title:
On "Strong control, conservative point estimation and simultaneous
  conservative consistency of false discovery rates": Does a large number of
  tests obviate confidence intervals of the FDR?

abstract:
  A previously proved theorem gives sufficient conditions for an estimator of
the false discovery rate (FDR) to conservatively converge to the FDR with
probability 1 as the number of hypothesis tests increases, even for small
sample sizes. It does not follow that several thousand tests ensure that the
estimator has moderate variance under those conditions. In fact, they can hold
even if the test statistics have long-range correlations, which yield
unacceptably wide confidence intervals, as observed in genomic data when there
are 8 or 16 individuals (microarrays) per group. Thus, informative FDR
estimation will include some measure of its reliability.


title:
Probabilities of spurious connections in gene networks: Application to
  expression time series

abstract:
  Motivation: The reconstruction of gene networks from gene expression
microarrays is gaining popularity as methods improve and as more data become
available. The reliability of such networks could be judged by the probability
that a connection between genes is spurious, resulting from chance fluctuations
rather than from a true biological relationship. Results: Unlike the false
discovery rate and positive false discovery rate, the decisive false discovery
rate (dFDR) is exactly equal to a conditional probability without assuming
independence or the randomness of hypothesis truth values. This property is
useful not only in the common application to the detection of differential gene
expression, but also in determining the probability of a spurious connection in
a reconstructed gene network. Estimators of the dFDR can estimate each of three
probabilities: 1. The probability that two genes that appear to be associated
with each other lack such association. 2. The probability that a time ordering
observed for two associated genes is misleading. 3. The probability that a time
ordering observed for two genes is misleading, either because they are not
associated or because they are associated without a lag in time. The first
probability applies to both static and dynamic gene networks, and the other two
only apply to dynamic gene networks. Availability: Cross-platform software for
network reconstruction, probability estimation, and plotting is free from
http://www.davidbickel.com as R functions and a Java application.


title: Dynamics of Acute Inflammation

abstract:
  When the body is infected, it mounts an acute inflammatory response to rid
itself of the pathogens and restore health. Uncontrolled acute inflammation due
to infection is defined clinically as Sepsis and can culminate in organ failure
and death. We consider a three dimensional ordinary differential equation model
of inflammation consisting of a pathogen, and two inflammatory mediators. The
model reproduces the healthy outcome and diverse negative outcomes, depending
on initial conditions and parameters.when key parameters are changed and
suggest various therapeutic strategies. We suggest that the clinical condition
of sepsis can arise from several distinct physiological states, each of which
requires a different treatment approach. We analyze the various bifurcations
between the different outcomes


title:
Pair approximation of the stochastic
  susceptible-infected-recovered-susceptible epidemic model on the hypercubic
  lattice

abstract:
  We investigate the time-evolution and steady states of the stochastic
susceptible-infected-recovered-susceptible(SIRS) epidemic model on one- and
two- dimensional lattices. We compare the behavior of this system, obtained
from computer simulations, with those obtained from the mean-field
approximation(MFA) and pair-approximation(PA). The former(latter) approximates
higher order moments in terms of first(second) order ones. We find that the PA
gives consistently better results than the MFA. In one dimension the
improvement is even qualitative.


title:
Model evaluation for glycolytic oscillations in yeast biotransformations
  of xenobiotics

abstract:
  Anaerobic glycolysis in yeast perturbed by the reduction of xenobiotic
ketones is studied numerically in two models which possess the same topology
but different levels of complexity. By comparing both models' predictions for
concentrations and fluxes as well as steady or oscillatory temporal behavior we
answer the question what phenomena require what kind of minimum model
abstraction. While mean concentrations and fluxes are predicted in agreement by
both models we observe different domains of oscillatory behavior in parameter
space. Generic properties of the glycolytic response to ketones are discussed.


title: How does the chromatin fiber deal with topological constraints?

