Abstract
Fractals are self-similar geometric patterns that are inherently embedded throughout nature. Their discovery and application have produced significant benefits across a wide variety of biomedical applications. Recently, complex physiological systems (e.g., neurological, respiratory, cardiovascular) have been shown to exhibit fractal dimensions that are capable of distinguishing among physiologic function versus dysfunction and, in turn, health versus disease. Additionally, fractal data suggest that the immune system operates under similar patterned relationships, and this is in keeping with the recent findings that immune-based diseases are organized according to specific patterns. This review considers the potential benefits of using fractal analysis along with considerations of nonlinearity, scaling, and chaos as calibration tools to obtain holistic information on immune–environment interactions. The potential uses of both synthetic and artificial immune systems for improved protection of the biological immune system are also discussed. The addition of holistic measures of immune status to currently collected biomarkers of immunotoxicity has the potential to increase the effectiveness of health risk assessment. The objective of extending fractal physiology analyses to the immune system would be to promote immune optimization as a public health benefit, which would include improved: (1) immunotoxicity testing and effective health risk reduction and (2) measures of effective immune management for children, adults, and aged individuals.
Introduction
Physiological systems such as the immune system should be managed in a holistic manner to optimize the health of both the individual and the population as a whole. Effective maintenance of the neurological, respiratory, cardiovascular, renal, endocrine, and immune systems are critical for the health of the individual. Additionally, immune dysfunction can impact not only the health of the individual but also the health risks of populations. A compromised immune system can create infectious agent reservoirs that facilitate the spread of global infectious diseases (Kostova Citation2007; Joh et al., Citation2009). As a result, impaired host defense can rapidly extend beyond the issue of an individual’s health to become a broader public health concern. This topic was recently considered using the model of environmental pollutant-induced immunotoxicity and global infectious disease (Winans et al., Citation2010). For this reason, immune management and optimization strategies should be a central part of public health planning.
To date, most of our system management efforts have been directed toward only one part of a holistic management process, the end process where medical treatment and drug therapy are used to minimize the health consequences of one or more already dysfunctional physiological systems. Although this is useful, it is essentially managing one or more diseases rather than managing the physiological system for the maintenance of health. Additionally, a therapeutic heavy treatment approach places significant burdens on already stressed health care systems (Dietert, Citation2010c). It is far more efficient to protect physiological systems and avoid or minimize system malfunction.
The benefits of womb-to-tomb management of the immune system were discussed in an earlier review (Dietert and Piepenbrink, Citation2008), and the concepts were further updated concerning the prioritization of immunomodulating environmental factors (Dietert and Dietert, Citation2010). However, what was absent from these prior discussions was a consideration of recently developed tools that could facilitate a global approach for holistically measuring and predicting environmental modulation of the immune system. A cadre of mathematical, computational, and in silico tools appears to offer precisely that. These tools are facilitated in part by the existence of mathematically predictable relationships that are recognized to exist in most complex physiological systems, for example, fractal geometry.
Fractals are geometric shapes that can be divided into parts, each of which is a reduced-size version of the whole. This property is termed self-similarity. Fractals represent the mathematical organization of nature and as such provide a unique and potentially useful way to assess complex biological systems (Mandelbrot, Citation1982). This review will consider the fractal nature of the immune system, the existence of patterns within the immune system, the importance of scaling behavior and nonlinear relationships for the immune system and the potential opportunities to better model environment–immune interactions via the use of a either a synthetic immune system (SIS) or an artificial immune system (AIS). The effective integration of these holistic approaches with more traditional biomarker strategies (CitationDietert, 2010a–c) has the potential to improve immune safety testing and contribute to enhanced public health protection.
