Theoretical Biology and Medical Modelling - Latest Comments

Cancer proliferation and quantum metabolism

Mark Henderson — 2010-02-13T00:00:00Z

Demetrius et al. [1] have re-investigated Warburg’s metabolic hypothesis of cancer origins in terms of quantum metabolism. Their paper seems timely because, as the authors note, there has been renewed interest in the Warburg hypothesis during recent years. Whether their interpretation will prove useful remains to be seen, but it should interest workers in the field.

My colleagues and I were therefore surprised by the comment from O’Kelly [2], which seeks to dismiss the proposals of Demetrius et al. [1] on grounds best described as dubious. O’Kelly appears to claim acquaintance with previous publications by Demetrius, but in his first paragraph he states that he does not understand the phrase ‘the methodology of quantum mechanics’. Yet it is clear, e.g. in [3], that quantum metabolism exploits Debye’s quantum theory of solids, using the same mathematical approach as Debye. Surely that is not a difficult idea? Later in the comment, O’Kelly tells us that ‘quanta’ have nothing to do with proton pumps but are ‘energy states of the electron… as discrete as different wavelengths of photons’. Physicists will find those remarks oddly worded, to say the least, but in any case they are irrelevant to quantum metabolism, which is not at all the same as quantum mechanics – it simply uses the same mathematical apparatus as Debye’s theory.

In the first paragraph of his comment, O’Kelly also identifies three definitions of ‘metabolic efficiency’ used in [1] and implies that they are incompatible; in fact, as anyone familiar with cellular metabolism will recognize, they are essentially equivalent. Later, he claims that ‘metabolic rate’ is used in several inconsistent ways in [1], but once again these ‘several ways’ are essentially the same, as any competent biochemistry undergraduate would know. O’Kelly’s suggestions that Demetrius et al. ‘reveal their lack of familiarity with the term (quanta)’ and ‘ability to appreciate [the Kleiber equation’s] power was limited… by failure to understand the quantum nature of chemical energy’ therefore appear ironic.

His comment [2] contains other curious statements. For example, we are told that a phosphorylation potential is not a form of energy; in other words, the chemiosmotic hypothesis (which is accepted by the overwhelming majority of biologists, including Demetrius [3]) is false. Proportionality constants can apparently be neither measured nor calculated but are ‘disembodied hypotheses’, an assertion that will no doubt alarm mathematicians throughout the world. And O’Kelly is unable to accept that fermentation and respiration involve different types of energy coupling, thus requiring every elementary biochemistry textbook to be rewritten and Pasteur’s classic experiments to be re-investigated.

The proposals of Demetrius et al. [1] are speculative and deserve critical scrutiny, but any such scrutiny needs to be based on a better understanding of basic science than is apparent in [2].

References

1. Demetrius LA, Coy JF, Tuszinsky JA: Cancer proliferation and therapy: the Warburg effect and quantum metabolism. Theor Biol Med Model 2010, 7:2.

2. O’Kelly G: Quantum metabolism and cancer – power in numbers. Theor Biol Med Model 7:2 comment.

3. Demetrius LA: The origin of allometric scaling laws in biology. J Theor Biol 243:455-467.

Quantum metabolism and cancer - power in numbers

Gregory O'Kelly — 2010-02-11T00:00:00Z

The authors describe quantum metabolism as 'an analytic theory of metabolic regulation which exploits the methodology of quantum mechanics to derive allometric rule relating cellular metabolic rate (MR) and cell size." I have no idea what quantum methodology is, or how it differs from scientific methodology. But the allometric scaling rule relating MR and cell mass, as proffered by Dr. Demetrius in his 2004 Journal of Gerontology article, has little if anything to do with quantum mechanics, whatever its alleged methodology. And what little it does have to do, relates to the nature of electrical charge transferred in the redox coupling necessary for the creation of covalent bonds. In fact Dr. Demetrius defines metabolic efficiency (ME) there as a ratio of amperes to amperes. In contrast, in this paper, the authors define ME as:
1. "Metabolic efficiency is the amount of ATP produced per unit of substrate material."
2. "The ratio of the energy stored in the various bio-molecules to the free energy released in redox reactions is the efficiency of the metabolic process, which can be as high as about 95%..."
3. "Metabolic efficiency is the amount of ATP produced per unit of substrate material."