abstract:
  In the nuclei of eukaryotic cells, DNA is packaged through several levels of
compaction in an orderly retrievable way that enables the correct regulation of
gene expression. The functional dynamics of this assembly involves the
unwinding of the so-called 30 nm chromatin fiber and accordingly imposes strong
topological constraints. We present a general method for computing both the
twist and the writhe of any winding pattern. An explicit derivation is
implemented for the chromatin fiber which provides the linking number of DNA in
eukaryotic chromosomes. We show that there exists one and only one unwinding
path which satisfies both topological and mechanical constraints that DNA has
to deal with during condensation/decondensation processes.


title:
Simulation, Experiment, and Evolution: Understanding Nucleation in
  Protein S6 Folding

abstract:
  In this study, we explore nucleation and the transition state ensemble of the
ribosomal protein S6 using a Monte Carlo Go model in conjunction with
restraints from experiment. The results are analyzed in the context of
extensive experimental and evolutionary data. The roles of individual residues
in the folding nucleus are identified and the order of events in the S6 folding
mechanism is explored in detail. Interpretation of our results agrees with, and
extends the utility of, experiments that shift f-values by modulating
denaturant concentration and presents strong evidence for the realism of the
mechanistic details in our Monte Carlo Go model and the structural
interpretation of experimental f-values. We also observe plasticity in the
contacts of the hydrophobic core that support the specific nucleus. For S6,
which binds to RNA and protein after folding, this plasticity may result from
the conformational flexibility required to achieve biological function. These
results present a theoretical and conceptual picture that is relevant in
understanding the mechanism of nucleation in protein folding.


title:
Site Model of Allometric Scaling and Fractal Distribution Networks of
  Organs

abstract:
  At the basic geometric level, the distribution networks carrying vital
materials in living organisms, and the units such as the nephrons and alveoli,
form a scaling structure named here the site model. This unified view of the
allometry of the kidney and lung of mammals is in agreement with the existing
data, and in some cases, improves the predictions of the previous fractal model
of the lung. Allometric scaling of surfaces and volumes of relevant organ parts
are derived from a few simple propositions about the collective characteristics
of the sites. New hypotheses and predictions are formulated, some verified by
the data, while others remain to be tested, such as the scaling of the number
of capillaries and the site model description of other organs.


title:
Protein Structure and Evolutionary History Determine Sequence Space
  Topology

abstract:
  Understanding the observed variability in the number of homologs of a gene is
a very important, unsolved problem that has broad implications for research
into co-evolution of structure and function, gene duplication, pseudogene
formation and possibly for emerging diseases. Here we attempt to define and
elucidate the reasons behind this observed unevenness in sequence space. We
present evidence that sequence variability and functional diversity of a gene
or fold family is influenced by certain quantitative characteristics of the
protein structure that reflect potential for sequence plasticity i.e. the
ability to accept mutation without losing thermodynamic stability.


title: Fast DNA translocation through a solid-state nanopore

abstract:
  We report translocation experiments on double-strand DNA through a silicon
oxide nanopore. Samples containing DNA fragments with seven different lengths
between 2000 to 96000 basepairs have been electrophoretically driven through a
10 nm pore. We find a power-law scaling of the translocation time versus
length, with an exponent of 1.26 $\pm$ 0.07. This behavior is qualitatively
different from the linear behavior observed in similar experiments performed
with protein pores. We address the observed nonlinear scaling in a theoretical
model that describes experiments where hydrodynamic drag on the section of the
polymer outside the pore is the dominant force counteracting the driving. We
show that this is the case in our experiments and derive a power-law scaling
with an exponent of 1.18, in excellent agreement with our data.


title: Resultants in Genetic Linkage Analysis

abstract:
  Statistical models for genetic linkage analysis of k-locus diseases are
k-dimensional subvarieties of a (3^k-1)-dimensional probability simplex. We
determine the algebraic invariants of these models with general characteristics
for k=1, in particular we recover, and generalize, the Hardy-Weinberg curve.
For k = 2, the invariants are presented as determinants of 32x32-matrices of
linear forms in 9 unknowns, a suitable format for computations with numerical
data.


