene translation. Even though answering these questions may sound straightforward, apparently many of these studies seem to contradict each other. In the present article, I provide four major non-mutually exclusive explanations related to this issue: (i) there are dozens of related relevant variables with unknown causal relationships; (ii) various biases in the relevant experimental data; (iii) drawing conclusions from specific examples; and (iv) challenges in experimentally modifying one biological variable without affecting the system via multiple biological feedback mechanisms. Specifically, some of the contradictions can be settled when considering these four points and/or via a multidisciplinary approach. The discussion reported in the present article is also relevant to many other biological/medical questions/fields.

Convergent Lines of Descent: Symptoms, Patterns, Constellations, and the Emergent Interface of Systems Biology and Chinese Medicine

During the first decade of the twenty-first century, a network composed of politicians, regulators, bioscientists, clinical researchers, and Chinese medicine specialists has emerged that seeks to bridge an imagined gulf between the modern West and ancient China in order to create a new type of personalized medicine. The central building block of this bridge is the Chinese medical concept of zheng /, variously translated into English as syndrome, pattern, or type. My paper places side by side two different genealogies of how zheng assumed this central role. The first genealogy examines the process by means of which zheng came to be considered as something shared by both ancient China and cutting-edge biological science and, by extension, how it manages to hold together the entire institutional, political, and economic framework into which this bridge is embedded and which it co-creates. The second genealogy shows zheng to be central to a much older series of redefinitions of Chinese medicine and Chinese medical practice that extend from the eleventh century to the present. Read together, these two genealogies-neither of which should be seen as exhaustive-raise three important issues that are further discussed in the conclusion of the paper. First, I explore how the concept of zheng has come to tie a medical tradition derided by its adversaries for being a pseudoscience to one of the most cutting-edge fields of bioscience research. I ask what is at stake in this synthesis, for whom, and why, and how it transforms Chinese medicine and/or systems biology along the way. Second, I am interested in finding out how and why the very same concept can be at the heart of two apparently agonistic visions of Chinese medicine's future as it is popularly imagined in China today. Finally, I insist that the medical humanities need to become actively involved in the construction of emergent articulations such as the ones I am exploring. Merely writing a history of the present will not be productive unless its critique can somehow be articulated into the very processes of emergence that historians or anthropologists seek to examine.

Synthetic biology and the technicity of biofuels

The principal existing real-world application of synthetic biology is biofuels. Several 'next generation biofuel' companies-Synthetic Genomics, Amyris and Joule Unlimited Technologies-claim to be using synthetic biology to make biofuels. The irony of this is that highly advanced science and engineering serves the very mundane and familiar realm of transport. Despite their rather prosaic nature, biofuels could offer an interesting way to highlight the novelty of synthetic biology from several angles at once. Drawing on the French philosopher of technology and biology Gilbert Simondon, we can understand biofuels as technical objects whose genesis involves processes of concretisation that negotiate between heterogeneous geographical, biological, technical, scientific and commercial realities. Simondon's notion of technicity, the degree of concretisation of a technical object, usefully conceptualises this relationality. Viewed in terms of technicity, we might understand better how technical entities, elements, and ensembles are coming into being in the name of synthetic biology. The broader argument here is that when we seek to identify the newness of disciplines, their newness might be less epistemic and more logistic.

Synthetic biology between technoscience and thing knowledge

Synthetic biology presents a challenge to traditional accounts of biology: Whereas traditional biology emphasizes the evolvability, variability, and heterogeneity of living organisms, synthetic biology envisions a future of homogeneous, humanly engineered biological systems that may be combined in modular fashion. The present paper approaches this challenge from the perspective of the epistemology of technoscience. In particular, it is argued that synthetic-biological artifacts lend themselves to an analysis in terms of what has been called 'thing knowledge'. As such, they should neither be regarded as the simple outcome of applying theoretical knowledge and engineering principles to specific technological problems, nor should they be treated as mere sources of new evidence in the general pursuit of scientific understanding. Instead, synthetic-biological artifacts should be viewed as partly autonomous research objects which, qua their material-biological constitution, embody knowledge about the natural world-knowledge that, in turn, can be accessed via continuous experimental interrogation.