Traditional immune assessment information and public health outcomes
Prevalence of environmentally affected, immune-based diseases has increased significantly in recent decades particularly among children (Dietert and Zelikoff, Citation2009) despite increased research efforts directed toward such conditions as childhood asthma (Clark et al., Citation2010) and Type 1 diabetes (Knip et al., Citation2010). Effective identification of human immunological risk is not a trivial consideration. Part of the challenge falls into three major categories: (1) the immune system and factors controlling its functional responses are among the most complex within humans; (2) adverse immunotoxic outcomes (e.g., diseases and conditions) are invariably addressed individually rather than holistically; and, (3) the variables involved in predicting environmentally induced adverse outcomes are numerous and often rely on problematic exposure information.
Knowledge of the science behind immunotoxicity and its application to immunotoxicity testing has made significant strides in recent decades (Luster et al., Citation2005; Luebke et al., Citation2006; Gerberick et al., Citation2007; Baken et al., Citation2008; Plunkett et al., Citation2010); but, at the same time, it appears that the gap between knowledge of a multitude of details involving environment–immune interactions and the prevalence of immune dysfunction among populations has actually widened. This will be termed the “immune health gap.” The gap reflects the fact that despite increased available information on environment–immune interactions, we are witnessing a poorer immune performance among populations across their lifespan. For this reason, it is useful to examine why our wealth of immune-related data has produced such apparently disappointing public health benefit.
A portion of the gap between application of the science of immunotoxicology and public health outcome may involve limitations in whether chemicals or drugs were ever screened for immune safety or were screened such that disease-relevant data were not collected. In some cases, lack of immune safety data means that we have no basis for determining whether a chemical or drug might cause immune-related health problems. A second contributing factor to the “immune health gap” may be the lack of translation between the data that do exist and regulatory action to protect the public. A third possible factor is that the collection of biomarkers currently employed in immunotoxicity safety testing may not be the most relevant for determining the risk of diseases of greatest concern. Finally, a fourth factor may be that the current focus in immune safety assessment lacks a key holistic component that could reduce the risk of the “immune health gap.”
In addressing the fractal immune system, this review will focus on the last two factors and the likely benefit of including more holistic data analysis in immune safety evaluation. It is stressed that the idea is not to discard potentially useful microlevel biomarker information for the immune system that has been developed over decades (Dietert, Citation2010a,Citationb). Instead, it is to continue this development by increasing the network of immune safety information to include data concerning fractals, scaling, nonlinearity, and immune patterns that are evident in health versus disease. provides a summary of the mathematical and computational tools considered in this review and the added value that they can offer toward the goal of immune optimization. For the purpose of this review, immune optimization is defined as intentional shifts in the balance of immune functional capacities designed to produce significant improvements in overall health and reduced risk of disease. Environmental factors important for optimization would include the spectrum of chemical and drug exposures, therapeutic regimes, diet, and other lifestyle choices. Immune functional goals would include enhanced broad-spectrum host defense, reduced risk of allergic, autoimmune and inflammatory diseases, improved tissue and organ homeostasis, and useful-rather than detrimental-responses to stress.
Table 1. Applications for enhanced immune analysis.
Networked information in immunotoxicology
Use of networked or nested data in immunotoxicity assessment has existed since the earliest origins of the science (Vos, Citation1980; Luster et al., Citation1988) and has been the foundation for immunotoxicity assessment (Luster et al., Citation1992). In fact, the networked data, in the form of a tier system of immune parameters, is predicated on the concept that a collection of certain parameters (even some that appear to be disparate) is greater in predictive value than the sum of the parts (Luster et al., Citation1992). When more information is desired within the tier system approach, there is a drilling down or increased focus in data collection that can both buttress prior findings and add additional detail to environment–immune interactions. In part, this additional detail broadens the landscape of information, but it also adds potentially mechanistically relevant information. So the tier system itself is designed to add increasingly detailed immune information having the effect of “drilling down” or “zooming in” as more immune assays are performed. Each new level of “drilling down” comes at a significant cost. However, what is rarely done in immunotoxicity evaluation is to consider the potential value of extending the tier concept in the opposite focal direction. That is to “zoom out.”
