Systems biology model databases and resources

2008 ◽  
Vol 45 ◽  
pp. 223-236 ◽  
Author(s):  
Carel van Gend ◽  
Jacky L. Snoep
Keyword(s):  
Experimental Data ◽  
Systems Biology ◽  
Kinetic Models ◽  
Complete System ◽  
Kinetic Modelling ◽  
Biological Systems ◽  
Standard Form ◽  
Computer Models ◽  
Data Sets ◽  
Quantitative Understanding

Systems biology aims at a quantitative understanding of systemic behaviour as a function of its components and their interactions. In systems biology studies computer models play an important role: (i) to integrate the components’ behaviour and (ii) to analyse experimental data sets. With the growing number of kinetic models that are being constructed for parts of biological systems, it has become important to store these models and make them available in a standard form, such that these models can be combined, eventually leading to a model of a complete system. In the present chapter we describe database initiatives that contain kinetic models for biological systems, together with a number of other systems biology resources related to kinetic modelling.

scholarly journals Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 76 ◽  
Author(s):  
Farhana R. Pinu ◽  
David J. Beale ◽  
Amy M. Paten ◽  
Konstantinos Kouremenos ◽  
Sanjay Swarup ◽  
...  
Keyword(s):  
New Zealand ◽  
Systems Biology ◽  
Life Science ◽  
Biological Systems ◽  
Research Community ◽  
Data Sets ◽  
Omics Data ◽  
Omics Integration ◽  
Traditional Approaches ◽  
Multiple Interactions

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

scholarly journals Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

2017 ◽  
Vol 17 (12) ◽  
pp. 8021-8029 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Kinetic Models ◽  
Reaction Rate ◽  
Rate Coefficients ◽  
Model Parameters ◽  
Data Sets ◽  
Model Input ◽  
Input Parameters

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools, allowing users to design experiments that should be particularly useful for constraining model parameters. We show that the MCGA has been used successfully to constrain parameters such as chemical reaction rate coefficients, diffusion coefficients, and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere–biosphere interface. While this study focuses on the processes outlined above, the MCGA approach should be portable to any numerical process model with similar computational expense and extent of the fitting parameter space.

scholarly journals Technical Note: Monte-Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

2017 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Atmospheric Chemistry ◽  
Kinetic Models ◽  
Reaction Rate ◽  
Rate Coefficients ◽  
Data Sets ◽  
Model Input ◽  
Input Parameters

Abstract. We present a Monte-Carlo Genetic Algorithm (MCGA) for efficient, automated and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find the solution of an optimization problem and to explore the space of solutions with similar model output. It addresses a problem inherent to complex models whose extensive input parameter sets might not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools. The MCGA algorithm has been used successfully to constrain parameters such as reaction rate coefficients, diffusion coefficients and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere-biosphere interface. It should be portable to any numerical model with similar computational expense and extent of the fitting parameter space.

A New Adsorption Rate Equation in Batch Systems

2018 ◽  
Author(s):  
yongson hong ◽  
Kye-Ryong Sin ◽  
Jong-Su Pak ◽  
Chol-Min Pak
Keyword(s):  
Experimental Data ◽  
Kinetic Analysis ◽  
Adsorption Kinetics ◽  
Rate Equation ◽  
Kinetic Models ◽  
Adsorption Kinetic ◽  
Adsorption Rate ◽  
Batch Systems ◽  
New View

<p><b>In this paper, the deficiencies and cause of previous adsorption kinetic models were revealed, new adsorption rate equation has been proposed and its validities were verified by kinetic analysis of various experimental data.</b> <b>This work is a new view on the adsorption kinetics rather than a comment on the previous adsorption papers.</b></p>

scholarly journals Polyconvex hyperelastic modeling of rubberlike materials

2021 ◽  
Vol 43 (7) ◽  
Author(s):  
Cyprian Suchocki ◽  
Stanisław Jemioło
Keyword(s):  
Experimental Data ◽  
Power Law ◽  
Curve Fitting ◽  
Constitutive Relations ◽  
Data Sets ◽  
Power Law Model ◽  
Stored Energy ◽  
Data Approximation ◽  
Exponential Power ◽  
Hyperelastic Models

