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Reversal of Informational Entropy and the Acquisition of Germ-like Immortality by Somatic Cells




We live within an increasingly technological, information-laden environment for the first time in human evolution. This subjects us (and will continue to subject us in an accelerating fashion) to an unremitting exposure to ‘meaningful information that requires action’. Directly dependent upon this new environment are novel evolutionary pressures, which can modify existing resource allocation mechanisms and may eventually favour the survival of somatic cells (particularly neurons) at the expense of germ line cells. In this theoretical paper I argue that persistent, structured information-sharing in both virtual and real domains, leads to increased biological complexity and functionality, which reflects upon human survival characteristics. Certain biological immortalisation mechanisms currently employed by germ cells may thus need to be downgraded in order to enable somatic cells to manage these new energy demands placed by our modern environment. Relevant concepts from a variety of disciplines such as the evolution of complex adaptive systems, information theory, digital hyper-connectivity, and cell immortalisation will be reviewed. Using logical, though sometimes speculative arguments, I will attempt to describe a new biology. A biology not driven by sex and reproduction but by information and somatic longevity.



Industrial and technology-driven developments have had a dramatic effect in lowering human mortality [1]. Evolutionary natural selection pressures at maturity push mortality at the lowest possible level, or to put it another way, the age of minimum mortality coincides with the time required to achieve Darwinian fitness. Nevertheless, the lowest possible mortality rates achieved by natural evolution are significantly above the lowest levels achieved through technology in post-industrial countries. In countries such as Japan and Sweden the level is significantly lower (in some cases over 200-fold lower) compared to what natural selection has ever achieved. The reduction in human mortality rates has been achieved mostly during the past 100 years, through technology (i.e. structured and targeted information) with improved health care and socio-political risk-reduction. Based on this, it could be argued that the appropriate use of technology could further alter human mortality rates, not just through general health improvements and reduction of risks, but also by other mechanisms directly related to technology (such as global information sharing and processing). This environmentally-dependent malleability in mortality is inherent in the human genome and allows increases in life expectancy that are not necessarily based on genetic change [1].

Theories, hypotheses and ideas about the causes of age related mortality are abundant [2]. However, these contain limiting and implicit assumptions based on a ‘component approach’: that the components of the organism or cell are damaged during ageing, and that their repair may lead to rejuvenation. More recent efforts at understanding ageing are based instead upon a ‘connection-approach’: that the study of how individual agents (cells, macromolecules, organisms) are interconnected can provide a better perception of the complex mechanisms involved in ageing [3]. The following discussion depends primarily on the ‘connection-approach’ and on our understanding of the opportunities afforded by strongly networked systems. It is specifically directed at studying the possible effects upon the maximum human lifespan. Four essential tenets in this discussion are:

1. Information plus organization creates complexity, and complexity increases functionality [4]. Increased functionality improves fitness, and this increases survival (Fig. 1). Thus, in a specified niche, information increases survival [5, 6] (Table 1). It was shown [7] that a high-information, stable entropy state is associated with healthy living systems, whereas a phase transition to a low-information state is associated with cancer and aging.

2. Nature tends, on the whole, to follow a ‘fromsimple-to-complex’ paradigm [8]. There is a general propensity that leads to an increased complexity, selforganisation and dynamical variability [9], [10]. Simple initial events progress hierarchically to reach, through emergence, higher levels of complexity [11]. Higher complexity means increased functionality and increased survival (Fig. 1).

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3. Ageing is accompanied by loss of information and complexity i.e. increased entropy over time [12-16]. This loss is rooted in the suboptimal conditions caused by the uneven distribution of resources favouring the survival of the germ-line versus somatic repair [17]. In addition, such information loss could be driven by genetic mechanisms as a form of antagonistic pleiotropy [18]. The rate of somatic repair tends to become progressively compromised as a function of age, resulting in accumulation of damaged biological material that reduces organisation and functionality i.e. a reduced information content, reduced complexity and thus reduced survival [19].

