BIO-complexity最新文献

{"title":"Who are the parents of Mycoplasma mycoides JCVI-syn1.0?","authors":"Scott T. Matuscak, C. Tan","doi":"10.5048/BIO-C.2016.2","DOIUrl":"https://doi.org/10.5048/BIO-C.2016.2","url":null,"abstract":"The rapid advancement of technology is causing people to re-think many ideas that were once considered certainties. During a TED (Technology, Entertainment, Design) conference in May 2010, Dr. Craig Venter stated that his team had created “the first self-replicating species we’ve had on the planet whose parent is a computer.” Their work was published in Science in July 2010. Briefly, the Venter team created a synthetic bacterium, Mycoplasma mycoides JCVI-syn1.0, whose genome sequence is composed of the genome sequence of M. mycoides , a yeast cloning vector, and some artificial DNA sequence. This paper provides a detailed analysis of their project and several possible indicators that the statement made by Dr. Craig Venter concerning the parents of the synthetic cells might not be altogether reliable, by following the various contributions made by M. mycoides , M. capricolum , yeast, E. coli , and humans.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stylus Experiments Made Easy--A Free App for Personal Computers","authors":"D. Axe, W. Ewert","doi":"10.5048/BIO-C.2016.1","DOIUrl":"https://doi.org/10.5048/BIO-C.2016.1","url":null,"abstract":"The Stylus model world offers intriguing parallels to the world of bacterial genetics. Combined with its computational tractability, this makes it an attractive system for evolutionary simulations. Here we describe a new app that adds ease of use to these advantages. The free Stylus app (for Mac, Windows, and Linux) brings all aspects of Stylus under the control of a simple graphical user interface and facilitates sharing of all key components of a Stylus project: genes, methods, and experiments. The ease of working with these components and of downloading data and graphics will suit both beginning and advanced users.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Modeling of Human History Part 1: Comparison of Common Descent and Unique Origin Approaches","authors":"O. Hössjer, A. Gauger, C. Reeves","doi":"10.5048/BIO-C.2016.3","DOIUrl":"https://doi.org/10.5048/BIO-C.2016.3","url":null,"abstract":"In a series of two papers (Part 1 and 2) we explore what can be said about human history from the DNA variation we observe among us today. Population genetics has been used to infer that we share a common ancestry with apes, that most of our human ancestors emigrated from Africa 50 000 years ago, that they possibly had some mixing with Neanderthals, Denisovans and other archaic populations, and that the early Homo population was never smaller than a few thousand individuals. Population genetics uses mathematical principles for how the genetic composition of a population develops over time through various forces of change, such as mutation, natural selection, genetic drift, recombinations and migration. In this article (Part 1) we investigate the assumptions about this theory and conclude that it is full of gaps and weaknesses. We argue that a unique origin model where humanity arose from one single couple with created diversity seems to explain data at least as well, if not better. We finally propose an alternative simulation approach that could be used in order to val- idate such a model. The mathematical principles of this model are described in more detail in our second paper (Part 2).","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Modeling of Human History Part 2: A Unique Origin Algorithm","authors":"O. Hössjer, A. Gauger, C. Reeves","doi":"10.5048/BIO-C.2016.4","DOIUrl":"https://doi.org/10.5048/BIO-C.2016.4","url":null,"abstract":"This paper presents a mathematical unique origin model of humanity. It suggests algorithms for testing different historical scenarios of the human population under the assumption that we all descend from one single couple. For each such scenario, DNA variation is repeatedly simulated from a sample of individuals of today in order to estimate statistics of DNA variation. Comparison of these statistics to real data makes model validation possible. Each simulation repeat is divided into three steps, where first the genealogy of the sampled individuals is simulated backwards in time until the founder generation is reached, then founder DNA is generated and thereafter spread forwards in time to the present, along the lineages of the ancestral tree. The model is applicable to predefined demographic scenarios that may include population expansions and bottlenecks. Colonization/range expansion and geographic migration is achieved by dividing the metapopulation into geographically separated, but more or less connected, subpopulations. Age structure is modeled in terms of overlapping generations, with various mating rules for males and females and reproduction rules of mating couples. On the genetic level, our model incorporates mitochondrial as well as nuclear (autosomal, X and Y chromosomal) DNA, ordinary (reciprocal) recombination events and gene conversion. The source of genetic variation is selectively neutral germline mutations, and for autosomal and X chromosomal DNA, a second source of variation is created diversity. An extension of the model allows for balancing selection. It combines forward and backward simulation of the genealogy. Our paper is a first step towards a future goal to compare a best fitting unique origin model with a common descent model where humans and other species have a shared ancestry.