Does ME differ from the efficiency of the metabolic process, where the former is an amount, or an amount/unit of substrate, and the latter is a ratio? There is a good deal of looseness to these formulations.

The authors make the claim that OxPhos has a higher metabolic efficiency (chose the definition), and thus a higher MR. This conflicts drastically with the Eq. (1) in Dr. Demetrius's 2004 which clearly indicates that for things of mass less than one gram in size, higher ME means lower MR. Problems loom. The authors also claim: "An increased metabolic rate entails an increased efficiency in the acquisition of resources. Hence, when limited resource conditions prevail, types with higher metabolic rates will be favoured," at a time when Demetrius's 2004 clearly shows that higher MR is something determined by the resources and the ability to use them (ME), and not something that exists independently of those resources.

That same math also clearly shows, contra the authors' claim that "Cancer cells may be considered as autonomous units which have an impared capacity to maintain the metabolic stability of the organism in which they reside," and "...one cell type replaces a related cell type by natural selection," that all cells share the ME of the host, and that this is the nature of biological organization. The authors seem to think the cells of an organism are instead in competition for energy resources such that ME may vary from once cell to another. And to this Darwinian competition are introduced 'adaptive mechanisms', 'regulatory interventions', and 'anti-tumour defenses', none of which appear in the math.

Examination of the definition of MR reveals MR is:
1. Rate of ATP production, and/or
2. "...the totality of chemical reactions in cells carried out by an organism," and/or
3. "...the rate at which an organism transforms nutrients into thermal energy and biological work", and/or
4. "Metabolic rate is the rate of ATP production per unit time..." [surely a redundancy], and/or
5. "... the minimum rate at which the cell uses energy to stay alive," and/or
6. "... determined by the proton conductance and the proton potential of the metabolic system...regulated by the phospholipid composition of the mitochondrial membrane...[that is modifiable by] exercise and diet." The authors don't say how the cells exercise but, instead, seamlessly move between basal and field metabolic rate, and fail to disclose at what scale Darwinian competition for energy is replaced by cooperation of the parts of the organism.

But this is not enough. In matters of ME, "...the transformation of nutrients into thermal energy and biological work involves the inter-converson of two forms of energy: the redox potential difference...[and] the phosphorylation potential..." Redox potentials and phosphorylation potentials are not forms of energy. Energy, chemical energy, is expressed in coulombs or amperes, not volts. Amperes are not potentials. The authors assert that thermogenesis is part of metabolism, which conflicts with the idea "Metabolic efficiency is the amount of ATP produced per unit of substrate material." Only when thermogenesis is considered part of metabolism do we find extremely high efficiencies like that the authors claim characterizes the Krebs Cycle - 95%, an efficiency rating characteristic, at best, of mechanical linkages.

The authors speak of "two classes of metabolic pathways" - fermentation and respiration - where those two things characterize different 'mode of coupling - chemical and electrical, as if electrochemistry did not include both, at a time when redox coupling efficiency (ME) is a term from electrochemistry. They make this distinction repeatedly through the paper, e.g., MR is "determined by the proton conductance and the proton potential of the metabolic system"; "In OxPhos, the coupling is electrical...In glycolysis, the coupling is chemical..."; "The coupling between the electron transport chain and ADP phosphorylation is generated by the flow of protons across the biomembrane"; "In OxPhos coupling is achieved by a single common intermediate between the oxidation of a variety of substrates and ATP formation. The intermediate is the trans-membrane proton gradient"; "All living systems exist in a steady state relatively far from thermodynamic equilibrium and this state is maintained by sustaining non-equilibrium concentration gradients across membranes"; and "The metabolic rate of normal cells is primarily determined by the proton conductance and the proton potential of the metabolic system."

