Bacterial Chemotaxis: Introverted or Extroverted? A Comparison of the Advantages and Disadvantages of Basic Forms of Metabolism-Based and Metabolism-Independent Behavior Using a Computational Model
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{"title"=>"Bacterial Chemotaxis: Introverted or Extroverted? A Comparison of the Advantages and Disadvantages of Basic Forms of Metabolism-Based and Metabolism-Independent Behavior Using a Computational Model", "type"=>"journal", "authors"=>[{"first_name"=>"Matthew D.", "last_name"=>"Egbert", "scopus_author_id"=>"35096042700"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84878079031", "pui"=>"368973453", "doi"=>"10.1371/journal.pone.0063617", "sgr"=>"84878079031", "pmid"=>"23717454"}, "id"=>"27d8fb41-e106-3adf-9fa6-0ec6f06ed0b6", "abstract"=>"Using a minimal model of metabolism, we examine the limitations of behavior that is (a) solely in response to environmental phenomena or (b) solely in response to metabolic dynamics, showing that basic forms of each of these kinds of behavior are incapable of driving survival-prolonging behavior in certain situations. Inspired by experimental evidence of concurrent metabolism-based and metabolism-independent chemotactic mechanisms in Escherichia coli and Rhodobacter sphaeroides, we then investigate how metabolism-independent and metabolism-based sensitivities can be integrated into a single behavioral response, demonstrating that a simple switching mechanism can be sufficient to effectively integrate metabolism-based and metabolism-independent behaviors. Finally, we use a spatial simulation of bacteria to show that the investigated forms of behavior produce different spatio-temporal patterns that are influenced by the metabolic-history of the bacteria. We suggest that these patterns could be a way to experimentally derive insight into the relationship between metabolism and chemotaxis in real bacteria.", "link"=>"http://www.mendeley.com/research/bacterial-chemotaxis-introverted-extroverted-comparison-advantages-disadvantages-basic-forms-metabol", "reader_count"=>15, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>1, "Student > Master"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>1, "Student > Master"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Environmental Science"=>1, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>6, "Philosophy"=>1, "Physics and Astronomy"=>2, "Psychology"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>1, "China"=>1, "United Kingdom"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1547855"], "description"=>"<p>In the presented model, the system is only considered alive if . Given this constraint and the intrinsic metabolic dynamics described by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063617#pone.0063617.e010\" target=\"_blank\">Equation 3</a>, two regions emerge: the precarious region (states for which must be changed if the system is to persist) and the viable region (states for which no change in is necessary to avoid death).</p>", "links"=>[], "tags"=>["Computational biology", "Metabolic networks", "systems biology", "Evolutionary biology", "Animal behavior", "microbiology", "bacteriology", "Bacterial biochemistry", "Bacterial physiology", "Theoretical biology", "Computer modeling", "Nonlinear dynamics", "viable", "precarious", "regions", "dynamical"], "article_id"=>1068300, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Matthew D. Egbert"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0063617.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Equilibria_and_the_viable_and_precarious_regions_in_the_dynamical_model_of_the_metabolism_/1068300", "title"=>"Equilibria, and the viable and precarious regions in the dynamical model of the metabolism.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-22 07:30:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1547857"], "description"=>"<p>Each of the left-hand plots indicate six example trajectories through the state space described in Section 2.1. The starting points for these trajectories (indicated by small, filled circles) are the same for each behavior. The right-hand plots indicate the “survivable initial conditions” for the same behaviors. The shade of gray on these graphs indicates at for the initial condition indicated by the location of the point on the plot. The white and black regions indicates initial conditions that end in the death states, and respectively. The mid-tones indicate initial conditions which survived. For all calculations, the system appeared to have come close to equilibrium before the end of the simulation.</p>", "links"=>[], "tags"=>["Computational biology", "Metabolic networks", "systems biology", "Evolutionary biology", "Animal behavior", "microbiology", "bacteriology", "Bacterial biochemistry", "Bacterial physiology", "Theoretical biology", "Computer modeling", "Nonlinear dynamics", "trajectories", "survivable", "forms"], "article_id"=>1068302, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Matthew D. Egbert"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0063617.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_trajectories_and_survivable_initial_conditions_for_four_different_forms_of_behavior_/1068302", "title"=>"Example trajectories and survivable initial conditions for four different forms of behavior.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-22 07:30:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1547858"], "description"=>"<p>The black columns indicate the fitness of each behavior as evaluated by the fitness function. Fitness is the sum of two terms: , the proportion of tested initial conditions that are survived by the behavior and , a normalized score that describes the mean distance of the final concentration of from the death states for all tested initial conditions (higher is better for all measurements). See Section 2.2 for further details of how these scores were calculated. Also plotted are the proportion of survival in the spatial simulations (see Section 3.5). Note that the spatial model survival rates follow a similar pattern to the fitness evaluation in the more abstract model.</p>", "links"=>[], "tags"=>["Computational biology", "Metabolic networks", "systems biology", "Evolutionary biology", "Animal behavior", "microbiology", "bacteriology", "Bacterial biochemistry", "Bacterial physiology", "Theoretical biology", "Computer modeling", "Nonlinear dynamics"], "article_id"=>1068303, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Matthew D. Egbert"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0063617.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_behaviors_/1068303", "title"=>"Evaluation of behaviors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-22 07:30:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1547869"], "description"=>"<p>Spatial location () plotted against the initial metabolic state of the simulated bacteria . The distribution of in the environment is indicated by the black curve. Blue points indicate bacteria that have died due to excess (), red points indicate bacteria that have died due to running low of autocatalyst (), and green points indicated bacteria that are alive (). The four behaviors produce qualitatively different spatio-temporal distributions.</p>", "links"=>[], "tags"=>["Computational biology", "Metabolic networks", "systems biology", "Evolutionary biology", "Animal behavior", "microbiology", "bacteriology", "Bacterial biochemistry", "Bacterial physiology", "Theoretical biology", "Computer modeling", "Nonlinear dynamics", "distributions", "performing"], "article_id"=>1068314, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Matthew D. Egbert"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0063617.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatio_temporal_distributions_of_bacteria_performing_the_four_different_behaviors_/1068314", "title"=>"Spatio-temporal distributions of bacteria performing the four different behaviors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-22 07:30:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1547870"], "description"=>"<p>Parameters.</p>", "links"=>[], "tags"=>["Computational biology", "Metabolic networks", "systems biology", "Evolutionary biology", "Animal behavior", "microbiology", "bacteriology", "Bacterial biochemistry", "Bacterial physiology", "Theoretical biology", "Computer modeling", "Nonlinear dynamics"], "article_id"=>1068315, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Matthew D. Egbert"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0063617.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_/1068315", "title"=>"Parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-22 07:30:32"}

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Relative Metric

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