Is defining life pointless? Operational definitions at the frontiers of Biology

Despite numerous and increasing attempts to define what life is, there is no consensus on necessary and sufficient conditions for life. Accordingly, some scholars have questioned the value of definitions of life and encouraged scientists and philosophers alike to discard the project. As an alternative to this pessimistic conclusion, we argue that critically rethinking the nature and uses of definitions can provide new insights into the epistemic roles of definitions of life for different research practices. This paper examines the possible contributions of definitions of life in scientific domains where such definitions are used most (e.g., Synthetic Biology, Origins of Life, Alife, and Astrobiology). Rather than as classificatory tools for demarcation of natural kinds, we highlight the pragmatic utility of what we call operational definitions that serve as theoretical and epistemic tools in scientific practice. In particular, we examine contexts where definitions integrate criteria for life into theoretical models that involve or enable observable operations. We show how these definitions of life play important roles in influencing research agendas and evaluating results, and we argue that to discard the project of defining life is neither sufficiently motivated, nor possible without dismissing important theoretical and practical research

When one model is not enough: Combining epistemic tools in systems biology

In recent years, the philosophical focus of the modeling literature has shifted from descriptions of general properties of models to an interest in different model functions. It has been argued that the diversity of models and their correspondingly different epistemic goals are important for developing intelligible scientific theories (Levins, 2006; Leonelli, 2007). However, more knowledge is needed on how a combination of different epistemic means can generate and stabilize new entities in science. This paper will draw on Rheinberger’s practice-oriented account of knowledge production. The conceptual repertoire of Rheinberger’s historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue that the use of multiple means of representations is an essential part of the dynamic of knowledge generation. It is because of – rather than in spite of – the diversity of constraints of different models that the interlocking use of different epistemic means creates a potential for knowledge production

Scale-dependency and Downward Causation in Biology

This paper argues that scale-dependence of physical and biological processes offers resistance to reductionism and has implications that support a specific kind of downward causation. I demonstrate how insights from multiscale modeling can provide a concrete mathematical interpretation of downward causation as boundary conditions for models used to represent processes at lower scales. The autonomy and role of macroscale parameters and higher-level constraints are illustrated through examples of multiscale modeling in physics, developmental biology, and systems biology. Drawing on these examples, I defend the explanatory importance of constraining relations for understanding the behavior of biological systems

Lovin\u27 You

Contains advertisements and/or short musical examples of pieces being sold by publisher.https://digitalcommons.library.umaine.edu/mmb-vp/7180/thumbnail.jp

Cancer beyond genetics: On the practical implications of downward causation

Discussions about reductionism and downward causation are often assumed to be primarily of interest to philosophers. Often, however, the question of whether multi-scale systems can be understood “bottom-up” has important practical implications for scientific inquiry. Cancer research, I argue, is one such example. While the focus on genetic factors has intensified with recent investments in cancer genomics, the importance of biomechanical factors within the tumor microenvironment is increasingly acknowledged. I suggest that role of solid-state tissue properties in tumor progression can be interpreted as a form of downward causation, understood as constraining relations between tissue-scale and micro-scale variables. Experimental demonstrations of these sort of influences reveal limitations of reductionist accounts and expose the dangers of what Wimsatt calls functional localization fallacies. The latter relate to the common bias of downgrading factors that – as a practical necessity – are left out of scientific analysis. Any heuristic, experimental or theoretical, involves foregrounding some aspects while ignoring others, and the complexity of cancer leaves room for the co-existence of many different partial perspectives. These perspectives are not reducible to one another, but neither do they in this case make up a neatly integrated “causal mosaic” of different influences. At present, the picture of cancer research looks more like a fragmented cubist painting in need of a more balanced attention to difference-making factors at higher levels or scales

Can biological complexity be reverse engineered?

