Recent advances in biosensing technologies have led to applications of biosensor probe arrays for rapid identification of biological agents such as drugs, gene expressions, proteins, cholesterol and fats in an input sample. However, monitoring the simultaneous presence of multiple agents in a sample is still a challenging task. Multiple agents may often attach to the same probes, leading to low specificity. By using microarrays as a specific example, we introduce two methods based on conditional deduction and non-unique probes to detect multiple targets. We introduce three quality metrics, namely: effectiveness, cost and reliability to evaluate different designs of microarrays and propose two ILP/Pseudo-Boolean models for optimizing on these metrics. By applying on various synthetic and real datasets, we demonstrate the importance of these quality metrics in designing microarrays for multiple target detections.
{"title":"Methods for Designing Reliable Probe Arrays","authors":"M. Lombardi, L. Benini, A. Garg, G. Micheli","doi":"10.1109/BIBE.2010.66","DOIUrl":"https://doi.org/10.1109/BIBE.2010.66","url":null,"abstract":"Recent advances in biosensing technologies have led to applications of biosensor probe arrays for rapid identification of biological agents such as drugs, gene expressions, proteins, cholesterol and fats in an input sample. However, monitoring the simultaneous presence of multiple agents in a sample is still a challenging task. Multiple agents may often attach to the same probes, leading to low specificity. By using microarrays as a specific example, we introduce two methods based on conditional deduction and non-unique probes to detect multiple targets. We introduce three quality metrics, namely: effectiveness, cost and reliability to evaluate different designs of microarrays and propose two ILP/Pseudo-Boolean models for optimizing on these metrics. By applying on various synthetic and real datasets, we demonstrate the importance of these quality metrics in designing microarrays for multiple target detections.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128243563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Xanthopoulos, Steffen Rebennack, Chang-Chia Liu, Jicong Zhang, G. Holmes, B. Uthman, P. Pardalos
Absence seizures are characterized by sudden loss of consciousness and interruption of ongoing motor activities for a brief period of time lasting few to several seconds and up to half a minute. Due to their brevity and subtle clinical manifestations absence seizures are easily missed by inexperienced observers. Accurate evaluation of their high frequency of recurrence can be a challenge even for experienced observers. We present a novel method for detecting and analyzing absence seizures acquired from electroencephalogram (EEG) recordings in patients with absence seizures. Six patients were included in this study, two seizure free, of a total recording time of 26 hours, and four experiencing over 100 seizures within 14.5 hours of total recordings. Our algorithm detected only one false positive finding in the first seizure free patients and 148 of 186 continuous uninterrupted 3Hz spike and wave discharge (SWD) epochs in the rest of the patients. Out of the total 38 missed SWD epochs 28 were
{"title":"A Novel Wavelet Based Algorithm for Spike and Wave Detection in Absence Epilepsy","authors":"P. Xanthopoulos, Steffen Rebennack, Chang-Chia Liu, Jicong Zhang, G. Holmes, B. Uthman, P. Pardalos","doi":"10.1109/BIBE.2010.12","DOIUrl":"https://doi.org/10.1109/BIBE.2010.12","url":null,"abstract":"Absence seizures are characterized by sudden loss of consciousness and interruption of ongoing motor activities for a brief period of time lasting few to several seconds and up to half a minute. Due to their brevity and subtle clinical manifestations absence seizures are easily missed by inexperienced observers. Accurate evaluation of their high frequency of recurrence can be a challenge even for experienced observers. We present a novel method for detecting and analyzing absence seizures acquired from electroencephalogram (EEG) recordings in patients with absence seizures. Six patients were included in this study, two seizure free, of a total recording time of 26 hours, and four experiencing over 100 seizures within 14.5 hours of total recordings. Our algorithm detected only one false positive finding in the first seizure free patients and 148 of 186 continuous uninterrupted 3Hz spike and wave discharge (SWD) epochs in the rest of the patients. Out of the total 38 missed SWD epochs 28 were","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133354646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a global clustering approach to identify putative intergenic non-coding RNAs based on the RNA polymerase II and Histone 3 lysine 4 trimethylation signatures. Both of these signatures are processed from the digital sequencing tags produced by chromatin immunoprecipitation, a high-throughput massively parallel sequencing (ChIP-Seq) technology. Our method compares favorably to the comparison method. We characterize the intergenic non-coding RNAs to have conservative promoters. We predict that these nc-RNAs are related to metabolic process without lipopolysaccharides (LPS) treatment, but shift towards developmental and immune-related functions with LPS treatment. We demonstrate that more intergenic nc-RNAs respond positively to LPS treatment, rather than negatively. Using QPCR, we experimentally validate 8 out of 11 nc-RNA regions respond to LPS treatment as predicted by the computational method.