title: A toy model of the brain

abstract:
  We have designed a toy brain and have written computer code that simulates
it. This toy brain is flexible, modular, has hierarchical learning and
recognition, has short and long term memory, is distributed (i.e. has no
central control), is asynchronous, and includes parallel and series processing.
We have simulated the neurons calculating their internal voltages as a function
of time. We include in the simulation the ion pumps of the neurons, the
synapses with glutamate or GABA neurotransmitters, and the delays of the action
pulses in axons and dendrites. We have used known or plausible circuits of real
brains. The toy brain reads books and learns languages using the Hebb
mechanism. Finally, we have related the toy brain with what might be occurring
in a real brain.


title: Spreading of families in cyclic predator-prey models

abstract:
  We study the spreading of families in two-dimensional multispecies
predator-prey systems, in which species cyclically dominate each other. In each
time step randomly chosen individuals invade one of the nearest sites of the
square lattice eliminating their prey. Initially all individuals get a
family-name which will be carried on by their descendants. Monte Carlo
simulations show that the systems with several species (N=3,4,5) are
asymptotically approaching the behavior of the voter model, i.e., the survival
probability of families, the mean-size of families and the mean-square distance
of descendants from their ancestor exhibit the same scaling behavior. The
scaling behavior of the survival probability of families has a logarithmic
correction. In case of the voter model this correction depends on the number of
species, while cyclic predator-prey models behave like the voter model with
infinite species. It is found that changing the rates of invasions does not
change this asymptotic behavior. As an application a three-species system with
a fourth species intruder is also discussed.


title: Information theory in molecular biology

abstract:
  This article introduces the physics of information in the context of
molecular biology and genomics. Entropy and information, the two central
concepts of Shannon's theory of information and communication, are often
confused with each other but play transparent roles when applied to statistical
ensembles (i.e., identically prepared sets) of symbolic sequences. Such an
approach can distinguish between entropy and information in genes, predict the
secondary structure of ribozymes, and detect the covariation between residues
in folded proteins. We also review applications to molecular sequence and
structure analysis, and introduce new tools in the characterization of
resistance mutations, and in drug design.


title: Self Trapping of a Single Bacterium in its Own Chemoattractant

abstract:
  Bacteria (e.g. E. Coli) are very sensitive to certain chemoattractants (e.g.
asparate) which they themselves produce. This leads to chemical instabilities
in a uniform population. We discuss here the different case of a single
bacterium, following the general scheme of Brenner, Levitov and Budrene. We
show that in one and two dimensions (in a capillary or in a thin film) the
bacterium can become self-trapped in its cloud of attractant. This should occur
if a certain coupling constant $g$ is larger than unity. We then estimate the
reduced diffusion D_eff of the bacterium in the strong coupling limit, and find
D_eff ~ 1/g.


title:
Proteins associated with diseases show enhanced sequence correlation
  between charged residues

abstract:
  Function of proteins or a network of interacting proteins often involves
communication between residues that are well separated in sequence. The classic
example is the participation of distant residues in allosteric regulation.
Bioinformatic and structural analysis methods have been introduced to infer
residues that are correlated. Recently, increasing attention has been paid to
obtain the sequence properties that determine the tendency of disease related
proteins (Abeta peptides, prion proteins, transthyretin etc.) to aggregate and
form fibrils. Motivated in part by the need to identify sequence
characteristics that indicate a tendency to aggregate, we introduce a general
method that probes covariations in charged residues along the sequence in a
given protein family. The method, which involves computing the Sequence
Correlation Entropy (SCE) using the quenched probability Psk(i,j) of finding a
residue pair at a given sequence separation sk, allows us to classify protein
families in terms of their SCE. Our general approach may be a useful way in
obtaining evolutionary covariations of amino acid residues on a genome wide
level.


