In part I of Predicting Responses in Complex Systems, I addressed how the word predict is used in science and whether scientists claim that animal models are predictive models. In part II, I address two more retorts.
Retort #3. “No one uses the terminology predictive modality of predictive model. Greek made these up.”
A PubMed search on October 19, 2012, produced 3407 results for “predictive model”. Examples include the article titled, “Predictive model for spontaneous preterm labor among pregnant women with contractions and intact amniotic membranes:”
. . . Accuracy, sensitivity, specificity and predictive values were used to measure associations of predicted probabilities and to check the ability of the model to predict outcomes. The predictive analyses were based on logistic regression models, with calculation of odds ratios and 95 % confidence intervals.
The incidence of preterm delivery was 32.80 % (23/70). After validation, the predictive model proposed showed accuracy of 87.88 %, sensitivity of 78.26 % and specificity of 93.02 %.
The model presented good accuracy with correspondence between predictions and observations, and has the capacity to become a useful tool for management of pregnant women with preterm labor and intact amniotic membranes. 
Sundaram et al, in an article titled, “King's College Hospital Criteria for Non-Acetaminophen Induced Acute Liver Failure in an International Cohort of Children,” state: “Parameters beyond the KCHC should be evaluated to create a predictive model for PALF.” 
Kavanagh et al., in their article, “A predictive model of suitability for minimally invasive parathyroid surgery in the treatment of primary hyperparathyroidism:”
With the predictive scoring model, a score of ≥ 3 had a positive predictive value of 100% for single-gland disease.
A scoring model encompassing preoperative biochemical and imaging data can be successfully employed to predict suitability for minimally invasive surgery in the majority of patients with single-gland disease. 
The following PubMed articles contain the term predictive modality:
Predictive value of biochemical markers in stroke.
Fowler SB, Mancini B.
J Neurosci Nurs. 2007 Feb;39(1):58-60. Review.
A new predictive modality of cranial bone thickness.
Elahi MM, Watkin KL, Hakim MS, Schloss MD, Lessard ML.
Ann Plast Surg. 1999 Jun;42(6):651-7.
Flow cytometry as a predictive modality in prostate cancer.
Deitch AD, deVere White RW.
Hum Pathol. 1992 Apr;23(4):352-9. Review.
The term modality is commonly used in medicine.
Retort #4. “There is no such thing as a complex system.” “There is no such thing as a complexity theory or complexity science.” And or, “Scientists can understand complex systems and make predictions about them.”
Note the variety, and mutually exclusive nature, of the above retorts to the complexity argument. In part I of Predicting Responses in Complex Systems, I quoted Duncan Watts, PhD mathematics and author of the book Everything is Obvious*: Once You Know The Answer. Watts again:
In making this bold leap, however, Laplace [who claimed everything was predictable after Newton discovered certain laws of nature] obscured a critical difference between two different sorts of systems, which for the sake of argument I'll call "simple" and "complex." Simple systems are those for which a model can capture all or most of the variation in what we observe. The oscillations of pendulums and the orbits of satellites are therefore "simple" in this sense, even though it's not necessarily a simple matter to be able to model and predict them.
Complex systems are another animal entirely. Nobody really agrees on what makes a complex system "complex," but it's generally accepted that complexity arises out of many interdependent components interacting in nonlinear ways. The U.S. economy, for example, is the product of the individual actions of millions of people, as well as hundreds of thousands of firms, thousands of government agencies, and countless other external and internal factors, ranging from the weather in Texas to interest rates in China.
Out of all these interactions arise two countervailing features of complex systems: on the one hand, tiny disturbances in one part of the system can at times get amplified to produce large effects somewhere else -- reminiscent of the "butterfly effect" from chaos theory -- while, on the other hand, very large shocks can at other times get absorbed with remarkable ease. And because it's impossible to determine in advance which of these two fates any given shock will experience, it follows that the sorts of mechanistic models that work well for simple systems -- and that enabled the likes of Newton and Laplace to make such accurate predictions -- work poorly for complex systems. . . . for complex systems it is no more possible to predict outcomes with certainty than it is to predict the outcome of a die roll.
