Bayesian history of science: The case of Watson and Crick and the structure of DNA

https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00233

Connecting empirical findings to theories is fundamental to science. Many of these connections are surprising and unexpected: for example, that gravity can bend light as predicted by general relativity, or that the speed of light can be deduced from electromagnetic theory as James Maxwell did in the 19th century. Many such surprises are hidden inside scientific problems and are experienced only by scientists working on them. For example, James Watson and Francis Crick were surprised when they found a configuration of specific pairs of DNA bases that were hydrogen bonded inside two sugar-phosphate backbones. The recent Nobel Prize winner David Julius and his team were surprised when they discovered that they could clone a pain receptor (Julius, 2021).

One way of understanding surprise is Bayesian analysis, when we have a low expectation of success in solving a problem and then find a solution. Surprise can come about if our prior probability is low, but, on consideration of the evidence, our probability increases abruptly. Alternatively, a result that was considered well confirmed and thus had high probability is undermined by new evidence, resulting in a sudden drop in its probability. In yet other instances, a new theory is found that accounts for the evidence dramatically better than the existing theory, such as might occur in a scientific revolution.

In the Bayesian framework, our expectation about the validity of a theory is expressed as a prior probability, and the impact of evidence on a theory leads to a posterior probability, the probability of the theory given the evidence. This change is mediated by conditional probabilities that express how well the old and new theories explain or do not explain the evidence. An equivalent formulation of the probability of a theory given the evidence is the joint probability, expressed as P(T & E), that is, the probability that theory T and evidence E agree with one another. When that happens, we can be surprised if our initial expectation of agreement was low.

In this model, we can think of science as a gigantic jigsaw puzzle consisting of a mixture of theoretical and empirical pieces that we are attempting to fit together. We would not expect two pieces selected at random to fit together, although some pieces might come close. This puzzle must be hyperdimensional, like a complex network with some pieces linking to many others but others linking to only a few (Price, 1986, p. 268). The problem with this jigsaw puzzle model of science is that the pieces keep changing shape. A new or modified theory becomes a new puzzle piece. The evidence pieces will change too when experimental accuracy increases or when new devices and experiments are devised that yield novel findings. As this puzzle dynamically changes, occasions arise when parts of the already assembled puzzle may need to be radically rearranged, and perhaps totally dismantled and rebuilt, as in the case of a scientific revolution.

Theories in the psychological sense used here are statements or generalizations claiming to be universally true about which we have varying degrees of confidence. These can range from Kepler’s first law that planets follow elliptical orbits to Bohr’s theory of the hydrogen atom. But we also take theories to include hypotheses, presuppositions, and models, for example, Guillemin and Schally’s model of thyrotropin releasing factor (TRF) as a peptide (Latour & Woolgar, 1979), and Hershko and Ciechanover’s ubiquitin system for protein degradation (Fry, 2022). If a theory agrees with empirical observations, we might say that it was merely a fluke or coincidence, that somehow the theory was rigged to explain the experiment, or we might conclude that the agreement was because this is the way the world works. In any event, it seems natural to say, as Bayesians do, that a theory has some probability of being true depending on how well it fits the evidence, allowing for the possibility that other current or future theories might fit the evidence as well or better.

Competition among theories is especially visible when there are a series of attempts to model an entity or phenomenon, such as the atom in the early 20th century, high-temperature superconductivity in the late 20th century (Hartmann, 2008), or a specific substance, such as DNA in the 1950s, as will be discussed in this paper. In such a sequence of attempts, it seems reasonable to use the probability of a previous model as the prior probability of the next model. As the prior probability reflects our confidence in the correctness of some idea, if we or others have made attempts to solve a problem, our level of confidence will increase or decrease depending on previous successes or failures. A string of failures will make us less confident that we are on the right track, but a string of near successes might encourage us to keep trying.

In testing theories scientists rely on multiple forms of evidence. Each piece of evidence can be taken one at a time using Bayes’ theorem or all the available evidence can be applied at the same time. In the latter case we need a formulation of Bayes’ theorem that accommodates multiple kinds of evidence. A good candidate is the naïve Bayes model where, in network terms, a theory is like the hub of a wheel with various forms of evidence radiating out like spokes (Figure 1).

The numerator can be interpreted as a joint probability of independent forms of evidence E₁ to E_N: P(T & E₁ & E₂ & E₃ … & E_N) = P(T) * P(E₁|T) * P(E₂|T) * P(E₃|T) … * P(E_N|T). So, the probability of the theory given all the forms of evidence is proportional to the product of the prior probability of the theory and the probabilities of each form of evidence given the theory under consideration. In the denominator of Eq. 1 the first term is the same as the numerator and can be interpreted as the probability that the evidence fits with the theory. The second term is the probability the evidence fits with the alternative theories P(∼T) * P(E₁|∼T) * P(E₂|∼T) * P(E₃|∼T) … * P(E_N|∼T). If these two terms are equal, then the probability the theory is correct given all forms of evidence (the posterior) is equal to 0.5. Thus, if there is no reason to favor T over ∼T we assign a probability of 0.5 to both. Another attractive feature of a 0.5 prior is that it allows the widest range of confirming or disconfirming posterior probabilities.

Confirmation of the theory is indicated if the posterior probability is greater than the prior probability, P(T|E_1,N) > P(T) and disconfirmation if P(T|E_1,N) < P(T). If theory T is part of a series of attempts to model some phenomena, then the posterior can be used as the prior for the next attempt. Whether multiple forms of evidence are taken all at one time, as in Eq. 1, the result is the same as if each form of evidence had been evaluated separately, setting the prior of the successor theory equal to the posterior of the predecessor theory.

