Information – a fundamental entity for understanding life!


Werner Gitt

German Federal Institute of Physics and Technology. Former Director and Head of Information Technology.


Dr. Gitt obtained an undergraduate degree in engineering from the Technical University of Hannover in 1968 and completed his Ph.D. summa cum laude in 1970 from the Technical University of Aachen which also awarded him its prestigious “Borchers Medal.” In 1971 Werner Gitt started his career at the German Federal Institute of Physics and Technology in Brunswick, being promoted to Director and Professor in 1978. He served as Head of “Information Technology” from 1971 to 2002, when he retired. He is the author of numerous research papers dealing with information science, numerical mathematics, and control engineering. List his latest book and mention his extensive speaking engagements – “he is a widely sought-after author and speaker”.



Good science and logical deductions require that critical terms in any field be unambiguously defined. Although everyone has an idea of the meaning of the term “information,” it has not been unambiguously defined. After working out a definition of information, I proceed to formulate scientific laws for this nonmaterial entity, information, from which it is possible to draw sound conclusions. These laws exclude the possibility that information, including biological information, can arise purely from matter and energy without reference to an intelligent agent. As such these laws show that the neo-Darwinian theory of evolution cannot in principle account for the most fundamental biological phenomenon. In addition, the laws here presented give positive ground for attributing the origin of biological information to the conscious, willful action of a sender. The far-reaching implications of these laws are discussed.



It is commonly argued that the spectacular increase in order which has occurred on Earth does not violate the second law of thermodynamics because the Earth is an open system, and anything can happen in an open system as long as entropy increases outside the system compensate the entropy decreases inside the system. However, if we define “X-entropy” to be the entropy associated with any diffusing component X (for example, X might be heat), and, since entropy measures disorder, “X-order” to be the negative of X-entropy, a closer look at the equations for entropy change shows that they do not only say that the X-order cannot increase in a closed system, they also say that in a open system the X-order cannot increase faster than it is imported through the boundary. Thus the equations for entropy change do not support the “compensation” idea, they instead illustrate the tautology that “if an increase in order is extremely improbable when a system is closed, it is still extremely improbable when the system is open, unless something is entering which makes it not extremely improbable.” Thus unless we are willing to argue that the influx of solar energy into the Earth makes the appearance of spaceships, computers and the internet not extremely improbable, we have to conclude that the second law has in fact been violated here.


A second look at the second law


Granville Sewell

Mathematics Department, University of Texas, El Paso.


Granville Sewell is Professor of Mathematics at the University of Texas at El Paso (UTEP). He completed his Ph.D in Mathematics at Purdue University, and has subsequently been employed by (in chronological order) Universidad Simon Bolivar (Caracas), Oak Ridge National Laboratory, Purdue University,  IMSL Inc. (Houston), UTEP, The University of Texas Center for High Performance Computing (Austin), and Texas A&M University, and is currently back at UTEP. He spent one semester (Fall 1999) teaching at Universidad Nacional de Tucuman in Argentina, on a Fulbright grant, and returned to Universidad Simon Bolivar to teach summer courses in 2005 and 2008. Sewell has written three books on numerical analysis, and is the author of a widely-used finite element computer program (video at


Pragmatic Information


John W. Oller, Jr.

Hawthorne Regents Professor IV, Department of Communicative Disorders, University of Louisiana at Lafayette.


John W. Oller, Jr., Ph.D. founded the Department of Linguistics at the University of New Mexico in 1972 and the Applied Language and Speech Sciences Ph.D Program at UL Lafayette in 2001. Oller’s research has concentrated on the theory and experimental measurement of linguistic processes in education, high stakes testing, the diagnosis of disorders, the success of social interactions, and more recently on genetic systems, biochemistry, repair and disease defenses, etc. Winner of the Mildenberger Prize offered by the Modern Language Association, Oller is the author of over 200 peer-reviewed papers and monographs along with 16 books largely in experimental measurement and research on theories of linguistics and sign systems in general. His 2010 works include a book on the causes of autism, an encyclopedic reclassification of communication disorders and related disease

conditions, and a monograph-sized contribution to the peer-reviewed multidisciplinary open source journal Entropy. The latter deals with the process of pragmatic mapping (as in referring to an object, person, event, relation, or sequence of them) and as found in genetics, the dynamics of immune systems, and the distinct neuroarchitecture of the human brain.