What is problematic with this view of energy is its complete un-relatedness to the quanta of metabolism. Proton gradients, proton pumps, proton conductance, etc., have absolutely nothing to do with quanta. Quanta are the energy states of the electron. They are as discrete as different wavelengths of photons. Use of the word in the favored phrase 'quantum metabolism', in the manner favored by the authors, merely reveals their lack of familiarity with the term. There is a long history to this bit of insular elision that dates to 1902 and Julius Bernstein who sought to explain the electromagnetism of the nervous system in terms of thermodynamics. Bernstein turned to the Nernst equation, a thermodynamic equation of Walter Nernst that spoke of 'elementary particles'. These elementary particles could be any particles in solution or in an ideal gas, i.e., they could be ions, protons, electrons, molecules, whatever. The volts of Nernst were units of osmotic pressure, and had nothing to do with electromagnetism. The claim that there are two modes of energy transduction, one chemical and one electrical, is spurious, because all chemical energy transduction is electrochemical, not osmotic.

"Quantum Metabolism predicts that the metabolic rate of cells utilizing OxPhos and cells utilizing glycolysis will have the same scaling exponents but will differ in terms of the proportionality constants"; and "Quantum metabolism, a new bio-energetic theory of metabolic regulation in cells [dating to 1902], shows the proportionality constants in the scaling laws for metabolic rates of cells utilizing OxPhos and glycolysis pathways are contingent on the different modes of coupling..."

The proportionality constant depends upon the difference in redox coupling modes (ME), a difference obviated when the quantum world replaces the world of thermodynamic electricity, so that the two modes collapse into one. Proportionality constants are not measured, nor are they calculated. They are disembodied hypotheses meant to rescue a tortured scheme. At the same time when MR is determined entirely by scaling exponents (Demetrius 2004), the authors propose "The evolutionary argument rests on differences in the MR of cells utilizing OxPhos and glycolytic pathways, respectively," Demetrius's 2004 shows that when scaling exponents for cells are the same and there is a difference in MR, the difference is due to the mass of the cell, nothing else. The scaling exponents differ if and only if the value for ME differs from one cell to another, something not likely when ME is determined by the organism, and not the cells which comprise it.

The authors are now proposing 'non-invasive' 'regulatory interventions on the basis of quantum metabolism' 'to combat cancer.' "The hypothesis, in its simplest form, asserts that cancer is primarily a disease of metabolic dysregulation..." "...therapeutic strategies based on arresting the transition from normal... to... malignant...may be effective in complementing traditional methods..." Typically, the authors specify no such interventions outside of hints at drugs and pharmaceuticals that might be complementary. They hope to influence MR this way at a time when Demetrius's 2004 shows MR is dependent entirely upon biomass and ME. Presumably ME can be varied for the cell with adequately targeted drugs that act upon modes of energy processing.

Demetrius's 2004, which also supports the hypothesis of cancer as due to metabolic dysregulation, does not stoop to this, the idea of modes of energy processing. Instead of hypothesizing regulatory interventions that might complement traditional methods however, the mathematics suggest the traditional methods target the cell but not the cause of the mutation, a matter of ME, and should be rejected outright. The curves suggest the way to act upon MR is to manipulate ME through the application of electrochemistry. The curves model how the cause of mutation is increased ME from the reduced denominator of its redox coupling ratio, and how this drives cells, whose MRs are collapsing, to degenerate by alternately reducing the numerator of the ratio. This allows the cell to survive in an organism of high ME, which the cell shares. John Cairnes called this 'starvation induced mutation'.

I will always be disappointed Dr. Demetrius never looked into this equation more thoroughly, but I understand his ability to appreciate its power was limited by his understanding of the quantum nature of chemical energy, and how this understanding's electrophysiology was formulated before the quantum revolution.

Disclaimer: TBioMed 2009, 6:25

Paul Agutter — 2009-11-17T00:00:00Z

As Editor-in-Chief of the journal, I respectfully draw the attention of readers to the author's disclaimer in the Acknowledgments section of this paper. Dr Smieszek is conerned that no liability issues arise from the publication of the data in the Supplementary File. The Editors share that concern, for ethical as well as business reasons.

