Why model?

By O. Wolkenhauer. Frontiers in Physiology, 2013

665 citations recorded by the source corpus.

Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations may however not be satisfied if the number of patient groups with similar characteristics is going to be very large. I therefore doubt that mining sequence data will give us an understanding of why and when therapies work. For understanding the mechanisms underlying diseases, an alternative approach is to model small networks in quantitative mechanistic detail, to elucidate the role of gene and proteins in dynamically changing the functioning of cells. Here an obvious critique is that these models consider too few components, compared to what might be relevant for any particular cell function. I show here that mining approaches and dynamical systems theory are two ends of a spectrum of methodologies to choose from. Drawing upon personal experience in numerous interdisciplinary collaborations, I provide guidance on how to model by discussing the question “Why model?”

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/600a474dcb1e1dc4a58954923d1ee995110ccf41

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Citations in the corpus

How Computational Modeling Can Force Theory Building in Psychological Science (2020, background): “…men-tees that modeling is neither extremely complex nor requires extra skills beyond those we already expect that they master, for example, programming, experimental design, literature review, and statistical-analysis techniques (e.g., Epstein, 2008; Wills et al., 2017; Wilson & Collins, 2019).”
Ecological Macroeconomic Models: Assessing Current Developments (2017, background): “They can therefore only ever be understood as tools that can help to formalise our assumptions and theories, to identify gaps in empirical research, to explore dynamics that might not be immediately obvious, or to explore potential future scenarios (Epstein, 2008).”
The natural selection of bad science (2016, background): “Such a model simultaneously verifies the logic of a hypothesis and helps to refine its predictions, so that it can be more easily falsified (Wimsatt, 1987; Epstein, 2008).”
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “Recognising that a STDT has purpose beyond prediction such as explanation is the ﬁrst response [187].”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, background): “A theoretical model is an abstract representation of the real system which helps in understanding the real system [51, 52, 53, 54, 55, 56, 57, 58].”
Lack of Theory Building and Testing Impedes Progress in The Factor and Network Literature (2020, background): “…mathematical or computational models to escape vagaries of language is a critical step forward, and has been successful in many other disciplines (Epstein, 2008; Lakens & Debruine, 2020; Morton, 2009; Oberauer & Lewandowsky, 2019; Robinaugh, Haslbeck, et al., 2019; Smaldino, 2017, 2019; Szollosi…”
Models Are Stupid, and We Need More of Them (2017, background): “For example, studying computational models of complex systems can help us to build mental models of some emergent phenomena whose dynamics are otherwise difficult to visualize (Nowak, Rychwalska, & Borkowski, 2013), and the process of model construction can illuminate core uncertainties in one’s kno”