Prostate Cancer Models Index

This page is an index of my prostate cancer modeling review project, the goal of which is to summarize as many published prostate cancer models as possible. In this index, I provide a link to a concise summary of each model. Summaries include the model equations, main assumptions and a concise statement of results. When relevant, I also provide MatLab LiveScripts with analyses of the model.

In general, I try to streamline notation and use consistent symbols for quantities used across models. For example, p(t) and x(t) and their variants will always represent PSA and tumor size (volume, mass or number of cells), respectively. Therefore, the summaries will often deviate from the notation used by the original authors. For convenience I provide a table of notation in each model summary.

Source Summary Notes Focus
Vollmer and Humphrey (2003) pdf LiveScript PSA and tumor dynamics
Swanson et al. (2001) pdf LiveScript PSA and tumor dynamics
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Life Sciences Symposium Abstract, Spring 2017

Symposium Abstracts:

Talk 1:

Population dynamics in a fragmented landscape with small patches: The Bodie pikas

Sabrina Jones1,2, Andrew Nemecek3, Lorida Llaci4, and John D. Nagy2,5
1School of Life Sciences, Arizona State University, 2School of Mathematical and Statistical Sciences, Arizona State University, 3Geography Department, University of Montana, 4Translational Genomics Research Institute, Phoenix, AZ, 5Department of Life Science, Scottsdale Community College

A population of American pikas (Ochotona princeps) inhabiting an anthropogenic landscape in the ghost mining town of Bodie, CA has historically been interpreted as a true metapopulation, where dispersal among patches of habitat plays a definitive role in its population dynamics. However, this assumption has never been explicitly demonstrated; in fact, it has been challenged by two competing hypotheses. The first suggests that, rather than patches being roughly equal in size and connectivity as in a metapopulation, large patches act as mainlands, making the landscape a classical MacArthur-Wilson island-mainland system. The second hypothesis suggests that observed occupancy patterns are a result of spatially correlated extinction events; in this hypothesis, dispersal plays a negligible role. Here we show, using 20 years of empirical patch occupancy data, that dispersal must be a key driver of the population dynamics of the Bodie pikas. Furthermore, a Hanski Incidence Function Model, which has become a standard modeling framework for metapopulations, fits the data better than do models of the other two hypotheses. In addition, the metapopulation concept has much more predictive and explanatory power. The Bodie pika population is well-suited to provide insight into fragmented population dynamics because it is distributed over discrete habitat patches, and we possess a series of high-quality censuses of the population from 1972 to 2010. It has become a standard empirical model of the effects of habitat fragmentation; therefore, it is critical that we have an accurate picture of the drivers of its population dynamics.

Talk 2:

Artificial selection for dispersal in experimental metapopulations of Tribolium confusum

Adam T. Hrabovsky1, Sarah H. Ung2, Michele V. Moreno2, Kerry J. Calhoun2,
Perry Olliver3, John D. Nagy2,4
1School of Life Sciences, Arizona State University; 2Department of Life Sciences, Scottsdale Community College; 3School of Molecular Sciences, Arizona State University; 4School of Mathematical and Statistical Sciences, Arizona State University

In metapopulations, dispersal connects subpopulations residing in discrete patches of habitat surrounded by uninhabitable matrix. In the 1970s Levins showed that metapopulation persistence requires that colonization rates equal extinction rates, which in turn requires adequate dispersal. Dispersal rate, on the other hand, is determined by evolutionary forces acting on individual fitness, not population persistence. The dynamics of this interplay are not entirely understood. Any experimental study of such dynamics requires a species in which dispersal has high heritability. Here we investigate the heritability of dispersal in artificial metapopulations of confused flour beetles (Tribolium confusum). We show that dispersal in T. confusum has a strong heritable component, but also exhibits a high degree of plasticity depending on environmental conditions. The key environmental determinant appears to be humidity. We also corroborate the results of Ogden and others who suggest that dispersal can be artificially selected in this species, which also supports the conclusion of high heritability of dispersal behavior.

