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Quasi-Species ~~ Evolution at the Speed of Light ~~

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Quasi-Species ~~ evolution at the speed of light ~~ Signals and Systems in Biology Kushal Shah @ EE, IIT Delhi Quasi-Species : Introduction I Species : Single genotype I Quasispecies I Large group of genotypes with high mutation rate I RNA viruses, Macromolecules like RNA/DNA I Proposed by Manfred Eigen and Peter Schuster in 1970s J. J. Bull et. al., PLOS Computational Biology 1, e61 (2005) C. O. Wilke, BMC Evolutionary Biology 5:44 (2005) Quasi-Species : Introduction I Species : Single genotype I Quasispecies I Large group of genotypes with high mutation rate I RNA viruses, Macromolecules like RNA/DNA I Proposed by Manfred Eigen and Peter Schuster in 1970s J. J. Bull et. al., PLOS Computational Biology 1, e61 (2005) C. O. Wilke, BMC Evolutionary Biology 5:44 (2005) Quasi-Species : Introduction I Species : Single genotype I Quasispecies I Large group of genotypes with high mutation rate I RNA viruses, Macromolecules like RNA/DNA I Proposed by Manfred Eigen and Peter Schuster in 1970s J. J. Bull et. al., PLOS Computational Biology 1, e61 (2005) C. O. Wilke, BMC Evolutionary Biology 5:44 (2005) Quasi-Species : Introduction I Species : Single genotype I Quasispecies I Large group of genotypes with high mutation rate I RNA viruses, Macromolecules like RNA/DNA I Proposed by Manfred Eigen and Peter Schuster in 1970s J. J. Bull et. al., PLOS Computational Biology 1, e61 (2005) C. O. Wilke, BMC Evolutionary Biology 5:44 (2005) RNA Virus I Contains single- or double-stranded RNA as its genetic material I SARS, influenza, hepatitis C, West Nile fever, polio and measles I Retrovirus : Replication process through a DNA intermediate I HIV-1 and HIV-2 I Ribovirus : RNA virus that does not use an DNA intermediate I Very small genome size I RNA Virus ~ 1.7 Kb to 10 Kb I Largest Virus : 1180 Kbp (Mimivirus) I Bacteria ~ 160 Kbp to 13 Mbp I Human ~ 3Gbp I Polychaos dubium ~ 670 Gbp (amoeba) RNA Virus I Contains single- or double-stranded RNA as its genetic material I SARS, influenza, hepatitis C, West Nile fever, polio and measles I Retrovirus : Replication process through a DNA intermediate I HIV-1 and HIV-2 I Ribovirus : RNA virus that does not use an DNA intermediate I Very small genome size I RNA Virus ~ 1.7 Kb to 10 Kb I Largest Virus : 1180 Kbp (Mimivirus) I Bacteria ~ 160 Kbp to 13 Mbp I Human ~ 3Gbp I Polychaos dubium ~ 670 Gbp (amoeba) RNA Virus I Contains single- or double-stranded RNA as its genetic material I SARS, influenza, hepatitis C, West Nile fever, polio and measles I Retrovirus : Replication process through a DNA intermediate I HIV-1 and HIV-2 I Ribovirus : RNA virus that does not use an DNA intermediate I Very small genome size I RNA Virus ~ 1.7 Kb to 10 Kb I Largest Virus : 1180 Kbp (Mimivirus) I Bacteria ~ 160 Kbp to 13 Mbp I Human ~ 3Gbp I Polychaos dubium ~ 670 Gbp (amoeba) RNA Virus I Contains single- or double-stranded RNA as its genetic material I SARS, influenza, hepatitis C, West Nile fever, polio and measles I Retrovirus : Replication process through a DNA intermediate I HIV-1 and HIV-2 I Ribovirus : RNA virus that does not use an DNA intermediate I Very small genome size I RNA Virus ~ 1.7 Kb to 10 Kb I Largest Virus : 1180 Kbp (Mimivirus) I Bacteria ~ 160 Kbp to 13 Mbp I