4. A reversal of age-related information loss must lead to improved survival. Based on the assumption that there is an adaptive value in the tendency to progress ‘from simple to complex’ it is reasonable to suggest that, if there is an artificial way to increase biological complexity, then this would have an impact upon somatic survival characteristics [20]. Increased input of meaningful and actionable information (see Appendix) may be useful in restoring the information content of the organism [21] and thus it may herald a return to the ‘simple to complex’ progression model, with increased functionality, and thus increased survival (Fig. 1). This discussion will examine the details and mechanisms of premise number 4, namely that an appropriate increase of informational exposure (Table 2) may have a positive impact upon functionality, modulating energy allocation mechanisms which may shift more resources for somatic repair, thus resulting in somatic rejuvenation and increased survival of the individual.


It is well known that a challenging environment induces strategy adjustments that can up-regulate several parameters involved not only with brain function, such as BDNF (Brain Derived Neurotrophic Factor) [22] but also with age-associated immune function [23], and other somatic benefits [24]. An enriched environment (in the most general sense), is essentially a situation associated with increased exposure to actionable information, which will need to be assimilated and used in order to improve the function of the organism.

However, the long-range benefits of these improvements are not sufficiently known. While the health benefits of Environmental Enrichment (EE) have been well studied (for example [25]) it is not clear what impact an enriched environment has on prolonging healthy lifespan, and certainly it is not clear if EE can prolong human lifespan well beyond the current maximum limit (approximately 122 years). Here, an attempt will be made to clarify some possible mechanisms involved in this respect, particularly with regards to examining whether EE (and thus information) can have any effects on prolonging human lifespan beyond the current maximum limits. With particular reference to the premise number 4 (above) the argument will examine the sequence of events between the detection and manipulation of information by neurons, the resources used for this process, and the impact this may have on the immortalisation mechanisms employed by germ cells.

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It is necessary to describe in some detail the exact mechanisms that take place during the detection, acquisition, assimilation and elaboration of new information by the neuron. In order to generate signals and thus be able to control information, neurons use a substantial amount of energy. In an energy-efficient, low entropy organism the allocation of this energy must follow guidelines which are evolutionarily beneficial to the survival of the organism. Resources are finite and the organism must make arrangements for trade-offs (such as maintenance of the function of the germ line at the expense of somatic repair). However, the processes of handling a relentless arrival of actionable new information may eventually cause a shift from this scenario with remarkable consequences.

Using the example of a visual input which carries information that can be beneficial to the organism, we can study the sequence of events that are associated with the process. Photon catch and signal processing by retinal cells is dependent upon optimisation models that regulate parameters such as optical blur, random neural noise, and energy usage [26, 27]. Following the successful activation of the retinal cell with the new information, there is generation of action potentials towards the visual cortex. For example, in the primary visual cortex V1, an early response consists of tiled sets of selective spatiotemporal filters. In the spatial domain, the functioning of V1 can be thought of as similar to many spatially local, complex Gabor transforms [28]. The filters must be sufficiently effective to distinguish between useful (potentially relevant) and not useful (background noise) information, thus these need to be operating at an efficient level, more so when the influx of new information is sustained. Na+/K+ pumps concentrated in the axons and at synapses require significant amounts of energy in order to signal efficiently. Neurotransmitters and second messengers also add to the energy cost.

All of the processes mentioned in this example use energy, for example, through hydrolysation of ATP, which recycles signalling messengers and other molecules [29]. This energy is precious within an organism which allocates resources to the germ-line so efficiently. The supply of metabolic energy constrains the efficient transmission of information and repair of the neurons. However, if new information arrives at the sensory organ, it must be processed and thus the process will inevitably demand energy from any available resources. Within this framework, we can hypothesize that, if the amount of meaningful and actionable information is sufficiently large, and sufficiently sustained, then there will come a point when the ratio between the resources available to the germ line and resources available to the soma (for information processing AND subsequent somatic cell repair) will be forcibly shifted in favour of the latter. In other words, the information assimilation process may initiate a sequestration sequence that diverts resources from germ cells to somatic cells. It is important to emphasize that this is taken in the evolutionary context of increased fitness within a technological, information-laden niche.

