Seminar material

Ein paar Sachen sind eingescannt und daher von nicht besonderer Qualit├Ąt, sorry.

Pattern formation

  • Meinhardt group at MPI for Developmental Biology
  • The classical paper (Turing Patterns): Turing, A.M. (1952) The Chemical Basis of Morphogenesis. Phil. Trans. R. Soc. B 237,641:37-72. <pdf>
  • Dasselbe nochmal etwas einfacher dargestellt: aus dem Fachkurs ‚Modellierung biologischer Systeme‘, HU 2000. <pdf>
  • An instructive book: Meinhardt H. (1982) Models of biological pattern formation. Academic Press. 211p. <pdf>
  • Gierer A., Meinhardt H. (1972) A Theory of Biological Pattern Formation. Kybernetik 12:30-39. <pdf>
  • Gierer A. Meinhardt H.: A generative principle of pattern formation based on lateral inhibition, local instability and global stability. <pdf>
  • Lee K.J. et al. (1992) Pattern Formation by Interacting Chemical Fronts. Science 261:192-194 <pdf>
  • Pearson J.E. (1993) Complex Patterns in a Simple System. Science 261:189-192. <pdf>
  • Mathematical Biology by Murray (2001) I: An introduction. Chapter 8: BZ oscillating reactions. <pdf>
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    Population dynamics

  • Mathematical Biology by Murray (2001) I: An introduction. Chapter 2: Discrete populations models for a single species. <pdf>
  • Mathematical Biology by Murray (2001) I: An introduction. Chapter 3: Models for interacting populations. <pdf>
  • Wright (1983) Catastrophe Theory in Management Forecasting and Decision Making. J. Opl Res. Soc. 34:935-942 <pdf>
  • Peterman (1977) A simple mechanism that causes collabsing stability regions in exploited salmoid populations. J. Fish. Res. Bd. Can. 34:1130-1142. <pdf>
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    Molecular evolution

  • Molecular Evolution by Li (1997). Chapter 2: Dynamics of genes in populations. <pdf>
  • A review on the ‚Nearly Neutral Theory of Evolution‘: Otha (1992) The nearly neutral theory of molecular evolution. Annu. Rev. Ecol. Syst. 23:263-86 <pdf>
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    For the mathematically scilled and/or interested reader: some classical Genetics papers of Kimura:

  • Kimura (1954) Process leading to quasi-fixation of genes in natural populations due to random fluctuation of selection intensities. Genetics 39:280-295. <pdf>
  • Kimura (1962) On the probability of fixation of mutant genes in a populations. Genetics 47:713-719. <pdf>
  • Kimura et al. (1963) The mutation lead in small populations. Genetics 48:1303-1312. <pdf>
  • Kimura & Crow (1964) The number of alleles that can be maintained in a fiite population. Genetics 49:725-738. <pdf>
  • Kimura & Maruyama (1966) The mutational load with epistatic gene interactions in fitness. Genetics 54:1337-1351. <pdf>
  • Kimura (1969) The number of heterozygous nucleotide sites mainted in a finite population due to steady state flx of mutations. Genetics 61:893-903. <pdf>
  • Kimura & Ohta (1970) Probability of fixation of a mutant gene in a finite population when selective advantage decreases with time. Genetics 65:525-534. <pdf>
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    Sequence evolution

  • Competition and Selection in Biological Systems: aus dem Fachkurs ‚Modellierung biologischer Systeme‘, HU 2000. <pdf>
  • Eigen & Schuster (1977) The Hypercycle. A Principle of Natural Self-Organisation. Part A: Emergence of the Hypercycle. Naturwissenschaften 64:541-565. <pdf>
  • Eigen & Schuster (1978) The Hypercycle. A Principle of Natural Self-Organisation. Part B: The Abstract Hypercycle. Naturwissenschaften 65:7-41. <pdf>
  • Eigen & Schuster (1978) The Hypercycle. A Principle of Natural Self-Organisation. Part C: The Realistic Hypercycle. Naturwissenschaften 65:341-369 <pdf>
  • Swetina & Schuster (1982) Self-replication with errors. A model for polynucleotide replication. Biophys. Chem. 16:329-345. <pdf>
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    Evolutionary Algorithms

    Some good online resources:

  • A Survey of Global Optimization Methods
  • Global Optimization
  • Gene Expression Programming
  • The classical SA paper: Metropolis et al. (1953) J. Chem. Phys. 21. <pdf>
  • Another classical SA paper: Kirkpatrick et al. (1983) Science 220. <pdf>
  • An introduction to Optimization. The first chapter of the book ‚Differential Evolution by Price et al. <pdf>
  • Storn et al. (1997) Differential evolution paper. <pdf>
  • Genetic Programming: Koza, J.R. (1990), Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems, Stanford University Computer Science Department technical report.<pdf>
  • Gene Expression Programming: Ferreira (2001) Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87-129. <pdf>