BacSim, a simulator for individual-based modeling of bacterial colony growth. The generic, quantitative, spatially explicit, individual-based model BacSim was developed to simulate growth and behaviour of bacteria. The potential of this approach is in relating the properties of microscopic entities – cells – to the properties of macroscopic, complex systems such as biofilms. Here, the growth of a single Escherichia coli cell into a colony was studied. The object-oriented program BacSim is an extension of Gecko, an ecosystem dynamics model which uses the Swarm toolkit for multi-agent simulations. The model describes bacterial properties including substrate uptake, metabolism, maintenance, cell division and death at the individual cell level. With the aim of making the model easily applicable to various bacteria under different conditions, the model uses as few as eight readily obtainable parameters which can be randomly varied. For substrate diffusion, a two-dimensional diffusion lattice is used. For growth-rate-dependent cell size variation, a conceptual model of cell division proposed by Donachie was examined. A mechanistic version of the Donachie model led to unbalanced growth at higher growth rates, whereas including a minimum period between subsequent replication initiations ensured balanced growth only if this period was unphysiologically long. Only a descriptive version of the Donachie model predicted cell sizes correctly. For maintenance, the Herbert model (constant specific rate of biomass consumption) and for substrate uptake, the Michaelis-Menten or the Best equations were implemented. The simulator output faithfully reproduced all input parameters. Growth characteristics when maintenance and uptake rates were proportional to either cell mass or surface area are compared. The authors propose a new generic measure of growth synchrony to quantify the loss of synchrony due to random variation of cell parameters or spatial heterogeneity. Variation of the maximal uptake rate completely desynchronizes the simulated culture but variation of the volume-at-division does not. A new measure for spatial heterogeneity is introduced: the standard deviation of substrate concentrations as experienced by the cells. Spatial heterogeneity desynchronizes population growth by subdividing the population into parts synchronously growing at different rates. At a high enough spatial heterogeneity, the population appears to grow completely asynchronously.

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  1. Soleimani, Meisam: Finite strain visco-elastic growth driven by nutrient diffusion: theory, FEM implementation and an application to the biofilm growth (2019)
  2. Mattei, Maria Rosaria; Frunzo, Luigi; D’Acunto, B.; Pechaud, Yoan; Pirozzi, Francesco; Esposito, Giovanni: Continuum and discrete approach in modeling biofilm development and structure: a review (2018)
  3. Degond, Pierre; Ferreira, Marina A.; Motsch, Sebastien: Damped Arrow-Hurwicz algorithm for sphere packing (2017)
  4. Machineni, Lakshmi; Rajapantul, Anil; Nandamuri, Vandana; Pawar, Parag D.: Influence of nutrient availability and quorum sensing on the formation of metabolically inactive microcolonies within structurally heterogeneous bacterial biofilms: an individual-based 3D cellular automata model (2017)
  5. Xu, Feifei; Bierman, Robert; Healy, Frank; Nguyen, Hoa: A multi-scale model of \textitEscherichiacoli chemotaxis from intracellular signaling pathway to motility and nutrient uptake in nutrient gradient and isotropic fluid environments (2016)
  6. Bolea Albero, Antonio; Ehret, Alexander E.; Böl, Markus: A new approach to the simulation of microbial biofilms by a theory of fluid-like pressure-restricted finite growth (2014)
  7. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  8. Johnson, Leah R.; Briggs, Cheryl J.: Parameter inference for an individual based model of chytridiomycosis in frogs (2011)
  9. Johnson, Leah R.: Microcolony and biofilm formation as a survival strategy for bacteria (2008)
  10. Murphy, James T.; Walshe, Ray; Devocelle, Marc: A computational model of antibiotic-resistance mechanisms in methicillin-resistant \textitStaphylococcusaureus (MRSA) (2008)
  11. Prats, Clara; Giró, Antoni; Ferrer, Jordi; López, Daniel; Vives-Rego, Josep: Analysis and IbM simulation of the stages in bacterial lag phase: basis for an updated definition (2008)
  12. Tindall, M. J.; Maini, P. K.; Porter, S. L.; Armitage, J. P.: Overview of mathematical approaches used to model bacterial chemotaxis. II: Bacterial populations (2008)
  13. Tindall, M. J.; Porter, S. L.; Maini, P. K.; Gaglia, G.; Armitage, J. P.: Overview of mathematical approaches used to model bacterial chemotaxis. I: The single cell (2008)
  14. Ferrer, Jordi; Vidal, Jaume; Prats, Clara; Valls, Joaquim; Herreros, Esperanza; Lopez, Daniel; Giró, Antoni; Gargallo, Domingo: Individual-based model and simulation of \textitPlasmodiumfalciparum infected erythrocyte \textitinvitro cultures (2007)
  15. Hellweger, Ferdi L.; Kianirad, Ehsan: Individual-based modeling of phytoplankton: evaluating approaches for applying the cell quota model (2007)
  16. Saoud, Narjès Bellamine-Ben; Mark, Gloria: Complexity theory and collaboration: An agent-based simulator for a space mission design team (2007) ioport
  17. Schaller, Gernot; Meyer-Hermann, Michael: A modelling approach towards epidermal homoeostasis control (2007)
  18. Prats, Clara; López, Daniel; Giró, Antoni; Ferrer, Jordi; Valls, Joaquim: Individual-based modelling of bacterial cultures to study the microscopic causes of the lag phase (2006)
  19. Ginovart, Marta; López, Daniel; Gras, Anna: Individual-based modelling of microbial activity to study mineralization of C and N and nitrification process in soil (2005)
  20. Hare, M.; Deadman, P.: Further towards a taxonomy of agent-based simulation models in environmental management (2004)

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