Talk 3:

Evolution of treatment resistance in advanced prostate cancer

Khoa Dang Ho1, Paige N. Mitchell1, William J. Baker2, Jonathan Trautman2, Chandler M. Grant2, Alaina E. Daum2,3, and John D. Nagy2,4
1School of Life Sciences, Arizona State University; 2Department of Life Sciences, Scottsdale Community College; 3School of Molecular Sciences, Arizona State University; 4School of Mathematical and Statistical Sciences, Arizona State University

Recurrent and advanced prostate cancers are typically treated with total androgen blockade. However, androgen deprivation almost inevitably leads to castration resistance. Molecular mechanisms of castration resistance have been elucidated–the most common of which is amplification of the androgen receptor (AR) gene. But the ultimate cause of resistance remains unknown. Two hypotheses have been suggested: (i) resistance arises from cell plasticity; and (ii) resistance is caused by natural selection acting on mutant clones within the tumor. Here we show that evolution by natural selection is likely to be the ultimate cause of treatment resistance in prostate cancer. We found that, in a sample of 55 patients treated with intermittent androgen deprivation, the velocity of serum prostate specific antigen (PSA) decline tends to decrease over sequential on-treatment phases. In contrast, off-treatment PSA velocity exhibits no specific pattern in sequential cycles. These observations are consistent with treatment generating directional selection for castration resistance during treatment periods only. These results corroborate a predictive mathematical model that includes natural selection for AR expression under androgen deprivation. Such a model promises to be a key tool in managing castration protocols to mitigate the effects of treatment resistance in prostate cancer.

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Philosophy of Achievement


Many paths lead to achievement. People regularly achieve greatness–as measured by wealth, influence and status–by the adroit use of self-promotion, its sibling, marketing, and the handmaiden to both, deceit. Bribery, coercion and other forms of persuasion have also led many to “greatness.” All such paths are controversial, exciting some and dismaying others. But all, when navigated by the proper type of person, are demonstrably effective. In this lab, however, we have no interest in wealth, influence or status. Instead, we focus exclusively on a goal we see as thoroughly laudable and significant—the deepest possible understanding of the universe. For us there exists only one possible route to success, the signpost to which is found on a NASA mission patch, of all places.

The National Aeronautics and Space Administration loves patches. As a NASA scientist once told me, “if you don’t have a patch, you don’t have a mission.” Even those who control missions have their own patch. The Mission Control patch was designed by the legendary flight director, Eugene (Gene) Kranz, and Robert McCall, one of the greatest American artists of the 20th Century (and who contributed much of the artwork adorning the Natural Sciences buildings at SCC). Mission Control is short for Mission Operations Control Room, or MOCR (pronounced “Mow-kur”). But it was much more than just a room. Here, flight control teams worked continuously with the astronauts to monitor the spacecraft and other mission hardware, solved problems and generally protected the lives of the flight crew while focused on success of the mission. Mission Control was, quite literally, an indispensable part of the only successful attempt in Earth’s history to set humans down on another world. And Gene Kranz was indispensable to Mission Control. He has been intimately involved in the flights of all U.S. crewed spacecraft. He was assistant flight director for Mercury, flight director (head of a MOCR team) for all Gemini and Apollo missions, promoted to Deputy Director and then Director of the Mission Operations Directorate during the Space Shuttle years, and finally retired after 34 years at NASA following the successful repair of the Hubble Space Telescope by the STS-61 crew (December 1993). Kranz knows something about achievement.

McCall credits Kranz for the design of the patch, saying he (McCall) did little more than make it easily reproducible. This is probably something of an understatement–those familiar with McCall’s work would recognize his hand even if they knew nothing of his role in making the emblem–but essentially all elements of the patch reflect Kranz’s leadership style. The patch is circular, with a “legacy ring” surrounding a central picture. The picture is anchored by the Earth at the bottom, from which rises a Saturn V rocket boosting an Apollo spacecraft on a jet of flame toward the moon and sun while a satellite orbits Earth below. The “Mission Control” identifier is overlain by the greek letter sigma (∑) representing teamwork (summing up). At the bottom of the “legacy ring” sit icons for Mercury, Gemini and Apollo, all crewed U.S. spacecraft up to that time. The top of the ring holds prominent the Latin phrase, res gesta par excellentiam [sic]–“achieve through excellence.”

news-091214dOriginal Mission Control patch designed by Gene Kranz and Robert McCall in 1973.

