RMBNToolbox: random models for biochemical networks

Aho, Tommi; Smolander, Olli‐Pekka; Niemi, Jari; Yli‐Harja, Olli

doi:10.1186/1752-0509-1-22

Cited by 5 publications

(19 citation statements)

References 41 publications

(30 reference statements)

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“…While P P C accounts only for linear or monotonic relationships, the mutual information takes into account all types of dependence. Given The Venn diagram for mutual information M I (2) of two variables two random variables X and Y with the joint density function f X,Y and marginal density functions f X , f Y , the mutual information M I (2) of two variables X and Y [8] is defined as follows:…”

Section: Mutual Information Between Two Variablesmentioning

confidence: 99%

“…(we use the superscript number 2 to emphasize that the mutual information here is for 2 variables) If X and Y are independent, the mutual information M I (2) (X, Y ) = 0; if they are perfectly dependent, M I (2) (X, Y ) approaches infinity.…”

Section: Mutual Information Between Two Variablesmentioning

confidence: 99%

“…When applying on the synthetic perturbation data of full-random networks (all structures, kinetic laws and parameter values are randomly generated, [2]) as well as of a semi-random networks, the human red blood cell metabolism ( [14,20]), our method gave promising results of interaction subsets that are close to the validated metabolic reactions. The interaction subsets with highest mutual information found from our method often correspond to metabolic reactions in the original networks, also many original reactions have been found in the results of our software.…”

Section: Introductionmentioning

confidence: 99%

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Computational reconstruction of metabolic networks from high-throughput profiling data

Diệp¹,

Hoan²,

Bao³

et al. 2012

JCC

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Tóm tȃt. Hầ u hết các phu. o. ng pháp tính toán tái hiê. n ma. ng sinh ho. c hiê. n nay mó. i chı ' tâ. p trung tìm các tu. o. ng tác giũ. a hai phân tu. ' , trong khi dó ma. ng chuyê ' n hóa la. i bao gồm các pha 'nú. ng liên quan dến tù. 2 dến 6 chất. Vì vâ. y mà các phu. o. ng pháp khám phá ma. ng sinh ho. c dang tồn ta. i không thích ho. . p dê ' tái hiê. n các pha 'nú. ng sinh hóa có nhiề u ho. n hai chất tham gia. Bài báo này gió. i thiê. u mô. t phu. o. ng pháp tính toán tái hiê. n ma. ng các chất chuyê ' n hóa tù. dũ. liê. u do nồng dô. /khối lu. o. . ng các chất o. ' các diề u kiê. n hoȃ. c thò. i diê ' m khác nhau. Phu. o. ng pháp không chı ' phát hiê. n các tu. o. ng tác giũ. a hai phân tu. ' mà còn phát hiê. n du. o. . c các tu. o. ng tác nhiề u ho. n hai phân tu. ' , dó là các tu. o. ng tác ba chất, tu. o. ng tác bốn chất, v.v. Trong phu. o. ng pháp dề xuất, chúng tôi su. ' du. ng dô. do thông tin phu. thuô. c bâ. c ba dê ' dò tìm các tu. o. ng tác da chất. Chúng tôi cung cấp mô. t cách nhìn mó. i về dô. do thông tin phu. thuô. c bâ. c ba mà thích ho. . p trong viê. c phát hiê. n các tu. o. ng tác nhiề u ho. n hai biến. Hiê. u nȃng cu 'a phu. o. ng pháp dề xuất dã du. o. . c dánh giá trên các dũ. liê. u mô pho 'ng các hê. chuyê ' n hóa sinh ho. c. Tính chính xác cu 'a phu. o. ng pháp tái hiê. n la. i ma. ng chuyê ' n hóa du. o. . c dánh giá o. ' hai mú. c: các tu. o. ng tác hai chất và các tu. o. ng tác ba chất. Kết qua ' tái hiê. n cu 'a Phu. o. ng pháp dề xuất là rất triê ' n vo. ng.

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Section: Mutual Information Between Two Variablesmentioning

confidence: 99%

Section: Mutual Information Between Two Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational reconstruction of metabolic networks from high-throughput profiling data

Diệp¹,

Hoan²,

Bao³

et al. 2012

JCC

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“…More precisely, for each data vector y, we employed the RMBNToolbox (MATLAB) [26] a random stoichiometric matrix SI with the desired dimensions and a full column rank, where each species participated in at least one reaction. For the specified number of exchange fluxes mE, we assigned the first mE metabolites (rows) of SI as the species whose exchange fluxes were measured.…”

Section: Random Metabolic Modelsmentioning

confidence: 99%

Assessing and Resolving Model Misspecifications in Metabolic Flux Analysis

Hutter

2017

Preprint

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Abstract:Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell's metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention.Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue.Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions.Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

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“…MFinder and FANMOD are programs for finding over-represented network motifs [54,87]. Dizzy, BioNetS and RMBNToolbox are network kinetics simulators [88][89][90] (Table 1). The functions and performances of 12 different modeling tools have been compared in detail [91].…”

Section: An Outlookmentioning

confidence: 99%