Eq. 1 can be derived by enumerating all the terms in the probability function in Figure 1 for the theory and evidence nodes, which must sum to 1 (Koller & Friedman, 2009, p. 292). To get the posterior probability of the theory given the evidence being true, we omit the conditional probability terms where the evidence nodes are set to “false” and divide by the “total probability,” that is, the sum of probabilities of T being true, and T not being true.

In applying the Bayesian framework to an actual historical case, we need a way of specifying both the prior probability of the theory or model and the conditional probabilities that the available evidence can be explained by the theory (Salmon, 1970, 1990). This applies to both the theory being evaluated and any alternative or competing theories that are relevant in the historical context. Thus, Bayesian analysis is always a comparative exercise.

Of course, we do not have direct access to an individual’s subjective probabilities. In contemporary science we could access the full text of scientific papers and aggregate statements to give a collective assessment of probabilities (Small, 2022). However, for historical cases focused on individual scientists, we need to rely on the statements of the scientists involved or on the accounts of historians, and especially on statements regarding whether evidence reflects favorably or unfavorably on a theory.

To implement a Bayesian approach, such evidence statements have been manually coded to reflect the approximate strength of the scientists’ conviction that a theory is consistent or inconsistent with the evidence. The scale was constructed with a limited number of discrete values between 0 and 1 to simplify judgments and avoid unwarranted accuracy. Only five degrees of strength are allowed, which are mapped to preset values of conditional probability (see Table 1). The probabilities assigned ranged from 0.7 for “strongly consistent” to 0.3 for “strongly inconsistent,” with 0.5 signifying a neutral stance. A neutral probability means that there is a 50/50 chance that the theory T is consistent with the evidence E in the expression P(E|T). The range of values in Table 1 is of course arbitrary and other scales could have been used, which would have changed the absolute values of the posteriors computed but not their relative values. For example, a five-point scale from 0.1 to 0.9 leads to more extreme values of the posteriors for a series of models, which seemed at odds with the uncertainties expressed by the historical participants. An “inconsistent” conditional P(E|T) indicates that the theory was unlikely to explain or predict the evidence, whereas a “consistent” probability means that the theory was compatible to some degree with the evidence.

For example, regarding Watson and Crick’s first DNA model, a triple helix, Watson admitted: “The awkward truth became apparent that the correct DNA model must contain at least 10 times more water than was found in our model.” (Watson, 1968, p. 94) Thus, the “water content” was incorrect evidence and was coded 0.3 as “strongly inconsistent.” On the other hand, the crystallographic data required that the model conform to a specific geometry: “Three chains twisted about each other in a way that gave rise to a crystallographic repeat every 28 Angstroms along the helical axis” (Watson, 1968, p. 89). The crystallographic evidence was coded as only “weakly consistent” because the triple helix model had to be designed to satisfy this constraint.

Rather than trying to directly infer probabilities from the historical record, the approach is to qualitatively assess the scientist’s opinion on how well or poorly the evidence fits with the theory and then assign a probability from the prespecified scale. This approach can be contrasted with that of Dorling (1979), specifying approximate values for specific probabilities based on general historical considerations but not the opinions of the scientists involved.

In addition to conditional probabilities, prior probabilities must be set. Here we can also rely on the statements of scientists regarding their initial confidence in a theory. A special circumstance arises when the theory under consideration is the latest in a line of prior attempts. For example, Kepler attempted to account for Tycho’s observations on Mars using a variety of orbital shapes prior to his success with elliptical orbits. In such cases it is reasonable to assign the prior for the most recent version of the theory to the posterior of the immediately preceding unsuccessful theory. Failures should engender lower expectations for future success. This, however, leaves the case of the first theory in the sequence without a prior. In the absence of any written expression of confidence, or lack thereof, assigning a neutral 50/50 prior of 0.5 seems reasonable, and is the case, as noted above, when the theory and competing theories are equally probable. There are numerous examples in the history of Bayesian analyses where even odds have been used (McGrayne, 2011).

We can now apply this framework to an historical example: the attempts to construct a molecular model of DNA. Watson describes four models that were devised in the early 1950s:

1. A triple-helix model developed by Watson and Crick based on an analogy to Pauling’s alpha helix for proteins;

2. A triple-helix model proposed independently by Pauling and Corey;

3. A double helix with like-to-like base pairing by Watson; and

4. A final double helix with adenine to thymine and guanine to cytosine base pairing by Watson and Crick (Watson, 1968).

Different kinds of evidence were brought to bear on each model which either supported or undermined the validity of each model. Only evidence brought to bear at the time the model was evaluated is considered. Bayes’ theorem also requires us to evaluate the evidence for or against the competing or alternative models if they exist.

As to the prior probability for the first model in the series, there may be a sense of what the community of researchers regards as a prevailing or generally accepted view. For example, when Avery, MacLeod, and McCarthy proposed that DNA was the “transforming substance,” it was generally believed that proteins with their varying sequences of amino acids governed heredity, not DNA (Judson, 1979, p. 30). In the case of DNA some researchers had entertained the vague notion of a single linear chain of nucleotides (Watson, 1968, p. 52) but this idea was not sufficiently defined to serve as a testable model.