The emblem has changed since then. NASA turns to Michael Okuda, a graphic designer largely responsible for look of the fictional technology in Star Trek movies and The Next Generation television shows, whenever it feels the need to update the patch. During these redesigns, the Apollo-Saturn stack has been replaced first by the shuttle and then by a stylized spacecraft that, according to one website, “represent[s] the growing variety of U.S. space vehicles in operation.” (This is, of course, disingenuous–NASA has no crewed spacecraft in operation, and they are not about to put a Russian rocket on a U.S. patch.) The stars were rearranged to represent U.S. astronauts who died in the Apollo 1, STS-51-L (Challenger) and STS-107 (Columbia) missions. “Mission Control” became “Mission Operations” and then “Flight Operations.” The sun was replaced by Mars, and then both Moon and Mars were deemphasized and shown as barely visible crescents. The orbiting satellite was replaced by the International Space Station and then by…something. The legacy ring has added Skylab, the shuttle, the Russian MIR space station and the ISS. But throughout all these alterations one thing never changed: the Latin inscription at the top (except for a slight grammatical correction).

MOD2Mission Operations Emblem during the Space Shuttle era.

There is a reason for that, extending beyond NASA flight operations. The NASA flight directors during the Apollo era were among the greatest leaders the world has ever produced. I am sure there are those who dismisses this statement as childish hyperbole, but they are wrong. Listen to the flight director’s audio loop during Glynn Lunney’s first shift following the Apollo 13 accident, and then consider the qualities of flight directors who helped achieve a goal so extraordinary that a significant fraction of subsequent generations refuse to believe it was really achieved. But it was. And because they were great leaders, those responsible have taught us how they did it because they realized that the lesson applies to anyone accepting the challenge of significant goals: res gesta per excellentiam.

news-091214cCurrent Flight Operations Emblem.

Excellence is also our path to achievement. It is the only one available to those of us compelled to study Nature honestly. But what does it mean, “achieve through excellence?” This is explained in the “Foundations of Mission Control,” published by Kranz in his book, “Failure is not an option” (Kranz 2000). Here are the foundations, slightly edited to fit our purpose:

To instill within ourselves these qualities essential for professional excellence:

Discipline. Being able to follow as well as lead, knowing that we must master ourselves before we can master our task.

Competence. There being no substitute for total preparation and complete dedication, for [Nature] will not tolerate the careless or indifferent.

Confidence. Believing in ourselves as well as others, knowing that we must master fear and hesitation before we can succeed.

Responsibility. Realizing that it cannot be shifted to others, for it belongs to each of us; we must answer for what we do, or fail to do.

Toughness. Taking a stand when we must; to try again, and again, even if it means following a more difficult path.

Teamwork. Respecting and utilizing the ability of others, realizing that we work toward a common goal, for success depends on the efforts of all.

To recognize that the greatest error is not to have tried and failed, but that in trying, we did not give it our best effort.

This is what is meant by res gesta per excellentiam. This is our path, and we will do our utmost to remain on it at all times.

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J. B. S. Haldane and “Familial Selection”

Figure: Phase portraits of J. B. S. Haldane’s “familial selection” model. Nullclines (colored curves) and example solutions (black lines) are shown. Arrows show direction of time. Shaded portions are not part of the phase space for this model. (A) Selection favors the dominant allele. (B) Selection favors the recessive allele. Download essay (here) for details.