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model and Laboratory Demonstrations That Evolutionary Optimization Works Well Only If Preceded by Invention—Selection Itself Is Not Inventive","authors":"D. Axe, A. Gauger","doi":"10.5048/BIO-C.2015.2","DOIUrl":"https://doi.org/10.5048/BIO-C.2015.2","url":null,"abstract":"0 0 1 320 1828 Biologic Institute 15 4 2144 14.0 Normal 0 false false false EN-US JA X-NONE 0 0 1 320 1828 Biologic Institute 15 4 2144 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ \u0000table.MsoNormalTable \u0000 {mso-style-name:\"Table Normal\"; \u0000 mso-tstyle-rowband-size:0; \u0000 mso-tstyle-colband-size:0; \u0000 mso-style-noshow:yes; \u0000 mso-style-priority:99; \u0000 mso-style-parent:\"\"; \u0000 mso-padding-alt:0in 5.4pt 0in 5.4pt; \u0000 mso-para-margin:0in; \u0000 mso-para-margin-bottom:.0001pt; \u0000 mso-pagination:widow-orphan; \u0000 font-size:12.0pt; \u0000 font-family:\"Times New Roman\"; \u0000 mso-fareast-language:JA;} \u0000 Since biological inventions only benefit their possessors after they work, their origins cannot be attributed to their selective effects. One proposed solution to this conundrum is that selection perfects activities that already existed in rudimentary form before they became beneficial. An example of this idea for protein origins is the promiscuity hypothesis, which claims that minor aberrant side-reactions in enzymes can be evolutionary starting points for proficient new enzymes. Another example—the junk hypothesis—claims that proteins arising from accidental expression of non-genic DNA may likewise have slight activities that, through evolutionary optimization, lead to proficient enzymes. Here, we tested these proposals by observing how the endpoint of simple evolutionary optimization depends on the starting point. Beginning with optimization of protein-like constructs in the Stylus computational model, we compared promiscuous and junk starting points, where design elements specific to the test function were completely absent, to a starting point that retained most elements of a good design (mutation having disrupted some). In all three cases, evolutionary optimization improved activities by a large factor. The extreme weakness of the original activities, however, meant even large improvements could be inconsequential. Indeed, the endpoint was itself a proficient design only in the case where this design was largely present from the outset. Laboratory optimization of ampicillin-resistance proteins derived from a natural beta lactamase produced similar results. Our junk protein here was a deletion mutant that somehow confers weak resistance without the original catalytic mechanism (much of the active site having been lost). Evolutionary optimization was unable to improve that mutant. In contrast, a comparably weak mutant that retained the active site surpassed the natural beta lactamase after six rounds of selection. So, while mutation and selection can improve the proficiency of good designs through small structural adjustments, they seem unable to convert fortuitous selectable activities into good designs.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2015 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70593973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overabundant mutations help potentiate evolution: The effect of biologically realistic mutation rates on computer models of evolution","authors":"W. Ewert","doi":"10.5048/BIO-C.2015.1","DOIUrl":"https://doi.org/10.5048/BIO-C.2015.1","url":null,"abstract":"Various existing computer models of evolution attempt to demonstrate the efficacy of Darwinian evolution by solving simple problems. These typically use per-nucleotide (or nearest analogue) mutation rates orders of magnitude higher than biological rates. This paper compares models using typical rates for genetic algorithms with the same models using a realistic mutation rate. It finds that the models with the realistic mutation rates lose the ability to solve the simple problems. This is shown to be the result of the difficulty of evolving mutations that only provide a benefit in combination with other mutations.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2015 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systems Biology as a Research Program for Intelligent Design","authors":"D. Snoke","doi":"10.5048/BIO-C.2014.3","DOIUrl":"https://doi.org/10.5048/BIO-C.2014.3","url":null,"abstract":"Opponents of the intelligent design (ID) approach to biology have sometimes argued that the ID perspective discourages scientific investigation. To the contrary, it can be argued that the most productive new paradigm in systems biology is actually much more compatible with a belief in the intelligent design of life than with a belief in neo-Darwinian evolution. This new paradigm in system biology, which has arisen in the past ten years or so, analyzes living systems in terms of systems engineering concepts such as design, information processing, optimization, and other explicitly teleological concepts. This new paradigm offers a successful, quantitative, predictive theory for biology. Although the main practitioners of the field attribute the presence of such things to the outworking of natural selection, they cannot avoid using design language and design concepts in their research, and a straightforward look at the field indicates it is really a design approach altogether.