Common true narrative representations (TNRs), such as “I had yogurt for breakfast,” or any viable expression of a genome in an organism, relative to all other representations, are “perfect” (complete). They are deeply layered and pervasively interdependent as Sanford and others show. Logicomathematical proofs show why mutating genetic TNRs with toxins, viruses, and radiant energy must lead eventually to disorder, mortality, and even extinction. Deeply interdependent TNRs are prerequisite to the biosphere. The unique perfections of TNRs cannot be found in fictions, errors, lies, or nonsense fragments. Those perfections refute neo-Darwinism. Pragmatic information, absolutely dependent on TNRs, is provably infinitely complex, abstract, eternal, and immaterial. TNRs connect with each other and generalize to the whole material universe. Proofs show that TNRs, provide the only basis for communication, valid measurements, mathematical representations, and life itself. Also, each presupposes the rest, so they cannot arise piecemeal.

Multiple overlapping genetic codes profoundly reduce the
probability of beneficial mutation


George D. Montañez

BS Computer Science, University of California -- Riverside (2004), MS Computer Science, Baylor University (2011)


George D. Montañez is a graduate student in the Machine Learning department, School of Computer Science, at Carnegie Mellon University. His research interests include predictive state model reconstruction, information properties of genetic algorithms, conservation of information in machine learning, and machine learning methods for textual data mining. He served as a research assistant to Dr. Robert J. Marks II at Baylor University.


There is growing evidence that much of the DNA in higher genomes is poly-functional, with the same nucleotide contributing to more than one type of code. Such poly-functional DNA should logically be multiply constrained in terms of the probability of sequence improvement via random mutation. We describe a model of this relationship, which relates the degree of poly-functionality and the degree of constraint on mutational improvement. We show that:  a) the probability of beneficial mutation is inversely related to the degree that a sequence is already optimized for a given code; b) the probability of beneficial mutation drastically diminishes as the number of overlapping codes increases. The growing evidence for a high degree of optimization in biological systems, and the growing evidence for multiple levels of poly-functionality within DNA, both suggest that mutations which are unambiguously beneficial must be especially rare. The theoretical scarcity of beneficial mutations is compounded by the fact that most of the beneficial mutations that do arise should confer extremely small increments of improvement in terms of total biological function. This makes such mutations invisible to natural selection. Beneficial mutations which are below a population’s selection threshold are effectively neutral in terms of selection, and so should be largely unproductive from an evolutionary perspective. We conclude that beneficial mutations that are unambiguous (not deleterious at any level) and useful (subject to natural selection) should be extremely rare.


This paper provides a general framework for understanding targeted search. It begins by defining the search matrix, which makes explicit the sources of information that can affect search progress. The search matrix enables a search to be represented as a probability measure on the original search space. This representation facilitates tracking the information cost incurred by successful search (success being defined as finding the target). To categorize such costs, various information and efficiency measures are defined, notably, active information. Conservation of information charac­terizes these costs and is precisely formulated via two theorems, one restricted (proved in previous work of ours), the other general (proved for the first time here). The restricted version assumes a uniform probability search baseline, the general, an arbitrary probability search baseline. When a search with probability q of success displaces a baseline search with probability p of success where q > p, conservation of information states that raising the probability of successful search by a factor of q/p (> 1) incurs an information cost of at least log(q/p). Conservation of information shows that information, like money, obeys strict accounting principles.