In the first of a series of papers called, “A Mathematical Theory of Artificial and Natural Selection” (Haldane 1924), J. B. S. Haldane rationalizes Darwin’s notion of natural selection with Mendel’s idea of particulate inheritance. Therefore this paper is a central document in the Modern Synthesis. It is also important because it lays much of the foundation of modern population genetics, introducing the selection coefficient and basic Mendelian selection model (as discussed in another essay). It also introduces nuances not often explored in modern textbooks, including selection on one sex only and selection when family sizes are strictly regulated. In this essay we take up the latter situation, what Haldane called “familial selection,” and save the former (selection in one sex) for a follow-up article. Haldane was not always transparent regarding the assumptions he was making–he played especially fast-and-loose when linking discrete biological processes with mathematical representations in the continuum–so this essay develops the model carefully and brings to it a more modern expression and analysis.

Entire essay in pdf format

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The Selection Coefficient and Early Selection Models of J. B. S. Haldane (1924)

In 1924, J. B. S. Haldane published the first of a series of 10 papers with the main title, “A Mathematical Theory of Artificial and Natural Selection”. He expressed his motivation in the very first sentence of the first paper: “A satisfactory theory of natural selection must be quantitative” (Haldane 1924, pg. 19). From the start, Haldane challenged an opinion, common in 1924, that Mendelian genetics and evolution by natural selection were incompatible. That opinion presumed that mutation could yield only large, immediate changes in phenotype, which contradicts Darwin’s notion of gradual change via selection. This idea, called “mutationism,” is not just an argument about tempo. In its more extreme (and earlier) forms, it argued against selection as the mode, envisioning evolution via a type of drift involving only large phenotypic changes.

For example, it was well known in 1924 that peppered moths (Biston betularia) around Manchester, England had been predominantly light in color in 1848, but were almost all dark 50 years later. The mutationists argued that this change was caused by a single mutation, perhaps occurring in more than one individual, but was essentially independent of selection.

The idea of mutationism follows from Mendel’s discovery of particulate inheritance. Since traits are linked to discrete genes, changes in genes would presumably cause discrete changes in phenotype. Therefore, evolution should be saltational–that is, it should occur in jumps. (This is a different notion than modern saltational evolution à la Eldredge and Gould (1977).) Since mutations are discrete, how could evolution be continuous and gradual? (Keep in mind that at this time molecular genetic mechanisms were completely mysterious, and experiments has shown that phenotypes can, indeed, undergo massive change in a single generation. Offspring expressing the distinctly new phenotypes were called “sports.”)

This was the logic that Haldane challenged. He wrote (pg. 19),

In order to establish the view that natural selection is capable of accounting for the known facts of evolution, we must show not only that it can cause a species to change, but that it can cause it to change at a rate which will account for present and past transmutations.

From the modern viewpoint, Haldane here takes up the task of reconciling Darwin’s notion of natural selection with Mendel’s of particulate inheritance, making this paper one of the key primary sources of the Modern Synthesis. He develops mathematical models of evolution which incorporate discrete mutations but show that evolution in populations can still be gradual. In the process he establishes the foundations of modern evolutionary modeling. In this essay we will analyze Haldane’s simplest models using modern dynamical systems theory.

Click here for a PDF of the entire essay, plus references.

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October aurora borealis in Lapland

A huge sunspot traversed the face of the sun the previous two weeks (last 2 weeks of October, 2014). It is officially designated AR 12192, and it is the largest sunspot recorded in about 25 years. It released some 10 large solar flares, with the result that the arctic enjoyed outstanding aurora displays for the last couple of weeks. There’s an excellent video at (shot by a group called Lights over Lapland, in northern Sweden; “Lapland” can refer to the area of Finland north of the Gulf of Bothnia or, more generally, to the ancient homeland of the Lapps (Sami), which includes the portions of Finland, Sweden, Norway and part of Russia mostly north of the Arctic Circle).Whether or not we could see the displays down in Turku, which is a good bit farther south (roughly 450 miles, 725 km) is moot. It’s been cloudy almost the entire two-week stretch.

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What did G. H. Hardy, of Hardy-Weinberg fame, actually show?