Linus Pauling’s model for protein structure called the alpha helix (Pauling, Corey, & Branson, 1951) had shown that a long-chain polypeptide molecule could have a helical structure. Despite not providing direct evidence for a helical structure for a long-chained nucleic acid such as DNA, Pauling’s alpha helix made this possibility plausible. Watson stated: “Pauling’s success with the polypeptide chain had naturally suggested to Francis [Crick] that the same tricks might also work for DNA …. We could thus see no reason why we should not solve DNA in the same way. All we had to do was to construct a set of molecular models and begin to play—with luck, the structure would be a helix.” (Watson, 1968, pp. 48, 50). In addition to being a powerful influence on Watson’s and Crick’s thinking, the alpha-helix idea served as a justification for their model of DNA because, by analogy, this was the natural structure for a long-chained molecule (Kuhn, 2000; Salmon, 1990; Thagard, 1992).

In their first attempt at a structure of DNA, Watson and Crick formulated a triple helix consisting of three polynucleotide chains. They placed the intertwined sugar-phosphate backbones on the inside and the bases (adenine, cytosine, guanine, and thymine) on the outside of the backbones (Watson, 1968, p. 79). Prior to their work on DNA, Crick, along with Cochran and Vand, had developed a theory that predicted how a helical molecule would diffract X-rays, although at that time no such X-ray pictures existed for DNA matching the predicted pattern (Cochran, Crick, & Vand, 1952; Schindler, 2008). The X-ray evidence that did exist, from Rosalind Franklin at King’s College, London, as well as earlier X-ray pictures by Astbury and Bell, suggested that DNA had a regular crystal structure (Astbury, 1947). There was a crystallographic repeat at about 28 angstroms along the helical axis and the nucleotides were flat and 2.3 angstroms thick.

In November of 1951 Watson attended a colloquium at King’s College, London, organized by Maurice Wilkins, where Rosalind Franklin presented X-ray diffraction results for DNA based on what would later be called the crystalline or “A-form” of DNA (Olby, 1974, pp. 349–350). Wilkins supported a three-stranded polynucleotide configuration based on density considerations, and Franklin, from her lecture notes, favored a spiral structure with a structural repeat every 28 angstroms. On hearing about the colloquium from Watson, Crick concluded that “only a small number of formal solutions were compatible both with the Cochran-Crick theory and with Rosy’s [Rosalind Franklin’s] experimental data … and perhaps a week of fiddling with the molecular models would be necessary to make us absolutely sure we had the right answer” (Watson, 1968, p. 77).

They were already committed to the idea that DNA was a helix from Pauling’s alpha helix, and the general idea that DNA contained a large number of nucleotides linked together linearly. The X-ray pictures showing a regular crystal implied that the sugar-phosphate backbones were packed in a regular manner, although these ideas were too vague to constitute a concrete model.

Following Wilkins’ suggestion, Watson and Crick then began playing with molecular models involving three helical strands of sugar-phosphate polynucleotide chains coiled around each other that would give rise to the observed crystallographic repeat (Watson, 1968, p. 89). This model was thus consistent, by design, with the X-ray evidence at that time. Olby states that “At the time Watson and Crick were highly pleased with this 3-stranded helix …” (Olby, 1974, p. 361).

However, three points of evidence were strongly inconsistent with the triple helix model. First, there was a need for positive ions, so-called salt bridges, to hold the helical strands in place, because the chains had a negative charge due to the ionization of the phosphate groups on the backbones. However, there was no evidence that DNA contained positive ions such as Mg++. Watson also acknowledged that some of the bond lengths between atoms were “too close for comfort,” and finally that he had grossly underestimated the water content of the DNA samples used for Franklin’s X-ray pictures, which would have affected the structure in an indeterminate manner (Watson, 1968, pp. 80, 89).

The defects of the model were made clear in a meeting in Cambridge involving Watson and Crick and the group from King’s College. After the meeting, news of the unsuccessful model reached the head of the Cavendish Lab in Cambridge, Sir Lawrence Bragg, and Crick and Watson were instructed to stop working on DNA. Crick later described this model as a “complete waste of time” (Olby, 1974, p. 360) and Watson called it a “fiasco” (Crick, 1988; Watson, 1968, p. 201).

Table 2 summarizes the evidence Watson and Crick brought to bear on this initial triple helix model and estimates of the conditional probabilities of the evidence they considered relevant. Even though according to Olby they were initially pleased with their model, there is no indication in the historical record that they were confident that it was correct. Thus, a prior probability of 0.5, even odds, seems reasonable reflecting its equal chance of being correct or incorrect. Crick later commented that in retrospect he wished they had waited a week before presenting it. There was no coherent alternative model to the triple helix.

The evidence derived from the existing X-ray diffraction pictures is coded as “weakly consistent” because the model was specifically designed to account for that data. The analogy of the DNA helical structure to Pauling’s alpha helix for proteins is also coded as “weakly consistent” (see Table 1). The incorrect water content for the X-ray pictures, the inaccurate bond lengths, and the absence of the positive ions to hold the three chains together are each coded as “strongly inconsistent.” In summary, there were two weakly supporting points of evidence and three in strong opposition. The resulting posterior probability, based on the naïve Bayes formulation, was 0.24, indicating disconfirmation compared to a prior of 0.5, a decrease in probability of 52% with respect to the prior.