But the situation is actually worse for the vivisection activist, as he must argue that predictions can be made for one complex system based on the outcome in a second and that such predictions are correct a very high percentage of the time. Higgins writes:
Many achievements in medicine have come from applying linear theory to problems. Most current methods of data analysis use linear models, which are based on proportionality between two variables and/or relationships described by linear differential equations. However, nonlinear behavior commonly occurs within human systems due to their complex dynamic nature; this cannot be described adequately by linear models.
Nonlinear thinking has grown among physiologists and physicians over the past century, and nonlinear system theories are beginning to be applied to assist in interpreting, explaining, and predicting biological phenomena. Chaos theory describes elements manifesting behavior that is extremely sensitive to initial conditions, does not repeat itself and yet is deterministic. Complexity' theory goes one step beyond chaos and is attempting to explain complex behavior that emerges within dynamic nonlinear systems.
Nonlinear modeling still has not been able to explain all of the complexity present in human systems, and further models still need to be refined and developed. However, nonlinear modeling is helping to explain some system behaviors that linear systems cannot and thus will augment our understanding of the nature of complex dynamic systems within the human body in health and in disease states. 
So, there are complex systems and there are biological complex systems. Complex systems are characterized by various properties including the following:
- The presence of feedback loops
- Complex systems are not simulable, cannot be modeled.
- “Many strongly interdependent variables, with multiple inputs contributing to observed outputs. Because the variables in a complex system are so strongly dependent on each other, changes to system inputs can have unintended, unanticipated consequences.
- Chaotic behavior: extreme sensitivity to initial conditions, fractal geometry, and self-organized criticality.
- Multiple (meta)stable states, where a small change in conditions may precipitate a major change in the system.
- A non-Gaussian distribution of outputs, often where outcomes that are far away from the average are more likely than you might think.” 
- A system is a collection of interacting elements. Behavior of the system is distinct from the behavior of its parts or elements. Output is not proportional to input and output to a perturbation may vary over time. 
These are some of the reasons why why prediction is difficult when examining a complex system, if not impossible, and why inter-system extrapolation is impossible in terms of yielding PPVs and NPVs high enough to be of use to medical scientists studying drug and disease response.
I am not alone in stating that complexity has implications for predicting responses in biological systems. Krakauer et al:
Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints. Biological theories have rarely reached a level of generality and predictive power comparable to physical theories. This discrepancy is explained through a combination of frozen accidents, environmental heterogeneity, and widespread non-linearities observed in adaptive processes. 
Uhl et al, discussing The 8th American College of Veterinary Pathologists (ACVP) Symposium, stated:
Dr Robert Hamlin, The Ohio State University, opened with the challenges for studying cardiovascular disease in a talk entitled: “Animals as models of human cardiovascular disease: the search to overcome outdated evolutionary homeostatic mechanisms.” He addressed the fact that animals used to model human diseases often have very different cardiovascular physiology from humans and how these differences affect the predictive value of safety studies. As an example, cardiovascular drug safety testing was expanded significantly upon discovering that the antihistamine terfenadine is cardiotoxic when not completely metabolized. Its toxic effect is induced by the inhibition of the cytochrome P450 3A4 (CYP 3A4) isoform, a circumstance that caused death in a small percentage of people due to an unexpected interaction with other medications [8, 9]. CYP 3A4 is inhibited by a variety of commonly prescribed drugs as well as grapefruit . In its toxic form, terfenadine induces a prolonged QT interval and thus can trigger a fatal arrhythmia ; however, this effect could not be modeled in rats and mice since they do not have the hERG ion channel that caused the human arrhythmia. . . . To summarize, Dr Hamlin questioned how we can expect to model a heterogeneous population with a homogeneous animal model when disease characteristics may be determined by population heterogeneity. 
Támas Vicsek of the Eötvös Loránd University, Department of Biological Physics in Nature:
In the past, mankind has learned to understand reality through simplification and analysis. Some important simple systems are successful idealizations or primitive models of particular real situations — for example, a perfect sphere rolling down an absolutely smooth slope in a vacuum. This is the world of Newtonian mechanics, and it ignores a huge number of other, simultaneously acting factors. Although it might sometimes not matter that details such as the motions of the billions of atoms dancing inside the sphere’s material are ignored, in other cases reductionism may lead to incorrect conclusions. In complex systems, we accept that processes that occur simultaneously on different scales or levels are important, and the intricate behaviour of the whole system depends on its units in a nontrivial way. Here, the description of the entire system’s behaviour requires a qualitatively new theory, because the laws that describe its behaviour are qualitatively different from those that govern its individual units. 
