Cell biologists, including those seeking molecular mechanistic explanations of cellular phenomena, frequently rely on experimental strategies focused on identifying the cellular context relevant to their investigations. We suggest that such practices can be understood as a guided decomposition strategy, where molecular explanations of phenomena are defined in relation to natural contextual (cell) boundaries. This “top-down” strategy contrasts with “bottom-up” reductionist approaches where well-defined molecular structures and activities are orphaned by their displacement from actual biological functions. We focus on the central (...) role of microscopic imaging in cell biology to uncover possible constraints on the system. We show how identified constraints are used heuristically to limit possible mechanistic explanations to those that are biologically meaningful. Historical examples of this process described here include discovery of the mechanism of oxidative phosphorylation in mitochondria, molecular explanation of the first steps in protein secretion, and identification of molecular motors. We suggest that these instances are examples of a form of downward causation or, more specifically, constraining relations, where higher-level structures and variables delimit and enable lower-level system states. The guided decomposition strategy in our historical cases illustrates the irreducibility of experimentally identified constraints in explaining biological activities of cells. Rather than viewing decomposition and recomposition as separate epistemic activities, we contend that they need to be iteratively integrated to account for the ontological complexity of multi-level systems. (shrink)

The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if (...) and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self‐conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine. (shrink)

There have been repeated attempts in the history of comparative biology to provide a mechanistic account of morphological homology. However, it is well-established that homologues can develop from diverse sets of developmental causes, appearing not to share any core causal architecture that underwrites character identity. We address this challenge with a new conceptual model of Character Identity Mechanisms. ChIMs are cohesive mechanisms with a recognizable causal profile that allows them to be traced through evolution as homologues despite having a diverse (...) etiological organization. Our model hypothesizes that anatomical units at different levels of organization—cell types, tissues, and organs—have level-specific ChIMs with different conserved parts, activities, and organization. Relying on a methodology of conceptual engineering, we show how the ChIM concept advances our understanding of the developmental basis of morphological characters, while forging an important link between comparative and mechanistic biology. (shrink)

We begin this article by delineating the explanatory gaps left by prevailing gene‐focused approaches in our understanding of phenotype determination, inheritance, and the origin of novel traits. We aim not to diminish the value of these approaches but to highlight where their implementation, despite best efforts, has encountered persistent limitations. We then discuss how each of these explanatory gaps can be addressed by expanding research foci to take into accountbiological agency—the capacity of living systems at various levels to participate in (...) their own development, maintenance, and function by regulating their structures and activities in response to conditions they encounter. Here we aim to define formally what agency and agents are and—just as importantly—what they are not, emphasizing that agency is an empirical property connoting neither intention nor consciousness. Lastly, we discuss how incorporating agency helps to bridge explanatory gaps left by conventional approaches, highlight scientific fields in which implicit agency approaches are already proving valuable, and assess the opportunities and challenges of more systematically incorporating biological agency into research programs. (shrink)

Reconstructing ancestral species is a challenging endeavour: fossils are often scarce or enigmatic, and inferring ancestral characters based on novel molecular approaches has long been controversial. A key philosophical challenge pertinent at present is the lack of a theoretical framework capable of evaluating inferences of homology made through integration of multiple kinds of evidence. Here, I present just such a framework. I start with a brief history and critical assessment of attempts at inferring morphological homology through developmental genetics. I then (...) bring attention to a recent model of homology, namely Character Identity Mechanisms, intended partly to elucidate the relationships between morphological characters, developmental genetics, and homology. I utilise and build on this model to construct the evaluative framework mentioned above, which judges the epistemic value of evidence of each kind in each particular case based on three proposed criteria: effectiveness, admissibility, and informativity, as well as providing a generalised guideline on how it can be scientifically operationalised. I then point out the evolution of the eumetazoan body plan as a case in point where the application of this framework can yield satisfactory results, both empirically and conceptually. I will conclude with a discussion on some potential implications for more general philosophy of biology and philosophy of science, especially surrounding evidential integration, models and explanation, and reductionism. (shrink)