G. H. Hardy was a very successful mathematician of the early 20th century. Even today he is highly esteemed among professional mathematicians. He made seminal contributions to a variety of subdisciplines, including number theory and analysis, and he was among the most important British mathematicians of his day. His immortalization in the title of the founding theorem of population genetics therefore seems quite appropriate. But as is well-known among experts, it’s actually quite ironic.

To start with, Hardy’s contribution to the Hardy-Weinberg theorem was nothing more than a trivial mathematical observation expressed in a letter to the editor in Science. Two short paragraphs in the 1-page letter sufficed to convey the substance of the argument. The remainder is devoted to a contrived example and an expression of astonishment that “biologists” were not aware of something so straightforward. In fact, some biologists already knew of the result. In Germany, Wilhelm Weinberg [3]  made the same observation at about the same time, but even he wasn’t the first. Karl Pearson was. But Hardy was led to the belief that biologists were in the dark by an uncharacteristically misbegotten notion published by G. Udny Yule [1], who expressed a mistaken intuition, common among neophytes, that any dominant trait would eventually come to represent 3/4 of the population if the trait was not under selection. Hardy’s motivation was to show that Yule was wrong, which required nothing more sophisticated than grade-school algebra. So the brilliant mathematician forever became linked by name to a mathematically trivial result.

And mathematically trivial it is. But one should not confuse mathematical simplicity with biological triviality. The Hardy-Weinberg theorem quite rightly serves as the foundation for population genetics, and among other things provides us a tool to identify traits that are evolving in real populations. So it is, I think, important to understand precisely what Hardy contributed to the theory.

Hardy’s Result

In 1908, one approached problems in population genetics–the seeds of which had just been sown–often from different perspectives than the modern one. In this case, Hardy focused on absolute numbers of individuals of each genotype and neither on their frequencies (despite subsequent misinterpretations) nor the allele frequencies, as we tend to do now. So, first we’ll look at his result using his notation and argument, and then demonstrate how it can be obtained more directly with modern ideas.

Hardy defined the numbers of individuals with genotypes AA, Aa and aa as p, 2q and r, respectively. Keep in mind that p and q do not mean what we typically teach in modern textbooks. These are numbers of individuals, not proportions. So, the total number of individuals in the population is p + 2q +r. If mating is random, the population is really large (or we ignore statistical fluctuations), sex ratios for each genotype are all unity, and there’s no selection, then the number of matings among each genotype can be obtained by expanding

(1)                     (p + 2q + r)2,

and the number of individuals of each genotype in the next generation can be obtained by segregating their expected proportions. If we work it all out (which Hardy left the reader to do), we obtain the results in the following Table:

Now we just use  Table 1 to partition up the mating frequencies to determine the number of each genotype in the next generation. For example, let pn be the number of AA individuals in generation n, and we want pn+1. We know that all offspring from AAxAA matings will be AA, while only half of the offspring from AAxAa matings and 1/4 of those from AaxAa will be AA. So,

           pn+1 = pn2 + (1/2) (4pn qn) + (1/4) (4qn2) = (pn + qn)2.

This is the brute force approach. A more elegant way is to rewrite expression (1) as

(2)          ((p+q) + (q+r))2.

If we square the first term, then we get the number of AA offpspring. Squaring the second term gives the number of aa offspring. The number of heterozygotes is the sum of the two cross-terms. Either way you look at it, you get the following:

Hardy did none of this, nor did he quite use this notation. He assumed the reader grasped it without his help.

Our goal is to find a fixed point of system (3). Let that fixed point be (p, q, r). If Yule were right, then p + 2 q = 3r (there would be three times more dominant phenotypes than recessives at equilibrium). But, notice that a fixed point, by definition and equations (3a) and (3c), satisfies

Substituting these results into equation (3b) tells us that at a fixed point we must also have

This evidently is not Yule’s result. In addition, Hardy also stated without demonstration (because it was easy enough for the reader to figure out, or so he thought) that the numbers of each genotype would not change in successive generations following the first. Then he contrived an example to show in a concrete way how it worked.