Human ~ 3Gbp I Polychaos dubium ~ 670 Gbp (amoeba) RNA Virus I Contains single- or double-stranded RNA as its genetic material I SARS, influenza, hepatitis C, West Nile fever, polio and measles I Retrovirus : Replication process through a DNA intermediate I HIV-1 and HIV-2 I Ribovirus : RNA virus that does not use an DNA intermediate I Very small genome size I RNA Virus ~ 1.7 Kb to 10 Kb I Largest Virus : 1180 Kbp (Mimivirus) I Bacteria ~ 160 Kbp to 13 Mbp I Human ~ 3Gbp I Polychaos dubium ~ 670 Gbp (amoeba) Natural Selection The process by which traits become more or less common in a population due to consistent effects upon the survival or reproduction of their bearers. ~~ Survival of the fittest ~~ Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Fitness (natural selection) vs. Mutation rate I Consider a group of genotypes existing together I If mutation rate is low, natural selection works I If mutation rate is high, the idea of ‘fittest’ becomes meaningless I 2 genotypes could just have a difference of 1 nucleotide I Equilibrium between various genotypes I Error catastrophe I Loss of a fitter genotype in a population due to high mutation rate I Mutagenesis : Increase the mutation rate of viruses using drugs I Extinction : All genotypes become extinct Simple case of 2 genotypes Quasispecies : Mathematical Model Assuming discrete time-steps 0 n1 = n1w1 (1 − m1) + n2w1m3 0 n2 = n1w2m1 + n2w2 (1 − m2) N0 = MN where w1 (1 − m1) w1m3 M = w2m1 w2 (1 − m2) n1 N = n2 w ≥ 0 and 0 ≤ m ≤ 1 Quasispecies : Mathematical Model Assuming discrete time-steps 0 n1 = n1w1 (1 − m1) + n2w1m3 0 n2 = n1w2m1 + n2w2 (1 − m2) N0 = MN where w1 (1 − m1) w1m3 M = w2m1 w2 (1 − m2) n1 N = n2 w ≥ 0 and 0 ≤ m ≤ 1 Quasispecies : Mathematical Model Assuming discrete time-steps 0 n1 = n1w1 (1 − m1) + n2w1m3 0 n2 = n1w2m1 + n2w2 (1 − m2) N0 = MN where w1 (1 − m1) w1m3 M = w2m1 w2 (1 − m2) n1 N = n2 w ≥ 0 and 0 ≤ m ≤ 1 Quasispecies : Mutation-Selection Balance w1 (1 − m1) w1m3 n1 M = N = w2m1 w2 (1 − m2) n2 N0 = MN At equilibrium, N0 = lN ) MN = lN If m3 = 0, N1 w1 (1 − m1) − w2 (1 − m2) l1 = w1 (1 − m1) = N2 w2m1 l2 = w2 (1 − m2) N1 = 0 w1 (1 − m1) = w2 (1 − m2): Error Threshold Quasispecies : Mutation-Selection Balance w1 (1 − m1) w1m3 n1 M = N = w2m1 w2 (1 − m2) n2 N0 = MN At equilibrium, N0 = lN ) MN = lN If m3 = 0, N1 w1 (1 − m1) − w2 (1 − m2) l1 = w1 (1 − m1) = N2 w2m1 l2 = w2 (1 − m2) N1 = 0 w1 (1 − m1) = w2 (1 − m2): Error Threshold Quasispecies : Mutation-Selection Balance w1 (1 − m1) w1m3 n1 M = N = w2m1 w2 (1 − m2) n2 N0 = MN At equilibrium, N0 = lN ) MN = lN If m3 = 0, N1 w1 (1 − m1) − w2 (1 − m2) l1 = w1 (1 − m1) = N2 w2m1 l2 = w2 (1 − m2) N1 = 0 w1 (1 − m1) = w2 (1 − m2): Error Threshold Quasispecies : Mutation-Selection Balance w1 (1 − m1) w1m3 n1 M = N = w2m1 w2 (1 − m2) n2 N0 = MN At equilibrium, N0 = lN ) MN = lN If m3 = 0, N1 w1 (1 − m1) − w2 (1 − m2) l1 = w1 (1 − m1) = N2 w2m1 l2 = w2 (1 − m2) N1 = 0 w1 (1 − m1) = w2 (1 − m2): Error Threshold Quasispecies : Mutation-Selection Balance w1 (1 − m1) w1m3 n1 M = N = w2m1 w2 (1 − m2) n2 N0 = MN At equilibrium, N0 = lN ) MN = lN If m3 = 0, N1 w1 (1 − m1) − w2 (1 − m2) l1 = w1 (1 − m1) = N2 w2m1 l2 = w2 (1 − m2) N1 = 0 w1 (1 − m1) = w2 (1 − m2): Error Threshold w1 > w2 m1 = km m2 = m k > 1 l1 = w1 (1 − m1) l2
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  • The Magnitude and Diversity of Infectious Diseases