Because the alternative model was assigned even odds for all forms of evidence, it serves as a null or baseline model for comparison to the triple helix model. Other options for the alternative model were explored but led to similar results. For example, a single helical strand of nucleotides was posited as a possible hypothetical model and evaluated on the same forms of evidence. In this case the absence of positive ions to keep the strands together was “strongly consistent” as only one strand was present, but the X-ray data called for a higher density of strands and was “strongly inconsistent” with the single strand model. Bond lengths were set to “weakly consistent” because having only one strand imposed fewer structural constraints. As far as we know none of these judgments were shared by the participants and are purely hypothetical. Nevertheless, the resulting posterior of 0.3 was only slightly higher than the comparative baseline model of 0.24, but still disconfirming.

When Linus Pauling wrote up his triple helix model of DNA (Pauling & Corey, 1953), he was unaware that Watson and Crick had made a similar attempt some months earlier, which was unpublished. Pauling considered his a “promising structure” (Olby, 1974, pp. 381, 383), although serious issues regarding interatomic distances arose in the days following the paper’s submission for publication. As we did for Watson and Crick’s triple helix, we adopt a 50/50 prior probability.

We can evaluate Pauling’s model from either Pauling’s point of view or Watson and Crick’s. Pauling was in the same position as Watson and Crick in that there was no competing model. Pauling also appealed to his alpha helix for proteins to justify a helical structure for the long-chained nucleic acid (Pauling et al., 1951). He constructed his model to be consistent with the X-ray diffraction data available to him, namely the work of Astbury and Bell (Astbury, 1947), including their density calculation, which suggested to Pauling that three-polynucleotide chains were wrapped in a helical structure. The only troubling feature from Pauling’s point of view was that the model involved “a tight squeeze for nearly all the atoms” (Olby, 1974, p. 383). Scoring the poor fit with interatomic distances as “strongly inconsistent,” as we did for Watson and Crick’s triple helix, gives a posterior probability of 0.46 versus the null model, narrowly disconfirming Pauling’s model from his point of view. Again, we use 50/50 odds for the hypothetical alternative model’s conditional probabilities, as we did for the Watson and Crick triple helix (Table 3).

Seen from the Watson and Crick point of view, however, the situation is different. News of Pauling’s model reached Cambridge via Pauling’s son Peter, then a student at Cambridge, who gave the manuscript to Watson. After Watson’s initial surprise that the model was “suspiciously like our aborted effort” of the previous year, he read the paper carefully and concluded that the molecule could not be acidic because all the hydrogen atoms were bonded: “Everything I knew about nucleic-acid chemistry indicated that phosphate groups never contained bound hydrogen atoms” (Watson, 1968, p. 160). Watson did not investigate the question of bond lengths in Pauling’s model but learned later in a letter from Pauling that they were having problems with them (Olby, 1974, p. 409).

Because we are looking at Pauling’s model from Watson and Crick’s point of view, we use their triple helix as the alternative model and its posterior probability of 0.24 as the prior for Pauling’s model, thus expressing their diminished confidence in the model based on their prior experience. Scoring the lack of acidity and the inaccurate bond lengths as “strongly inconsistent” gives a disconfirming posterior probability of 0.16 (Table 4).

As the competing theory shared features with the Pauling model, such as helical structure and consistency with X-ray data, these features are not advantages for the Pauling model because they are scored with the same conditional probabilities as Watson and Crick’s triple helix. Pauling’s difficulty with the interatomic distances also does not have an impact on the posterior because the Watson and Crick triple helix suffered the same defect. Thus, disconfirmation is due solely to the lack of acidity of the model, the other features canceling each other out. The lower absolute value of the posterior is in part due to the lower prior used.

The critical new piece of evidence in 1953 was the X-ray picture of the wet or B-form of DNA taken by Rosalind Franklin in 1952, but not seen by Watson until January 1953. This so-called cross-ways or black cross picture of DNA confirmed the helical nature of DNA via the Cochran-Crick-Vand theory and also work by the King’s College physicist Alexander Stokes. These theories showed how a helical molecule would diffract X-rays. Watson had traveled to London to show the King’s College group the DNA structure paper by Pauling and Corey. But when he saw the new X-ray picture by Franklin “… my mouth fell open and my pulse began to race …. The black cross of reflections which dominated the picture could arise only from a helical structure” (Watson, 1968, p. 167). The new pictures of the B or wet form of DNA meant that there was a crystallographic repeat every 34 angstroms rather than the 28 angstrom repeat seen in the A-form. Additional information was obtained from an MRC report from the King’s College group that had been distributed in December 1952. The MRC report revealed to Crick that DNA was a member of the C2 space group and had dyadic symmetry, that “the molecule of DNA, rotated a half turn, came back to congruence with itself” (Olby, 1974, p. 412).

On his way back to Cambridge Watson decided to try a two- rather than three-chain model. Olby and Crick suggest that this was based on a density calculation of the more compact A-form going to the more stretched out, and less dense, B-form, making a two-chain model more feasible. Watson claims that it was from his conviction that “important biological objects come in pairs” (Olby, 1974, p. 398). Whether this decision was motivated by evidence is not clear.

However, Watson had difficulty fitting two chains on the inside and bases on the outside, as they had done with the three-chain model. Crick suggested he try putting the two chains on the outside and try to fit the bases between them. Meanwhile, Watson had been reading about titration of DNA and concluded that most of the bases were hydrogen bonded to other bases. His first guess was that the bases were hydrogen bonded to bases of the same type (e.g., adenine to adenine) and the available textbook diagrams of the bases seemed to confirm that the bases could be hydrogen bonded like-to-like. This would make the sequence of bases on each of the two chains identical and suggested to Watson a mechanism for gene replication where one chain would serve as a template for the other, duplicating the sequences of bases (Watson, 1968, p. 186).