title: Predicting protein functions with message passing algorithms

abstract:
  Motivation: In the last few years a growing interest in biology has been
shifting towards the problem of optimal information extraction from the huge
amount of data generated via large scale and high-throughput techniques. One of
the most relevant issues has recently become that of correctly and reliably
predicting the functions of observed but still functionally undetermined
proteins starting from information coming from the network of co-observed
proteins of known functions.
  Method: The method proposed in this article is based on a message passing
algorithm known as Belief Propagation, which takes as input the network of
proteins physical interactions and a catalog of known proteins functions, and
returns the probabilities for each unclassified protein of having one chosen
function. The implementation of the algorithm allows for fast on-line analysis,
and can be easily generalized to more complex graph topologies taking into
account hyper-graphs, {\em i.e.} complexes of more than two interacting
proteins.


title: Dynamic Phase Transitions in Cell Spreading

abstract:
  We monitored isotropic spreading of mouse embryonic fibroblasts on
fibronectin-coated substrates. Cell adhesion area versus time was measured via
total internal reflection fluorescence microscopy. Spreading proceeds in
well-defined phases. We found a power-law area growth with distinct exponents
a_i in three sequential phases, which we denote basal (a_1=0.4+-0.2), continous
(a_2=1.6+-0.9) and contractile (a_3=0.3+-0.2) spreading. High resolution
differential interference contrast microscopy was used to characterize local
membrane dynamics at the spreading front. Fourier power spectra of membrane
velocity reveal the sudden development of periodic membrane retractions at the
transition from continous to contractile spreading. We propose that the
classification of cell spreading into phases with distinct functional
characteristics and protein activity patterns serves as a paradigm for a
general program of a phase classification of cellular phenotype. Biological
variability is drastically reduced when only the corresponding phases are used
for comparison across species/different cell lines.


title: Exact Sequence Analysis for Three-Dimensional HP Lattice Proteins

abstract:
  We have exactly enumerated all sequences and conformations of HP proteins
with chains of up to 19 monomers on the simple cubic lattice. For two variants
of the hydrophobic-polar (HP) model, where only two types of monomers are
distinguished, we determined and statistically analyzed designing sequences,
i.e., sequences that have a non-degenerate ground state. Furthermore we were
interested in characteristic thermodynamic properties of HP proteins with
designing sequences. In order to be able to perform these exact studies, we
applied an efficient enumeration method based on contact sets.


title: Topological thermal instability and length of proteins

abstract:
  We present an analysis of the effects of global topology on the structural
stability of folded proteins in thermal equilibrium with a heat bath. For a
large class of single domain proteins, we computed the harmonic spectrum within
the Gaussian Network Model (GNM) and determined the spectral dimension, a
parameter describing the low frequency behaviour of the density of modes. We
find a surprisingly strong correlation between the spectral dimension and the
number of amino acids of the protein. Considering that larger spectral
dimension value relate to more topologically compact folded state, our results
indicate that for a given temperature and length of the protein, the folded
structure corresponds to the less compact folding compatible with thermodynamic
stability.


title: Coarse-Graining and Self-Dissimilarity of Complex Networks

abstract:
  Can complex engineered and biological networks be coarse-grained into smaller
and more understandable versions in which each node represents an entire
pattern in the original network? To address this, we define coarse-graining
units (CGU) as connectivity patterns which can serve as the nodes of a
coarse-grained network, and present algorithms to detect them. We use this
approach to systematically reverse-engineer electronic circuits, forming
understandable high-level maps from incomprehensible transistor wiring: first,
a coarse-grained version in which each node is a gate made of several
transistors is established. Then, the coarse-grained network is itself
coarse-grained, resulting in a high-level blueprint in which each node is a
circuit-module made of multiple gates. We apply our approach also to a
mammalian protein-signaling network, to find a simplified coarse-grained
network with three main signaling channels that correspond to cross-interacting
MAP-kinase cascades. We find that both biological and electronic networks are
'self-dissimilar', with different network motifs found at each level. The
present approach can be used to simplify a wide variety of directed and
nondirected, natural and designed networks.


