Data without models merging with models without data

Krohs, Ulrich; Callebaut, Werner

doi:10.1016/b978-044452085-2/50011-5

Cited by 45 publications

(37 citation statements)

References 60 publications

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“…Researchers in all areas of biology are busy exploring the functional significance of those structural data. This leads to the accumulation of even more data of different types, including data about gene expression, morphological effects correlated to 'knocking out' specific genes, and so forth (Krohs and Callebaut 2007). These results are obtained through experimentation on a relatively small set of species whose features are particularly tractable through the available laboratory techniques.…”

Section: Making Facts About Organisms Travelmentioning

confidence: 99%

On the Locality of Data and Claims about Phenomena

Leonelli

2009

Philos. of Sci.

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and Woodward (1988) characterise data as embedded in the context in which they are produced ('local') and claims about phenomena as retaining their significance beyond that context ('non-local'). This view does not fit sciences such as biology, which successfully disseminate data via packaging processes that include appropriate labels, vehicles and human interventions. These processes enhance the evidential scope of data and ensure that claims about phenomena are understood in the same way across research communities. I conclude that the degree of locality of both data and claims about phenomena varies depending on the packaging used to make them travel and on the research setting in which they are used.

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Section: Making Facts About Organisms Travelmentioning

confidence: 99%

On the Locality of Data and Claims about Phenomena

Leonelli

2009

Philos. of Sci.

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“…There is distributed functionality, where a biological system is not controlled by a limited number of privileged entities or activities, but where the dynamics of how the system behaves and is functionally regulated is of a more holistic nature (Krohs & Callebaut, 2007;Moreno, 2007;Westerhoff & Kell, 2007). 6 Section 4.4 discussed that robustness is often a distributed process, for instance when a gene's activity is part of a gene regulatory network, yet the deactivation of this gene does not make a difference to the network operation's final outcome, due to compensatory changes to the activities of other genes throughout the network.…”

Section: Mechanismsmentioning

confidence: 99%

“…Scientific models are conceived as necessarily partial representations of reality, so that many models are needed and different accounts are developed for different purposes, integrating different items of empirical data and different mathematical models. Many systems biologists take a non-reductionist stance by assuming that biological processes cannot be understood in terms of genes alone (Noble, 2006) or by endorsing the notion of distributed functionality as opposed to localized control by certain entities (Krohs & Callebaut, 2007;Moreno, 2007;Wolkenhauer & Muir, 2011).…”

Section: Systems Biologymentioning

confidence: 99%

“…Top-down approaches, in contrast, proceed from a system's overall behavior and attempt to analyze it, e.g., by breaking system functionality down into functional components. Krohs and Callebaut (2007) distinguish three 'roots' of systems biology in terms of how much information they deliver about component dynamics, system dynamics, and component structure. First, pathway modeling has a tradition of studying enzyme kinetics, the flux in and regulation of metabolic pathways, and also signaling pathways.…”

Section: Systems Biologymentioning

confidence: 99%

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Systems biology and the integration of mechanistic explanation and mathematical explanation

Brigandt

2013

Studies in History and Philosophy of Science Part C: Studies in

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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.

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“…Top-down systems biologists doubt whether this can be done, and insist on the need for more general principles that emerge at higher levels of organization, and constrain the behavior of constituents. This is a relatively crude dichotomy, of course, and the consensus among biologists involved in these projects is that some combination of the two will be needed for systems biology to succeed (Krohs and Callebaut [2007] offer criticism of the dichotomy just mentioned). This is exactly what should be expected in the light of the preceding discussion: the capacities of parts will require explanation through reductive, bottom-up approaches; but a top-down approach is required to understand their actual behavior, and to identify the capacities that need to be explained.…”

Section: Systems Biologymentioning

confidence: 99%

It Is Not Possible to Reduce Biological Explanations to Explanations in Chemistry and/or Physics

Dupré

2009

Contemporary Debates in Philosophy of Biology

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In this paper, I argue that the traditional notion that complex systems, such as those found in biology, can be fully understood from a sufficiently detailed knowledge of their constituents is mistaken. My central claim is that the properties of constituents cannot themselves be fully understood without a characterization of the larger system of which they are part. This claim is elaborated through a defence of the concepts of emergence and of downward causation, causation acting from a system on its constituent parts. Although much of this argument can be read as having only epistemological or methodological force, the final section of the paper defends a more robust metaphysical reading: even purely metaphysical understandings of reductionism such as are commonly represented by supervenience theses are misguided.

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Data without models merging with models without data

Cited by 45 publications

References 60 publications

On the Locality of Data and Claims about Phenomena

On the Locality of Data and Claims about Phenomena

Systems biology and the integration of mechanistic explanation and mathematical explanation

It Is Not Possible to Reduce Biological Explanations to Explanations in Chemistry and/or Physics

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