Editorial [Ground-Breaking Mathematical Models for Basic and Applied Research (Executive Editors: A.O. Vassilev and H.E. Tibbles)] Part-I

In recent years, biomedical research and drug design became one of the fastest growing branches of scientific and industrial development. The tremendous scale (number of projects and volume of information to process) promoted by trillions of dollars invested in these fields called for completely new approaches to obtain, analyze, and apply information to clinical practice, pharmacological research and the medical industry. This issue was put together as a result of our long-standing interest in the various aspects of research and drug design. It is dedicated to the use of new mathematical models in various fields of medical research. Our previous Current Pharmaceutical Design issue (2004) addressed the concept of multi-functional drug targets in diverse model systems [1]. This issue, accordingly, continues our inquiry into various types of models used in research and the subsequent creation of novel agents and improved therapies. This issue aims to give the reader an inside view into the concepts of intelligent research design and biomedical information processing. The first three reviews [2-4] are orientated on the use of mathematics in basic science research to uncover the processes underlining the most complex events that are so crucial to understand in order to successfully conduct medical research, design drugs and simply practice medicine nowadays. In the first review Yang and Hamer [2] discuss an important topic in bioinformatics and systems biology - identifying functional sites in proteins. They focus on the variants of Bio-basis Function Neural Networks (BBFNN) and their applications in mining protein sequence data. Next, Goutsias and Lee [3] discuss four gene regulatory networks models: gene networks, transcriptional regulatory systems, Boolean networks, and dynamical Bayesian networks. The authors review state-of-theart functional genomics techniques, such as gene expression profiling, cis-regulatory element identification, TF target gene identification, and gene silencing by RNA interference, which can be used to extract information about gene regulation. In the third review by Qazi, Chamberlin, and Nigam [4], the authors describe the use of the difference method to investigate the information processing capabilities of GABAA receptors and predict how pharmacological agents may modify these properties. They suggest that understanding this process of transmitter-receptor interactions may be useful in the development of more specific and highly targeted modes of action. The next three reviews [5-9] are dedicated to predicting capabilities of using mathematical models in biomedical research and in medicine, such as the prediction of drug delivery efficiency or patient treatment outcome. First, Panteleev et al. discuss the use of mathematical models of blood coagulation and platelet-mediated primary hemostasis and thrombosis in clinical practice, research and drug development [5]. Micheli, Sperduti and Starita [6] introduce the reader to new developments in neural networks and Kernel machines concerning the treatment of structured domains. Focusing more on the computational side than on the experimental one, they discuss the research on these relatively new models to introduce a novel and more general approach to QSPR/QSAR analysis. Artificial intelligence approaches for rational drug design is then reviewed by Duch, Swaminathan, and Meller [7]. A special emphasis is made on methods that “enable an intuitive interpretation of the results” and facilitate gaining an insight into the nature of the problem. This discussion is continued in the next issue We would like to thank all the authors for their contributions and hope that this two part-issue will stimulate new communication and collaborations. References [1] Current Pharmaceutical Design, Volume 10, Number 15, June 2004. [2] Yang ZR, Hamer R. Bio-basis Function Neural Networks in Protein Data Mining. Curr Pharm Des 2007; 13(14): 1403-1413. [3] Goutsias J, Leeb NH. Computational and Experimental Approaches for Modeling Gene Regulatory Networks. Curr Pharm Des 2007; 13(14): 1415-1436. [4] Qazi S, Caberlin M, Nigam N. Mechanism of psychoactive drug action in the brain: Simulation modeling of GABAA receptor interactions at non-equilibrium conditions. Curr Pharm Des 2007; 13(14): 1437-1455. [5] Panteleev MA, Ananyeva NM, Radtke K-P, Ataullakhanov FI, and Saenko EL, Mathematical models of blood coagulation and platelet adhesion: clinical application. Curr Pharm Des 2007; 13(14): 1457-1467. [6] Micheli A, Sperduti A, Starita A. An Introduction to Recursive Neural Networks and Kernel Methods for Cheminformatics. Curr Pharm Des 2007; 13(14): 1469-1495. [7] Duch W, Swaminathan K, Meller J. Artificial Intelligence Approaches for Rational Drug Design and Discovery. Curr Pharm Des 2007; 13(14): 1497-1508.