This idea was called into question when a crystallographer in their lab, Jerry Donohue, asserted that the textbook diagrams were wrong and Watson had used the wrong tautomeric forms for the bases—the enol rather than the keto form. However, adopting these alternative forms disrupted the hydrogen bonding between the like bases and resulted in an even poorer fit of bases between the two chains (Watson, 1968, p. 193). Crick added three more objections to Watson’s like-with-like model. Crick ruled out the 34 angstrom repeat for the model on X-ray diffraction grounds. In addition, the C2 symmetry deduced from the MRC report would be violated (Olby, 1974, p. 411). Finally, the model did not provide an explanation of Chargaff’s rules, regarding the ratio of bases in DNA, which Crick had taken more seriously than Watson (Fry, 2016, p. 218). Chargaff had determined that the purine bases (adenine and guanine) and pyrimidines bases (thymine and cytosine) occurred in DNA in a 1 to 1 ratio (Chargaff, Zamenhof, & Green, 1950). These rules had not been relevant to their previous model with bases unconstrained on the outside.

Table 5 shows the estimated conditional probabilities for the like-with-like model using the Watson and Crick triple helix model as the competing theory. The rationale for using this latter model as the competing one, rather than Pauling’s triple helix, is that it was more psychologically relevant to use their own model as a basis of comparison, and Pauling’s model was not acidic in Watson’s view. The new B-form X-ray pictures from King’s, in combination with the Cochran-Crick-Vand theory, provided strong confirmation for the helical structure of DNA. However, the triple-helix model was also helical and was thus supported by the new B-form photos. The 34 angstrom crystallographic repeat derived from the X-ray picture, however, was inconsistent with the like-to-like model according to Crick and is thus scored as “strongly inconsistent.” The triple helix model was based on the now incorrect 28 angstrom repeat from the earlier A-form picture and thus is also “strongly inconsistent.” Likewise, the interatomic distances were violated in the like-to-like model whether the keto or enol forms for the bases were used, causing a buckling of the backbones, and hydrogen bonding was disrupted because of the incorrect tautomeric forms. Hydrogen bonding had been ruled out for the triple helix (Olby, 1974, p. 360) violating Watson’s new expectations. Bond lengths were also violated in triple helix, and C2 symmetry was not fulfilled by either the like-with-like model or the triple helix and hence both were “strongly inconsistent,” as were both models for their failure to account for Chargaff’s rules. The only bright spot for the like-with-like model was its potential explanation of gene replication, which is scored as “weakly consistent” because it was only a conjecture. The triple helix model offered no such explanation.

The posterior probability considering these seven forms of evidence was 0.32, which was, however, an increase of 34% over the posterior of the Watson and Crick triple helix used as the prior. This confirmation was due only to the prospect for a mechanism of gene replication offered by the like-with-like model. The reason that the five sources of negative evidence did not lead to disconfirmation was that the alternative model, the Watson and Crick triple helix, suffered from the same defects. Had the Pauling triple helix been used as the competing model, the like-with-like model would still have been confirmed, but the absolute value of the posterior would have been lower due to the lower posterior of the Pauling model.

The “black-cross” X-ray pictures gave no advantage to the like-with-like model, as the triple helix was equally supported by it. Including as evidence the argument in favor of a double helix advocated by Crick and Olby, that new density evidence favored a double helix, and scoring it as 0.6 for the like-with-like model and 0.4 for the triple helix, would have given the like-with-like model an improved posterior of 0.42. Hence, the like-with-like model could have been seen as a step in the right direction.

Only a few days elapsed between Watson’s proposal of the like-with-like model and the Watson and Crick final model with purine to pyrimidine hydrogen bonding, adenine to thymine and guanine to cytosine. Although the evidence remained the same, the model changed in a significant way. Watson’s failure to fit like bases together prompted him to make cardboard cutouts of the bases in the enol configurations recommended by Donohue. “Shifting the bases in and out of various pairing possibilities” Watson hit on the solution: “the adenine-thymine pair held together by two hydrogen bonds was identical in shape to a guanine-cytosine pair …” (Watson, 1968, p. 194).

All the pieces of evidence then seemed to fall into place. “I suspected that we now had the answer to the riddle of why the number of purine residues exactly equaled the number of pyrimidine residues …. Chargaff’s rules then suddenly stood out as a consequence of a double-helical structure for DNA.” Furthermore, “This type of double helix suggested a replication scheme much more satisfactory than my briefly considered like-with-like pairing” (Watson, 1968, p. 196). Shortly after this realization Crick “… spotted the fact that the two glycosidic bonds (joining the base and sugar on the backbone) of each base pair were systematically related by a dyad axis perpendicular to the helical axis. Thus, both pairs could be flipflopped …” (Watson, 1968, p. 197). Hence, the C2 symmetry criterion was also fulfilled. Watson’s description of these realizations is close to what Koestler called a “Eureka moment” (Koestler, 1964, p. 107). But Watson also knew that they would have to verify all the stereochemical contacts. This did not deter Crick from announcing at lunch that they had discovered the “secret of life” (Watson, 1968, p. 197).