Although this may seem counterintuitive based on classical approaches to toxicological assessment of physiological systems, it is the very nature of how complex systems like the immune system are organized that points toward the need to look more broadly to identify minute, yet clinically significant alterations. It will be argued that immunotoxicity evaluation would benefit by expanding out in data analysis to obtain information that is less reductionistic. In most cases, such information could be obtained without incurring the same costs required to “drill down.” Additionally, the new information would reflect a more holistic and integrative view of immune status permitting the status of the immune network to be viewed across scaling and in its entirety.
Patterning in natural systems
Patterns are an important part of both biological and nonliving systems (Hazen, Citation2009). They also provide a holistic view into systems with integrity versus systems that are in decay (Wu et al., Citation2009). In fact, even in highly complex systems there exist hierarchical relationships that may not be evident until they are subject to certain mathematical analyses. These hierarchical relationships are rarely evident until and unless one is collecting and integrating across holistic and specialized data. One example would be the emerging universality of what is known as Zipf’s law (named after the linguist George K. Zipf) (Corominas-Murtra and Solé, Citation2010). Zipf’s law provides a framework of scaling pattern distributions that pertains to everything from language (Ferrer et al., Citation2003) and chess openings (Blasius and Tönjes, Citation2009) to the normalization of microarrays (Lu et al., Citation2005), proteome topology (Schubert et al., Citation2006), and the distribution of sequences reflecting antibody diversity (Mora et al., Citation2010). The latter example of the application of Zipf’s law to antibody diversity is only one example of the extent to which hierarchical analysis could be applied to immune assessment and management. Once baseline scaling patterns are established for a healthy optimized immune system, then those relationships provide a previously untapped benchmark measure of immunotoxicity. Knowledge of the existence of such patterning and scaling relationships can provide an advantage in health risk assessment once the range of applicability of Zipf’s law has been established. In fact, there is some evidence that Zipf’s law has applicability in a consideration of human risk preferences (Weber et al., Citation2004), which could directly affect public health strategies.
Patterning in immune dysfunction and disease
At the core of health risk assessment and decisions concerning environmental protection is the capacity to determine if a given exposure is likely to cause significant biological or clinical harm. Often disparate pieces of biological information or groupings of biomarkers are used to test for significant deviation from averages based on environmental exposures or conditions. One of the concerns is the extent to which the information collected actually predicts risk of disease.
In immunotoxicology, this concern is analogous to asking whether a statistically significant elevation in IgM production in a T-dependent antibody response (TDAR) assay, a centerpiece assay in immune safety assessment, accurately predicts the risk of allergic, autoimmune, or inflammatory disease? The answer is: we don’t know. Those data are not particularly relevant for those health risks. Additionally, we approach diseases either as broad categories (e.g., autoimmune disease, which encompasses at least 60 different conditions some involving quite distinct mechanisms) or as single entities. In reality, neither is likely to be the base organizing unit linking physiological system dysfunction (e.g., immune dysfunction) to disease. Diseases are organized as specific patterns (Sharpe and Skakkebaek, Citation2008; Dietert and Zelikoff, Citation2010; Dietert et al., Citation2010) somewhat analogous to the branching of the vascular system. It is the recognition of patterns—whether in physiological systems themselves or among the diseases connected to those dysfunctional systems—that underscores the value of considering fractals.
Using fractal analysis for calibrating health and disease
The concept of incorporating holistic analyses in an evaluation of health versus disease states is not new. Critically important and complex biological systems have been evaluated for balance and function using what was recently termed “fractalomics.” In such analysis, biological systems can be seen to exhibit self-similar patterns and scaling properties that can be applied for useful comparisons (Losa, Citation2009). Fractal dynamics are central to several physiological systems and can be used to distinguish among healthy, normal function versus disease-associated states (Goldberger et al., Citation2002). Varela et al. (Citation2010) argued that the fractal dynamics associated with pathophysiological states offers a particularly useful tool for detecting health risk that is not afforded by the classic anatomo-clinic paradigm. After the onset of disease, fractals can be used to monitor the improvement or worsening of conditions during treatment (Thamrin et al., Citation2010).