{"title":"A Clustering Approach to Identify Intergenic Non-coding RNA in Mouse Macrophages","authors":"L. Garmire, S. Subramaniam, D. Garmire, C. Glass","doi":"10.1109/BIBE.2010.10","DOIUrl":"https://doi.org/10.1109/BIBE.2010.10","url":null,"abstract":"We present a global clustering approach to identify putative intergenic non-coding RNAs based on the RNA polymerase II and Histone 3 lysine 4 trimethylation signatures. Both of these signatures are processed from the digital sequencing tags produced by chromatin immunoprecipitation, a high-throughput massively parallel sequencing (ChIP-Seq) technology. Our method compares favorably to the comparison method. We characterize the intergenic non-coding RNAs to have conservative promoters. We predict that these nc-RNAs are related to metabolic process without lipopolysaccharides (LPS) treatment, but shift towards developmental and immune-related functions with LPS treatment. We demonstrate that more intergenic nc-RNAs respond positively to LPS treatment, rather than negatively. Using QPCR, we experimentally validate 8 out of 11 nc-RNA regions respond to LPS treatment as predicted by the computational method.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes and differ based on the composition, affinity and lifetime of the association. A vast amount of PPI data for various organisms is avaiable from MIPS, DIP and other sources. The identification of functional modules in PPI network is of great interest because they often reveal unknown functional ties between proteins and hence predict functions for unknown proteins. In this paper, we propose using functional flow simulation and the topology of the network for the functional module detection and function prediction problem. Our approach is based on the functional influence model that quantifies the influence of a biological component on another. We introduce a flow simulation algorithm to generate a functional profile for each component. In addition, a new clustering method FMD (Functional Module Detection) is designed to associate with functional profiles to detect functional modules. We evaluate the proposed technique on three different yeast networks with MIPS functional categories and compare it with several other existing techniques in terms of precision and recall. Our experiments show that our approach achieves better accuracy than other existing methods.
{"title":"Functional Flow Simulation Based Analysis of Protein Interaction Network","authors":"Lei Shi, Young-Rae Cho, A. Zhang","doi":"10.1109/BIBE.2010.32","DOIUrl":"https://doi.org/10.1109/BIBE.2010.32","url":null,"abstract":"Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes and differ based on the composition, affinity and lifetime of the association. A vast amount of PPI data for various organisms is avaiable from MIPS, DIP and other sources. The identification of functional modules in PPI network is of great interest because they often reveal unknown functional ties between proteins and hence predict functions for unknown proteins. In this paper, we propose using functional flow simulation and the topology of the network for the functional module detection and function prediction problem. Our approach is based on the functional influence model that quantifies the influence of a biological component on another. We introduce a flow simulation algorithm to generate a functional profile for each component. In addition, a new clustering method FMD (Functional Module Detection) is designed to associate with functional profiles to detect functional modules. We evaluate the proposed technique on three different yeast networks with MIPS functional categories and compare it with several other existing techniques in terms of precision and recall. Our experiments show that our approach achieves better accuracy than other existing methods.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132074149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polony technology is a low-cost, high-throughput platform employed in several applications such as DNA sequencing, haplotyping and alternative pre-mRNA splicing analysis. Owing to their random placement, however, overlapping polonies occur often and may result in inaccurate or unusable data. Accurately identifying polony positions and sizes is essential for maximizing the quantity and quality of data aquired in an image, however, most existing identification algorithms do not handle overlapping polonies well. In this paper, we present a novel polony identification approach combining both a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. Experiments on simulated and real images of highly overlapping polonies show that our algorithm has a 10% to 20% increase in recall compared with the existing algorithms, while keeping precision at the same level.