In Table 6, Watson’s like-with-like model was used as the competing model, and its posterior as the prior probability for the new double helix model. The reason for the strong confirmation of the final double helix was that it was consistent with five of the seven pieces of evidence that the like-with-like model was inconsistent with: the 34 angstrom crystallographic repeat for the B-form, bond distances and angles, C2 symmetry, Chargaff’s rules, and hydrogen bonding. The posterior of 0.97 was a 203% increase over the prior probability, which was the posterior of the like-with-like model. The dramatic increase in posterior probability can be seen by plotting the posteriors for the four models as shown in Figure 2. If there is such a phenomenon as “Bayesian surprise” this is certainly such a case. A similar and even more dramatic trend from model to model is seen in the likelihood ratio (Eq. 3), which is not dependent on the prior probability.

For many years after their discovery, Watson and Crick had to fend off various challenges to their model, including rival models and skeptical colleagues, and made minor tweaks, such as adding one more hydrogen bond to the base pairing (Crick, 1988). But the basic model remained the same, the major change being the gradual accumulation of confirming evidence.

The concept of “Bayesian surprise” has been discussed in a number of papers from the fields of cognitive science and neuroscience (Baldi & Itti, 2010; Gijsen, Grundei et al., 2021; Visalli, Capizzi et al., 2021). These papers develop Bayesian models of “surprise” using experimental results on human subjects responding to perceptual stimuli for studying attention, learning, and belief updating often using electroencephalographic methods. Under the more general rubric of the “Bayesian brain” (Friston, 2012), these studies assert that the brain generates predictions of future sensory input based on some internal model of the environment that is continuously updated as new sensory input arrives using Bayesian inference. In turn, the brain attempts to minimize surprise or entropy by adjusting its internal model of reality. Whether these neurological findings are applicable to surprising findings in science is beyond the scope of this paper, but we can speculate that the types of scientific findings that we come to label as “discoveries” are perhaps a byproduct of a dramatic increase in the probability of a theory. Calling the double helix model of DNA a “discovery” allows us to update our prior expectations and adjust to a new normal so we can move on to the next question. Incoming evidence and the model in our brain are clearly tightly interlocked in this process. A mismatch needs to be resolved or minimized either by modifying our model or by disputing the evidence. A match between model and evidence reduces entropy and uncertainty.

On the evidence side we have allowed certain forms of “soft” evidence to play a role in addition to harder evidence of an experimental or quantitative nature. For the early triple helix models, for example, an analogy to Pauling’s successful helical model of proteins provided weak evidence that a similar approach could be taken to nucleic acids. In his later writings, Kuhn has pointed out the neglected role played by analogy in theory change (Kuhn, 2000, p. 30). Thagard also used analogy to enhance the “coherence” of one theory over another in his network activation scheme (Thagard, 1992).

In addition, evidence was considered “weakly consistent” if the model was purposefully designed to accommodate the evidence, as in the case of the crystallographic repeat of the triple helix. The rationale for considering this as evidence is that a physical model still needed to be devised to meet that requirement, and the model could not be deduced directly from that evidence. The prospect for a mechanism for gene replication offered by the last two models was also considered as weak support. This is in line with Kuhn’s criterion of the “fruitfulness” of a theory, because the models held promise of providing an explanation of gene replication (Kuhn, 1977, p. 322; Salmon, 1990).

Some clear implications follow from the Bayesian formulas. First, confirmation or disconfirmation is dependent only on the values of the conditional probabilities and not on the prior. This is clear from the formula for the “likelihood ratio” (Eq. 2) which depends only on the conditional probabilities of the target theory and competing theories. On the other hand, the absolute value of the posterior depends on the value of the prior. But, similar to the posterior, the likelihood ratio shows a sharp increase for the final double helix model (from 1.5 to 69.2). In fact, the likelihood ratio increased on a percentage basis 20 times faster than the posterior going from the like-to-like to the final double helix model. The fact that both the posterior and likelihood ratio show similar trends suggests that either method can be applied to historical cases, although the likelihood ratio is more volatile.

One consequence of this is that it is not imperative to set the prior probability of a new model equal to the posterior of a preceding model to get the same verdict on confirmation or disconfirmation. This convention was adopted because, in a subjective interpretation of probabilities, the prior should reflect the initial degree of confidence of participants on the validity of the model, which depends in part on the success of previous models. Although this convention will not affect whether the model is confirmed or disconfirmed, it will result in a more meaningful trend of posterior probabilities.

For a theory that does not have a clearly defined predecessor, such as Watson and Crick’s or Pauling’s triple helix, we have assigned a prior of 0.5, which would be the value of the posterior if the theory and a hypothetical predecessor theory were equally probable. Conditional probabilities for the hypothetical theory’s ability to account for the evidence are also set at 0.5. This provides a null or baseline theory against which the new theory can be evaluated and allows the initiation of the Bayesian process. We have also explored using a preliminary hunch such as the single nucleotide chain as an alternative model. But, as this model is undefined, and apparently not taken seriously by the participants, its fit with evidence remains conjectural. Nevertheless, assuming some initial hypothetical comparison is performed, a subsequent model can utilize the first model’s posterior as its prior as well as serve as the alternative theory for comparison against subsequent theories, that is, become part of “not T” (∼T) in Eq. 1. If more than one predecessor theory exists, we can use the previous theory with the highest posterior as the alternative theory, consistent with the perspective of the evaluators, in our case Watson and Crick. For example, Pauling’s model is not used as the alternative theory for the like-with-like model, but rather Watson and Crick’s triple helix. Another consideration that makes the initial prior for a sequence of models less important is called the washing out or swamping of priors. This can occur if confirming (or nonconfirming) evidence accumulates (Earman, 1992, p. 141). This is clearly the case for the final double helix model, where confirming evidence became overwhelming.

