Current
Pharmaceutical Design
ISSN: 1381-6128
Current Pharmaceutical Design
Volume 13, Number 14, 2007
Contents
Part-I
Ground-Breaking Mathematical Models for Basic and Applied
Research
Executive Editors: A.O. Vassilev and H.E. Tibbles
Editorial: Pp. 1401
Bio-Basis Function Neural Networks in Protein
Data Mining Pp. 1403-1413
Z.R. Yang and R. Hamer
[Abstract]
Computational and Experimental Approaches for Modeling
Gene Regulatory Networks Pp. 1415-1436
J. Goutsias and N.H. Lee
[Abstract]
Mechanism of Psychoactive Drug Action in the Brain:
Simulation Modeling of GABAA
Receptor Interactions at Non-Equilibrium Conditions
Pp. 1437-1455
S. Qazi, M. Caberlin and N. Nigam
[Abstract]
Mathematical Models of Blood Coagulation and Platelet
Adhesion: Clinical Applications Pp. 1457-1467
M.A. Panteleev, N.M. Ananyeva, F.I. Ataullakhanov and
E.L. Saenko
[Abstract]
An Introduction to Recursive Neural Networks and Kernel
Methods for Cheminformatics Pp. 1469-1495
A. Micheli, A. Sperduti and A. Starita
[Abstract]
Artificial Intelligence Approaches for Rational Drug
Design and Discovery Pp. 1497-1508
W. Duch, K. Swaminathan and J. Meller
[Abstract]
Abstracts
[Back to top]
Editorial: Ground-Breaking Mathematical Models for
Basic and Applied Research
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-the-art 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.
Alexei O. Vassilev Ph.D
avassilev@hotmail.com
Heather E. Tibbles
tibbles_2@hotmail.com
[Back to top]
Bio-Basis Function Neural Networks in Protein Data Mining
Z.R. Yang and R. Hamer
Accurately identifying functional sites in proteins is one
of the most important topics in bioinformatics and systems
biology. In bioinformatics, identifying protease cleavage
sites in protein sequences can aid drug/inhibitor design.
In systems biology, post-translational protein-protein interaction
activity is one of the major components for analyzing signaling
pathway activities. Determining functional sites using laboratory
experiments are normally time consuming and expensive. Computer
programs have therefore been widely used for this kind of
task. Mining protein sequence data using computer programs
covers two major issues: 1) discovering how amino acid specificity
affects functional sites and 2) discovering what amino acid
specificity is. Both need a proper coding mechanism prior
to using a proper machine learning algorithm. The development
of the bio-basis function neural network (BBFNN) has made
a new way for protein sequence data mining. The bio-basis
function used in BBFNN is biologically sound in well coding
biological information in protein sequences, i.e. well measuring
the similarity between protein sequences. BBFNN has therefore
been outperforming conventional neural networks in many subjects
of protein sequence data mining from protease cleavage site
prediction to disordered protein identification. This review
focuses on the variants of BBFNN and their applications in
mining protein sequence data.
[Back to top]
Computational and Experimental Approaches for Modeling
Gene Regulatory Networks
J. Goutsias and N.H. Lee
To understand most cellular processes, one must understand
how genetic information is processed. A formidable challenge
is the dissection of gene regulatory networks to delineate
how eukaryotic cells coordinate and govern patterns of gene
expression that ultimately lead to a phenotype. In this paper,
we review several approaches for modeling eukaryotic gene
regulatory networks and for reverse engineering such networks
from experimental observations. Since we are interested in
elucidating the transcriptional regulatory mechanisms of colon
cancer progression, we use this important biological problem
to illustrate various aspects of modeling gene regulation.
We discuss four important models: gene networks, transcriptional
regulatory systems, Boolean networks, and dynamical Bayesian
networks. We review state-of-the-art 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. We can employ this information,
in conjunction with appropriately designed reverse engineering
algorithms, to construct a computational model of gene regulation
that sufficiently predicts experimental observations. In the
last part of this review, we focus on the problem of reverse
engineering transcriptional regulatory networks by gene perturbations.
We mathematically formulate this problem and discuss the role
of experimental resolution in our ability to reconstruct accurate
models of gene regulation. We conclude, by discussing a promising
approach for inferring a transcriptional regulatory system
from microarray data obtained by gene perturbations.
