Metabolite and isotopomer balancing in the analysis of metabolic cycles: I. Theory

Klapa, Maria I.; Park, Sung M.; Sinskey, Anthony J.; Stephanopoulos, Gregory

doi:10.1002/(sici)1097-0290(19990220)62:4<375::aid-bit1>3.3.co;2-f

Cited by 12 publications

(27 citation statements)

References 8 publications

(8 reference statements)

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“…Using concepts of stoichiometry in a slightly different fashion, Wiechert and others developed isotopomer and cumomer methods for flux rate estimation using radioactive labeling techniques [105–108]. The technique is based on the fact that specifically labeled atoms in an input molecule, such as 1- 13 C-glucose, have known metabolic fates.…”

Section: Kinetic Model Constructionmentioning

confidence: 99%

Recent developments in parameter estimation and structure identification of biochemical and genomic systems

Chou

Voit

2009

Mathematical Biosciences

356

319

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The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using "top-down" or "inverse" approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms.Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification.The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational "work-flow" that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.

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Section: Kinetic Model Constructionmentioning

confidence: 99%

Recent developments in parameter estimation and structure identification of biochemical and genomic systems

Chou

Voit

2009

Mathematical Biosciences

356

319

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“…Moreover, unknown pathways or metabolic channels may be present (van Winden et al, 2001a). This leads to the problem of model discrimination which can be solved based on the / 2 criterion to judge how well a model fits Klapa et al, 1999).…”

Section: Identifiability Analysis Optimal Experimental Design and Mmentioning

confidence: 99%

“…The approaches of Klapa et al (1999) and Sauer et al (1997) are based on explicit formulas for flux determination. Thus, they are limited to a certain network topology and do not make use of the full measurement information.…”

Section: Software Frameworkmentioning

confidence: 99%

13C Metabolic Flux Analysis

Wiechert

2001

Metabolic Engineering

728

610

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“…It should also be noted that experimental measurements can be incorporated as constraints to aid in the calculation of the entire metabolic flux distribution (Vallino and Stephanopoulos, 1993; Zupke and Stephanopoulos, 1994; Wiechert and de Graaf, 1996; Sauer et al. , 1997; Klapa et al , 1999); however, the measurements do not depict ‘hard‐wired’ constraints, but rather constraints to the specific condition.…”

Section: Flux‐balance Analysis (Fba)mentioning

confidence: 99%