title: Technique(s) for Spike - Sorting

abstract:
  Spike-sorting techniques attempt to classify a series of noisy electrical
waveforms according to the identity of the neurons that generated them.
Existing techniques perform this classification ignoring several properties of
actual neurons that can ultimately improve classification performance. In this
chapter, after illustrating the spike-sorting problem with real data, we
propose a more realistic spike train generation model. It incorporates both a
description of "non trivial" (ie, non Poisson) neuronal discharge statistics
and a description of spike waveform dynamics (eg, the events amplitude decays
for short inter-spike intervals). We show that this spike train generation
model is analogous to a one-dimensional Potts spin glass model. We can
therefore use the computational methods which have been developed in fields
where Potts models are extensively used. These methods are based on the
construction of a Markov Chain in the space of model parameters and spike train
configurations, where a configuration is defined by specifying a neuron of
origin for each spike. This Markov Chain is built such that its unique
stationary density is the posterior density of model parameters and
configurations given the observed data. A Monte Carlo simulation of the Markov
Chain is then used to estimate the posterior density. The theoretical
background on Markov chains is provided and the way to build the transition
matrix of the Markov Chain is illustrated with a simple, but realistic, model
for data generation . Simulated data are used to illustrate the performance of
the method and to show that it can easily cope with neurons generating spikes
with highly dynamic waveforms and/or generating strongly overlapping clusters
on Wilson plots.


title: What one can learn from experiments about the elusive transition state?

abstract:
  We present the results of an exact analysis of a model energy landscape of a
protein to clarify the notion of the transition state and the physical meaning
of the $\phi$ values determined in protein engineering experiments. We
benchmark our findings to various theoretical approaches proposed in the
literature for the identification and characterization of the transition state.


title: Prediction of RNA pseudoknots by Monte Carlo simulations

abstract:
  In this paper we consider the problem of RNA folding with pseudoknots. We use
a graphical representation in which the secondary structures are described by
planar diagrams. Pseudoknots are identified as non-planar diagrams. We analyze
the non-planar topologies of RNA structures and propose a classification of RNA
pseudoknots according to the minimal genus of the surface on which the RNA
structure can be embedded. This classification provides a simple and natural
way to tackle the problem of RNA folding prediction in presence of pseudoknots.
Based on that approach, we describe a Monte Carlo algorithm for the prediction
of pseudoknots in an RNA molecule.


title:
A statistical framework for the design of microarray experiments and
  effective detection of differential gene expression

abstract:
  Four reasons why you might wish to read this paper: 1. We have devised a new
statistical T test to determine differentially expressed genes (DEG) in the
context of microarray experiments. This statistical test adds a new member to
the traditional T-test family. 2. An exact formula for calculating the
detection power of this T test is presented, which can also be fairly easily
modified to cover the traditional T tests. 3. We have presented an accurate yet
computationally very simple method to estimate the fraction of non-DEGs in a
set of genes being tested. This method is superior to an existing one which is
computationally much involved. 4. We approach the multiple testing problem from
a fresh angle, and discuss its relation to the classical Bonferroni procedure
and to the FDR (false discovery rate) approach. This is most useful in the
analysis of microarray data, where typically several thousands of genes are
being tested simultaneously.


title:
Position Dependent and Independent Evolutionary Models Based on
  Empirical Amino Acid Substitution Matrices

abstract:
  Evolutionary models measure the probability of amino acid substitutions
occurring over different evolutionary distances. We examine various
evolutionary models based on empirically derived amino acid substitution
matrices. The models are constructed using the PAM and BLOSUM amino acid
substitution matrices. We rescale these matrices by raising them to powers to
model substitution patterns that account for different evolutionary distances.
We also examine models that account for the dissimilarity of substitution rates
along a protein sequence. We compare the models by computing the likelihood of
each model across different alignments. We also present a specific example to
illustrate the subtle differences in the estimation of evolutionary distance
computed using the different models.


