Thermodynamic State Ensemble Models of cis-Regulation
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{"title"=>"Thermodynamic state ensemble models of cis-regulation", "type"=>"journal", "authors"=>[{"first_name"=>"Marc S.", "last_name"=>"Sherman", "scopus_author_id"=>"55221205700"}, {"first_name"=>"Barak A.", "last_name"=>"Cohen", "scopus_author_id"=>"7402977852"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84861120417", "pmid"=>"22479169", "pui"=>"364830899", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "scopus"=>"2-s2.0-84861120417", "doi"=>"10.1371/journal.pcbi.1002407", "issn"=>"1553734X"}, "id"=>"52153c2c-18f1-3b3b-a364-1231b9dcb5f8", "abstract"=>"A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2) the binding constants that describe the affinity of the protein-protein and protein-DNA interactions that occur in each state, and (3) whether each state is capable of transcribing. Using these components, we demonstrate how to compute a cis-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own cis-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of cis-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial cis-regulation and in generating formalisms that accurately predict gene expression from DNA sequence.", "link"=>"http://www.mendeley.com/research/thermodynamic-state-ensemble-models-cisregulation", "reader_count"=>130, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>11, "Researcher"=>38, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>41, "Student > Postgraduate"=>3, "Student > Master"=>11, "Other"=>3, "Student > Bachelor"=>13, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>11, "Researcher"=>38, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>41, "Student > Postgraduate"=>3, "Student > Master"=>11, "Other"=>3, "Student > Bachelor"=>13, "Professor"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>20, "Mathematics"=>6, "Agricultural and Biological Sciences"=>78, "Medicine and Dentistry"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>7, "Chemistry"=>3, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>78}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>20}, "Mathematics"=>{"Mathematics"=>6}, "Unspecified"=>{"Unspecified"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Colombia"=>1, "Canada"=>2, "Netherlands"=>1, "Austria"=>1, "United States"=>10, "Ukraine"=>1, "Denmark"=>1, "United Kingdom"=>2, "Italy"=>1, "France"=>2, "Germany"=>2, "Spain"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/338892"], "description"=>"<div><p>A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1) the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2) the binding constants that describe the affinity of the protein–protein and protein–DNA interactions that occur in each state, and (3) whether each state is capable of transcribing. Using these components, we demonstrate how to compute a <em>cis</em>-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own <em>cis</em>-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of <em>cis</em>-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial <em>cis</em>-regulation and in generating formalisms that accurately predict gene expression from DNA sequence.</p> </div>", "links"=>[], "tags"=>["thermodynamic", "ensemble", "models"], "article_id"=>126989, "categories"=>["Physics", "Biological Sciences", "Genetics"], "users"=>["Marc S. Sherman", "Barak A. Cohen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002407", "stats"=>{"downloads"=>7, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Thermodynamic_State_Ensemble_Models_of_cis_Regulation/126989", "title"=>"Thermodynamic State Ensemble Models of <em>cis</em>-Regulation", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-03-29 01:56:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/660510"], "description"=>"<p>(A) Four states are allowed in this example, two where transcription is inactive (states 1 and 3) and two states where transcription is active (states 2 and 4). (B) The function is composed of the concentrations of transcriptionally active states summed in the numerator divided by the sum of the concentrations of all possible states.</p>", "links"=>[], "tags"=>["ensemble"], "article_id"=>331003, "categories"=>["Physics", "Biological Sciences", "Genetics"], "users"=>["Marc S. Sherman", "Barak A. Cohen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002407.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Thermodynamic_state_ensemble_model_example_/331003", "title"=>"Thermodynamic state ensemble model example.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-29 00:16:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/660575"], "description"=>"<p>Proteins/complexes are represented as ovals, binding sites as rectangles. (A) Repressor-RNAP competition with activator release model, see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002407#pcbi-1002407-box002\" target=\"_blank\">Box 2</a>. The ovals represent RNAP (blue), repressor (red), and activator (green). Note that the repressor and RNAP binding sites are overlapping to reflect competition between sites. (B) Sequential binding model, see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002407#pcbi-1002407-box002\" target=\"_blank\">Box 2</a>. The ovals correspond to RNAP (blue), activator A1 (dark green), and activator A2 (light green).</p>", "links"=>[], "tags"=>["representations", "thermodynamic"], "article_id"=>331068, "categories"=>["Physics", "Biological Sciences", "Genetics"], "users"=>["Marc S. Sherman", "Barak A. Cohen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002407.g003", "stats"=>{"downloads"=>6, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphical_representations_of_thermodynamic_cis_regulatory_functions_/331068", "title"=>"Graphical representations of thermodynamic <i>cis</i>-regulatory functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-29 00:17:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/660470"], "description"=>"<p>A basal promoter is composed of two states, one where DNA is bound with RNAP and is transcriptionally active, and another where DNA is free and inactive.</p>", "links"=>[], "tags"=>["basal"], "article_id"=>330962, "categories"=>["Physics", "Biological Sciences", "Genetics"], "users"=>["Marc S. Sherman", "Barak A. Cohen"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002407.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_States_of_a_basal_promoter_/330962", "title"=>"States of a basal promoter.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-29 00:16:02"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"7", "full-text"=>"2", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[325, 522, 627, 718, 804, 884, 969, 1052, 1131, 1207, 1277, 1346, 1415, 1478, 1542, 1605, 1663, 1723, 1776, 1839, 1895, 1955, 2008, 2066, 2123]}]}
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