A general theory of information cost incurred by successful search


William A. Dembski

Discovery Institute, 208 Columbia Street, Seattle, WA 98104.


William A. Dembski received the B.A. degree in psychology, the M.S. degree in statistics, the Ph.D. degree in philosophy, and the Ph.D. degree in mathematics in 1988 from the University of Chicago, Chicago, IL, and the M.Div. degree from Princeton Theological Seminary, Princeton, NJ, in 1996. He was an Associate Research Professor with the Conceptual Foundations of Science, Baylor University, Waco, TX. He is currently also a Senior Fellow with the Center for Science and Culture, Discovery Institute, Seattle, WA. He has held National Science Foundation graduate and postdoctoral fellowships. He has published articles in mathematics, philosophy, and theology journals and is the author/editor of more than a dozen books.


Tierra is a digital simulation of evolution for which the stated goal was the development of open-ended complexity and a digital “Cambrian Explosion.” However, Tierra fails to produce such a result. A closer inspection of the programs produced by the process of evolution within Tierra shows very few instances of adaptation through novelty. Instead, most changes result from removing or rearranging the existing pieces within a Tierra program. The open-ended development of complexity depends on the ability to generate novelty, but Tierra fails on precisely that point.

Tierra: wasteland of novelty


Winston Ewert

Electrical & Computer Engineering, One Bear Place #97356, Baylor University, Waco, TX 76798-7356


Winston Ewert received the B.Sc. in Computer Science from Trinity Western University in Langley, B.C., and a Ph.D. at Baylor University where he was a member of Evolutionary Informatics Lab. Together with Dr. Robert Marks, Dr. William Dembski, and George Montañez, he is an author on a number of papers investigating the informational content of evolution-inspired search algorithms. He now works as a Software Engineer.

di Biologia / Biology Review, and he has co-authored articles in Development and Proceedings of the National Academy of Sciences USA. He is also the author of several books, including Charles Hodge’s Critique of Darwinism, Icons of Evolution and The Politically Incorrect Guide to Darwinism and Intelligent Design, and he is the co-author (with William Dembski) of The Design of Life. His most recent book, The Myth of Junk DNA, was published in 2011.



In the 1950s Francis Crick formulated the Central Dogma of molecular biology, which states (in effect) that DNA makes RNA makes protein makes us. By 1970, however, biologists knew that the vast majority of our genome does not encode proteins, and the non-protein-coding fraction became known as “junk DNA.” Yet data from recent genome projects show that most nuclear DNA is transcribed into RNAs – many of which perform sequence-dependent functions in cells and tissues – so the notion of “junk DNA” is obsolete, and the amount of information in the genome far exceeds the information in protein-coding regions. Various sequence-independent functions of non-protein-coding DNA and RNA have also been proposed or demonstrated. In addition to summarizing newly discovered sequence-dependent functions, this paper describes some sequence-independent functions (such as the three-dimensional organization of chromatin and nuclei) and asks whether they require us to expand our concept of biological information beyond that which applies to the specified complexity of nucleotide sequences.

Sequence-dependent and sequence-independent functions of "junk" DNA: do we need an expanded concept of biological information?


Jonathan Wells

Discovery Institute, Seattle WA 98104.


Jonathan Wells holds an A.B. in Physical Sciences from the University of California at Berkeley. In 1985 he received a Ph.D. in Religious Studies from Yale University, with a dissertation on Charles Hodge and the nineteenth-century Darwinian controversies. In 1994 he received a second Ph.D. in Molecular and Cell Biology from the University of California at Berkeley, with a dissertation on frog embryology. From 1995 to 1998 he worked as a hospital laboratory supervisor and did postdoctoral research at Berkeley. He then moved with his family to Seattle, where he is now a Senior Research Fellow at the Discovery Institute. He has authored scientific articles in BioSystems, The Scientist, The American Biology Teacher and Rivista


Background. In a companion paper we use careful numerical simulation to show that there is a quantifiable selection threshold below which low impact deleterious mutations escape purifying selection and therefore accumulate without limit. In that study we developed the statistic, STd, which is the mid-point of the transition zone between selectable and unselectable deleterious mutations. We showed that under most natural circumstances STd values should be surprisingly high, such that the large majority of all deleterious mutations should be unselectable. Does a similar selection threshold exist for beneficial mutations?