AbstractIn this work a number of selected, isotropic, invariant-based hyperelastic models are analyzed. The considered constitutive relations of hyperelasticity include the model by Gent (G) and its extension, the so-called generalized Gent model (GG), the exponential-power law model (Exp-PL) and the power law model (PL). The material parameters of the models under study have been identified for eight different experimental data sets. As it has been demonstrated, the much celebrated Gent’s model does not always allow to obtain an acceptable quality of the experimental data approximation. Furthermore, it is observed that the best curve fitting quality is usually achieved when the experimentally derived conditions that were proposed by Rivlin and Saunders are fulfilled. However, it is shown that the conditions by Rivlin and Saunders are in a contradiction with the mathematical requirements of stored energy polyconvexity. A polyconvex stored energy function is assumed in order to ensure the existence of solutions to a properly defined boundary value problem and to avoid non-physical material response. It is found that in the case of the analyzed hyperelastic models the application of polyconvexity conditions leads to only a slight decrease in the curve fitting quality. When the energy polyconvexity is assumed, the best experimental data approximation is usually obtained for the PL model. Among the non-polyconvex hyperelastic models, the best curve fitting results are most frequently achieved for the GG model. However, it is shown that both the G and the GG models are problematic due to the presence of the locking effect.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

scholarly journals Future of Personalized Medicine in Oncology: A Systems Biology Approach

2010 ◽  
Vol 28 (16) ◽  
pp. 2777-2783 ◽  
Author(s):  
Ana Maria Gonzalez-Angulo ◽  
Bryan T.J. Hennessy ◽  
Gordon B. Mills
Keyword(s):  
Systems Biology ◽  
Patient Outcomes ◽  
Cost Effective ◽  
Molecular Medicine ◽  
Multidimensional Data ◽  
Data Sets ◽  
Therapeutic Modalities ◽  
Molecular Tests ◽  
Therapeutic Decision Making ◽  
The Masses

The development of cost-effective technologies able to comprehensively assess DNA, RNA, protein, and metabolites in patient tumors has fueled efforts to tailor medical care. Indeed validated molecular tests assessing tumor tissue or patient germline DNA already drive therapeutic decision making. However, many theoretical and regulatory challenges must still be overcome before fully realizing the promise of personalized molecular medicine. The masses of data generated by high-throughput technologies are challenging to manage, visualize, and convert to the knowledge required to improve patient outcomes. Systems biology integrates engineering, physics, and mathematical approaches with biologic and medical insights in an iterative process to visualize the interconnected events within a cell that determine how inputs from the environment and the network rewiring that occurs due to the genomic aberrations acquired by patient tumors determines cellular behavior and patient outcomes. A cross-disciplinary systems biology effort will be necessary to convert the information contained in multidimensional data sets into useful biomarkers that can classify patient tumors by prognosis and response to therapeutic modalities and to identify the drivers of tumor behavior that are optimal targets for therapy. An understanding of the effects of targeted therapeutics on signaling networks and homeostatic regulatory loops will be necessary to prevent inadvertent effects as well as to develop rational combinatorial therapies. Systems biology approaches identifying molecular drivers and biomarkers will lead to the implementation of smaller, shorter, cheaper, and individualized clinical trials that will increase the success rate and hasten the implementation of effective therapies into the clinical armamentarium.

New analysis of the low-energy π±p differential cross-sections of the CHAOS Collaboration

2015 ◽  
Vol 24 (07) ◽  
pp. 1550050 ◽  
Author(s):  
E. Matsinos ◽  
G. Rasche
Keyword(s):  
Experimental Data ◽  
Cross Sections ◽  
Differential Cross ◽  
Form Factors ◽  
Partial Wave Analysis ◽  
Meson Mass ◽  
Data Sets ◽  
Differential Cross Sections ◽  
Pion Nucleon ◽  
Meson Factory

In a previous paper, we reported the results of a partial-wave analysis (PWA) of the pion–nucleon (πN) differential cross-sections (DCSs) of the CHAOS Collaboration and came to the conclusion that the angular distribution of their π+p data sets is incompatible with the rest of the modern (meson factory) database. The present work, re-addressing this issue, has been instigated by a number of recent improvements in our analysis, namely regarding the inclusion of the theoretical uncertainties when investigating the reproduction of experimental data sets on the basis of a given "theoretical" solution, modifications in the parametrization of the form factors of the proton and of the pion entering the electromagnetic part of the πN amplitude, and the inclusion of the effects of the variation of the σ-meson mass when fitting the ETH model of the πN interaction to the experimental data. The new analysis of the CHAOS DCSs confirms our earlier conclusions and casts doubt on the value for the πN Σ term, which Stahov, Clement and Wagner have extracted from these data.

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