Personification and pseudoscience

If you study the philosophy of science — and sometimes even if you just study science — then at some point you might get the urge to figure out what you mean when you say ‘science’. Can you distinguish the scientific from the non-scientific or the pseudoscientific? If you can then how? Does science have a defining method? If it does, then does following the steps of that method guarantee science, or are some cases just rhetorical performances? If you cannot distinguish science and pseudoscience then why do some fields seem clearly scientific and others clearly non-scientific? If you believe that these questions have simple answers then I would wager that you have not thought carefully enough about them.

Karl Popper did think very carefully about these questions, and in the process introduced the problem of demarcation:

The problem of finding a criterion which would enable us to distinguish between the empirical sciences on the one hand, and mathematics and logic as well as ‘metaphysical’ systems on the the other

Popper believed that his falsification criterion solved (or was an important step toward solving) this problem. Unfortunately due to Popper’s discussion of Freud and Marx as examples of non-scientific, many now misread the demarcation problem as a quest to separate epistemologically justifiable science from the epistemologically non-justifiable pseudoscience. With a moral judgement of Good associated with the former and Bad with the latter. Toward this goal, I don’t think falsifiability makes much headway. In this (mis)reading, falsifiability excludes too many reasonable perspectives like mathematics or even non-mathematical beliefs like Gandy’s variant of the Church-Turing thesis, while including much of in-principle-testable pseudoscience. Hence — on this version of the demarcation problem — I would side with Feyerabend and argue that a clear seperation between science and pseudoscience is impossible.

However, this does not mean that I don’t find certain traditions of thought to be pseudoscientific. In fact, I think there is a lot to be learned from thinking about features of pseudoscience. A particular question that struck me as interesting was: What makes people easily subscribe to pseudoscientific theories? Why are some kinds of pseudoscience so much easier or more tempting to believe than science? I think that answering these questions can teach us something not only about culture and the human mind, but also about how to do good science. Here, I will repost (with some expansions) my answer to this question.
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Ecology of cancer: mimicry, eco-engineers, morphostats, and nutrition

One of my favorite parts of mathematical modeling is the opportunities it provides to carefully explore metaphors and analogies between disciplines. The connection most carefully explored at the MBI Workshop on the Ecology and Evolution of Cancer was, as you can guess from the name, between ecology and oncology. Looking at cancer from the perspective of evolutionary ecology can offer us several insights that the standard hallmarks of cancer approach (Hanahan & Weingerg, 2000) hides. I will start with some definitions for this view, discuss ecological concepts like mimicry and ecological engineers in the context of cancer, unify these concepts through the idea of morphostatic maintenance of tissue microarchitecture, and finish with the practical importance of diet to cancer.

A very energetic proponent of the connection between ecology and cancer is Joel Brown, who delivered the second talk on Tuesday, September 16th. To make the analogies easier to see, he focused on definitions. Brown is an evolutionary ecologist, so from his perspective “metastatic cancer is the evolution of a new single-celled, asexual protist”. This means that we need to understand evolution — the change in heritable characteristics of a population over time; and we need to understand ecology — interaction between organisms and their environment. Together: studying organisms, populations, communities, and ecosystems through the adaptation of the organisms. This gives us three tools to study cancer that we already use to study nature more generally: (1) looking at the recipe of inheritance with genetics, (2) tracing a historical process with phylogenetics, and (3) understanding the fit of form and function — adaptation.

In analogy to other biological systems, Brown sees the driver behind adaptation as natural selection: a force of evolution that promotes heritable traits (which we can more easily call ‘strategies’) that maximize the average growth rate in the population given the circumstances. He views the whole human body as an ecosystem with different organs, tissues, spatial locations within tissues, and matrix of healthy cells corresponding the the more specific circumstances. Surprisingly, in the application to chancer, the question of what constitutes a population — especially in clinical settings where many different cell-types with slight variations in both phenotype and genotype are interacting — is the more difficult one. Measuring these populations of cancer cells in patients to test our theories is even more difficult, a problem that is less common when studying more classical ecological systems like squirrels.

