The Present and the Future Perspectives of Biological Network Inference

Biological network inference is based on a series of studies and computational approaches to the deduction of the connectivity of chemical species, the reaction pathway, and the reaction kinetics of complex reaction systems from experimental measurements. Inference for network structure and reaction kinetics parameters governing the dynamics of a biological system is currently an active area of research. In the era of post-genomic biology, it is a common opinion among scientists that living systems (cells, tissues, organs and organisms) can be understood in terms of their network structure as well as in term of the evolution in time of this network structure. In this chapter, the authors make a survey of the recent methodologies proposed for the structure inference and for the parameter estimation of a system of interacting biological entities. Furthermore, they present the recent works of the authors about model identification and calibration. DOI: 10.4018/978-1-61350-435-2.ch006

[1]  Darren J. Wilkinson,et al.  Bayesian methods in bioinformatics and computational systems biology , 2006, Briefings Bioinform..

[2]  Hesham H. Ali,et al.  Bioinformatics: Concepts, Methodologies, Tools, and Applications , 2013 .

[3]  Xiaohui Xie,et al.  Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent , 2010, BMC Systems Biology.

[4]  Mitja Peruš,et al.  Biological and Quantum Computing for Human Vision: Holonomic Models and Applications , 2010 .

[5]  D. Kihara,et al.  Discovering Protein-Protein Interaction Sites from Sequence and Structure , 2009 .

[6]  J. Ross,et al.  A Test Case of Correlation Metric Construction of a Reaction Pathway from Measurements , 1997 .

[7]  Alan Forrest,et al.  SPLINDID: a semi-parametric, model-based method for obtaining transcription rates and gene regulation parameters from genomic and proteomic expression profiles , 2005, Bioinform..

[8]  Mark Gerstein,et al.  Analyzing cellular biochemistry in terms of molecular networks. , 2003, Annual review of biochemistry.

[9]  Raya Khanin,et al.  Bayesian model-based inference of transcription factor activity , 2007, BMC Bioinformatics.

[10]  See-Kiong Ng,et al.  Biological Data Mining in Protein Interaction Networks , 2009 .

[11]  Athina Lazakidou,et al.  Biocomputation and Biomedical Informatics: Case Studies and Applications , 2009 .

[12]  A. S. Torralba,et al.  Experimental test of a method for determining causal connectivities of species in reactions , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Matthew Browne,et al.  A geometric approach to non-parametric density estimation , 2007, Pattern Recognit..

[14]  Maria Rodriguez-Fernandez,et al.  A hybrid approach for efficient and robust parameter estimation in biochemical pathways. , 2006, Bio Systems.

[15]  Nir Friedman,et al.  Inferring Cellular Networks Using Probabilistic Graphical Models , 2004, Science.

[16]  Darren J. Wilkinson,et al.  Bayesian inference for nonlinear multivariate diffusion models observed with error , 2008, Comput. Stat. Data Anal..

[17]  Paola Lecca,et al.  Network inference from time-dependent Omics data. , 2011, Methods in molecular biology.

[18]  Peter J. Woolf,et al.  Bayesian analysis of signaling networks governing embryonic stem cell fate decisions , 2005, Bioinform..

[19]  Corrado Priami,et al.  Algorithmic systems biology , 2009, CACM.

[20]  M. Tomita,et al.  Reverse engineering of biochemical equations from time-course data by means of genetic programming. , 2005, Bio Systems.

[21]  Corrado Priami,et al.  Algorithmic Systems Biology: An Opportunity for Computer Science , 2008 .

[22]  A. Lazakidou,et al.  E-Health Project Implementation: Privacy and Security Measures and Policies , 2010 .

[23]  Aboul Ella Hassanien,et al.  Integrated Features Based on Gray-Level and Hu Moment-Invariants with Ant Colony System for Retinal Blood Vessels Segmentation , 2012 .

[24]  Jason H. Moore,et al.  Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge , 2011, Handbook of Research on Computational and Systems Biology.

[25]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[26]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[27]  Paola Lecca,et al.  Correlation-Based Network Inference and Modelling in Systems Biology: The NF-kappa B Signalling Network Case Study , 2010, 2010 International Conference on Intelligent Systems, Modelling and Simulation.

[28]  Adam Arkin,et al.  On the deduction of chemical reaction pathways from measurements of time series of concentrations. , 2001, Chaos.

[29]  Junbin Gao,et al.  Simulated maximum likelihood method for estimating kinetic rates in gene expression , 2007, Bioinform..

[30]  Gregory Stephanopoulos,et al.  Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data. , 2004, Genome research.

[31]  Mathieu Roche,et al.  Information Retrieval in Biomedicine - Natural Language Processing for Knowledge Integration , 2009, Information Retrieval in Biomedicine.

[32]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[33]  H. Kitano.  Systems Biology: A Brief Overview , 2002, Science.

[34]  J. Ross,et al.  Determination of causal connectivities of species in reaction networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Dominic Waithe,et al.  Bridging the gap between in silico and cell‐based analysis of the nuclear factor‐κB signaling pathway by in vitro studies of IKK2 , 2007, The FEBS journal.

[36]  T. Ideker.  A systems approach to discovering signaling and regulatory pathways--or, how to digest large interaction networks into relevant pieces. , 2004, Advances in experimental medicine and biology.

[37]  Qiang Du,et al.  Grid generation and optimization based on centroidal Voronoi tessellations , 2002, Appl. Math. Comput..

[38]  P. McSharry,et al.  Mathematical and computational techniques to deduce complex biochemical reaction mechanisms. , 2004, Progress in biophysics and molecular biology.

[39]  Amr Mohamed,et al.  Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals , 2012, Int. J. Knowl. Discov. Bioinform..

[40]  J. Collins,et al.  Size matters: network inference tackles the genome scale , 2007, Molecular systems biology.

[41]  Adam P. Arkin,et al.  Statistical Construction of Chemical Reaction Mechanisms from Measured Time-Series , 1995 .

[42]  Mudita Singhal,et al.  COPASI - a COmplex PAthway SImulator , 2006, Bioinform..

[43]  Denis Noble,et al.  The rise of computational biology , 2002, Nature Reviews Molecular Cell Biology.

[44]  Paul P. Wang,et al.  Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..

[45]  R. Albert.  Network Inference, Analysis, and Modeling in Systems Biology , 2007, The Plant Cell Online.

[46]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[47]  J Timmer,et al.  Parameter estimation in stochastic biochemical reactions. , 2006, Systems biology.

[48]  Carmen G. Moles,et al.  Parameter estimation in biochemical pathways: a comparison of global optimization methods. , 2003, Genome research.

[49]  Paola Lecca,et al.  Calibration of dynamic models of biological systems with KInfer , 2010, European Biophysics Journal.