The modern way of dealing with this is similar to Hardy’s, although instead of numbers of each genotype, we focus on allele frequencies. So, beware of a change in notation here. Let p now represent the frequency of allele A and q be that of allele a. We assume these are the only alleles for the trait in the population, so p + q = 1. If mating is random, alleles are partitioned identically between the sexes–i.e., p is the same for both males and females–the population is large with no migration or selection, and mutation can be neglected, then the frequencies of all genotypes in the next generation are generated by the binomial square:

This is already a fixed point since, for any p,

and therefore qn+1 = qn. The genotype frequencies may shift in the first round of breeding, as Hardy noted, but the allele frequencies are fixed from the beginning. In the modern theory of dynamical systems, every point in the phase space, p in [0,1], is a neutrally stable fixed point. Where you start is where you stay forever.

One can also easily recover Hardy’s condition, but before showing that let’s disambiguate the notation. Let NAA, NAa and Naa be the number of individuals of each genotype. Hardy’s observation was that “the condition for” system (3) to have a fixed point is

(Remember, in Hardy’s notation, q is half the number of Aa individuals.) In the modern approach, random mating and all the other assumptions are expressed mathematically as the expansion of (p + q)2, so that the genotype frequencies after a single mating must be

In other words, random mating and all the other assumptions are true if and only if these are the genotype frequencies in all rounds of mating after the first. From here it is trivially easy to see that these frequencies hold if and only if

which is precisely Hardy’s condition up to a constant equal to the population size. In summary then, if we are at an equilibrium for the genotypes, then condition (8) holds, and if condition (8) holds, we are at an equilibrium. Therefore, Hardy’s somewhat cryptic, “it is easy to see that the condition for [the distribution being at an equilibrium] is q2 = pr” means that his condition is necessary and sufficient for an equilibrium.

Essay available as a pdf here.

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Tactical vs. Strategic Modeling

Theoreticians whose primary tools are mathematical models operate in such a bewildering variety of ways that someone attempting to summarize the programmatic approach to modeling–a textbook author, for example–is easily frustrated. However, with a bit of effort one begins to see a broad dichotomy among approaches. The two tines of this dichotomy I’ll call “tactical” and “strategic.” In the first, one identifies a problem or question in biology (or physics or chemistry or …) and then looks for specific analytical tools to solve or answer it. The second approach addresses the analytical tools themselves and attempts to extend and improve them.

For example, the origin of our first cancer model was the question, are tumors more like ecosystems, which are dominated by competition and segregation, or like tissues, which comprise cooperating cells? Since ordinary differential equations and adaptive dynamics were tools that we had mastered, they are what we applied to the problem. This is an example of the tactical approach. The strategic outlook is well illustrated in a recent paper by our colleagues Kalle Parvinen (who I am visiting in Turku, Finland as I write this), Mikko Heino and Ulf Dieckmann (J. Math. Biol. 67:509). As they note in the introduction, “adaptive dynamics was originally formulated for scalar strategies”–that is, for strategies that can be represented as a real number. However, there are times when the strategy cannot be so represented. For example, the strategy could be vector-valued (characterized by a set of real numbers) or function-valued (represented as a real function of a real variable). In previous work, they develop adaptive dynamics theory for a certain class of models with function-valued strategies using calculus of variations. But models of this class suffer key limitations that restrict the theory’s ability to analyze realistic scenarios. So, in the paper quoted, they extend the results to a more general class using optimal control theory. This is what I mean by “strategic.”

This dichotomy, of course, is one person’s perception. Another person could debate it on the grounds that these two approaches are not mutually exclusive. I would completely agree. Strategic advances must be directed by tactical needs. In other words, one has to have a good reason to extend a modeling theory. On the other hand, when addressing a tactical problem, or even deciding which problem to tackle, one refers to the analytical toolkit at hand, leading to the common perception that modelers tend to shoehorn empirical problems to fit their “favorite” analytical tool. Although such a view (which has been expressed to me many times) is a little unfair, there is some truth in it. But one should not allow such criticisms, among others, to obscure the facts that the dichotomy, if perhaps a little simplistic, is real, and that neither tactical nor strategic programs can survive without the other.