Among the uses of fractal analysis that have been pursued are: (1) improved characterization of normal biological changes such as cell differentiation (Galvão et al., Citation2010) and aging (Fukusaki et al., Citation2000; Kriete et al., Citation2006); (2) earlier or improved diagnosis of pathological conditions (Beuchée et al., Citation2009); (3) identification of pathology-associated molecular networks; and, (4) fractal-based pharmocokinetic modeling (Pereira, Citation2010).
To date, the most highly developed examples of fractal analysis are the applications for physiological systems such as the neurological (Esteban et al., Citation2010), cardiovascular (Cheung et al., Citation2010), and respiratory systems (Ionescu et al., Citation2010) and for the analysis of cancers (Bedin et al., Citation2010). Additionally, fractals are significant in evaluating maturational and pathological changes affecting human gait (Hausdorff, Citation2007; Scafetta et al., Citation2009). Fractal analysis can be used to predict not only long-term trends in physiological status but also short-term changes in physiological rhythms. For example, shifts in the fractal dimension (FD) of EEG signals can be used to predict the transitions from wakeful-to-drowsy states in humans (Bojic et al., Citation2010).
In a review of fractal physiology and the cardiovascular system, Sharma (Citation2009) describes five types of behaviors that are distributed among physiological systems: equilibrium, periodicity, quasi-periodicity, deterministic chaos, and random behavior. Fractal geometry can provide insight into physiological behavior involving pathways, networks, and macro- and micromolecular scaling. In the latter case of micromolecular scaling, Bancaud et al. (Citation2009) showed that the organization and maintenance of heterochromatin rely on a fractal architecture. On a macromolecular scale case of the cardiovascular system, loss of fractal properties and variability in heart rate are associated with increased mortality rates (Mäkikallio et al., Citation2001). Not surprisingly, a similar change in the loss of fractal properties has been reported among obese children (Vanderlei et al., Citation2010). Evaluating the FD of the retinal vasculature has been a readily accessible and useful measure (reviewed in Masters, Citation2004). Changes in this FD have been related to kidney damage and disease (Sng et al., Citation2010). Pirici et al. (Citation2009) found that the fractal dimensional values of astrocytes in the brain for ischemic and hemorrhagic lesion patients could be readily distinguished from those of Alzheimer’s disease patients. With a focus on the molecular aspects of Alzheimer’s disease and relevant protein interactions, Wu et al. (Citation2009) found that the relevant protein network exhibited fractal-like properties.
Those systems in which fractal analysis has already been extensively applied have similarity to the immune system in the complexity of cellular components and the extensively dispersed nature of the systems. For this reason, it is not surprising that fractal immunology has begun to emerge as a consideration.
Fractal immunology
Concepts fundamental to fractal immunology have existed for over a decade. Most of the initial work has been focused on patterning within antibody repertoires. For example, Burgos (Citation1996) described murine B-lymphocyte repertoire in response to influenza virus hemagglutinin using a FD. Across the B-lymphocyte repertoire, the author concluded that there existed a linguistical analogy of Zipf-like scaling behavior that is held in common with most natural languages. When his same approach was applied to evaluate the cytotoxic T-lymphocyte repertoire of conventional versus chimeric versus athymic mice, it became clear that the FD was altered among these groups and could prove a useful tool for measuring current immune status (Burgos and Moreno-Tovar, Citation1996). This led to later attempts to provide a more comprehensive fractal analysis of the immune system (Bentley and Timmis, Citation2004). These early approaches focused on fractal proteins (as subsets of the Mandelbrot set) (Bentley, Citation2004) and gene regulatory networks (Bentley, Citation2003) with application to the immune system.
