scholarly journals Deciphering the regulation mechanism in biochemical networks by a systems-biology approach

2017 ◽  
Author(s):  
Bernardo A. Mello ◽  
Yuhai Tu
Keyword(s):  
Systems Biology ◽  
Molecular Mechanisms ◽  
Biochemical Networks ◽  
System Level ◽  
Regulation Mechanism ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Complex Processes ◽  
Regulation Mechanisms

To decipher molecular mechanisms in biological systems from system-level input-output data is challenging especially for complex processes that involve interactions among multiple components. Here, we study regulation of the multi-domain (P1-5) histidine kinase CheA by the MCP chemoreceptors. We develop a network model to describe dynamics of the system treating the receptor complex with CheW and P3P4P5 domains of CheA as a regulated enzyme with two substrates, P1 and ATP. The model enables us to search the hypothesis space systematically for the simplest possible regulation mechanism consistent with the available data. Our analysis reveals a novel dual regulation mechanism wherein besides regulating ATP binding the receptor activity has to regulate one other key reaction, either P1 binding or phosphotransfer between P1 and ATP. Furthermore, our study shows that the receptors only control kinetic rates of the enzyme without changing its equilibrium properties. Predictions are made for future experiments to distinguish the remaining two dual-regulation mechanisms. This systems-biology approach of combining modeling and a large input-output data-set should be applicable for studying other complex biological processes.

GENERATING BALANCED LEARNING AND TEST SETS FOR FUNCTION APPROXIMATION PROBLEMS

2011 ◽  
Vol 21 (03) ◽  
pp. 247-263 ◽  
Author(s):  
J. P. FLORIDO ◽  
H. POMARES ◽  
I. ROJAS
Keyword(s):  
Function Approximation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Original Data ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Out Of Sample ◽  
Test Sets ◽  
Approximation Problems

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

scholarly journals SABIO-RK: A data warehouse for biochemical reactions and their kinetics

2007 ◽  
Vol 4 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Olga Krebs ◽  
Martin Golebiewski ◽  
Renate Kania ◽  
Saqib Mir ◽  
Jasmin Saric ◽  
...  
Keyword(s):  
Systems Biology ◽  
Kinetic Data ◽  
Biochemical Networks ◽  
System Level ◽  
Kinetic Properties ◽  
Biological Processes ◽  
Biochemical Reactions ◽  
Biochemical Pathways ◽  
Application Potential ◽  
Promising Application

Abstract Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.

Piecewise Linear Virtual Inputs/Outputs in Interval DEA

2013 ◽  
Vol 4 (2) ◽  
pp. 36-49 ◽  
Author(s):  
Yiannis G. Smirlis ◽  
Dimitris K. Despotis
Keyword(s):  
Piecewise Linear ◽  
Linear Representation ◽  
Data Sets ◽  
Data Envelopment ◽  
Input Output ◽  
Artificial Data ◽  
Output Data ◽  
Data Set ◽  
Marginal Value ◽  
Linear Approach

A recent development in data envelopment analysis (DEA) concerns the introduction of a piece-wise linear representation of the virtual inputs and/or outputs as a means to model situations where the marginal value of an output (input) is assumed to diminish (increase) as the output (input) increases. Currently, this approach is limited to crisp data sets. In this paper, the authors extend the piece-wise linear approach to interval DEA, i.e. to cases where the input/output data are only known to lie within intervals with given bounds. The authors also define appropriate interval segmentations to implement the piece-wise linear forms in conjunction with the interval bounds of the input/output data and the authors propose a new models, compliant with the interval DEA methodology. They finally illustrate their developments with an artificial data set.

scholarly journals Teaching Systems Biology: An Active-learning Approach

2005 ◽  
Vol 4 (4) ◽  
pp. 323-329 ◽  
Author(s):  
Anuj Kumar
Keyword(s):  
Systems Biology ◽  
Active Learning ◽  
Molecular Mechanisms ◽  
Cell Function ◽  
Student Response ◽  
Homology Search ◽  
Learning Approach ◽  
Data Sets ◽  
Data Set ◽  
Ann Arbor

With genomics well established in modern molecular biology, recent studies have sought to further the discipline by integrating complementary methodologies into a holistic depiction of the molecular mechanisms underpinning cell function. This genomic subdiscipline, loosely termed“ systems biology,” presents the biology educator with both opportunities and obstacles: The benefit of exposing students to this cutting-edge scientific methodology is manifest, yet how does one convey the breadth and advantage of systems biology while still engaging the student? Here, I describe an active-learning approach to the presentation of systems biology. In graduate classes at the University of Michigan, Ann Arbor, I divided students into small groups and asked each group to interpret a sample data set (e.g., microarray data, two-hybrid data, homology-search results) describing a hypothetical signaling pathway. Mimicking realistic experimental results, each data set revealed a portion of this pathway; however, students were only able to reconstruct the full pathway by integrating all data sets, thereby exemplifying the utility in a systems biology approach. Student response to this cooperative exercise was extremely positive. In total, this approach provides an effective introduction to systems biology appropriate for students at both the undergraduate and graduate levels.

