[1] Philosophers of science then began paying increasing attention to biology, from the rise of Neodarwinism in the 1930s and 1940s to the discovery of the structure of DNA in 1953 to more recent advances in genetic engineering.
[7] Holism in science is the view that emphasizes higher-level processes, phenomena at a larger level that occur due to the pattern of interactions between the elements of a system over time.
The old positivist approach used in physics emphasised a strict determinism and led to the discovery of universally applicable laws, testable in the course of experiment.
[9] Standard philosophy of science seemed to leave out a lot of what characterised living organisms - namely, a historical component in the form of an inherited genotype.
But teleological explanations relating to purpose or function have remained useful in biology, for example, in explaining the structural configuration of macromolecules and the study of co-operation in social systems.
[14] David Copp responded to Street by arguing that realists can avoid this so-called dilemma by accepting what he calls a “quasi-tracking” position.
[16] Building on current research in fields such as bioinformatics and biocomputing, as well as on work in the history of science (particularly the work of Georges Canguilhem, Lily E. Kay, and Hans-Jörg Rheinberger), Thacker defines biomedia as entailing "the informatic recontextualization of biological components and processes, for ends that may be medical or non-medical...biomedia continuously make the dual demand that information materialize itself as gene or protein compounds.
This includes work by scholars such as Melinda Cooper, Luciana Parisi, Paul Rabinow, Nikolas Rose, and Catherine Waldby.
[dead link][23] This is especially the case where the availability of high throughput screening techniques for the different "-omics" fields such as genomics, whose complexity makes them predominantly data-driven.
[25][26] As Krakauer et al. put it: "machine learning is a powerful means of preprocessing data in preparation for mechanistic theory building, but should not be considered the final goal of a scientific inquiry.
[30] Dougherty and Bittner argue that for biology to progress as a science, it has to move to more rigorous mathematical modeling, or otherwise risk to be "empty talk".
[31] In tumor biology research, the characterization of cellular signaling processes has largely focused on identifying the function of individual genes and proteins.
This principal was formulated by first considering how macroscopic order is generated in a simple non-biological system far from thermodynamic equilibrium, and subsequently extending consideration to short, replicating RNA molecules.