Thus, information processing carries an adaptive value within this specific niche. It is possible that restrictions are already hardcoded into our genome, which are likely to make this process more difficult. Any attempt at removing these restrictions may still have to go through the process of natural selection, and so it is conceivable that this process may take a very long time. However, we should not discard the possibility that regulatory mechanisms already exist in hitherto uncharted regions of the DNA (‘junk’ DNA, or in transposable elements), or that these mechanisms can be made to operate in a more favourable fashion, through putative epigenetic regulation. Any resulting biological advantages will then be selected or amplified in a much shorter time.

While it is true that the energy used during signalling constrains the flow of information both within and between neurons, a sustained input of information makes such energy demands on the system that may eventually cause a transition to a new state whereby the energy available to the cell will be (in some way) proportionate to the amount of information which the cell is required to process. This energy may thus be used not only for information processing but, necessarily, also for cell repair in order to make information processing possible. It is unlikely that a high level of efficient information processing takes place in a damaged cell. The cell needs to be repaired (this includes age-related damage repair) in order to be in a position to process the information. Indeed, studies have estimated that 50-80% of energy consumption is allocated to signalling, and the remainder is allocated to cell maintenance and repairs [30]. There is evidence to show that the organism tries to economise in energy expenditure by minimising the costs associated with signal processing, such as minimising the signal-to-noise ratio and band-width [31]. However, a cost-efficient neural system increases the numbers of active cells and reduces waste by reducing inactive neurons. Here a balance must be found between the energy costs of information processing and the contribution to overall fitness made by this information. If the fitness of the organism increases as a result of the new information, then it means that the cost of processing that information was worth it. This will have a secondary effect on other associated processes such as metabolic, immune, vascular and respiratory mechanisms, all of which must also be repaired. It must be reiterated that this discussion refers specifically to the evolutionary fitness of modern humans who live in an increasingly technological niche, where survival depends on adapting successfully to, ultimately, relentless information processing.


Recent developments in our understanding of phase transitions provide useful insights applicable to this discussion. A phase transition is a profound structural re-organisation when there is a large change in resource availability for maintenance. Sudden, rapid-acceleration phase transitions are associated with emergence, mutations, breakthroughs and development of autocatalytic systems which change the status quo and result in new situations which may improve fitness and survival [32].The study of such threshold dynamics gives interesting insights into plausible mechanisms for artificial manipulation of the process [33]. Kiss et al. [34] suggest that in order for a phase transition to take place it is necessary to have increased complexity and increased exposure to new challenges and a reduction in available resources. In the case under consideration here, the continuous exposure to new actionable information, and the unrelenting energy demands associated with it, may lead to a critical point where the value of selective pressure induces a phase transition in certain basic biological parameters.

Theoretically, it was shown that any continual additions to a set of agents lead to a critical transition from disconnected to connected dynamics. This phase transition depends not only on the quality and quantity of the added agent but also on critical thresholds within the set of agents [35]. To mention a biological analogy, it was shown that sensory stimuli trigger rapid and chaotic firing of neurons, which is fed through feedback loops and gradually re-synchronises neural firing. Cognition involves sudden transitions in cortical electroencephalography oscillations, and suggests that there is a dependence on connectivity phase transitions [36].

The suggestion that a sustained exposure to actionable information forcibly induces a change in basic hitherto stable evolutionary processes is not an entirely speculative one. It has been proposed that increased aerobic activity (thus in this case physical, instead of cognitive, effort) had a direct evolutionary impact on the human brain [37]. The increased and sustained aerobic activity of hunter-gatherers resulted in the up-regulation of peripheral BDNF and increased its concentration to such a degree that it eventually crossed the bloodbrain barrier and directly influenced the function and survival of the neurons. The plasma concentrations of other factors that are normally found in the periphery (i.e. not in the brain) such as insulin-like growth factor 1 (IGF-1) and vascular endothelial growth factor (VEGF) all of which increase during physical activity, were elevated by the sustained physical effort and, having crossed the blood-brain barrier, reached the brain resulting in improved neurogenesis. Thus, it is hypothesized that other sustained activities, in this case cognitive stimulation, may have physical effects which cause a phase transition from one evolutionary stage to another.