In the next talk after Brown, Ruchira Datta proposed more specific insights we could draw from ecology. She explored the connection between mimicry and a tumor’s interaction with the immune system. In the mimicry that ecologists are familiar with, a mimic emits a signal imitation the model, the dupe receives the signal and mistakes the mimic for the model, acting toward the mimic as if it was the model and thus providing the mimic with some advantage. In the case of cancer, she proposed the hypothesis that cancerous cells mimic the phenotype of wounded tissue, this dupes the immune system into executing a wound healing program, and thus leading the immune system to cooperate in carcinogenesis. In contrast to the popular view that “cancer is the wond that never heals” (Pierce & Speers, 1988; Riss et al., 2006), David Axelrod summarized Datta’s hypothesis as “cancer is the wound that keeps on healing”.

In the second to last talk of Monday, September 15th, Kenneth Pienta pushed further by introducing the analogy of cancer cells as ecological engineers (Pienta et al., 2008; Yang, et al., 2014). Pienta’s guiding question is: why do people die from solid tumors like those of prostate cancer? Although we know many of the immediate causes of death — metabolic death 40% of the time, embolus 20%, pain treatment 30% and respiratory failure 10% — the unifying ultimate cause is mysteries. Pietka believes that the mystery is cytokine overproduction, the oncological equivalent of ecology’s swamp gas that slowly poisons the patient. Cancer cells are ecological engineers that are building a ‘swamp’ in two different ways:

  • Allogenic — like beavers, cancer cells mechanically alter their environment. This can be done by deforming the cell matrix they are embedded in, or by attracting new blood vessels that alter the local spatial heterogeneity and creating new static edges to exploit.
  • Autogenic — like trees, changing themselves with growth over time. As the tumor grows in size, it changes the local architecture and pH concentrations through things like the Warburg effect from a lack of oxygenation.

The resulting niche helps the cancer cells more easily continue reproducing and avoiding our natural defenses.

An important aspect of cancer ecology to remember, is that human cells are largely organized in tissues and are not (maybe with the exception of blood) well modeled by an inviscid population. When we look at cancer cells, we need to understand not just the individual cells, but their interaction with their local architecture and environment. The overly reductionist textbook account of cancer — as presented by Hanahan & Weingerg’s (2000) hallmarks of cancer, for example — tends to mostly ignore this by focusing on individual mutations, and only mentioning tissue in the context of angiogenesis — the recruitment and formation of new blood vessels. An ecological perspective must depart from this viewpoint, and that is just what John Potter did in the third talk on Monday with his introduction of morphostats.

As regular readers of TheEGG can recall, Alan Turing’s most cited work was not in computer science, but in biology. In 1952, to understand the systematic break of spherical symmetry in embryos, Turing introduced the idea of morphogenes. The morphogenetic fields he defined help organize the dynamic tissue morphology of a growing embryo. In analogy to this, several researchers (Tarin, 1972; Potter, 2001; van den Brink, 2001; for an overview, see Potter, 2007) have suggested morphostatic fields as a way to maintain homeostatic tissue microarchitecture in adults, and a mechanism for resisting cancer. The offers a change in perspective by focusing on the microarchitecture rather than just differentiation of cells.

By corrupting the morphostatic control, cancer cells can recruit normal cells into tumours. This disrupted areas can quickly become the swamps of Pienta’s presentation. Since morphastatic control is believed to be related to wound healing (Potter, 2007), its large scale disruption might envision the sort of immune-system mimicry that Datta hypothesized. Most importantly, this focus on maintenance of the local environment can take us out of the world of abstract connections between ecology and oncology, and into the world of concrete interventions — nutrition.

It is important to remember that both development (the part that morphogenes are relevant to) and homoestatic maintenance (the part that morphostats are relevant to) are ruled not only by a genetic program but by the environment. An important and often overlooked part of the human cellular ecosystem is nutrition — in fact, it is often overlooked (or oversimplified) on purpose by doing studies on mice with fixed feed. These environmental effects of nutrition are often downplayed by the hallmarks of cancer approach, which tends to focus on carcinogens that facilitate mutations instead of general effects of nutrition on microarchitecture of the gut. Potter provided us with an onslaught of evidence that suggested that the dominant theory of carcinogenesis does not account for the drastic effects of nutrition on cancer progression and prevention (Hirayama, 1979; Reddy et al., 1980; Willett, 2000; Anand et al., 2008; Gonzalez & Riboli, 2010). I wish that I could go into more detail on this connection, but I’d rather not scare you off your dinner.