The question arises of whether taking more than one alternative theory would affect the results. For example, we might take both the like-with-like theory and Watson and Crick’s triple helix as the alternative theories for the final double helix (see Eq. 3). This means that we need to combine the various forms of evidence used for the three models and score each model for each form updated to the time the double helix was proposed. This results in nine forms of evidence to consider for each of the three models. To set the prior probabilities for the alternative theories we use 0.32 for the double helix (the posterior of the like-with-like model) and split the remainder (1 − 0.32 = 0.68) between the two alternatives, weighting them by their posterior probabilities. The outcome of this exercise, however, results in increasing the posterior for the double helix from 0.97 to 0.98. The reason for this increase appears to be that some of the defects of the triple helix model remained valid (water content and absence of positive ions) and some of its apparent advantages (the crystallographic repeat of the A-form) were nullified by new evidence.

It is an open question whether it is legitimate to compare theories devised at different points in time, as we have done above, using evidence valid either for the earlier or the later period. In an extreme case discussed by Kuhn as “incommensurability,” he claimed that it is impossible to compare Aristotle’s theory of motion with Newton’s because their terms of reference were completely different (Kuhn, 2000, p. 16). Nevertheless, if a suitable mapping of the theoretical and empirical terms (old to new theory, old to new evidence) can be achieved there is no reason in principle that such a comparison could not be made using a Bayesian approach (Earman, 1992, Ch. 8).

The prior probability plays a somewhat different role in the evaluation of theories than it does in other statistical applications where quantitative rather than subjective priors are used. In the case of quantitative priors, the prior represents a “base rate” for some event, such as the incidence of a disease in a population (Kahneman, 2011, p. 166; Pearl & Mackenzie, 2018, p. 106) where we are interested in our chance of having the disease given the results of a test. In this case the “base rate” often plays a decisive role in the posterior probability, notably when other forms of evidence are unavailable, and is often mistakenly overlooked by human subjects. Technically, belief in the validity of a theory could also be measured for a population of researchers and used as the prior. But in the case of individuals, such as Watson, Crick, or Pauling, our only access to their levels of confidence is through contemporaneous writings or reports. For example, we have shown that Pauling’s view of his own model and Watson’s view of Pauling’s model would differ regarding the prior probability of the model as well as what evidence was deemed relevant.

Another consequence of the Bayesian formulas and the fact that the posterior is a function of the products of conditional probabilities is that the increase of the probability for one form of evidence can be offset by a decrease in some other form of evidence, the multiplication of probabilities being order independent. Similarly, if the target theory and the competing theory are both equally consistent or inconsistent with some form of evidence, there will be no change in the posterior. For example, if two successive models are consistent with the same form of evidence and the earlier model is used as the competing model, then the target and competing models can offset each other, resulting in no change in the posterior. This occurred, for example, when the B-form X-ray pictures showed the black cross pattern predicted by theory as indicative of a helix. However, both the target and competing models were based on helices and this was thus moot.

Not including some form of evidence can also lead to a different posterior. In the present study two or more consistent accounts of events were used where possible to verify each form of evidence. For example, Crick’s claim that the decision to try a double helix was based on the lower density of the B-form of DNA was not consistent with Watson’s account of his reason for taking up a double helix, namely, that biological objects should come in pairs. Because of this inconsistency, the evidence was not considered. However, including either Watson’s or Crick’s line of reasoning would have increased the posterior of the like-with-like model from 0.3 to 0.4, but would not have affected the final double helix model because both models employed double helices.

One clear finding from this case study is that the number of forms of evidence increases over time, from model to model, and in some cases the evidence changes as well. For example, when the X-ray evidence changed from the A-form to the B-form pictures, the “crystallographic repeat” changed from 28 angstroms to 34 angstroms. Consequently, one form of evidence favoring the Watson and Crick triple helix became inconsistent. Because it was also inconsistent with the like-with-like model (Watson, 1968, p. 193), it had no net effect on the posterior.

Another “new” form of evidence for the two later models was the Chargaff rules, which were not considered in the earlier triple helix models presumably because the bases were on the outside of the backbone and were hence unconstrained. Also, Crick’s realization that the X-ray evidence necessitated C2 symmetry of the bases was only a factor for the final two models, as was hydrogen bonding, which was initially dismissed as not playing a role but later became critical when the bases were placed on the inside of the backbones. This illustrates how evidence only takes on meaning in the light of theory. Thus, finding new forms of evidence is critical to the development of theory.

A more complex example of new data having relevance is when the X-ray diffraction picture of the B, or wet form, of DNA showed a “black cross” or “cross-ways” pattern. A theory of how a helical molecule would diffract X-rays was developed by Cochran, Crick, and Vand prior to the proposal of the various models for DNA considered by Watson and Crick. Schindler argues that deductive reasoning from this theory “played a crucial part in the discovery of the DNA structure” (Schindler, 2008, p. 627). In effect, the Cochran-Crick-Vand theory allowed the X-ray picture to be deduced from a helical model. Figure 1 shows the Cochran-Crick-Vand theory (T₂) and the empirical finding of the “black cross” X-ray picture (E₁) as separate nodes allowing the helical X-ray theory to intervene between the helical molecular model (T) and the “cross-ways” X-ray picture (i.e., the DNA model causes the helical X-ray theory to predict a “cross-ways” pattern, which is then observed). Ironically, however, this new certainty regarding the helical nature of the DNA molecule did not improve the posterior of the like-with-like model because the earlier competing model was also helical. Thus, for new evidence to benefit a new theory it is necessary for the new evidence not to benefit the older, competing theory.