It was shown that, by increasing transcriptional noise (through the manipulation of free energy availability, and thus the selective pressure) it may be possible to trigger a structural phase transition where genetic networks change their topology from random to segregated [38]. In this case, most genes are regulated by a smaller and denser subset of genes (i.e. by transcription factor genes that regulate all others). This could be a mechanism used in germ line immortalisation and it could possibly also be made to operate in putative somatic cell immortalisation, through manipulation of the parameters. The process is described by the order parameter:

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where br is the value of b* -the average error (the main fitness criterion) for a fully random network, and bmin is the smallest possible value of b* for a given k (the average number of inputs per node). bmin is given by Σk pk mk (p) (m denotes any cell from a system of many cells)

When ø =0 the network is fully random (i.e. unfit), whereas when ø=1 the network has the largest possible value of fitness. The process therefore is dependent upon the number of inputs per node (k), and the noise level p, that is to say, it is dependent upon the overall degree of information input, which may modify selection pressure in a way favourable to somatic cells. Theoretical studies of evolutionary phase transitions in complex adaptive systems (CAS) [39] show that, when agents compete for limited resources, they tend to self-segregate into opposing groups depending on the ‘prize-to-fine’ ratio (i.e. the probability of fitness success vs fitness failure). The evolutionary kinetics of both germ-cells and somatic cells (both are CAS) can then be studied using the above model. Both groups of cells can be considered as agents (of systems) competing for resources and are thus subjected to evolutionary dynamics rules. It was shown that these systems may change their characteristics from ’cautious’ agents to ‘extreme’ ones and back (i.e. from random to segregated, via a phase transition as described above) depending on the values of the prize-to-fine ratio, i.e. when the value of resources available for maintenance changes [40]. This may explain the mechanisms of the phase transition which depend on resource distribution: when the ‘prize’ of following a certain decision to allocate resources and repair something is higher than the ‘fine’ associated with that repair [i.e. within a defined fitness landscape, when the fitness increase following a particular repair is more than the fitness reduction associate with that repair], then there will be a transition that favours that particular repair which carries a better survival value. Of course, a better survival value is currently achieved through favourable repairs to germ line cells at the expense of somatic cells. However, it is suggested that this balance may be shifted at the favour of somatic if the prize to fine ration is changed, via increased demand for resources following informational exposure thus via manipulating the number of inputs per node(cell).


Germ cells achieve immortality by ensuring that they maintain robustness – the redundancy that counteracts the effects of random noise. Evolution drives the balance of the right trade-off between robustness and maintenance resources. The trade-off between survival of the somatic cells and reproduction could be due to factors such as:

a. The impossibility to maintain all processes within the body indefinitely, due to lack of resources. Or

b. The damage to the somatic cells could be incurred during the normal course of reproduction [41].

Generally, there is a direct relationship between increasing age and genomic instability in somatic cells. However, the situation is quite more complex. There exist several tissue-specific effects, resulting for example in accumulation of damaged DNA in some tissues but not in others [42]. Maintenance resource reallocation favours reproduction at the expense of somatic cell senescence. However, it is possible to encounter soma-to-germ line transformation of gene expression which is normally encountered only in germ-line. It was shown [43] that somatic cells of insulin-like signalling mutants are efficiently protected against genotoxic stress and exhibit more germ-line features. Therefore, the possible germ line-like (and thus preservation-affording) transformation of somatic cells is not, a priori, excluded.

The rejuvenation process encountered in germ line erases the age-related damage that accumulates over the years [44]. Three mechanisms have been suggested to account for germ line immortality [45]:

1. A generally more efficient and increased rate of cellular repair and maintenance, as well as specific repair and rejuvenation mechanisms.

2. Efficient selection of fully functional germ cells which are allowed to propagate [46] at the expense of less efficient germ cells. A relevant concept here is that of apoptosis regulation.