This is my third post of a series on the MBI Workshop on the Ecology and Evolution of Cancer. The previous posts were: Colon cancer, mathematical time travel, and questioning the sequential mutation model; Experimental and comparative oncology: zebrafish, dogs, elephants.

References

Anand, P., Kunnumakara, A. B., Sundaram, C., Harikumar, K. B., Tharakan, S. T., Lai, O. S., … & Aggarwal, B. B. (2008). Cancer is a preventable disease that requires major lifestyle changes. Pharmaceutical Research, 25(9): 2097-2116.

Gonzalez, C. A., & Riboli, E. (2010). Diet and cancer prevention: Contributions from the European Prospective Investigation into Cancer and Nutrition(EPIC) study. European Journal of Cancer, 46(14): 2555-2562.

Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. Cell, 100(1): 57-70.

Hirayama, T. (1979). Diet and cancer. Nutrition and Cancer, 1(3): 67-81.

Pienta, K. J., McGregor, N., Axelrod, R., & Axelrod, D. E. (2008). Ecological therapy for cancer: defining tumors using an ecosystem paradigm suggests new opportunities for novel cancer treatments. Translational Oncology, 1(4): 158-164.

Pierce, G. B., & Speers, W. C. (1988). Tumors as caricatures of the process of tissue renewal: prospects for therapy by directing differentiation. Cancer Research, 48(8): 1996-2004.

Potter, J.D. (2001). Morphostats a missing concept in cancer biology. Cancer Epidemiology Biomarkers & Prevention, 10(3): 161-170.

Potter, J. (2007). Morphogens, morphostats, microarchitecture and malignancy. Nature Reviews Cancer, 7 (6), 464-474 DOI: 10.1038/nrc2146

Reddy, B. S., Cohen, L. A., David McCoy, G., Hill, P., Weisburger, J. H., & Wynder, E. L. (1980). Nutrition and its relationship to cancer. Advances in Cancer Research, 32: 237-345.

Riss, J., Khanna, C., Koo, S., Chandramouli, G. V., Yang, H. H., Hu, Y., … & Barrett, J. C. (2006). Cancers as wounds that do not heal: differences and similarities between renal regeneration/repair and renal cell carcinoma. Cancer Research, 66(14): 7216-7224.

Tarin, D. (1972). Tissue interactions in morphogenesis, morphostasis and carcinogenesis. Journal of Theoretical Biology, 34(1): 61-72.

Turing, A.M. (1952). The Chemical Basis of Morphogenesis. Philosophical Transactions of the Royal Society of London, 237(641): 37–72.

van den Brink, G. R., Hardwick, J. C., Tytgat, G. N., Brink, M. A., Ten Kate, F. J., Van Deventer, S. J., & Peppelenbosch, M. P. (2001). Sonic hedgehog regulates gastric gland morphogenesis in man and mouse. Gastroenterology, 121(2): 317-328.

Willett, W. C. (2000). Diet and cancer. The Oncologist, 5(5): 393-404.

Yang, K. R., Mooney, S., Zarif, J. C., Coffey, D. S., Taichman, R. S., & Pienta, K. J. (2014). Niche inheritance: a cooperative pathway to enhance cancer cell fitness though ecosystem engineering. Journal of Cellular Biochemistry.

Models and metaphors we live by

MetaphorsGeorge Lakoff and Mark Johnson’s Metaphors we live by is a classic, that has had a huge influence on parts of linguistics and cognitive science, and some influence — although less so, in my opinion — on philosophy. It is structured around the thought that “[m]etaphor is one of our most important tools for trying to comprehend partially what cannot be comprehended totally”.