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Do tumor vessels regress?

Yes, despite a remarkably durable contrary dogma. Evidence exists, and has for decades, that neovessels regress in the absence of growth factor support, and in fact mature vessels can also regress. Here’s one passage from a 1999 abstract from Holash, Wiegand and Yancopoulos (1999; New Model of Tumor Angiogenesis: Dynamic Balance Between Vessel Regression and Growth Mediated by Angiopoietins and VEGF. Oncogene 18:5356–5362):

“Our analyses in several different tumor settings challenge the prevailing view that malignancies and metastases generally initiate as avascular masses that only belatedly
induce vascular support. Instead, we find that malignant cells rapidly coopt existing host vessels to form an initially well-vascularized tumor mass. Paradoxically, the coopted vasculature does not undergo angiogenesis to support the growing tumor, but instead regresses (perhaps as part of a normal host defense mechanism) via a process that involves disruption of endothelial cell/smooth muscle cell interactions and endothelial cell apoptosis. This vessel regression in turn results in necrosis within the central part of the tumor.” [Italics added]

Holash et al. show regression of existing, mature vessels in situ in a living mouse, challenging the apparently still prevalent idea that mature vasculature is static. Further, in our angiogenesis model, most of the vessels are not fully mature, and it is well-known that such neovessels are susceptible to regression on removal of growth factor support (see chapter 4 in Reza Forogh [ed] (2006; New Frontiers in Angiogenesis) written by Nicosia, Zhu and Aplin (Regulation of Postangiogenic Vascular Regression, pp. 79-95). This observation, and others, imply the regression term in the microvessel equation of our model. It’s also important to note that this regression term does not depend on angiogenic signaling (and that VEGF is not the only angiogenic factor, nor is its translation the only energetic cost associated with the angiogenic signal).

To the lab members, I would like to remind you of something that I recently forgot. Whenever we present or publish, we can never count on a sympathetic audience. That’s a good thing. It requires us to prepare all communications with exquisite care. Especially before giving a talk, we must have all details of our previous work and results from earlier studies available for immediate recall, even if we feel they are well-established. We will be challenged, perhaps more than most. Only by thorough preparation can we defend our work effectively and professionally. Our work is worth defending.

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We reject the curious ethical stance suggested by Praveen Chaddah in recent issue of Nature

The July 10 issue of Nature contains an opinion piece by Praveen Chaddah I would like everyone in the lab to read [1]. (He is a physicist who directed the University Grants Commission of the Indian Department of Atomic Energy Consortium for Scientific Research). In this article, Chaddah espouses some views in diametric opposition to the ethical standards of this lab. To avoid confusion, I address some of the more dismaying claims in the paper and formally lay out our standards of publication.

1. We categorically reject Chaddah’s statement that, “…scientists are not writers. We value the originality of ideas more than of language.” In no context is this statement correct. Scientists are writers. We are also public speakers. Our work lives and dies not just by the quality and originality of our ideas, but on how well we express them. The scientific arena is now more competitive than ever in history. A brilliant idea expressed poorly in this environment has very little chance of influencing anyone but  the originator’s mind. Writing is the basic skill of composition, which is the act of expressing ideas in a permanent medium. The value of effective writing in science is therefore at its highest premium.

All members of this lab will endeavor to master the craft of technical writing. We will continually hone our skills in composition throughout our careers and present only our best efforts in all submissions: manuscripts for publication, grant proposals, blog entries, official letters and all other forms of professional communication.

2. In this article, Chaddah identifies “three forms of plagiarism”–so-called “text plagiarism,” which is essentially cutting and pasting passages from another author’s work without proper attribution, along with “idea” and “data” plagiarism. Of these three, he argues that text plagiarism is the least offensive and easiest to recognize. In contrast, he claims that, “[a]uthors who have plagiarized ideas or results have crossed a serious ethical line and should be sanctioned by their institutes…” [italics added].