Extracting fuzzy rules for modeling of complex processes using neural networks

2013 ◽  
Vol 227 (12) ◽  
pp. 2861-2869 ◽  
Author(s):  
A Jamali ◽  
SJ Motevalli ◽  
N Nariman-zadeh
Keyword(s):  
Neural Networks ◽  
Fuzzy Rule ◽  
Structure Design ◽  
Fuzzy Rules ◽  
Group Method ◽  
Rule Base ◽  
Input Output ◽  
Output Data ◽  
Complex Processes ◽  
Linguistic Rules

Modeling of complex processes often leads to complex mathematical relationships between inputs and outputs, which do not reflect the influence of the independent variables on the output parameters. In this article, an innovative technique based on neural networks is presented to extract fuzzy linguistic rules for modeling some processes using some input–output data. In this way, genetic algorithm is used both for optimal structure design of those group method of data handling-type neural networks and for subsequent optimization of sub-bounds of fuzzy singleton antecedents to further optimize the obtained fuzzy rule base. Three different input–output data tables related to some complex problems of a nonlinear mathematical system, an explosive cutting process and the probability of failure estimation of a two mass-spring system are modeled by some fuzzy rules, using the technique discussed in this article.

Robust intelligent modeling for giant magnetostrictive actuators with rate‐dependent hysteresis

2012 ◽  
Vol 5 (4) ◽  
pp. 500-514
Author(s):  
Wei Zhang ◽  
Jianqin Mao
Keyword(s):  
Active Vibration Control ◽  
Modeling Method ◽  
Single Frequency ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Content Type ◽  
Intelligent Modeling ◽  
Rate Dependent ◽  
Magnetostrictive Actuator

PurposeThis paper proposes a robust modeling method of a giant magnetostrictive actuator which has a rate‐dependent nonlinear property.Design/methodology/approachIt is known in statistics that the Least Wilcoxon learning method developed using Wilcoxon norm is robust against outliers. Thus, it is used in the paper to determine the consequence parameters of the fuzzy rules to reduce the sensitiveness to the outliers in the input‐output data. The proposed method partitions the input space adaptively according to the distribution of samples and the partition is irrelative to the dimension of the input data set.FindingsThe proposed modeling method can effectively construct a unique dynamic model that describes the rate‐dependent hysteresis in a given frequency range with respect to different single‐frequency and multi‐frequency input signals no matter whether there exist outliers in the training set or not. Simulation results demonstrate that the proposed method is effective and insensitive against the outliers.Originality/valueThe main contributions of this paper are: first, an intelligent modeling method is proposed to deal with the rate‐dependent hysteresis presented in the giant magnetostrictive actuator and the modeling precision can fulfill the requirement of engineering, such as the online modeling issue in the active vibration control; and second, the proposed method can handle the outliers in the input‐output data effectively.

Application of Multiple Regression and Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness in EDM

2012 ◽  
Author(s):  
S. S. Baraskar ◽  
S. S. Banwait
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Multiple Regression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy

A manufacturing system is oriented towards higher production rate, better quality and reduced cost and time to make a product. Surface roughness is an index parameter for determining the quality of a machined product and is influenced by various input process parameters. Surface roughness prediction in Electrical Discharge Machine (EDM) is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of hybrid intelligent technique, multiple regression and adaptive neuro-fuzzy inference system (ANFIS) for prediction of surface roughness in EDM. An experimental data set is obtained with current, pulse-on time and pulse-off time as input parameters and surface roughness as output parameter. Central composite rotatable design was used to plan the experiments. Multiple regression model is developed using the experimental data, to generate additional input-output data set. The input-output data set is used for training and validation of the proposed technique. After validation, data are forwarded for prediction of surface roughness. The proposed hybrid model for the prediction of surface roughness has very good agreement with the experimental results.

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