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."

The search for organizing principles as a cure against reductionism in systems medicine

Biological complexity has forced scientists to develop highly reductive approaches, with an ever-increasing degree of specialization. As a consequence, research projects have become fragmented, and their results strongly dependent on the experimental context. The general research question, that originally motivated these projects, appears to have been forgotten in many highly specialized research programmes. We here investigate the prospects for use of an old regulative ideal from systems theory to describe the organization of cellular systems 'in general' by identifying key concepts, challenges and strategies to pursue the search for organizing principles. We argue that there is no tension between the complexity of biological systems and the search for organizing principles. On the contrary, it is the complexity of organisms and the current level of techniques and knowledge that urge us to renew the search for organizing principles in order to meet the challenges that are arise from reductive approaches in systems medicine. Reductive approaches, as important and inevitable as they are, should be complemented by an integrative strategy that de-contextualizes through abstractions, and thereby generalizes results.

Are we doing synthetic biology?

Synthetic Biology is a singular, revolutionary scenario with a vast range of practical applications but, is SB research really based on engineering principles? Is it contributing to the artificial synthesis of life or using approaches "sophisticated" enough to fall outside the scope of biotechnology or metabolic engineering? We have reviewed the state of the art on synthetic biology and we conclude that most research projects actually describe an extension of metabolic engineering. We draw this conclusion because the complexity of living organisms, their tight dependence on evolution and our limited knowledge of the interactions between the molecules they are made of, actually make life difficult to engineer. We therefore propose the term synthetic biology should be used more sparingly.

Bridging experiments, models and simulations: an integrative approach to validation in computational cardiac electrophysiology

Computational models in physiology often integrate functional and structural information from a large range of spatiotemporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and skepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace, and refine animal experiments. A fundamental requirement to fulfill these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated. The present study aims at informing strategies for validation by elucidating the complex interrelations among experiments, models, and simulations in cardiac electrophysiology. We describe the processes, data, and knowledge involved in the construction of whole ventricular multiscale models of cardiac electrophysiology. Our analysis reveals that models, simulations, and experiments are intertwined, in an assemblage that is a system itself, namely the model-simulation-experiment (MSE) system. We argue that validation is part of the whole MSE system and is contingent upon 1) understanding and coping with sources of biovariability; 2) testing and developing robust techniques and tools as a prerequisite to conducting physiological investigations; 3) defining and adopting standards to facilitate the interoperability of experiments, models, and simulations; 4) and understanding physiological validation as an iterative process that contributes to defining the specific aspects of cardiac electrophysiology the MSE system targets, rather than being only an external test, and that this is driven by advances in experimental and computational methods and the combination of both.

The roles of integration in molecular systems biology

A common way to think about scientific practice involves classifying it as hypothesis- or data-driven. We argue that although such distinctions might illuminate scientific practice very generally, they are not sufficient to understand the day-to-day dynamics of scientific activity and the development of programmes of research. One aspect of everyday scientific practice that is beginning to gain more attention is integration. This paper outlines what is meant by this term and how it has been discussed from scientific and philosophical points of view. We focus on methodological, data and explanatory integration, and show how they are connected. Then, using some examples from molecular systems biology, we will show how integration works in a range of inquiries to generate surprising insights and even new fields of research. From these examples we try to gain a broader perspective on integration in relation to the contexts of inquiry in which it is implemented. In today's environment of data-intensive large-scale science, integration has become both a practical and normative requirement with corresponding implications for meta-methodological accounts of scientific practice. We conclude with a discussion of why an understanding of integration and its dynamics is useful for philosophy of science and scientific practice in general.