We reject the notion that “text plagiarism” is less offensive than any other form of intellectual theft, including theft of ideas and results. High-quality scientists have always worked diligently to craft their technical compositions, even when Latin was the lingua franca of science and yet no one’s mother tongue. That many scientists today and in the past have failed to master technical discourse highlights the skill of those who have. These professionals are rightfully proud of their command of the language. That others devalue the fruits of such skill does not diminish the offensiveness of its theft.

In this lab, we deeply appreciate all creative contributions of our colleagues–their ideas, their results, their syntheses, their writing and even the occasional interpretive dance. We therefore treat all such contributions with the utmost respect. In our communications, we always endeavor to provide proper attribution to ideas, results and writing. Although we highly value expressing all ideas–ours or others’–in our own crafted language, we recognize that direct quotes can sometimes have rhetorical value. However, if we use another author’s words, we set them out in quotes, note our own editorial alterations by setting them with square brackets, identify removed passages with ellipses (three periods in a row), and identify the source of the quote, including page number(s) (if not obvious), immediately following the quoted text. We also recognize that mistakes happen, possibly because, as Chaddah notes, one “had read previous works that left an indelible mark on their subconscious.” In that unlikely event, we would immediately, voluntarily and publicly take responsibility for, and correct to the best of our ability, the error.

3. Chaddah further argues that plagiarism should not result in retraction of a published article, which is the current standard of practice. Instead, he suggests that plagiarism should be corrected in the published piece with some sort of flag indicating that the passage, idea or result was plagiarized. This policy is presumably proposed to protect novel results in the plagiarizing paper from being blocked because of “mere” text plagiarism. He also argues that “[t]he wording of the correction must make clear that the offense was plagiarism, not fraud.”

We agree that plagiarism is not fraud. It is theft. We reject that theft is a lesser crime than fraud. We also reject the claim that retraction is too stiff a penalty for intellectual theft. If the plagiarizing paper otherwise has merit, then a revised manuscript with all plagiarism removed will have access to the body of literature through normal peer-review publication channels. The original offensive submission need not remain.

Therefore, I recommend the following policy to individuals in this lab who have begun reviewing peer manuscripts. If during the review process one finds a plagiarized passage, idea or result, one should immediately recommend rejection of the paper to the handling editor and provide a clear explanation and documentation of the plagiarized passage(s). One need not seek out all plagiarized passages nor evaluate other merits of the plagiarizing paper. However, if there appears to be merit or perhaps you are not sure, then indicate to the editor that you would be willing to review an alternate submission of the same result in which all plagiarism has been eliminated. If the authors resubmit, then review the contribution on its merits. If plagiarism is still evident, I recommend immediate rejection with a letter indicating that you will entertain no further submissions from the authors involved.

4. Finally, many individuals commenting on Chaddah’s piece on Nature’s website seem to have conflated his main points with another common problem–self-plagiarism, in which one publishes substantial portions of their own writing in more than one article. While not theft, strictly speaking (unless the copyright is held by a 3rd party), such redundancy muddies the literature and wastes researchers’ time. Therefore, it should be avoided, except in the following case. It is acceptable for an author to include one of his or her previously published papers essentially wholesale as a chapter in a book they have also authored. This practice does not include collections of papers one is editing, but traditional books on which one is a traditional author. Even in this case, however, the previously published material should contribute a minimum (a chapter or two at most) of text to the book. Publishing an anthology of one’s work is rarely appropriate, especially when one’s scientific corpus is still developing. If we do include previously published work in a book, then we will do so only under the following conditions: (i) we own the copyright or the text is in the public domain; (ii) we clearly mark, on the first page of the chapter, that the chapter is substantially the same as our previously published work, with a complete citation so others can find the original paper; and (iii) we edit it to fit the flow of the book.

We voluntarily adopt all of the policies expressed above out of respect for our colleagues, our profession and science. Any relaxation of standards proposed or adopted by our community of scholars or forced upon us from outside influences are irrelevant.

[1] Chaddah, P. 2014. Not all plagiarism requires a retraction. Nature 511:127.

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