Andrew S. Grove, PhD, of Intel Corporation:
Humans are incredibly complex biological systems, and working with them has to be subject to safety, legal, and ethical concerns…. The result is wide-scale experimentation with animal models of dubious relevance, whose merit principally lies in their short lifespan. 
Although biology has always been a science of complex systems, complexity itself has only recently acquired the status of a new concept, partly because of the advent of electronic computing and the possibility of simulating complex systems and biological networks using mathematical models (Emmeche, 1997; Alm & Arkin, 2003). Because complex systems have emergent properties, it should be clear from the preceding discussion that their behaviour cannot be understood or predicted simply by analysing the structure of their components. The constituents of a complex system interact in many ways, including negative feedback and feed-forward control, which lead to dynamic features that cannot be predicted satisfactorily by linear mathematical models that disregard cooperativity and non-additive effects. In view of the complexity of informational pathways and networks, new types of mathematics are required for modelling these systems (Aderem & Smith, 2004). 
The only people claiming that inter-complex system extrapolation, or prediction, yields a high enough PPV and NPV to meet the standard for medical science are vivisection activists. Mathematicians, electrical engineers, physicists, and even other biologists agree that such high predictive values are impossible when using one complex system to predict outcomes for another. (For an analysis of which levels of organization of complex systems are amenable to inter-system extrapolation resulting in high PPVs and NPVs, see our paper Animal models and conserved processes. Suffice it to say, the level where disease and drug response occurs is not one of them.)
I will address more fallacies and misrepresentation in future blogs.
1. de Oliveira RV, Martins Mda G, Rios LT, Araujo Junior E, Simoes VM, Nardozza LM, Moron AF: Predictive model for spontaneous preterm labor among pregnant women with contractions and intact amniotic membranes. Arch Gynecol Obstet 2012, 286:893-900.
2. Sundaram V, Shneider BL, Dhawan A, Ng VL, Im K, Belle S, Squires RH: King's College Hospital Criteria for Non-Acetaminophen Induced Acute Liver Failure in an International Cohort of Children. J Pediatr 2012.
3. Kavanagh DO, Fitzpatrick P, Myers E, Kennelly R, Skehan SJ, Gibney RG, Hill AD, Evoy D, McDermott EW: A predictive model of suitability for minimally invasive parathyroid surgery in the treatment of primary hyperparathyroidism [corrected]. World J Surg 2012, 36:1175-1181.
4. Higgins JP: Nonlinear systems in medicine. The Yale journal of biology and medicine 2002, 75:247-260.
5. Kastens KA, Manduca CA, Cervato C, Frodeman R, Goodwin C, Liben LS, Mogk DW, Spangler TC, Stillings NA, Titus S: How Geoscientists Think and Learn. Eos Trans AGU 2009, 90.
6. Williams G: Chaos Theory Tamed. Washington, D.C: Joseph Henry Press; 1997.
7. Krakauer DC, Collins JP, Erwin D, Flack JC, Fontana W, Laubichler MD, Prohaska SJ, West GB, Stadler PF: The challenges and scope of theoretical biology. Journal of theoretical biology 2011, 276:269-276.
8. Dresser GK, Spence JD, Bailey DG: Pharmacokinetic-pharmacodynamic consequences and clinical relevance of cytochrome P450 3A4 inhibition. Clinical Pharmacokinetics 2000, 38:41-57.
9. Salvi V, Karnad DR, Panicker GK, Kothari S: Update on the evaluation of a new drug for effects on cardiac repolarization in humans: issues in early drug development. British Journal of Pharmacology 2010, 159:34-48.
10. Uhl EW, Whitley E, Galbreath E, McArthur M, Oglesbee MJ: Evolutionary Aspects of Animal Models. Veterinary pathology 2012, 49:876-878.
11. Vicsek T: The bigger picture. Nature 2002, 418:131.
12. Grove AS: Efficiency in the health care industries: a view from the outside. JAMA 2005, 294:490-492.
13. Van Regenmortel M: Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological complexity and overcome the limitations of reductionism. EMBO Rep 2004, 5:1016-1020.