The authors spend the first part of the book giving a very convincing argument that “even our deepest and most abiding concepts — time, events, causation, morality, and mind itself — are understood and reasoned about via multiple metaphors.” These conceptual metaphors structure our reality, and are fundamentally grounded in our sensory-motor experience. For them, metaphors are not just aspects of speech but windows into our mind and conceptual system:

Our ordinary conceptual system, in terms of which we both think and act, is fundamentally metaphorical in nature. … Our concepts structure what we perceive, how we get around the world, and how we relate to others. Our conceptual system thus plays a central role in defining our everyday realities. … Since communication is based on the same conceptual system that we use in thinking and actiong, language is an important source of evidence for what that system is like.

I found the book incredibly insightful, and in large agreement with many of my recent thoughts on the philosophies of mind and science. After taking a few flights to finish the book, I wanted to take a moment to provide a mini-review. The hope is to convincing you to make the time for reading this short volume.
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Limits of prediction: stochasticity, chaos, and computation

Some of my favorite conversations are about prediction and its limits. For some, this is purely a practical topic, but for me it is a deeply philosophical discussion. Understanding the limits of prediction can inform the philosophies of science and mind, and even questions of free-will. As such, I wanted to share with you a World Science Festival video that THEREALDLB recently posted on /r/math. This is a selected five minute clip called “What Can’t We Predict With Math?” from a longer one and a half hour discussion called “Your Life By The Numbers: ‘Go Figure'” between Steven Strogatz, Seth Lloyd, Andrew Lo, and James Fowler. My post can be read without watching the panel discussion or even the clip, but watching the clip does make my writing slightly less incoherent.

I want to give you a summary of the clip that focuses on some specific points, bring in some of discussions from elsewhere in the panel, and add some of my commentary. My intention is to be relevant to metamodeling and the philosophy of science, but I will touch on the philosophy of mind and free-will in the last two paragraphs. This is not meant as a comprehensive overview of the limits of prediction, but just some points to get you as excited as I am about this conversation.

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Should we be astonished by the Principle of “Least” Action?

QuinceyFig2As one goes through more advanced expositions of quantum physics, the concept of action is gradually given more importance, with it being considered a fundamental piece in some introductions to Quantum Field Theory (Zee, 2003) through the use of the path integral approach. The basic idea behind using the action is to assign a number to each possible state of a system. The function that does so is named the Lagrangian function, and it encodes the physics of the system (i.e. how do different parts of the system affect each other). Then, to a trajectory of a system we associate the integral of this number over all the states in the trajectory. This contrasts with the classical Newtonian approach, where we study a system by specifying all the possible ways in which parts of the system exercise forces on each other (i.e. affect each other’s acceleration). Using the action usually results in nicer mathematics, while I’d argue that the Newtonian approach requires less training to feel intuitive.

In many of the expositions of the use of action in physics (see e.g. this one), I perceive an attempt at transmitting wonder about the world being such that it minimizes a function on its trajectory. This has indeed been the case historically, with Maupertuis supposed to have considered action minimization (and the corresponding unification of minimization principles between optics and mechanics) as the most definite proof available to him of the existence of God. However, along the spirit of this stack exchange question, I never really understood why such a wonder should be felt, even setting aside the fact that it assumes that our equations “are” the world, a perspective that Artem has criticized at length before.
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Philosophy of Science and an analytic index for Feyerabend

FeyerabendThroughout my formal education, the history of science has been presented as a series of anecdotes and asides. The philosophy of science, encountered even less, was passed down not as a rich debate and on-going inquiry but as a set of rules that best be followed. To paraphrase Gregory Radick, this presentation is mere propaganda; it is akin to learning the history of a nation from its travel brochures. Thankfully, my schooling did not completely derail my learning, and I’ve had an opportunity to make up for some of the lost time since.

