Environment-dependent morphology in plasmodium of true slime mold Physarum polycephalum and a network growth model

Atsuko Takamatsu, Eri Takaba, Ginjiro Takizawa

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Branching network growth patterns, depending on environmental conditions, in plasmodium of true slime mold Physarum polycephalum were investigated. Surprisingly, the patterns resemble those in bacterial colonies even though the biological mechanisms differ greatly. Bacterial colonies are collectives of microorganisms in which individual organisms have motility and interact through nutritious and chemical fields. In contrast, the plasmodium is a giant amoeba-like multinucleated unicellular organism that forms a network of tubular structures through which protoplasm streams. The cell motility of the plasmodium is generated by oscillation phenomena observed in the partial bodies, which interact through the tubular structures. First, we analyze characteristics of the morphology quantitatively, then we abstract local rules governing the growing process to construct a simple network growth model. This model is independent of specific systems, in which only two rules are applied. Finally, we discuss the mechanism of commonly observed biological pattern formations through comparison with the system of bacterial colonies.

Original languageEnglish
Pages (from-to)29-44
Number of pages16
JournalJournal of Theoretical Biology
Volume256
Issue number1
DOIs
Publication statusPublished - 2009 Jan 7

Keywords

  • Bacterial colony
  • Growth dynamics
  • Network
  • Oscillation
  • Pattern formation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Environment-dependent morphology in plasmodium of true slime mold Physarum polycephalum and a network growth model'. Together they form a unique fingerprint.

  • Cite this