Methods. As in our companion paper we here employ what we describe as genetic accounting to quantify the selection threshold STb for beneficial mutations, and we study how various biological factors combine to determine its value. 


Results. In all experiments that employ biologically reasonable parameters, we observe high STb values and a general failure of selection to preferentially amplify the large majority of beneficial mutations. High impact beneficial mutations strongly interfere with selection of all low impact mutations. 


Conclusions. A selection threshold exists for beneficial mutations similar in magnitude to the selection threshold for deleterious ones, but the dynamics of that threshold are different. Our results suggest that for higher eukaryotes, minimal values for STb are on the order of 10-4 or higher. It appears very likely that most functional nucleotides in a large genome have fractional contributions to fitness which are much smaller than this. This suggests that given our current understanding of how natural selection operates, we cannot explain the origin of the typical functional nucleotide.

Selection threshold severly constrains capture of beneficial mutations


John Sanford

Department of Horticulture, NYSAES, Cornell University, Geneva NY 14456


John Sanford has a Ph.D. in Plant Breeding/Genetics from the University of Wisconsin. He has been a Cornell professor for 30 years, conducting research in the areas of plant breeding, plant genetic engineering, and theoretical genetics. John conducted plant genetic research that resulted in many new crop varieties, more than 80 scientific publications, and 30 patents. John was the primary inventor of the biolistic “gene gun” process, which was used to produce a large fraction of the transgenic crops grown in the world today. John was team leader in the development of the program Mendel’s Accountant, the world’s first biologically realistic forward time genetic accounting program. John is the author of the book Genetic Entropy and the Mystery of the Genome. John is now semi-retired from Cornell, and continues to hold the position of Courtesy Associate Professor.



There is now abundant evidence that the continuous accumulation of deleterious mutations within natural populations poses a major problem for neo-Darwinian theory. It has been proposed that a viable evolutionary mechanism for halting the accumulation of deleterious mutations might arise if fitness depends primarily on an individual’s “mutation-count.”  This hypothetical “mutation-count mechanism” (MCM) is tested using numerical simulation to determine the viability of the hypothesis and to determine what biological factors affect the relative efficacy of this mechanism. MCM is shown to be operational only when all of the following circumstances prevail: 1) a very narrow range of mutational effects; 2) truncation selection; 3) zero environmental variance; and 4) sexual recombination. Therefore, MCM does not appear to occur under biologically realistic conditions. MCM is thus not a viable evolutionary hypothesis and is not capable of stopping deleterious mutation accumulation in natural populations.

Using numerical simulation to test the "mutation-count mechanism" for halting deleterious mutation accumulation in natural populations.


Wesley Brewer

Fluid Physics International.


Wesley Brewer is the sole proprietor of Fluid Physics International, a small consultancy specializing in developing numerical simulation software for modeling complex scientific phenomena. His primary research area is in computational hydrodynamics, but has also been working in computational genetics and numerical weather simulations. Since 2005, he has been part of the Mendel’s Accountant development team. Dr. Brewer holds a B.S. in engineering science and mechanics from the University of Tennessee, an M.S. in ocean engineering from the Massachusetts Institute of Technology, and a Ph.D. in computational engineering from Mississippi State University. Since 2007, Dr. Brewer spends much of his time teaching computer science in Korea