title: Kinetic barriers in RNA unzipping

abstract:
  We consider a simple model for the unfolding of RNA tertiary structure under
dynamic loading. The opening of such a structure is regarded as a two step
process, each corresponding to the overcoming of a single energy barrier. The
resulting two-barrier energy landscape accounts for the dependence of the
unfolding kinetics on the pulling rate. Furthermore at intermediate force, the
two barriers cannot be distinguished by the analysis of the opening kinetic,
which turns out to be dominated by a single macro-barrier, whose properties
depend non-trivially on the two single barriers. Our results suggest that in
pulling experiments on RNA molecule containing tertiary structures, the details
of the single kinetic barriers can only be obtained using a low pulling rate
value, or in the high force regime.


title:
Effects of neighbourhood size and connectivity on spatial Continuous
  Prisoner's Dilemma

abstract:
  The Prisoner's Dilemma, a 2-person game in which the players can either
cooperate or defect, is a common paradigm for studying the evolution of
cooperation, when individuals exhibit variable degrees of cooperation. It is
known that in the presence of spatial structure, when individuals ``play
against'' their neighbours, and ``compare to'' them, cooperative investments
can evolve to considerable levels. Here we examine the effect of increasing the
neighbourhood size: we find that the mean-field limit of no cooperation is
reached for a critical neighbourhood size of about five neighbours. We also
find the related result that in a network of players, the critical average
degree (number of neighbours) of nodes for which defection is the final state
depends only on the network topology. This critical average degree is
considerably higher for clustered networks, than for distributed random
networks. This result strengthens the argument that clustering is the mechanism
which makes the development and maintenance of the cooperation possible.


title: Geometry and symmetry presculpt the free-energy landscape of proteins

abstract:
  We present a simple physical model which demonstrates that the native state
folds of proteins can emerge on the basis of considerations of geometry and
symmetry. We show that the inherent anisotropy of a chain molecule, the
geometrical and energetic constraints placed by the hydrogen bonds and sterics,
and hydrophobicity are sufficient to yield a free energy landscape with broad
minima even for a homopolymer. These minima correspond to marginally compact
structures comprising the menu of folds that proteins choose from to house
their native-states in. Our results provide a general framework for
understanding the common characteristics of globular proteins.


title: The Orchestral Analog of Molecular Biology

abstract:
  Signal processing (SP) techniques convert DNA and protein sequences into
information that lead to successful drug discovery. One must, however, be aware
about the difference between information and entropy1. Eight other physical
properties of DNA and protein segments are suggested for SP analysis other than
ones already used in the literature. QSAR formulations of these properties are
suggested for ranking the amino acids that maximize efficiency of the amino
acids in proteins. Multiobjective programs are suggested for constraining or
searching the components of such sequences. Geometric maps of the networks of
proteins are preferable to scale-free descriptions in most cases. The genetic
code is presented as graphlets which show interesting correspondence to each
other, leading to possible new revelations.


title:
Spectral Analysis of Protein-Protein Interactions in Drosophila
  melanogaster

abstract:
  Within a case study on the protein-protein interaction network (PIN) of
Drosophila melanogaster we investigate the relation between the network's
spectral properties and its structural features such as the prevalence of
specific subgraphs or duplicate nodes as a result of its evolutionary history.
The discrete part of the spectral density shows fingerprints of the PIN's
topological features including a preference for loop structures. Duplicate
nodes are another prominent feature of PINs and we discuss their representation
in the PIN's spectrum as well as their biological implications.


title:
Scaling laws in the functional content of genomes: Fundamental constants
  of evolution?