Additional examples exist of fractal analysis for immune system components and/or to immune-related diseases. For example, ion current fluxuations in macrophages have memory and are best fitted by fractional analysis (Domínguez et al., Citation2009). Astrocyte fractal dimensional analysis has been used to compare the conditions of ischemic/hemorrhagic stroke versus Alzheimer’s disease (Pirici et al., Citation2009). Fractal analysis has been used in asthma to quantify airway remodeling, a complex multicomponent alteration (Boser et al., Citation2005).
Beyond the emerging applications of fractal analysis to immune-related disease, much of the mathematical modeling effort has been geared toward the development of both the SIS and the AIS. Both SIS and AIS are considered later in this review.
Fractals and the optimized immune system
The management of immune health has two significant environmental components: (1) immunotoxicity evaluation resulting in prevention of harmful environmental exposures and (2) time-sensitive introduction of selected nutrients, probiotics, and various immunomodulators that can promote effective immune balance across a lifetime (Dietert and Dietert, Citation2010). In addition to avoidance of immunotoxicants such as heavy metals (Bishayi and Sengupta, Citation2006), food contaminants (Meissonnier et al., Citation2008), traffic pollution (Williams et al., Citation2009), industrial chemicals (Peden-Adams et al., Citation2006; Heilmann et al., Citation2010), agricultural chemicals (Rowe et al., Citation2008), and some drugs (Frawley et al., Citation2010), other environmental factors falling into the immune management category would include birth delivery mode (Huurre et al., Citation2008), breast-feeding of infants (Verhasselt, Citation2010), dietary n-3 fatty acids (Gottrand, Citation2008), antioxidants (Novoselova et al., Citation2009), vitamin D (Hewison, Citation2010), probiotic bacteria that aid the establishment of gut microflora (MacDonald and Bell, Citation2010), and exposure to farm animals and/or their microbe-rich environments (Ege et al., Citation2006).
Ironically, until recently one of the consumer-driven goals was for products producing “immune enhancement.” But this is a naïve concept in immune management in light of the increasing prevalence of allergic and autoimmune disease even among children (Dietert and Zelikoff, Citation2009). In fact, one could argue that improper immune enhancement is at least as significant a health risk as immunosuppression. It seems likely that a well-functioning immune system will have fractal dynamic similarities to those already observed for the neurological, cardiovascular, and respiratory systems. In these cases, a range of FDs is usually associated with effective function and health, and a deviation from that range in either direction is predictive of dysfunction and disease.
Fractals, scaling, and public health
Fractal analysis is a potentially useful tool for evaluating environment–immune interactions including immunotoxicity. For example, if fractal analysis can be used to identify a suboptimum state for a physiological system associated with a likelihood of dysfunction and disease (Doubal et al., Citation2010), then it would appear to be a useful tool for identifying environmental conditions that could contribute to dysfunction and disease. This goes to the heart of cost-effective public health protection. A fundamental property of fractals, self-similarity in scaling, provides one of the benefits of such applications. Evidence of this scaling property in nature suggests that we can use scaling behavior to more effectively institute prohealth measures and protect against health risks at the level of both the individual and a population. This can be accomplished via the “zooming in” and “zooming out” capacities of fractal dimensional analysis.