Hartmann (2008), with reference to attempts to create a theory of high-temperature superconductivity, has discussed whether successive theories preserve the empirical successes of their predecessors. This was generally the case with the four models of DNA, for example, finding support for helical structures in different ways. Also, the replication mechanism continued from the like-with-like model to the final double helix. But there were steps backwards as well. The crystallographic repeat failed for the like-with-like model but was restored in the final double helix. What was more striking than the continuity of empirical success was the expansion of empirical criteria, as new evidence was found relevant.

The naïve Bayes formulation allows the comparison of a target theory with a competing theory or theories across multiple forms of evidence. The resulting posterior probability for the target theory can be compared to the prior probability, indicating whether the theory is confirmed or disconfirmed. The weighting of evidence according to the scale in Table 1 reflects the idea that some forms of evidence are better explained by a model than others. The conditional probabilities express a participant’s confidence that a specific form of evidence follows from the theory. In an historical case, these values must be inferred from the statements of the participants or from historical accounts. Whether other observers of these events would assign the same weights to the evidence given the historical accounts, and whether they would agree on the forms of evidence to be considered, remains to be seen and will require additional experimentation. It should also be noted that some philosophers, such as Norton (2021), advocate a non-Bayesian approach to induction grounded on physical “facts” and criticize Bayesians for interpreting all belief as probabilistic. However, this case study shows the adequacy of the Bayesian approach provided extreme values of probability are avoided (such as 0 or 1), which is consistent with the contingent and uncertain nature of scientific work.

Kuhn argued that for scientists, replacing an old theory with a new one in a scientific revolution is more like religious conversion than a matter of evidence. He maintained that it was not possible for “… an individual to hold both theories in mind together and compare them point by point with each other and with nature” (Kuhn, 1977, p. 338). This is precisely the strategy we have advocated in this paper using a Bayesian framework.

We can only speculate whether the Bayesian approach is an alternative to Kuhn’s theory of scientific revolutions (Earman, 1992; Kuhn, 1962; Weinert, 2014; Worrall, 2000). Like a Kuhnian revolution, a Bayesian approach involves pitting a new theory against an older competing theory, for example, the classic example of the Copernican versus the Ptolemaic system (Weinert, 2010) or Lavoisier’s oxygen theory versus the phlogiston theory of combustion (Pyle, 2000). Using the evidence available at the time, including soft as well as hard forms, may reveal that neither theory could claim a clear advantage, but that later developments in theory and experiment finally decide the issue. To conclude, however, that the only option is to see theory choice as a matter of “conversion” does not seem warranted (Norton, 2021).

In most work in the history of science, the approach is to show how a particular event or outcome was the result of various social and intellectual influences. Bayesian history of science, on the other hand, focuses on the lines of evidence relevant to the historical development to see if the direction taken by an individual or group of scientists was consistent or inconsistent with the evidence at hand. This turns the usual historical approach on its head. Both are valid approaches, but an approach focusing on evidence as the leading factor is less often taken. Indeed, sometimes influences can serve as evidence, as seen here in the case of Pauling’s alpha helix.

Even in a Bayesian approach it is important to take point of view into consideration when assessing whether a theory is confirmed or disconfirmed. This is illustrated by Pauling’s view of his own triple helix model versus Watson’s view of that model. The evidence brought to bear by different individuals can be different, as well as what competing theory is considered, and the strength of evidence. Worrall has discussed some of the limitations of the subjective Bayesian approach (Worrall, 2000).

Another obvious difficulty with a Bayesian approach to scientific discovery and theory choice is that it requires our brains to compute posterior probabilities. How this cognitive function is performed is a mystery. Perhaps such a mechanism has evolved to enhance our ability to survive (Friston, 2012), for example, to differentiate friend from foe, and prey from predator. Perhaps we are able somehow to simplify the multiple evidence inputs to just a few of the most salient forms, such as Watson’s realization of the purine to pyrimidine base pairing, and then, one by one, seeing how the other pieces of the puzzle fit together. Or as George Miller has speculated: “We might argue that in the course of evolution those organisms were most successful that were responsive to the widest range of stimulus energies in their environment” (Miller, 1967, p. 29). The double helix exemplifies such a wide range.

Whether or not our brains can perform Bayes-like operations, it seems fruitful to apply this methodology to other cases in the history and sociology of science, for example, to provide an alternative view on how theories are transformed into “facts” (Latour & Woolgar, 1979), and perhaps even to contemporary scientific or social debates that are yet to be resolved (e.g., Alzheimer’s disease). Despite not solving such problems, a Bayesian approach offers a systematic way of organizing the evidential pros and cons of competing views and a tentative verdict.

I would like to thank the three anonymous reviewers for stimulating several revisions and additions, Hung Tseng for useful commentary, Friedel Weinert for recommending the book by John Norton, and Ludo Waltman for help formatting equations.

The author has no competing interests.

No funding has been received for this research.

Data are available from the author.