Anyone who wishes to use the data in this paper must credit Dr Smieszek, and his institution (the data are owned by ETH Zurich). Two other persons also contributed to the data collection but did not qualify as authors of the article. Please contact Dr Smieszek, directly or via the Editors, for details.

Before making use of the data, please read the Acknowledgments in this paper, and cite the reference fully in all communications.

Corrections to TBioMed 1:6 (2004) communicated by the author

Paul Agutter — 2009-10-01T00:00:00Z

Dr Porteous has asked me to point out to readers that the following corrections should be made to the above article (doi: 10.1186/1742-4682-1-6):-

In Section 2.4 on 5th page, in the 2nd complete paragraph, in the 3rd line of text: the words “of the asymptote” should be deleted.

And on the 4th line: the words “on the asymptote” should be deleted; and, in this case, replaced by the words “of the flux response”.

Dr Porteous asks me to apologise for these errors in the published article.

The limits of thermodynamics; the shortcomings of WBE's Kleiber

Gregory O'Kelly — 2009-06-27T00:00:00Z

Comment:
The author is apparently not aware of the growing amount of data clearly deviating from the predictions of WBE's Kleiber (b=3/4) (DaSilva, Garcia et al. 2006; White, Seymour et al. 2006). These deviations are the result of refinement in metabolic energy measurement, to exclude thermogenesis, and to focus on sugar and oxygen consumption. The exponent 3/4 is an artifact of the data collected by earlier investigators into metabolic energy, like Max Kleiber, who measured this energy in terms of heat energy. That is why metabolic rate is still expressed in calories/sec rather than watts. This has roots in attempts to understand biological energy in terms of thermodynamics. Dr. Kurakin merely introduces the idea that for biology the thermodynamics should be non-equilibrium rather than equilibrium, rather than discarding thermodynamics entirely as inappropriate for understanding what is essentially an electrochemical phenomenon. Statistical mechanics are clearly incommensurable with electromagnetism, being about different things and requiring different mathematics.

Nor is the author aware that the term "metabolic efficiency" has no place in the equation favored by WBE, who instead assume an efficiency that is not part of the math. If it were, it would appear in the numerator such that, instead of 3/4, the numerator would be (4ME-1)/4ME, where ME is metabolic efficiency, a redox coupling efficiency ratio of amperes. Given this version of the equation, only values like 89 to 100% for ME result in MRs like what WBE show on their preferred graph of mass vs. MR. This eliminates any biological relevance since efficiencies like this are only found in the mechanical world. Biologists take the mechanism metaphor too seriously. Yet, even in mechanics, heat generation is not part of efficiency.

Dr. Kurakin is encouraged to examine the graph with the X axis as ME, the Y axis as MR, and a DIFFERENT CURVE FOR EACH BIOMASS VALUE in grams. Then he can examine how thermodynamic pressure, given ∆ME [fluctuations in energy supply, the denominator of ME] acts upon biomass through the numerator of ME, to stabilize MR, changing the gram value for biomass. This change can occur as division, growth, or development, as mutation, degeneration, or diminution. These pressures act on biomass against its inertial lagging before the immediacy of ∆ME, to maintain an average MR which, if not attainable, results in appropriate changes in that biomass. For Dr. Kurakin this would be the non-equilibrium whose pressure acts to achieve equilibrium by acting upon the biomass, and it is in this mathematical approach that the numerous issues he declares can be resolved by non-equilibrium considerations, can actually be modeled, including the energetic nature of all biological organization. Aside from this, thermodynamics is useless for biology.

Overlap in terminology of nucleic acid folding

Donald Forsdyke — 2008-09-20T00:00:00Z

There is a profusion of overlapping terminology in the field of nucleic acid folding studies. Readers might like to note that the author's three terms TFE, CFE and FFE(PFE) are very close to those introduced by Le & Maizel in 1989 (E, E_r, and E - E_r)[1], and by myself in the early 1990s (FONS, FORS-M and FORS-D)[2]. For example, see Table 1 of Forsdyke (2007), which may be accessed at BMC.