3. Non-autonomous contribution of the soma to germ line immortality [47].

Such strategies may also be present in somatic cells, but are significantly down-regulated [45]. The aim here is to examine the possibility that this rejuvenation process can be driven to operate in somatic cells [48]. It is important to highlight that certain mechanisms of germ line rejuvenation could be dependent upon epigenetic modifications and factors that regulate transcription [49]. Thus it is also conceivable that careful epigenetic co-ordination may have certain rejuvenating effects upon somatic cells.

Mechanism number 3 above, is particularly attractive as it raises the possibility that somatic cells may, under certain circumstances, withhold those immortality-affording contributions for their own repair (when for example, somatic cells are under intense pressure to maintain themselves). It is known that ablation of germ line extends lifespan in drosophila and in C. elegans, suggesting that defects in germ line immortality may provide beneficial resources to the soma [50].

Fontana [51] has suggested that when ‘good’ (i.e. immortality-affording) matching sequences are not present in the germ-line genome, then these ‘good’ sequences are created in the somatic cell and then migrate to the germ line through the bloodstream (Germ Line Penetration). In this case, it has been suggested that transposition of genomic elements in somatic cells drives differentiation in germ cells that drives evolution. As a consequence, it can also be argued that this process can conceivably be arrested, with somatic cells retaining the preservation-affording sequences and using these for their own repairs. Recent finding that neurons exhibit quasi-immortal characteristics (i.e. they are able to live longer than their host), may suggest that somatic cells which are evolutionarily beneficial are retained and maintained, perhaps indefinitely [52]. With extraordinary forthsight, over 10 years ago, Duesseldorf University prof Kurt Heininger summarised this view [53]:

“Somatic death is a germ cell-triggered event and has been established as evolutionary-fixed default state following asymmetric reproduction in a world of finite resources. Aging is the stress resistancedependent phenotype of the somatic resilience that counteracts this germ cell-inflicted death pathway. Thus, aging is a survival response and, in contrast to current beliefs, is antagonistically linked to death. It is … enforced upon the soma by the selfish (germ line) genes of the “enemy within”.


Kirkwood [54] describes menopause as the result of a convergence of biological and cultural evolution. Menopause has evolved as an adaptation to the biological value it has conferred upon older women. This implies, as a general concept, that if a situation confers sufficient fitness to a group of people then it will evolve and be favourably selected. Therefore, the same principle may be operating in the situation discussed here: that if the increased and continual input of information confers an increased fitness to individuals (within a niche of information technology) then the associated processes will be favourably selected and evolve. It is known that epigenetic regulation of gene expression responds to external environmental factors, such as increase environmental enrichment, a fact that provides a genetic basis for the improvement of the functional ability of the individual [55]. An example of how information-enriched environments may impact on evolution is the observed increased overall intelligence is recent times. Flynn [56] has shown that in the developed countries the average Intelligence Quotient has been rising by about 3 points per decade. A probable explanation for this is that higher intelligence is the result of environmental (i.e. artificial, information-laden) factors such as improved medical care and general health, better education and higher cognitive stimulation by living in an increasingly complex society [57]. This provides a direct link between increased meaningful information load and a resulting biological benefit.

Evolution depends on innovations in energy extraction mechanisms. Yun et al. [58] have suggested that energy efficiency is a driving force in evolution as it increases fitness, and that the ‘variation-selection’ method of Darwinian evolution may not be an all-encompassing biological paradigm. They suggest that a model of evolution based on the ‘concept of competition for energy’ “…encompasses prior notions of evolution and portends post-Darwinian mechanisms… Under these circumstances, indefinite persistence may become favored over life-death cycling, as usage of energy may then occur more efficiently within a single lifespan rather than over multiple generations”. If ‘competition for energy’ is a valid evolutionary model, then the possibility arises that increased competition for available energy resources may reverse the current supremacy of virtually immortal germ cells at the expense of somatic ones. This competition for resources may be intentionally intensified through strategies described in Table 2. These artificial strategies may have a deep impact upon our biological processes with regards to resource allocation. Within hierarchical networks, the element of cooperation is essential. It was shown that this co-operation can transpose fitness from a simpler level to a higher level, and in doing so it extends the fitness and survival of the network [64, 65]. In the specific example of human survival it may be argued that increased cooperation and network complexity will transcend the necessity of fitness of the germ line (germ cells and DNA) and increase, instead, the fitness of the more complex network (i.e. the brain, and as a consequence the entire organism). These more complex, globally co-operating networks are ideal solutions within our modern, rapidly changing technology-driven environment and they provide a situation whereby damage control, maintenance and restructuring may result in delayed ageing (i.e. delayed loss of complexity and functionality) [34]. These authors have also suggested that in order to preserve the evolvability of the network it is necessary to maintain a pool of creative elements within the network, such as molecular chaperones (in cellular networks), stem cells (in complex organisms) and creative persons (in social networks). Thus, the evolutionary need for maintaining the soma at the expense of the germ line becomes evident across all platforms and scales, and it can be tested (Table 3).

