Evolutionary optimization with data collocation for reverse engineering of biological networks
Kuan-Yao Tsai,Feng-Sheng Wang +1 more
181
TL;DR: In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations to obtain the approximate model profiles, which are then substituted into the algebraic system equations to decouple system interactions.
read more
Abstract: Motivation: Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems.
Results: We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.
Availability: The algorithm, implemented by Compaq Visual Fortran Professional Edition 6.6, and the supplements are available at http://www.che.ccu.edu.tw/~bioproc/index-english.html/. IMSL Math/Library is a commercial library included in Compaq Visual Fortran Professional Edition.
Contact: [email protected]
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems
I-Chun Chou,Eberhard O. Voit +1 more
TL;DR: The article presented here reviews the field of inverse modeling within BST 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.
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems
TL;DR: A novel metaheuristic, inspired by recent developments in the field of operations research, was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.
Estimating population parameters using the structured serial coalescent with Bayesian MCMC inference when some demes are hidden.
Greg Ewing,Allen G. Rodrigo +1 more
- 14 Feb 2007
TL;DR: This paper introduces a novel probabilistic method for prediction of protein-protein interactions using a new empirical probabilistic formula describing the loss of interactions between homologous proteins during the course of evolution.
319
Biochemical Systems Theory: A Review
TL;DR: This paper depicts major developments in BST up to the current state of the art in 2012 and is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.
203
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
TL;DR: This paper gives a comprehensive review of the application of metaheuristics to optimization problems in systems biology, mainly focusing on the parameter estimation problem (also called the inverse problem or model calibration).
189
References
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
•Book
Handbook of Evolutionary Computation
Thomas Bäck,David B. Fogel,Zbigniew Michalewicz +2 more
- 01 Jan 1997
TL;DR: The Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications, intended to become the standard reference resource for the evolutionary computation community.
3.1K
Program DYNAFIT for the Analysis of Enzyme Kinetic Data: Application to HIV Proteinase
TL;DR: A computer program with the code name DYNAFIT was developed for fitting either the initial velocities or the time course of enzyme reactions to an arbitrary molecular mechanism represented symbolically by a set of chemical equations.
1.5K
Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods
TL;DR: Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.
Minimizing the real functions of the ICEC'96 contest by differential evolution
Rainer Storn,Kenneth Price +1 more
- 20 May 1996
TL;DR: Two variants of DE are described which were used to minimize the real test functions of the ICEC'96 contest.
874