Predicting biological system objectives de novo from internal state measurements
<p>Abstract</p> <p>Background</p> <p>Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce by...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2008-01-01
|
| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/43 |
| _version_ | 1811257966439956480 |
|---|---|
| author | Maranas Costas D Burgard Anthony P Oberhardt Matthew A Gianchandani Erwin P Papin Jason A |
| author_facet | Maranas Costas D Burgard Anthony P Oberhardt Matthew A Gianchandani Erwin P Papin Jason A |
| author_sort | Maranas Costas D |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth) and the subsequent application of linear programming (LP) to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning.</p> <p>Results</p> <p>We present a novel method called <b>Biological Objective Solution Search (BOSS) </b>for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, <b>BOSS </b>identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant <it>in silico </it>flux distribution and available experimental (e.g., isotopomer) flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of <it>Saccharomyces cerevisiae</it>.</p> <p>Conclusion</p> <p>We illustrate how <b>BOSS </b>offers insight into the functional organization of biochemical networks, facilitating the interrogation of cellular design principles and development of cellular engineering applications. Furthermore, we describe how growth is the best-fit objective function for the yeast metabolic network given experimentally-measured fluxes.</p> |
| first_indexed | 2024-04-12T18:06:50Z |
| format | Article |
| id | doaj.art-064990c7c1834f38afbef315410674ad |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T18:06:50Z |
| publishDate | 2008-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-064990c7c1834f38afbef315410674ad2022-12-22T03:21:58ZengBMCBMC Bioinformatics1471-21052008-01-01914310.1186/1471-2105-9-43Predicting biological system objectives de novo from internal state measurementsMaranas Costas DBurgard Anthony POberhardt Matthew AGianchandani Erwin PPapin Jason A<p>Abstract</p> <p>Background</p> <p>Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth) and the subsequent application of linear programming (LP) to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning.</p> <p>Results</p> <p>We present a novel method called <b>Biological Objective Solution Search (BOSS) </b>for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, <b>BOSS </b>identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant <it>in silico </it>flux distribution and available experimental (e.g., isotopomer) flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of <it>Saccharomyces cerevisiae</it>.</p> <p>Conclusion</p> <p>We illustrate how <b>BOSS </b>offers insight into the functional organization of biochemical networks, facilitating the interrogation of cellular design principles and development of cellular engineering applications. Furthermore, we describe how growth is the best-fit objective function for the yeast metabolic network given experimentally-measured fluxes.</p>http://www.biomedcentral.com/1471-2105/9/43 |
| spellingShingle | Maranas Costas D Burgard Anthony P Oberhardt Matthew A Gianchandani Erwin P Papin Jason A Predicting biological system objectives de novo from internal state measurements BMC Bioinformatics |
| title | Predicting biological system objectives de novo from internal state measurements |
| title_full | Predicting biological system objectives de novo from internal state measurements |
| title_fullStr | Predicting biological system objectives de novo from internal state measurements |
| title_full_unstemmed | Predicting biological system objectives de novo from internal state measurements |
| title_short | Predicting biological system objectives de novo from internal state measurements |
| title_sort | predicting biological system objectives de novo from internal state measurements |
| url | http://www.biomedcentral.com/1471-2105/9/43 |
| work_keys_str_mv | AT maranascostasd predictingbiologicalsystemobjectivesdenovofrominternalstatemeasurements AT burgardanthonyp predictingbiologicalsystemobjectivesdenovofrominternalstatemeasurements AT oberhardtmatthewa predictingbiologicalsystemobjectivesdenovofrominternalstatemeasurements AT gianchandanierwinp predictingbiologicalsystemobjectivesdenovofrominternalstatemeasurements AT papinjasona predictingbiologicalsystemobjectivesdenovofrominternalstatemeasurements |