1. Le S-Y, Maizel JV: A method for assessing the statistical significance of RNA folding. J Theor Biol 1989 138:495-510.

2 Forsdyke DR: Calculation of folding energies of single-stranded nucleic acid sequences: conceptual issues.J Theor Biol 2007 248:745-753.

Correction in equations 2-5.

Hirotaka Ando — 2008-09-09T00:00:00Z

Dear readers,

Equations 2-5 include an incorrect term.

The correct equations are as follows.

Equation 2: dCr/dt = (Qr/Vr)(Ca-Cr/Kp,r)-CLrCa/Vr

Equation 3: dCgi/dt = (Qgi/Vgi)(Ca-Cgi/Kp,gi)-CLgiCa/Vgi

Equation 4: dClung/dt = (Qtot/Vlung)(Cv-Clung/Kp, lung)

Equation 5: dCi/dt = (Qi/Vi)(Ca-Ci/Kp,i)

On behalf of all the authors, I apologise for our error.

We will publish a correction article soon.

Sincerely,

Hirotaka Ando

Bedside Diagnosing Oncological Terrain and Inherited Oncological Real Risk in quantitative way.

Sergio Stagnaro — 2008-07-19T00:00:00Z

Editors,

I certainly find the paper by Kiranmoy Das and Rongling Wu fascinating, but really too technical to be utilized at the bedside by general practitioners. My 52-year long, well established clinical experience allows me to state that if we either ignore or overlook the reality of the Oncological Terrain (http://www.semeioticabiofisica.it) as well as the inherited ‘real risk’ of cancer, considered quantitatively, we cannot prevent or diagnose cancer promptly in its initial stage, i.e. Inherited Oncological Real Risk in a biological system [1-4]. In fact, both environmental risk factors and aging, which has been suggested as a risk factor for cancer, ‘could’ influence some biological functions in humans and/or bring about different disorders, such as cancers, exclusively in relation to the presence and intensity of CAEMH in a well-defined biological system. For instance, in spite of aging, NOT all aged individuals are affected by malignancy. As a consequence, we have to consider the importance of ‘genetic predispositions’, i.e. Oncological Terrain as well as Inherited Oncological Risk, located in biological system(s), in regard to the onset of many disorders including breast cancer. In conclusion, we need initially (i.e. starting any screening or research whatever) to investigate the presence and intensity of CAEMH in the ‘tested’ population, i.e. in ‘every’ ‘single’ patient, and quickly thereafter to assess the presence and intensity of the CAEMH-dependent ‘Oncological Terrain’ and the precise location of the congenital ‘real risk’ of cancer. Both always develop on the basis of the above-mentioned congenital mitochondrial cytopathology. In fact, without this alteration in the psycho-neuro-endocrine-immunological system, oncogenesis is not possible. This allows me to state that my 52-year-long clinical experience with Biophysical Semeiotics, Single Patient Based Medicine theory is based on [6]. Finally, these pathological conditions are characterized by microcirculatory remodelling, in which newborn-pathological, type I, ‘typical’ oncological subtype Endoarteriolar Blocking Devices play a key role [1-5, 14, 15].

1. Stagnaro-Neri M, Stagnaro S: Introduzione alla Semeiotica Biofisica. Il Terreno Oncologico. Roma: Travel Factory; 2004. http://www.travelfactory.it

2. Stagnaro S: There is another clinical, and overlooked tool, reliable in breast cancer prognosis evaluation. http://www.biomedcentral.com/1471-2407/5/70/comments#204473 2005

3. Stagnaro S: Mitochondrial Bed-Side Evaluation: a new Way in the War against Cancer (21 December 2005). Cancer Cell International http://www.cancerci.com/content/5/1/34/comments#218502

4. Stagnaro S, Stagnaro-Neri M: La Melatonina nella Terapia del Terreno Oncologico e del "Reale Rischio" Oncologico. Roma: Travel Factory; 2004. http://www.travelfactory.it/semeiotica_biofisica_2.htm

5. Stagnaro-Neri M, Stagnaro S: Cancro della mammella: prevenzione primaria e diagnosi precoce con la percussione ascoltata. Gazz Med It Arch Sc Med 1993, 152:447.