{"title":"Polony Identification Using the EM Algorithm Based on a Gaussian Mixture Model","authors":"Wei Li, Paul M. Ruegger, J. Borneman, Tao Jiang","doi":"10.1109/BIBE.2010.43","DOIUrl":"https://doi.org/10.1109/BIBE.2010.43","url":null,"abstract":"Polony technology is a low-cost, high-throughput platform employed in several applications such as DNA sequencing, haplotyping and alternative pre-mRNA splicing analysis. Owing to their random placement, however, overlapping polonies occur often and may result in inaccurate or unusable data. Accurately identifying polony positions and sizes is essential for maximizing the quantity and quality of data aquired in an image, however, most existing identification algorithms do not handle overlapping polonies well. In this paper, we present a novel polony identification approach combining both a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. Experiments on simulated and real images of highly overlapping polonies show that our algorithm has a 10% to 20% increase in recall compared with the existing algorithms, while keeping precision at the same level.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stuart King, Yanni Sun, James R. Cole, S. Pramanik
With the advent of next-generation sequencing and culture-independent methods, we now are accumulating an enormous amount of metagenomic data from microbial communities. These data sets are large, hard to assemble, and might encode rare or novel proteins, posing new computational challenges for protein homology search. This paper presents a novel protein homology search algorithm that combines the salient features of pairwise sequence alignment programs such as Blast and protein family based tools such as Hmmer. It is optimized for protein annotation in metagenomic data sets because: 1) it is fast, 2) it can classify short protein fragments encoded by individual sequence reads, 3) it can find homologs to novel or rare protein families when there is not enough member sequences to build a probabilistic model. Our algorithm builds a new indexing data structure called BlastTree, which can index a large sequence family database because of our effective compression techniques. In addition, BlastTree fully exploits sequence family membership information to improve homology search sensitivity. When the BlastTree Search algorithm is incorporated into Hmmer, it runs in a fraction of the time with comparable quality.
{"title":"BLAST Tree: Fast Filtering for Genomic Sequence Classification","authors":"Stuart King, Yanni Sun, James R. Cole, S. Pramanik","doi":"10.1109/BIBE.2010.74","DOIUrl":"https://doi.org/10.1109/BIBE.2010.74","url":null,"abstract":"With the advent of next-generation sequencing and culture-independent methods, we now are accumulating an enormous amount of metagenomic data from microbial communities. These data sets are large, hard to assemble, and might encode rare or novel proteins, posing new computational challenges for protein homology search. This paper presents a novel protein homology search algorithm that combines the salient features of pairwise sequence alignment programs such as Blast and protein family based tools such as Hmmer. It is optimized for protein annotation in metagenomic data sets because: 1) it is fast, 2) it can classify short protein fragments encoded by individual sequence reads, 3) it can find homologs to novel or rare protein families when there is not enough member sequences to build a probabilistic model. Our algorithm builds a new indexing data structure called BlastTree, which can index a large sequence family database because of our effective compression techniques. In addition, BlastTree fully exploits sequence family membership information to improve homology search sensitivity. When the BlastTree Search algorithm is incorporated into Hmmer, it runs in a fraction of the time with comparable quality.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116245814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. E. Allen, J. Norris, D. J. John, Stan J. Thomas, William H. Turkett, J. Fetrow
Multiple approaches for reverse-engineering bio-logical networks from time-series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co-temporal modeling. The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co-temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1) identify two types of entity relationships, parent and sibling, between biological entities, 2) deal with experimental sparse time course data, and 3) handle experimental noise seen in replicate data sets. These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co-temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.
{"title":"Comparison of Co-temporal Modeling Algorithms on Sparse Experimental Time Series Data Sets","authors":"E. E. Allen, J. Norris, D. J. John, Stan J. Thomas, William H. Turkett, J. Fetrow","doi":"10.1109/BIBE.2010.21","DOIUrl":"https://doi.org/10.1109/BIBE.2010.21","url":null,"abstract":"Multiple approaches for reverse-engineering bio-logical networks from time-series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co-temporal modeling. The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co-temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1) identify two types of entity relationships, parent and sibling, between biological entities, 2) deal with experimental sparse time course data, and 3) handle experimental noise seen in replicate data sets. These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co-temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121272084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jia-wei Zhang, Liping Wang, Xia Liu, Hong-hai Zhu, Jun Dong
Standard Electrocardiogram (ECG) database is prepared for testing the performance of automatic detection and classification algorithms. At present, there are three mainstream standard databases used by computer-aided ECG diagnosis researchers: MIT-BIH arrhythmia database, CSE multi-lead database and AHA database. By the progress of ECG in both equipment and diagnosis theory, fatal deficiency was found in these databases and a new one is needed for further studies. So Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data and detailed features with diagnosis result is proposed. It is distinguished not only by improving the raw ECG data’s technical parameters, but also introduces some morphology features. Investigation shows these features are utilized by experienced cardiologists effectively. CCDD is used in our group as well as aiming for other and others’ projects in the future.