The process of deleterious mutation accumulation is influenced by numerous biological factors, including the way in which the accumulating mutations interact with one another. The phenomenon of negative mutation-to-mutation interactions is known as synergistic epistasis (SE). It is widely believed that SE should enhance selective elimination of mutations and thereby diminish the problem of genetic degeneration. We apply numerical simulation to test this commonly expressed assertion. We find that under biologically realistic conditions, synergistic epistasis exerts little to no discernable influence on mutation accumulation and genetic degeneration. When the synergistic effect is greatly exaggerated, mutation accumulation is not significantly affected, but genetic degeneration accelerates markedly. As the synergistic effect is exaggerated still more, degeneration becomes catastrophic and leads to rapid extinction. Even when conditions are optimized to enhance the SE effect, selection efficiency against deleterious mutation accumulation is not appreciably influenced. We also evaluated SE using parameters that result in extreme and artificially high selection efficiency (truncation selection and perfect genotypic fitness heritability). Even under these optimized conditions, synergistic epistasis causes accelerated degeneration and only minor reductions in the rate of mutation accumulation. When we included the effect of linkage within chromosomal segments in our SE analyses, it made degeneration still worse and even interfered with mutation elimination. Our results therefore strongly suggest that commonly held perceptions concerning the role of synergistic epistasis in halting mutation accumulation are not correct.

Can synergistic epistasis halt mutation accumulation? Results from numerical simulation


John Baumgardner

Department of Earth and Environmental Sciences, Ludwig Maximilians University, Theresienstrasse 41, 80333 Munich, Germany.


Dr. Baumgardner has a B.S. in electrical engineering from Texas Tech University, a M.S. in electrical engineering from Princeton University, and a Ph.D. in geophysics and space physics from UCLA. From 1984 to 2004 he served as a staff scientist in the Theoretical Division of Los Alamos National Laboratory engaged in a variety of research projects in computational physics. Beginning in 2004 he has been part of the team which developed Mendel’s Accountant, a computer model for investigating research topics in population genetics. He is currently an adjunct staff scientist in the Department of Earth and Environmental Sciences at Ludwig Maximilians University in Munich, Germany.


Computational evolution experiments predict a net loss of genetic information despite selection in biological organisms


Chase W. Nelson

Research Scientist,


Chase W. Nelson is a biologist and musician currently pursuing a PhD in bioinformatics and molecular evolution. He graduated from Oberlin College in 2010, where he performed honors research on mutation accumulation in Arabidopsis. While at Oberlin, he became an NSF STEM Scholar in Computation and Modeling, and also took part in several research experiences, including an NIH IDeA Networks of Biomedical Research Excellence Fellowship at the University of Wyoming. He subsequently worked under Dr. John C. Sanford at Rainbow Technologies, Inc., where he examined the power of natural selection in digital organisms. His current studies under Dr. Austin L. Hughes focus on developing computational methods to detect natural selection at the nucleotide level. His design of novel tools for next-generation

sequence analysis and geographic information systems earned him an NSF GRFP Award in 2013. During the summer of 2013, he also undertook an NSF EAPSI Fellowship to study rice genetics under Dr. Wen-Hsiung Li at Academia Sinica (中央研究院) in Taipei, Taiwan.



Computational evolution experiments using the simulation Mendel’s Accountant have suggested that deleterious mutation accumulation may pose a threat to the long-term survival of many biological species. Contrarily, experiments using the program Avida have suggested that purifying selection is extremely effective and that novel genetic information can arise via selection for high-impact beneficial mutations. The present study shows that these approaches yield seemingly contradictory results only because of disparate parameter settings. Both agree when similar settings are used, and both reveal a net loss of genetic information under biologically relevant conditions. Further, both approaches establish the existence of three potentially prohibitive barriers to the evolution of novel genetic information: (1) the selection threshold and resultant genetic entropy; (2) irreducible complexity, or the waiting time to beneficial mutation; and (3) the pressure of reductive evolution, i.e., the selective pressure to shrink the functional genome and disable unused functions. The adequacy of mutation and natural selection for producing and sustaining novel genetic information cannot be assessed without a careful study of these issues.


research in the area of biomimetics where the minute combustion chamber of the bombardier beetle has inspired a patented novel spray technology with applications to fuel injectors, pharmaceutical sprays, fire extinguishers and aerosols. This research was awarded the 2010 Times Higher Educational award for the Outstanding Contribution to Innovation and Technology.