6. Stagnaro S, Stagnaro-Neri M: Single Patient Based Medicine.La Medicina Basata sul Singolo Paziente: Nuove Indicazioni della Melatonina. Roma: Travel Factory; 2005. http://www.travelfactory.it/libro_singlepatientbased.htm

7. Stagnaro S: Clinical tool reliable in bedside early recognizing pancreas tumour, both benign and malignant. World J Surg Oncol 2005, 3:62 doi:10.1186/1477-7819-3-62.

8. Stagnaro S: Bed-Side Evaluating Breast Cancer Real Risk. World J Surg Oncol 2005, 3:67 doi:10.1186/1477-7819-3-67. 2005 2005

9. Stagnaro S: Mitochondrial Bed-Side Evaluation: a new Way in the War against Cancer (21 December 2005). Cancer Cell Internat http://www.cancerci.com/content/5/1/34/comments#218502 2005

10. Stagnaro S: Cancer Risk Factors and Oncological Terrain. 2006. http://www.wjso.com/content/4/1/74/comments#247528 2006

11. Stagnaro S: Without Oncological Terrain oncogenesis is not possible. CMAJ. 23 March 2007 http://www.cmaj.ca/cgi/eletters/176/5/646

12. Stagnaro S: GPs, Biophysical Semeiotics, and bedside cancer diagnosis. 08 July 2007, International Seminar of Surgical Oncology, http://www.issoonline.com/content/4/1/11/comments#281539 , 2007

13. Stagnaro S: Bedside Evaluation Tobacco's actions on Biological Systems. The Lancet, October 13, 2007, http://www.thelancet.com/journals/lancet/article/

PIIS0140673607614822/comments?action=view&totalComments=2#1286 2007

14. Stagnaro S: Oncological Terrain and Inherited Oncological Real Risk: New Way in Malignancy Primary Prevention and early Diagnosis. Internat Sem Surg Oncol, 2007. http://www.issoonline.com/content/4/1/25/comments#290565

15. Stagnaro S: Bedside Biophysical-Semeiotic Diagnosis of Breast Cancer, since initial Stage. Internat Sem Surg Oncol, 2007, http://www.issoonline.com/content/4/1/21/comments

Biophysical semeiotics allows bedside recognition of Peter Pan Paradigm.

Sergio Stagnaro — 2008-02-02T00:00:00Z

According to my Angiobiopathy Theory (www.semeioticabiofisica.it) every

underdeveloped organ necessarily shows INHERITED microcirculatory

remodelling, characterized by newborn-pathological, type I, subtype (a)

oncological and (b) nonspeicific Endoarteriolar Blocking Devices (1-5). As a

consequence we are able to recognize it at birh with a simple stethoscope

and to administer adequate therapy such as conjugated melatonin and

application of NIR-LED; this is efficacious in ameliorating the underlying

congenital mitochondrial cytopathy, transmitted by the mother, which I have

termed Congenital Acidosic Enzyme Metabolic Histangiopathy.

References

1) Stagnaro-Neri M., Stagnaro S. Introduzione alla Semeiotica Biofisica. Il

Terreno Oncologico. Travel Factory, Roma, 2004.

http://www.travelfactory.it/semeiotica_biofisica.htm 2004

2) Stagnaro S. Pivotal role of Biophysical Semeiotic Constitutions in

Primary Prevention. Cardiovascular Diabetology, 2:1,

http://www.cardiab.com/content/2/1/13/comments#5753 2003

3) Stagnaro Sergio. Newborn-pathological Endoarteriolar Blocking Devices in

Diabetic and Dislipidaemic Constitution and Diabetes Primary Prevention. The

Lancet. March 06 2007.

http://www.thelancet.com/journals/lancet/article/PIIS0140673607603316/comments?totalcomments=1

4) Stagnaro Sergio. Rimodellamento Microvascolare, Costituzioni

Semeiotico-Biofisiche e Reale Rischio Semeiotico-Biofisico. Ruolo dei

Dispositivi Endoarteriolari di Blocco neoformati-patologici,

clicmedicina.it, 10/4/2007,

http://www.clicmedicina.it/pagine%20n%2028/rimodellamento.htm 2007

5) Stagnaro Sergio. Role of Coronary Endoarterial Blocking Devices in

Myocardial Preconditioning - c007i. Lecture, V Virtual International

Congress of Cardiology.

