Computer simulation of biological systems

Lumb, Judith Rae

doi:10.1007/bf00219423

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…One approach for solving this problem is simulation, which involves building mathematical or computer models of a system and using these models to study the properties of the system. 2,9,[31][32][33][34] There are different types of models such as dynamic, explanatory, descriptive or realistic.…”

Section: Dynamic Simulation Modelingmentioning

confidence: 99%

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Predictive Modeling Techniques in Prostate Cancer

Tewari

Porter²,

Peabody³

et al. 2001

Molecular Urology

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A number of new predictive modeling techniques have emerged in the past several years. These methods can be used independently or in combination with traditional modeling techniques to produce useful tools for the management of prostate cancer. Investigators should be aware of these techniques and avail themselves of their potentially useful properties. This review outlines selected predictive methods that can be used to develop models that may be useful to patients and clinicians for prostate cancer management.

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Section: Dynamic Simulation Modelingmentioning

confidence: 99%

“…Investigators should be aware of these new techniques that can be used independently or in combination with traditional statistical methods to develop accurate predictive models. [2][3][4][5][6][7][8][9][10][11][12][13][14] This review examines techniques that are available for developing predictive models for use in CaP management.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling Techniques in Prostate Cancer

Tewari

Porter²,

Peabody³

et al. 2001

Molecular Urology

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show abstract

“…Nonlinear differential equations are highly complex, so parameter estimation is difficult with the increasing size and nonlinearity of the fermentation pathway, leading to a corresponding increase in size and variable in the model [ 3 ]. Furthermore, the complicated equation increases the complexity of the model [ 4 ]. Parameter estimation is one of the critical steps in constructing a mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

Ismail

Remli

Choon

et al. 2023

Journal of Integrative Bioinformatics

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Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data.

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“…Clues from experiments are limited to the details of the sub-systems considered and can be difficult to interpret. Computer simulation allows for formulating a working model that can help explain experimental observations [ 2 , 3 ]. It can provide links between observations and unknowns in terms of a mathematical expression or numerical values, offering qualitative or quantitative insights.…”

Section: Introductionmentioning

confidence: 99%

BioSANS: A software package for symbolic and numeric biological simulation

Fajiculay

Hsu

2022

PLoS ONE

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Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable features including ease in preparing input, symbolic or analytical computation, parameter estimation, graphical user interface, and systems biology markup language (SBML) support are not seen concurrently in one software package. In this study, we developed a python-based software that supports these features, with both deterministic and stochastic propagations. The software can be used by graphical user interface, command line, or as a python import. We also developed a semi-programmable and intuitively easy topology input method for the biochemical reactions. We tested the software with semantic and stochastic SBML test cases. Tests on symbolic solution and parameter estimation were also included. The software we developed is reliable, well performing, convenient to use, and compliant with most of the SBML tests. So far it is the only systems biology software that supports symbolic, deterministic, and stochastic modeling in one package that also features parameter estimation and SBML support. This work offers a comprehensive set of tools and allows for better availability and accessibility for studying kinetics and dynamics in biochemical systems.

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Computer simulation of biological systems

Cited by 8 publications

References 21 publications

Predictive Modeling Techniques in Prostate Cancer

Predictive Modeling Techniques in Prostate Cancer

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

BioSANS: A software package for symbolic and numeric biological simulation

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