Are there laws of information exchange? And how do the principles of thermodynamics connect with the communication of information?


We consider first the concept of information and examine the various alternatives for its definition. The reductionist approach has been to regard information as arising out of matter and energy. In such an approach, coded information systems such as DNA are regarded as accidental in terms of the origin of life, and it is argued that these then led to the evolution of all life forms as a process of increasing complexity by natural selection operating on mutations in these first forms of life. However, scientists in the discipline of thermodynamics have long been aware that organizational systems are inherently systems with low local entropy, and have argued that the only way to have consistency with an evolutionary model of the universe and common descent of all life forms is to posit a flow of low entropy into the earth’s environment and in this second approach they suggest that islands of low entropy form organizational structures found in living systems A third alternative proposes that information is in fact non-material and that the coded information systems (such as, but not restricted to the coding of DNA in all living systems) is not defined at all by the biochemistry or physics of the molecules used to store the data. Rather than matter and energy defining the information sitting on the polymers of life, this approach posits that the reverse is in fact the case. Information has its definition outside the matter and energy on which it sits, and furthermore constrains it to operate in a highly non-equilibrium thermodynamic environment. This proposal resolves the thermodynamic issues and invokes the correct paradigm for understanding the vital area of thermodynamic / organizational interactions, which despite the efforts from alternative paradigms has not given a satisfactory explanation of the way information operates in biological systems.


Starting from the paradigm of information being defined by non-material arrangement and coding, one can then postulate the idea of laws of information exchange which have some parallels with the laws of thermodynamics which undergird such an approach. These issues are explored tentatively in this paper, and lay the groundwork for further investigative study.

Thermodynamics and information in living systems


Andy C. McIntosh

Professor University of Leeds, DSc, FIMA, C.Math, FInstE, CEng, FInstP, MIGEM, FRAeS


Andy McIntosh holds a research chair in Thermodynamics and Combustion Theory, and has lectured and researched in these fields for over 30 years. He has a PhD in combustion theory from the aerodynamics department of what was then Cranfield Institute of Technology (now Cranfield University), a DSc in Applied Mathematics from the University of Wales and worked for a number of years at the Royal Aircraft Establishment. He is a Fellow of the Institute of Mathematics and its Applications, the Institute of Energy, the Institute of Physics and the Royal Aeronautical Society. A chartered mathematician and engineer, and author of over 180 papers and articles, his research has been in combustion in fluids and solids. His work has also included investigations into the fundamental link between thermodynamics and information, and in the last few years he has been involved in


There are a number of standard models for the evolutionary process of mutation and competition as a dynamical system on a fitness space. We apply basic topology and dynamical systems results to prove that every such evolutionary dynamical system with a finite spatial domain is asymptotic to a recurrent orbit; to an observer the system will appear to repeat a known state infinitely often. In an evolutionary system driven by increasing fitness, the system will reach a point after which there is no observable increase in fitness.

Equilibria in fitness spaces


William Basener

School of Mathematics, Rochester Institute of Technology.


Dr. Basener is an associate professor in the School of Mathematical Sciences at the Rochester Institute of Technology and Chief Imaging Scientist for Spectral Solutions. He received a bachelor's degree in mathematics from Marist College and a Ph.D. in mathematics from Boston University in 2000. He has published research in dynamical systems, chaos, topology, population modeling, economics and remote sensing and is the author of an NSF-funded textbook Topology and Its Applications. He has also worked on projects funded by the Department of Defense, various corporations, and has worked as a contractor for the National Geospatial-Intelligence Agency.