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Quantifying Informational Input

During everyday life we are exposed to random unintentional challenges and information (cognitive challenges, novelty of the environment, new ideas and situations). This helps maintain brain function. At the same time, we experience increasing entropy, which eventually causes death because the degree of information input tends to zero with time, whereas entropy tends to infinity, therefore Ai =0 (death) – see below.

The proposal of this discussion is to introduce another variable, namely the sum of intentional exposure to information that requires action (i.e. intentional stimulation, engagement and integration with digital communication, goal-oriented behaviour, novelty-seeking etc. see Table 2) which adds robustness into the equation, delaying the Ai=0 scenario (as below).

The equation is as follows:

Screen Shot 2015-02-15 at 2.24.17 PM

Ai is the degree of activation of the system (in this case, the system is the brain)


[1] Burger O, Baudish A, Vaupel JW. Human mortality improvement in evolutionary context. Proc Natl Acad Sci USA 2012; 109(44): 18210-4.

[2] Viña J, Borrás C, Miquel J. Theories of ageing. IUBMB Life 2007; 59(4-5): 249-54.

[3] Cevenini E, Bellavista E, Tieri P, et al. Systems biology and longevity: an emerging approach to identify innovative anti-aging targets and strategies. Curr Pharm Des 2010; 16(7): 802-13.

[4] Heylighen F. Complexity and self-organization. In: Bates MJ, Maak MN (Eds). Encyclopedia of library and information sciences. Oxford: Taylor & Francis Publishers, 2008.

[5] Coffrey DS. Self-organisation, complexity and chaos: the new biology for medicine. Nat Med 1998; 4: 882-5.

[6] Ben-Jacob E, Cohen I, Levine H. The cooperative self-organisation of microorganisms. Adv Phys 2000; 49: 395-554.

[7] Frieden BR, Gatenby RA. Information dynamics in living systems: prokaryotes, eukaryotes, and cancer. PLoS One 2011; 6(7): e22085.

[8] William L. Benzon WL, Hays DG. A note on why natural selection leads to complexity. J Social Biol Struct 1990; 13(1): 33-40.

[9] Furusawa C, Kaneko K. Origin of complexity in multicellular organisms. Phys Rev Lett 2000; 84(26 Pt 1): 6130-3.

[10] Adami C, Ofria C, Collier TC. Evolution of biological complexity. Proc Natl Acad Sci USA 2000; 97(9): 4463-8.

[11] Heylighen F. Self-organization, emergence and the architecture of complexity. Proceedings of the 1st European Conference on System Science; 1989; AFCET Publisher: Pages 23-32.

[12] Takahashi AC, Porta A, Melo RC, et al. Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis. Intern Emerg Med 2012; 7(3): 229-35.

[13] Lipsitz LA. Aging as a process of complexity loss. In: Complex Systems Science in Biomedicine. Springer US 2006; pp. 641-54.

[14] Cheng D, Tsai SJ, Hong CJ, Yang AC. Reduced physiological complexity in robust elderly adults with the APOE epsilon4 allele. PLoS One 2009; 4(11): e7733.

[15] Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 2002; 23(1): 23-6.

[16] Lipsitz LA, Goldberger AL. Loss of complexity and aging. J Am Med Assoc 1992; 267: 1806-9.

[17] Kirkwood TB, Melov S. On the programmed/non-programmed nature of ageing within the life history. Curr Biol 2011; 21(18): R701-7.