[Back to top]
Mechanism of Psychoactive Drug Action in the Brain:
Simulation Modeling of GABAA
Receptor Interactions at Non-Equilibrium Conditions
S. Qazi, M. Caberlin and N. Nigam
Synaptic transmission requires that the binding of the transmitter
to the receptor to occur under rapidly changing transmitter
levels, and this binding interaction is unlikely to be at
equilibrium. We have sought to numerically solve for binding
kinetics using ordinary differential equations and simultaneous
difference equations for use in stochastic conditions. The
reaction scheme of GABA interacting with the ligand-gated
ion-channel demonstrates numerical stiffness. Implicit methods
(Backward Euler, ode23s) performed orders of magnitude better
than explicit methods (Forward Euler, ode23, RK4, ode45) in
terms of step size required for stability, number of steps
and cpu time. Interestingly, upon solving the system of 8
ordinary differential equations for the GABA reaction scheme
we observed the existence of low dimensional invariant manifolds
that may have important consequences for information processing
in synapses. We also describe a mathematical approach that
models complex receptor interactions in which the timing and
amplitude of transmitter release are noisy. Exact solutions
for simple bimolecular interactions that include stoichiometric
interactions and receptor transitions can be used to model
complex reaction schemes. We used the difference method to
investigate the information processing capabilities of GABAA
receptors and to predict how pharmacological agents may modify
these properties. Initial simulations using a model for heterosynaptic
regulation shows that signal to noise ratios can be decreased
in the presence of background presynaptic activity both in
the presence and absence of chlorpromazine. These types of
simulations provide a platform for investigating the effect
of psycho-active drugs on complex responses of transmitter-receptor
interactions in noisy cellular environments such as the synapse.
Understanding this process of transmitter–receptor interactions
may be useful in the development of more specific and highly
targeted modes of action.
[Back to top]
Mathematical Models of Blood Coagulation and Platelet
Adhesion: Clinical Applications
M.A. Panteleev, N.M. Ananyeva, F.I. Ataullakhanov and
E.L. Saenko
At present, computer-assisted molecular modeling and virtual
screening have become effective and widely-used tools for
drug design. However, a prerequisite for design and synthesis
of a therapeutic agent is determination of a correct target
in the metabolic system, which should be either inhibited
or stimulated. Solution of this extremely complicated problem
can also be assisted by computational methods. This review
discusses the use of mathematical models of blood coagulation
and platelet-mediated primary hemostasis and thrombosis as
cost-effective and time-saving tools in research, clinical
practice, and development of new therapeutic agents and biomaterials.
We focus on four aspects of their application: 1) efficient
diagnostics, i.e. theoretical interpretation of diagnostic
data, including sensitivity of various clotting assays to
the changes in the coagulation system; 2) elucidation of mechanisms
of coagulation disorders (e.g. hemophilias and thrombophilias);
3) exploration of mechanisms of action of therapeutic agents
(e.g. recombinant activated factor VII) and planning rational
therapeutic strategy; 4) development of biomaterials with
non-thrombogenic properties in the design of artificial organs
and implantable devices. Accumulation of experimental knowledge
about the blood coagulation system and about platelets, combined
with impressive increase of computational power, promises
rapid development of this field.
[Back to top]
An Introduction to Recursive Neural Networks and Kernel
Methods for Cheminformatics
A. Micheli, A. Sperduti and A. Starita
The aim of this paper is to introduce the reader to new developments
in Neural Networks and Kernel Machines concerning the treatment
of structured domains. Specifically, we discuss the research
on these relatively new models to introduce a novel and more
general approach to QSPR/QSAR analysis. The focus is on the
computational side and not on the experimental one.
[Back to top]
Artificial Intelligence Approaches for Rational Drug
Design and Discovery
W. Duch, K. Swaminathan and J. Meller
Pattern recognition, machine learning and artificial intelligence
approaches play an increasingly important role in rational
drug design, screening and identification of candidate molecules
and studies on quantitative structure-activity relationships
(QSAR). In this review, we present an overview of basic concepts
and methodology in the fields of machine learning and artificial
intelligence (AI). An emphasis is put on methods that enable
an intuitive interpretation of the results and facilitate
gaining an insight into the structure of the problem at hand.
We also discuss representative applications of AI methods
to docking, screening and QSAR studies. The growing trend
to integrate computational and experimental efforts in that
regard and some future developments are discussed. In addition,
we comment on a broader role of machine learning and artificial
intelligence approaches in biomedical research.
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