Although the old adage “you can’t see the forest for the trees” is useful to encourage changing one’s focus beyond a parochial view, it turns out that in safety evaluation and public health protection, with scaling you probably can “see” the forest when viewing an individual tree (as well as “see” a tree from the level of the forest). West et al. (Citation2009) provided evidence that rainforest dynamics are best represented as scaled versions of the branching system found in individual trees in the forest. This represents an example of “zooming out” using fractal geometry and scaling. In an example of “zooming in,” Birnbaum (Citation2001) found that forest canopy structure is defined by a nested system that can be reflected in individual or small groups of trees. The scaling of trees with specific branching patterns as they relate to an entire forest has a direct bearing on biomedical analysis of the health and well-being of physiological systems. Trees with their branching systems have a fractal architecture that is analogous to that of the bronchial and vascular trees (Kamiya and Takahashi, Citation2007). In biomedical examples, using fractal-based “zooming in” has enabled biomedical scans (e.g., ultrasound) that were previously incapable of detecting cancer to be able to provide early cancer detection (Moradi et al., Citation2006). But beyond the “zooming in” and “zooming out” within an individual, there is the opportunity to use the same scaling considerations in moving from the individual to the population.
For example, Kostova (Citation2007) recently used a mathematical model to describe viral spread and immune responses at the population level. The conclusions were that having a few individuals with weakened immune systems where the infection was maintained was the most important factor in maintaining the infection in the population as a whole. Such a conclusion would place more emphasis on environmental protection of the immune system as a top public health priority. This type of approach is in keeping with an antireductionist view of population health via a broader application of fractal geometry (as discussed by Glatte et al., Citation2008, Citation2009).
Nonlinearity and chaos theory in the immune system and host defense
Nonlinearity is an integral feature of physiological systems including the immune system. Nonlinear interactions of the immune system with the environment were proposed more than a decade ago (Dalgleish, Citation1999). Significant evidence suggests that nonlinearity is featured in immune maturation, immune functional processes, host defense, and the interaction between chemicals in the environment and the immune system. In immune maturation, nonlinear modeling of the interaction between human infant gut microbiota colonization has proved useful toward the goal of optimizing infant immune maturation (Trosvik et al., Citation2010). For immune processes, complement activation, both classical and alternative pathways, has been shown to exhibit nonlinear dynamics (Korotaevskiy et al., Citation2009). Likewise, TH1–TH2–Treg interactions in allergy and immunotherapy follow nonlinear dynamics (Gross et al., Citation2010). It has been suggested that general models of immune inflammation are most effectively modeled as reflecting nonlinear dynamics (Herald, Citation2010). In the case of host defense, Joh et al. (Citation2009) reported that prediction of disease outbreaks based on the interaction of environmental reservoirs of infectious agents and immune-based host defense follows nonlinear dynamics rather than more conventional linear alterations in equilibrium. Additionally, the initial immune reaction to a new tumor antigen has been reported to follow nonlinear dynamics (Prehn, Citation2010).
Likewise, the immune system’s interaction with toxins can follow nonlinear responses. This topic received an extensive review by Calabrese (Citation2005) under the framework of hormesis: low-dose exposures that produce opposite effects from higher-dose exposures to the same chemical. This review was supplemented by several additional perspectives on nonlinearity in drug- and chemical-induced immunotoxicity (Dietert, Citation2005; Hastings, Citation2005; Holladay et al., Citation2005; Ladics and Loveless, Citation2005). Nonlinear dose–response curves (i.e. U- or J-shaped curves) for immunotoxicity can be found in the case of exposure to heavy metals (Iavicoli et al., Citation2006, Citation2008) as well as to ionizing radiation (Lu, Citation2003; Ren et al., Citation2006).
Chaos theory has already made its way into considerations of human health protection. For example, it has provided a unifying framework for the examination of evolution of infectious diseases (Ogbunugafor et al., Citation2010). In silico modeling of inflammation has been used to provide a homeostatic context that is often missing from other types of analyses. The acute inflammatory response of immune cells to endotoxin has been investigated by Dong et al. (Citation2010) employing a nonlinear model. Additionally, Kolokotrones et al. (Citation2010) recently suggested that fundamental metabolic relationships are best represented by a convex curvature on a logarithmic scale. The authors suggested that aspects of metabolic scaling might be nonlinear.
The fractal and nonlinear nature of the immune system and particularly immune–environment interactions have led to two important technological developments, the SIS and the AIS. Although these two systems were designed for very different purposes, both would appear to be central to future efforts of immune management and optimization.