One of the philosophers of science that I’ve enjoyed reading the most has been Paul Feyerabend. His provocative writing in Against Method and advocation for what others have called epistemological anarchism — the rejection of any rules of scientific methodology — has been influential to my conception of the role of theorists. Although I’ve been meaning to write down my thoughts on Feyerabend for a while, now, I doubt that I will bring myself to do it anytime soon. In the meantime, dear reader, I will leave you with an analytic index consisting of links to the thoughts of others (interspersed with my typical self-links) that discuss Feyerabend, Galileo (his preferred historic case study), and consistency in science.
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Experimental and comparative oncology: zebrafish, dogs, elephants

One of the exciting things about mathematical oncology is that thinking about cancer often forces me to leave my comfortable arm-chair and look at some actually data. No matter how much I advocate for the merits of heuristic modeling, when it comes to cancer, data-agnostic models take second stage to data-rich modeling. This close relationship between theory and experiment is of great importance to the health of a discipline, and the MBI Workshop on the Ecology and Evolution of Cancer highlights the health of mathematical oncology: mathematicians are sitting side-by-side with clinicians, biologists with computer scientists, and physicists next to ecologists. This means that the most novel talks for me have been the ones highlighting the great variety of experiments that are being done and how they inform theory.In this post I want to highlight some of these talks, with a particular emphasis on using the study of cancer in non-humans to inform human medicine.
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Colon cancer, mathematical time travel, and questioning the sequential mutation model.

On Saturday, I arrived in Columbus, Ohio for the the MBI Workshop on the Ecology and Evolution of Cancer. Today, our second day started. The meeting is an exciting combination of biology-minded mathematicians and computer scientists, and math-friendly biologist and clinicians. As is typical of workshops, the speakers of the first day had an agenda of setting the scope. In this case, the common theme was to question and refine the established model as embodied by Hannah & Weinberg’s (2000) hallmarks of cancer outlined. For an accessible overview of these hallmarks, I recommend Buddhini Samarasinghe’s series of posts. I won’t provide a full overview of the standard model, but only focus on the aspects at issue for the workshop participants. In the case of the first two speakers, the standard picture in question was the sequential mutation model. In the textbook model of cancer, a tumour acquires the hallmark mutations one at a time, with each subsequent mutation sweeping to fixation. Trevor Graham and Darryl Shibata presented their work on colon cancer, emphasizing tumour heterogeneity, and suggesting that we might have to rewrite the sequential mutation page of our Cancer 101 textbooks to better discuss the punctuated model.
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Defining empathy, sympathy, and compassion

PaulBloomWhen discussing the evolution of cooperation, questions about empathy, sympathy, and compassion are often close to mind. In my computational work, I used to operationalize-away these emotive concepts and replace them with a simple number like the proportion of cooperative interactions. This is all well and good if I want to confine myself to a behaviorist perspective, but my colleagues and I have been trying to move to a richer cognitive science viewpoint on cooperation. This has confronted me with the need to think seriously about empathy, sympathy, and compassion. In particular, Paul Bloom‘s article against empathy, and a Reddit discussion on the usefulness of empathy as a word has reminded me that my understanding of the topic is not very clear or critical. As such, I was hoping to use this opportunity to write down definitions for these three concepts and at the end of the post sketch a brief idea of how to approach some of them with evolutionary modeling. My hope is that you, dear reader, would point out any confusion or disagreement that lingers.
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Transcendental idealism and Post’s variant of the Church-Turing thesis

KantPostOne of the exciting things in reading philosophy, its history in particular, is experiencing the tension between different schools of thought. This excitement turns to beauty if a clear synthesis emerges to reconcile the conflicting ideas. In the middle to late 18th century, as the Age of Enlightenment was giving way to the Romantic era, the tension was between rationalism and empiricism and the synthesis came from Immanuel Kant. His thought went on to influence or directly shape much of modern philosophy, and if you browse the table of contents of philosophical journals today then you will regularly encounter hermeneutic titles like “Kant on <semi-obscure modern topic>”. In this regard, my post is in keeping with modern practice because it could have very well been titled “Kant on computability”.

As stressed before, I think that it is productive to look at important concepts from multiple philosophical perspectives. The exercise can provide us with an increased insight into both the school of thought that is our eyes, and the concept that we behold. In this case, the concept is the Church-Turing thesis that states that anything that is computable is computable by a Turing machine. The perspective will be of (a kind of) cognitivism — thought consists of algorithmic manipulation of mental states. This perspective that can often be read directly into Turing, although Copeland & Shagrir (2013) better described him as a pragmatic noncognitivist. Hence, I prefer to attribute this view to Emil Post. Also, it would be simply too much of a mouthful to call it the Post-Turing variant of the Church-Turing thesis.
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