Concerns with the use of engineering approaches in biology have recently been raised. I examine two related challenges to biological research that I call the synchronic and diachronic underdetermination problem. The former refers to challenges associated with the inference of design principles underlying system capacities when the synchronic relations between lower-level processes and higher-level systems capacities are degenerate (many-to-many). The diachronic underdetermination problem regards the problem of reverse engineering a system where the non-linear relations between system capacities and lower-level mechanisms are changing over time. Braun and Marom argue that recent insights to biological complexity leave the aim of reverse engineering hopeless - in principle as well as in practice. While I support their call for systemic approaches to capture the dynamic nature of living systems, I take issue with the conflation of reverse engineering with naïve reductionism. I clarify how the notion of design principles can be more broadly conceived and argue that reverse engineering is compatible with a dynamic view of organisms. It may even help to facilitate an integrated account that bridges the gap between mechanistic and systems approaches

Cancer beyond genetics: On the practical implications of downward causation

Discussions about reductionism and downward causation are often assumed to be primarily of interest to philosophers. Often, however, the question of whether multi-scale systems can be understood “bottom-up” has important practical implications for scientific inquiry. Cancer research, I argue, is one such example. While the focus on genetic factors has intensified with recent investments in cancer genomics, the importance of biomechanical factors within the tumor microenvironment is increasingly acknowledged. I suggest that role of solid-state tissue properties in tumor progression can be interpreted as a form of downward causation, understood as constraining relations between tissue-scale and micro-scale variables. Experimental demonstrations of these sort of influences reveal limitations of reductionist accounts and expose the dangers of what Wimsatt calls functional localization fallacies. The latter relate to the common bias of downgrading factors that – as a practical necessity – are left out of scientific analysis. Any heuristic, experimental or theoretical, involves foregrounding some aspects while ignoring others, and the complexity of cancer leaves room for the co-existence of many different partial perspectives. These perspectives are not reducible to one another, but neither do they in this case make up a neatly integrated “causal mosaic” of different influences. At present, the picture of cancer research looks more like a fragmented cubist painting in need of a more balanced attention to difference-making factors at higher levels or scales

Scale-dependency and Downward Causation in Biology

This paper argues that scale-dependence of physical and biological processes offers resistance to reductionism and has implications that support a specific kind of downward causation. I demonstrate how insights from multiscale modeling can provide a concrete mathematical interpretation of downward causation as boundary conditions for models used to represent processes at lower scales. The autonomy and role of macroscale parameters and higher-level constraints are illustrated through examples of multiscale modeling in physics, developmental biology, and systems biology. Drawing on these examples, I defend the explanatory importance of constraining relations for understanding the behavior of biological systems

Cancer beyond genetics: On the practical implications of downward causation

Discussions about reductionism and downward causation are often assumed to be primarily of interest to philosophers. Often, however, the question of whether multi-scale systems can be understood “bottom-up” has important practical implications for scientific inquiry. Cancer research, I argue, is one such example. While the focus on genetic factors has intensified with recent investments in cancer genomics, the importance of biomechanical factors within the tumor microenvironment is increasingly acknowledged. I suggest that role of solid-state tissue properties in tumor progression can be interpreted as a form of downward causation, understood as constraining relations between tissue-scale and micro-scale variables. Experimental demonstrations of these sort of influences reveal limitations of reductionist accounts and expose the dangers of what Wimsatt calls functional localization fallacies. The latter relate to the common bias of downgrading factors that – as a practical necessity – are left out of scientific analysis. Any heuristic, experimental or theoretical, involves foregrounding some aspects while ignoring others, and the complexity of cancer leaves room for the co-existence of many different partial perspectives. These perspectives are not reducible to one another, but neither do they in this case make up a neatly integrated “causal mosaic” of different influences. At present, the picture of cancer research looks more like a fragmented cubist painting in need of a more balanced attention to difference-making factors at higher levels or scales

Cancer beyond genetics: On the practical implications of downward causation

Discussions about reductionism and downward causation are often assumed to be primarily of interest to philosophers. Often, however, the question of whether multi-scale systems can be understood “bottom-up” has important practical implications for scientific inquiry. Cancer research, I argue, is one such example. While the focus on genetic factors has intensified with recent investments in cancer genomics, the importance of biomechanical factors within the tumor microenvironment is increasingly acknowledged. I suggest that role of solid-state tissue properties in tumor progression can be interpreted as a form of downward causation, understood as constraining relations between tissue-scale and micro-scale variables. Experimental demonstrations of these sort of influences reveal limitations of reductionist accounts and expose the dangers of what Wimsatt calls functional localization fallacies. The latter relate to the common bias of downgrading factors that – as a practical necessity – are left out of scientific analysis. Any heuristic, experimental or theoretical, involves foregrounding some aspects while ignoring others, and the complexity of cancer leaves room for the co-existence of many different partial perspectives. These perspectives are not reducible to one another, but neither do they in this case make up a neatly integrated “causal mosaic” of different influences. At present, the picture of cancer research looks more like a fragmented cubist painting in need of a more balanced attention to difference-making factors at higher levels or scales