Synthetic immune system, computer immunology, and immunoinformatics
Computer-based modeling of immune interactions as well as the use of computer modeling to predict immune outcomes has recently shown promise (Chavali et al., Citation2008). This has led to such prediction models as the SIS (Mata and Cohn, Citation2007). Versions of the SIS have been used to model the immune response to cancer (Oprisan et al., Citation2000; de Pillis et al., Citation2005). In a related approach, the efficacy of cancer vaccination has been modeled using SIS (Palladini et al., Citation2010). It has also been used to model burn-induced systemic inflammation (Yang et al., Citation2010). Computer simulation has also been used to model the memory T-lymphocyte response to influenza (Naumova et al., Citation2008). Tomar and De (Citation2010) recently reviewed the landscape of what has been termed “immunoinformatics,” the field that intersects experimental immunology and computational strategies. Although the field has been applied primarily to the in silico modeling of vaccinations, there is no inherent reason why it could not have broader applications to immunotoxicology and immune safety.
Artificial immune systems and reverse engineering in health risk assessment
One of the ironies in environment–immune evaluation is that data to evaluate immunological health risks are collected as parochial snapshots of structural or functional status. Although biomarkers may be combined, a strategy to measure the system as whole is rarely utilized. Yet, the biological immune system as a whole serves as a template for real-world problem-solving outside of biology and medicine. The concept of an AIS is a recent (largely 21st century) development (de Castro and Von Zuben, Citation2001; de Castro and Timmis, Citation2002; Stepney et al., Citation2004), although the idea of using bioinspired systems for computational problem-solving has existed for decades (Farmer et al., Citation1986). Twycross and Auckelin (Citation2010) characterized the immune system as comprising multiple levels of information in a decentralized and distributed manner in which information is fused from individual immune cells but with no centralized controller. The organization of the immune system and mechanisms of control and communication are used to establish algorithmic-based AIS that are now in a second-generation iteration. One of the goals in these AIS is to develop decentralized information sensing networks that have the capability of adapting to changing conditions over a lifetime as would occur in an appropriate-functioning biological immune system (Twycross and Auckelin, Citation2010).
Current AIS models are constructed around combined innate and adaptive immunity, TH1 and TH2 cell function, cytokine and chemokine signals, and multiagent attack of the host (Twycross and Auckelin, Citation2010). One of the major applications is the development of an intrusion detection system for defense systems and computer networks that relies on algorithms for immune-based negative selection, positive selection, clonal selection/expansion, self versus non-self determination, and danger signals (Sobh and Mostafa, Citation2011). The AIS is designed to have important features of the human immune system, which have been labeled as: extendibility, scalability, adaptability, global analysis, and efficiency (Sobh and Mostafa, Citation2011). It is ironic that in immune safety evaluation for exposure to chemicals or drugs, we rarely collect data sets that could provide a glimpse of these super-biological characteristics of the immune system. That should be a concern. In fact, Forrest and Beauchemin (Citation2007) suggested that AIS has the potential to be reverse-applied or engineered for hypothesis testing within the biological immune system.
Clearly, the AIS is a derivation and is not precisely equivalent to a natural immune system. Nevertheless, the novel strategies that are being developed for optimizing defense while maintaining system integrity in the AIS have the potential to expand our repertoire of ideas and tools for protection and optimization of the natural immune system.
Some considerations for applying fractal-based analyses
Despite observations that the immune system, like the cardiovascular, neurological, and respiratory systems, is fractal in nature, analytical tools such as the use of fractal dynamics have not been applied for immune evaluation, to date. This may be due to the fact that fractal analyses themselves are still relatively new, and there is a significant challenge in identifying which immune measurements and parameters in which combinations would provide the most useful database for fractal-based mathematical analyses. In the latter case, there are a myriad of possibilities and such hypothesis testing for immune data input is generally beyond the scope of this introductory review. However, it is potentially worthwhile to describe some of the options.
