Browsing by Subject "Biological modeling"

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    Generalizations in Practice: Investigating Generality and Specificity in Developmental Biology
    (2023) Yoshida, Yoshinari
    Although there is a consensus that pursuits of general knowledge are crucial in almost all fields of science, the majority of philosophical analyses of generalizations have focused narrowly on universal generalizations or laws of nature and what role generalizations play in scientific explanations. This narrow focus has limited the scope of philosophical discussions about scientific generalizations. This dissertation proposes and exemplifies a broader inquiry into scientific generalizations that is motivated by the question: how do scientists pursue, formulate, reason about, utilize, and communicate generalizations? In other words, how are generalizations practiced in science? To address this broad set of questions, I focus on a particular field—developmental biology—and examine investigative and representational practices surrounding generalizations. Like many other fields, developmental biology seeks both widely shared regularities and the details of causal processes peculiar to specific systems. My analyses show how this dual interest in generality and specific details is interconnected and mutually contribute to each other. This dissertation is organized as follows. Chapter 1 provides a brief overview of how philosophers have discussed generalizations. I point out that the interests in laws and explanation have dominated the past discussions. In contrast, my approach focuses on investigative and representational practices of generalization, which have received very little philosophical attention. Chapter 2 analyzes two approaches to generalizations in developmental biology: mechanisms and principles. These are distinguished based on the relevance of abstraction. I show that the two approaches are associated with different investigative practices. This analysis illustrates what forms of non-universal generalizations developmental biologists seek and formulate, which serves as a basis for discussions in the following chapters. Chapter 3 explores generalizations from the perspective of modeling desiderata. I offer a characterization of what I call multiple-models juxtaposition (MMJ), a strategy for managing a trade-off between generality and detail in scientific models. MMJ displays models of distinct processes together and fulfills different desiderata both in the individual models and by a comparison of those models. I also clarify the distinction between MMJ and multiple-models idealization (MMI), which also uses multiple models to manage trade-offs among desiderata. Chapter 4 focuses on the use of model systems. Biologists often study particular biological systems as models of a phenomenon of interest, even if they know that the phenomenon is produced by diverse mechanisms and hence none of those systems alone can sufficiently represent it. I argue that even if generalizability of results from a single model system is significantly limited, generalizations concerning specific aspects of mechanisms often hold across certain ranges of biological systems. This enables multiple model systems to jointly represent such a phenomenon. Chapter 5 considers the question “how and why do scientists generalize?” by challenging three influential assumptions: (1) generalizations are expressed linguistically; (2) scientists generalize by formulating a single representation with wide applicability; and (3) generalizations are valuable because they enable scientific explanations. My analysis of a concrete example illustrates roles that visual representations play in generalizations. It also shows that formulating a single, unified representation is not the only way to generalize; scientists often generalize by configuring multiple representations. Finally, I argue that generalizations serve to facilitate cross-fertilization among studies of different target systems, which complements the explanation-centered view.
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    Reconstruction, Reconciliation, and Validation of Metabolic Networks
    (2018-05) Krumholz, Elias
    Metabolic networks are rigorous and computable representations of metabolism that describe the connections between genes, enzymes, reactions, and metabolites. The comprehensive nature of metabolic networks has allowed them to become the first truly “genome-scale” models, and they have served as a foundational framework for the broader effort of systems biology, which aims to model all aspects of cellular function. A more thorough and accurate understanding of metabolism has the potential to improve the synthesis of important biological compounds, better model metabolic diseases, and progress towards simulations of entire cells. The thesis research presented here focuses on the reconstruction of organism-specific metabolic networks from genome annotations and methods for improving metabolic networks by reconciling them with observed phenotypes, specifically the synthesis of essential cellular metabolites such as DNA, amino acids, and other small molecules. Gene sequence similarity and estimations of thermodynamic reaction parameters are used to guide network reconciliation through the use of numerical optimization algorithms. Particular attention is devoted to the validation of metabolic networks using experimental data, such as gene essentiality, and the development of computational controls using parameter randomization.