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2014 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Irreducible Complexity: A Survey of Irreducible Complexity in Computer Simulations","authors":"W. Ewert","doi":"10.5048/BIO-C.2014.1","DOIUrl":"https://doi.org/10.5048/BIO-C.2014.1","url":null,"abstract":"Irreducible complexity is a concept developed by Michael Behe to describe certain biological systems. Behe claims that irreducible complexity poses a challenge to Darwinian evolution. Irreducibly complex systems, he argues, are highly unlikely to evolve because they have no direct series of selectable intermediates. Various computer models have been published that attempt to demonstrate the evolution of irreducibly complex systems and thus falsify this claim. However, closer inspection of these models shows that they fail to meet the definition of irreducible complexity in a number of ways. In this paper we demonstrate how these models fail. In addition, we present another designed digital system that does exhibit designed irreducible complexity, but that has not been shown to be able to evolve. Taken together, these examples indicate that Behe’s concept of irreducible complexity has not been falsified by computer models.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2014 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enzyme Families--Shared Evolutionary History or Shared Design? A Study of the GABA-Aminotransferase Family","authors":"M. A. Reeves, A. Gauger, D. Axe","doi":"10.5048/BIO-C.2014.4","DOIUrl":"https://doi.org/10.5048/BIO-C.2014.4","url":null,"abstract":"The functional diversity of enzyme families is thought to have been caused by repeated recruitment events--gene duplications followed by conversions to new functions. However, mathematical models show this can only work if beneficial new functions are achievable by just one or two base changes in the duplicate genes. Having found no convincing demonstration that this is feasible, we previously chose a highly similar pair of E. coli enzymes from the GABA-aminotransferase-like (GAT) family, 2-amino-3-ketobutyrate CoA ligase (Kbl2) and 8-amino-7-oxononanoate synthase (BioF2), and attempted to convert the first to perform the function of the second by site-directed mutagenesis. In the end we were unable to achieve functional conversion by that rational approach. Here we take a complementary approach based on random mutagenesis. Focusing first on single mutations, we prepared mutated libraries of nine genes from the GAT family and tested for BioF2 function in vivo. None of the singly mutated genes had this function. Focusing next on double mutations, we prepared and tested 70% of the 6.5 million possible mutation pairs for Kbl2 and for BIKB, an enzyme described as having both Kbl2 and BioF2 activities in vitro. Again, no BioF2 activity was detected in vivo. Based on these results, we conclude that conversion to BioF2 function would require at least two changes in the starting gene and probably more, since most double mutations do not work for two promising starting genes. The most favorable recruitment scenario would therefore require three genetic changes after the duplication event: two to achieve low-level BioF2 activity and one to boost that activity by overexpression. But even this best case would require about 10^15 years in a natural population, making it unrealistic. Considering this along with the whole body of evidence on enzyme conversions, we think structural similarities among enzymes with distinct functions are better interpreted as supporting shared design principles than shared evolutionary histories.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"2014 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active Information in Metabiology","authors":"W. Ewert, W. Dembski, R. Marks","doi":"10.5048/BIO-C.2013.4","DOIUrl":"https://doi.org/10.5048/BIO-C.2013.4","url":null,"abstract":"Metabiology is a fascinating intellectual romp in the surreal world of the mathematics of algorithmic information theory. In this world, halting oracles hunt for busy beaver numbers and busy beaver numbers unearth Chaitin’s number, knowledge of which in turn allows resolution of numerous unsolved mathematical problems, many of whose solutions would earn large cash bounties. All this, despite the fact that halting oracles can’t be implemented on a computer, a computer can never make a list of busy beaver numbers, and Chaitin’s number, always a positive real number less than one, is proven to be unknowable. The fun of metabiology is the application of these ideas to illustrate Darwinian evolution. When metabiology’s evolutionary process is stripped of the glitter of algorithmic information theory, however, what remains is a procedure similar to that used in other attempts to model Darwinian evolution, like the ev and AVIDA computer programs. Metabiology, like ev and AVIDA, succeeds because available sources of knowledge about the solution being sought can be mined. We show the mining of information from a halting oracle has striking similarities to mining information from a simple Hamming oracle. Unlike a halting oracle, however, Hamming oracles can be implemented on a computer. We demonstrate that for both oracles, information can be mined by search strategies that are analogous in some respects even though the methods differ; in both cases the search strategy used greatly influences the result. Because metabiology’s process relies on unknowable numbers and infinite resources, its reported relative performance measures can only be expressed asymptotically. That is, the results of metabiology are only proven to be true on the largest possible scale. In fact, simple simulations using bounded resources suggest the asymptote is not always approached quickly, indicating that metabiology results may only hold for scales larger than any practical system.","PeriodicalId":89660,"journal":{"name":"BIO-complexity","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70594001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}