Certain types of cardiovascular, neurological, and respiratory data have provided insights into health, aging, and specific diseases through fractal analysis. The equivalent types of measures should be examined as an effective starting point for analysis of the immune system as well. For example, we need to know if measurements of the lymphatic system might have a similar utility for immune system analyses as various vascular system measurements have had for predicting both cardiovascular status and processes related to tumorigenesis. Where immune system structure exists in the form of networks and the data are accessible, such hypotheses need to be tested.
Beyond static or structural parameters, it seems likely that dynamic measurements will be useful. For human immune evaluation, we have relied heavily on static snapshots of peripheral blood parameters and compared these against large population averages to reach a determination of health versus risk of immune-related disease. But similar real-time measurements taken during environmental responses can provide measures of amplitudes, flux, and kinetics of the same parameters (e.g., numbers of circulating neutrophils or monocytes) and may be more relevant to immune processes occurring in tissues. We have the technology to collect such data and such data might be very useful in issues of vaccine safety, optimization, and public health-directed vaccine administration.
Likewise, we have relied heavily on such indices as the ratio of CD4:CD8 T-lymphocytes, which tends to change only under specific, and usually severe, shifts in immune function. But another hypothesis could be that mathematical slope calculations including measures of CD4:CD8 ratio, the ratio of Toll-like receptor expression in circulation, multiple cytokine levels in circulation, and exhaled nitric oxide levels have greater predictive utility for immune status than do CD4:CD8 ratios. Such immune data hypothesis testing will be needed for fractal-based applications to be effectively applied to immune evaluation. Fortunately, some robust immune databases already exist, others are currently being collected and computation modeling can aid in the needed myriad of hypothesis testing. This is a comparatively inexpensive way to increase the effectiveness of existing data and/or use data that are readily accessible.
Conclusions
Womb-to-tomb immune management and optimization includes both risk reduction and prohealth immune promotion. It is interfaced across the aging process stretching from prenatal development and the newborn through adulthood to the later years of life. Because immune performance affects host resistance to infectious agents and that, in turn, extends beyond the individual to affect the risk of populations, immune optimization should be a significant public health priority.
To date, a cadre of immune biomarkers has been used to assess both environmental risks for the immune system as well as useful immunomodulation. However, these biomarkers generally lack the capacity to provide a holistic frame of reference for a healthy versus a dysfunctional immune system. In addition, immune-based diseases have been increasing in prevalence in the population even in the face of recent safety testing and immune-based therapies. A significant “immune health gap” exists, and this suggests that modification of our current immune assessment approaches is needed. However, any modification needs to take into consideration both costs and potential animal usage.
Application of recently developed mathematical and computational tools holds the potential to help address the current “immune health gap.” Fractal analysis of immune system components has already been used in limited settings. But a broader application of fractals to the immune system and to environment–immune interactions has the potential to provide a holistic calibration that can be scaled to predict even local immune interactions. Such an approach would be analogous to the application of FDs currently used in the neurological, respiratory, and cardiovascular systems. Additionally, the existence of both SIS and AIS provides unique opportunities for alternative safety testing and effective immunomodulation. The application of system model testing could be based on informational loops where reverse engineering can provide new insights for issues of both immune assessment and optimization.
Acknowledgements
The author thanks Janice Dietert and Grant Dietert for their editorial assistance. During the preparation of this manuscript, Dr. Benoit B Mandelbrot passed away. It is with great honor that this publication is dedicated to Dr. Mandelbrot.
Declarations of interest
Rodney R. Dietert is employed by Cornell University. He also serves on advisory panels for the U.S. EPA, the Institute of Medicine, and the World Health Organization and consults for the US EPA on the Integrated Science Assessment for Pb. The author reports no declarations of interest regarding this manuscript. The author declares no conflict of interest.
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