[18] de Magalhães JP. Programmatic features of aging originating in development: aging mechanisms beyond molecular damage? FASEB 2012; 26: 4821-6.

[19] Weon BM, Je JH. Mathematical link of evolving aging and complexity. 2010; arxiv.1011.2829.

[20] Kyriazis M. Practical applications of chaos theory to the modulation of human ageing: nature prefers chaos to regularity. Biogerontology 2003; 4(2):75-90.

[21] Kyriazis M. Nonlinear stimulation and hormesis in human aging: practical examples and action mechanisms. Rejuvenation Res 2010; 13(4): 445-52.

[22] Vazquez-Sanroman D, Segura CS, Toledo R, Hernández ME, Manzo J, Miquel M. The effects of enriched environment on BDNF expression in the mouse cerebellum depending on the length of exposure. Behav Brain Res 2013; 243:118-28.

[23] Arranz L, De Castro NM, Baeza I, Maté I, Viveros MP, De la Fuente M. Environmental enrichment improves age-related immune system impairment: long-term exposure since adulthood increases life span in mice. Rejuvenation Res 2010; 13(4): 415-28.

[24] Herring A, Blome M, Ambrée O, Sachser N, Paulus W, Keyvani K. Reduction of cerebral oxidative stress following environmental enrichment in mice with Alzheimer-like pathology. Brain Pathol 2010; 20(1): 166-75.

[25] Kempermann G, Gast D, Gage FH. Neuroplasticity in old age: Sustained fivefold induction of hippocampal neurogenesis by longterm environmental enrichment. Ann Neurol 2002; 52 (2) 135-143.

[26] Atick JJ. Could information theory provide an ecological theory of sensory processing? Network 1992; 3: 213-251.

[27] Saxena A, Jacobson J, Yamanashi W, Scherlag B, Lamberth J, Saxena B. A hypothetical mathematical construct explaining the mechanism of biological amplification in an experimental model utilizing picoTesla (PT) electromagnetic fields. Med Hypotheses 2003; 60(6): 821-39.

[28] Cope D, Blakeslee B, McCourt ME. Simple cell response properties imply receptive field structure: balanced Gabor and/or bandlimited field functions. J Opt Soc Am A Opt Image Sci Vis 2009; 26(9): 2067-92.

[29] Rando RR. Membrane phospholipids as an energy source in the operation of the visual cycle. Biochemistry 1991; 30(3): 595-602.

[30] Laughlin S. Energy as a constraint on the coding and processing of sensory information. Curr Opin Neurobiol 2001; 11: 475-80.

[31] Anderson JS, Laughlin SB. Photoreceptor performance and the coordination of achromatic and chromatic inputs in the fly visual system. Vision Res 2000; 40: 13-31.

[32] de Rosnay, J. The Symbiotic Man: A New Understanding of the Organization of Life and a Vision of the Future, McGraw-Hill Professional Publishing, New York; 2000.

[33] Paperin G, Green DG, Saledin S. Dual-phase evolution in complex adaptive systems. J R Soc Interface 2011; 8(58): 609-629.

[34] Kiss HJM, Mihalik A, Nánási T, Őry B, Spiró Z, Sőti C, Csermely P. Ageing as a price of cooperation and complexity. BioEssays 2009; 31: 651-664.

[35] Erdös P. Rényi A. On the evolution of random graphs. Matematikai Kutató Intézetének Közleményei 1960; 5: 17-61.

[36] Freeman WJ. Mass action in the nervous system: examination of neurophysiological basis of adoptive behavior through the EEG. NY: Academic Press, 1975.

[37] Raichlen DA, Polk JD. Linking brains and brawn: exercise and the evolution of human neurobiology. Proc Biol Sci 2013; 280(1750): 20122250.

[38] Peixoto TP. Emergence of robustness against noise: a structural phase transition in evolved models of gene regulatory networks. Physical Rev 2012; E85: 041908.

[39] Scott A. Characterizing phase transitions in a model of natural evolutionary dynamics. Bull Am Phys Soc 2013; 58: number 1.