{"title":"Chinese Cardiovascular Disease Database (CCDD) and Its Management Tool","authors":"Jia-wei Zhang, Liping Wang, Xia Liu, Hong-hai Zhu, Jun Dong","doi":"10.1109/BIBE.2010.19","DOIUrl":"https://doi.org/10.1109/BIBE.2010.19","url":null,"abstract":"Standard Electrocardiogram (ECG) database is prepared for testing the performance of automatic detection and classification algorithms. At present, there are three mainstream standard databases used by computer-aided ECG diagnosis researchers: MIT-BIH arrhythmia database, CSE multi-lead database and AHA database. By the progress of ECG in both equipment and diagnosis theory, fatal deficiency was found in these databases and a new one is needed for further studies. So Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data and detailed features with diagnosis result is proposed. It is distinguished not only by improving the raw ECG data’s technical parameters, but also introduces some morphology features. Investigation shows these features are utilized by experienced cardiologists effectively. CCDD is used in our group as well as aiming for other and others’ projects in the future.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125064815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Respiratory and cardiac motions induce displacement and deformation of the tumor-volume in various internal organs. To accommodate this undesired movement and other errors, physicians incorporate a large margin around the tumor to delineate Planning Target Volume (PTV), so that the Clinical Target Volume (CTV) receives the prescribed radiation dose under any scenario. Consequently, a large volume of healthy tissue is irradiated and sometimes it is difficult to spare critical organs adjacent to the tumor. In this study we have proposed a novel approach to 4D Active Tracking and Dynamic Delivery (ATDD) together with tumor motion prediction. Proposed algorithm can predict tumor position and the robotic system can continuously track the tumor during radiation dose delivery, so that a precise dose is given to a moving target while reducing dose to nearby critical organs for improved patient treatment outcome. The efficacy of the proposed method has been investigated by extensive computer simulation. The results have been presented in this article.
{"title":"Tumor Motion Prediction and Tracking in Adaptive Radiotherapy","authors":"Ivan Buzurovic, T. Podder, Ke Huang, Yan Yu","doi":"10.1109/BIBE.2010.52","DOIUrl":"https://doi.org/10.1109/BIBE.2010.52","url":null,"abstract":"Respiratory and cardiac motions induce displacement and deformation of the tumor-volume in various internal organs. To accommodate this undesired movement and other errors, physicians incorporate a large margin around the tumor to delineate Planning Target Volume (PTV), so that the Clinical Target Volume (CTV) receives the prescribed radiation dose under any scenario. Consequently, a large volume of healthy tissue is irradiated and sometimes it is difficult to spare critical organs adjacent to the tumor. In this study we have proposed a novel approach to 4D Active Tracking and Dynamic Delivery (ATDD) together with tumor motion prediction. Proposed algorithm can predict tumor position and the robotic system can continuously track the tumor during radiation dose delivery, so that a precise dose is given to a moving target while reducing dose to nearby critical organs for improved patient treatment outcome. The efficacy of the proposed method has been investigated by extensive computer simulation. The results have been presented in this article.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116619731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giovanni Stracquadanio, R. Umeton, A. Papini, P. Lio’, Giuseppe Nicosia
We have studied the $mathbf{C_3}$ photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the $mathbf{C_3}$ photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the $mathbf{135%}$ from 15.486 $mathbf{mu mol~m^{-2}s^{-1}}$ to $mathbf{36.382~mu mol~m ^{-2}s^{-1}}$, and improving the previous best-found photosynthetic productivity value ($mathbf{27.261}$ $mathbf{mu mol~m ^{-2}s^{-1}}$, $mathbf{76%}$ of enhancement). Optimized enzyme concentrations express a maximal local robustness ($mathbf{100%}$) and a high global robustness ($mathbf{97.2%}$), satisfactory properties for a possible ``in vitro'' manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf $mathbf{CO_2}$ uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three $mathbf{c_i}$ concentrations referring to the estimate of $mathbf{CO_2}$ concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.
{"title":"Analysis and Optimization of C3 Photosynthetic Carbon Metabolism","authors":"Giovanni Stracquadanio, R. Umeton, A. Papini, P. Lio’, Giuseppe Nicosia","doi":"10.1109/BIBE.2010.17","DOIUrl":"https://doi.org/10.1109/BIBE.2010.17","url":null,"abstract":"We have studied the $mathbf{C_3}$ photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the $mathbf{C_3}$ photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the $mathbf{135%}$ from 15.486 $mathbf{mu mol~m^{-2}s^{-1}}$ to $mathbf{36.382~mu mol~m ^{-2}s^{-1}}$, and improving the previous best-found photosynthetic productivity value ($mathbf{27.261}$ $mathbf{mu mol~m ^{-2}s^{-1}}$, $mathbf{76%}$ of enhancement). Optimized enzyme concentrations express a maximal local robustness ($mathbf{100%}$) and a high global robustness ($mathbf{97.2%}$), satisfactory properties for a possible ``in vitro'' manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf $mathbf{CO_2}$ uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three $mathbf{c_i}$ concentrations referring to the estimate of $mathbf{CO_2}$ concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.","PeriodicalId":330904,"journal":{"name":"2010 IEEE International Conference on BioInformatics and BioEngineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116923744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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