2007.http://www.fac.org.ar/qcvc/llave/c007i/stagnaros.php

Updated Epiflex software available.

Brian Hanley — 2008-01-21T00:00:00Z

A new version of the Epiflex software is available by writing to the author at one of these email addresses:

bphanley@ucdavis.edu ; brian.hanley@ieee.org ; brian.p.hanley@att.net

Information for version 7.1

Fixes:

1. A bug was found that cut contact rates in cells with very large populations per cell. This has been corrected. This had no impact on the models used for the publication.

User warning:

When importing into Excel spreadsheets, data will be truncated at roughly 64,000 lines. You will need to break your files up into smaller ones if you execute large models and you use Excel to import files for processing.

Features:

1. New report file improvements

a. .ICX – Infection contagion detail statistics by area. This file is written at the close of processing.

Record contents: Area name, Host ID, Demographic group, number of other hosts infected by this host.

b. IDX – Infection demographic statistics by location. This file is optional, configured on the Run Configuration panel. If it is selected it will slow down processing significantly. (Tests show approximately 3-5 times increase in execution time.) It is written at the end of each cycle.

Record contents: Cycle, Area Name, Location name, cell#, Location population total, Demographic name, #Demographic , #new infections, #incubating, #prodrome, #manifestation, #chronic, #immune

Notes: List is not in time order within an area or location, only between cycles. Counts after demographic name are counts for the demographic within the location only. Only locations with active infections are shown.

c. INX - Infectious contacts details. This file is optional, configured on the Run Configuration panel. If it is selected it will slow down processing significantly. (Tests show approximately 3-5 times increase in execution time.)

Record contents: Cycle, Area Name, Location name, cell #, Infectee id, Infectee demographic group, Disease contracted, Causative contact, Infector id, Infector demographic group, Infector disease stage

Notes: List is not in time order within an area or location, only between cycles. (non-cellular location cell# = -1) Only new infection events are shown.

General Note: All files have format contents in a header to the file.

2. Optional automatic allocation of cells per location.

If cell count is set to zero in a model for a location, when the model is run it will allocate cells per location based on the maximum number of hosts that can move through a location based on demographic movement cycles and the number of hosts that are specified within each cell.

Notes: If a complex demographic pattern is specified some degree of over-allocation may occur since timing of demographic movement is not taken into account. This option should not be used for locations that exclusively use the random draw method for population. It is necessary to reset cell counts to zero each time it is desired to auto-allocate.

3. Return to home cells.

In the previous version, hosts did not necessarily return to the same cell they did before when they come around to it in their movement cycle again. This had mild skewing effects toward increasing R values in the model because, for instance, an individual would not always return to the same home they did before. This was done to save memory. Now, up to 100 locations can be specified in a movement cycle, and the person will return to the same one each time. As a result of this feature memory requirements have gone up for each host in the system, so optimal performance requires maximum amounts of RAM. This was implemented as a fixed array of 100 long integers because a linked list would be slower. But if you have insufficient RAM then your performance will suffer.

4. Age of demographic.

It is now possible to specify an age range for each demographic. Age is incremented on a 365 day calendar. Age specification is not required. Ages are randomly allocated within the age range without intentional skew.

5. Cross-immunity.

It is now possible to specify that an age range has some level of fractional immunity to a disease. If a demographic has age range specified, then that demographic will have some fractional degree of resistance to the disease. This will allow changing both how many of an age appropriate demographic become ill, and if they become ill, how severe the illness becomes. The intention of this feature is to allow modeling of age-related previous exposure to the same strain or a similar strain.

Note: Cross-immunity is how Epiflex now models asymptomatic disease carriers.