[40] Xu B, Jin G, Ma Y. Evolution kinetics and phase transitions of complex adaptive systems. Physical Rev 2005; E71: 026107; 1-5.

[41] Partridge L, Gems D, Withers DJ. Sex and death: what is the connection? Cell 2005; 120(4): 461-72.

[42] Garinis GA, van der Horst GT, Vijg J, Hoeijmakers JH. DNA damage and ageing: new-age ideas for an age-old problem. Nat Cell Biol 2008; 10(11): 1241-7.

[43] Curran SP, Wu X, Riedel CG, Ruvkun G. A soma-to-germline transformation in long-lived Caenorhabditis elegans mutants. Nature 2009; 459(7250): 1079-84.

[44] Mendvedev ZA. On the immortality of the germ line: genetic and biochemical mechanisms. A review. Mech Ageing Dev 1981; 17: 331-59.

[45] Smellick C, Ahmed S. Achieving immortality in the C. elegans germline. Ageing Res Rev 2005; 4: 67-82.

[46] Murphey P, McLean DJ, McMahan CA, Walter CA, McCarrey JR. Enhanced genetic integrity in mouse germ cells. Biol Reprod 2013; 88(1): 6.

[47] Kirkwood, TB. Immortality of the germ-line versus disposability of the soma. Basic Life Sci 1987; 42: 209-18.

[48] Avise JC. The evolutionary biology of aging, sexual reproduction and DNA repair. Evolution 1993; 47: 1293-1301.

[49] Santos F, Dean W. Epigenetic reprogramming during early development in mammals. Reproduction 2004; 127: 643-51.

[50] Arantes-Oliveira N. Apfeld J, Dillin A, Kenyon C. Regulation of life-span by germ-line stem cells in C. elegans. Science 2002; 295: 502-5.

[51] Fontana A. A hypothesis on the role of transposons. Biosystems 2010; 101(3): 187-93.

[52] Magrassia L, Letob K, Rossib F. Lifespan of neurons is uncoupled from organismal lifespan. Proc Natl Acad Sci USA 2013; 110(11): 4374-9.

[53] Heininger K. Aging is a deprivation syndrome driven by a germsoma conflict. Ageing Res Rev 2002; 1(3): 481-536.

[54] Kirkwood TBL. Understanding ageing from an evolutionary perspective. J Internal Med 2008; 263(2): 117-27.

[55] Vaiserman AM. Hormesis and epigenetics: is there a link? Ageing Res Rev 2011; 10(4): 413-21.

[56] Flynn JR. Massive IQ gains in 14 nations: What IQ tests really measure. Psychol Bull 1987; 101: 171-191.

[57] Heylighen F. Cognitive Systems: a cybernetic perspective of the new science of the mind. Lecture Notes ECCO, 2010; Vrije Universiteit Brussel.

[58] Yun AJ, Lee PY, Doux JD, Conley BR. A general theory of evolution based on energy efficiency: its implications for diseases. Med Hypotheses 2006; 66(3): 664-70.

[59] Kyriazis M. (2012) The longevity of real human avatars. H+ Magazine 2012. Accessed 19 February 2014.

[60] Wilson T. Human Information Behaviour. Informing Sci 2000; 3(2): 49-55.

[61] Peters T, Waterman RH, Ed. In search of excellence. Harpers Business, London 2004.

[62] Mumford MD, Gustafson S. Creativity syndrome: Integration, application, and innovation. Psychol Bull 1988; 103(1): 27-43.

[63] Strange BA, Duggins A, Penny W, Dolan RJ, Friston KJ. Information theory, novelty and hippocampal responses: unpredicted or unpredictable? Neural Netw 2005; 18(3): 225-30.

[64] Michod RE. The group covariance effect and fitness trade-offs during evolutionary transitions in individuality. Proc Natl Acad Sci USA 2006; 103: 9113-7.

[65] Heylighen F. Cybernetic Principles of Aging and Rejuvenation: the buffering/challenging strategy for life extension. Curr Aging Sci 2014; 7(1), 60-75.


Curr Aging Sci. 2014;7(1):9-16.

Article originally appears here. Republished with permission of the author.