    The Magnitude and Diversity of Infectious Diseases

    Chapter 1 The Magnitude and Diversity of Infectious Diseases “All interest in disease and death is only another expression of interest in life.” Thomas Mann THE IMPORTANCE OF INFECTIOUS DISEASES IN TERMS OF HUMAN MORTALITY According to the U.S. Census Bureau, on July 20, 2011, the USA population was 311 806 379, and the world population was 6 950 195 831 [2]. The U.S. Central Intelligence agency estimates that the USA crude death rate is 8.36 per 1000 and the world crude death rate is 8.12 per 1000 [3]. This translates to 2.6 million people dying in 2011 in the USA, and 56.4 million people dying worldwide. These numbers, calculated from authoritative sources, correlate surprisingly well with the widely used rule of thumb that 1% of the human population dies each year. How many of the world’s 56.4 million deaths can be attributed to infectious diseases? According to World Health Organization, in 1996, when the global death toll was 52 million, “Infectious diseases remain the world’s leading cause of death, accounting for at least 17 million (about 33%) of the 52 million people who die each year” [4]. Of course, only a small fraction of infections result in death, and it is impossible to determine the total incidence of infec- tious diseases that occur each year, for all organisms combined. Still, it is useful to consider some of the damage inflicted by just a few of the organisms that infect humans. Malaria infects 500 million people. About 2 million people die each year from malaria [4].
  • Guide to the Methods of Study and Identification of Soil Gymnamoebae

    Guide to the Methods of Study and Identification of Soil Gymnamoebae

    Protistology 3 (3), 148190 (2004) Protistology Guide to the methods of study and identification of soil gymnamoebae Alexey V. Smirnov1 and Susan Brown2 1 Department of Invertebrate Zoology, Faculty of Biology and Soil Sciences, St.Petersburg State University, Russia 2 CEH Dorset, Dorchester, UK Summary The present guide is an attempt to build a “bridge” between the general textbooks on protozoa and the guides to amoebae intended for specialists. We try to outline the subset of freshwater amoebae species that may be found in the soil and list them in the text. The extended introduction section provides detailed descriptions of the methods and shortcomings of amoeba investigation and gives one some ideas on the peculiarities, biology and ecology of soil amoebae. Special section guides the reader through the identification process to prevent him from potential errors. From our experience, dichotomous keys to amoebae are rather artificial and difficult in use, so this guide is based on a classification system of amoeba morphotypes, i.e. on the classification of the generalized shapes of the locomotive form of an amoeba. It allows easy and fast initial classification of an amoeba into one of 16 groups of species containing from two to twentyfive species. The section dedicated to each morphotype contains the sample plate of photographs and the list of relevant literature for further identification of species of the chosen morphotype. Key words: gymnamoebae, amoeba, systematics, identification, methods, guide Foreword morphological and ultrastructural data) requires establishment of cultures and both light (LM) and Amoeboid protists are among the most common electron (EM) microscopy examination, and is highly and abundant microbes in all types of soil habitats.