abstract:
  With the number of fully-sequenced genomes now well over a hundred it has
become possible to start investigating if there are any quantitative
regularities in the genetic make-up of genomes. In (physics/0307001), I
originally showed that the numbers of genes in different functional categories
scale as power laws in the total number of genes in the genome. In this chapter
I revisit these results with more recent data and go into considerable more
depth regarding the implications of these scaling laws for our understanding of
the regulatory design of cells. In addition, I further develop the evolutionary
model first proposed in (physics/0307001), which suggests that the exponents of
the observed scaling laws correspond to fundamental constants of the
evolutionary process. In particular, I put forward an hypothesis for the
approximately quadratic scaling of regulatory and signal transducing genes with
genome size.


title: Evolving eco-system: a network of networks

abstract:
  Ecology and evolution are inseparable. Motivated by some recent experiments,
we have developed models of evolutionary ecology from the perspective of
dynamic networks. In these models, in addition to the intra-node dynamics,
which corresponds to an individual-based population dynamics of species, the
entire network itself changes slowly with time to capture evolutionary
processes. After a brief summary of our recent published works on these network
models of eco-systems, we extend the most recent version of the model
incorporating predators that wander into neighbouring spatial patches for food.


title:
Modeling stochastic Ca$^{2+}$ release from a cluster of IP$_3$-sensitive
  receptors

abstract:
  We focused our attention on Ca$^{2+}$ release from the endoplasmic reticulum
through a cluster of inositol 1,4,5-trisphosphate (IP$_3$) receptor channels.
The random opening and closing of these receptors introduce stochastic effects
that have been observed experimentally. Here, we present a stochastic version
of Othmer-Tang model for IP$_3$ receptor clusters. We address the average
behavior of the channels in response to IP$_3$ stimuli. We found, by stochastic
simulation, that the shape of the receptor response to IP$_3$ (fraction of open
channels versus [IP$_3$]), is affected by the cytosolic Ca$^{2+}$ level. We
also study several aspects of the stochastic properties of Ca${2+}$ release and
we compare with experimental observations.


title: Universal Mortality Law, Life Expectancy and Immortality

abstract:
  Well protected human and laboratory animal populations with abundant
resources are evolutionary unprecedented, and their survival far beyond
reproductive age may be a byproduct rather than tool of evolution. Physical
approach, which takes advantage of their extensively quantified mortality,
establishes that its dominant fraction yields the exact law, and suggests its
unusual mechanism. The law is universal for all animals, from yeast to humans,
despite their drastically different biology and evolution. It predicts that the
universal mortality has short memory of the life history, at any age may be
reset to its value at a significantly younger age, and mean life expectancy
extended (by biologically unprecedented small changes) from its current maximal
value to immortality. Mortality change is rapid and stepwise. Demographic data
and recent experiments verify these predictions for humans, rats, flies,
nematodes and yeast. In particular, mean life expectancy increased 6-fold (to
"human" 430 years), with no apparent loss in health and vitality, in nematodes
with a small number of perturbed genes and tissues. Universality allows one to
study unusual mortality mechanism and the ways to immortality.


title: Scaling Laws in Cancer: The Dynamic Behaviour of the Power Exponent p

abstract:
  We have previously reported that a 'universal' growth law with fixed scaling
exponent p, as proposed by West and collaborators for all living organisms,
appears to be able to describe also the growth of tumors in vivo. Here, we
investigate in more detail the dynamic behaviour of p using data from the
literature. We show that p initially decreases before it again increases up to
or even beyond 3/4. These results support the notion that p can vary over time
and yet its dynamics are independent from the cancer type.


title:
General representation of collective neural dynamics with columnar
  modularity

abstract:
  We exhibit a mathematical framework to represent the neural dynamics at
cortical level. Our description of neural dynamics with columnar and functional
modularity, named fibre bundle representation (FBM) method, is based both on
neuroscience and informatics, whereas they correspond with the conventional
formulas in statistical physics. In spite of complex interactions in neural
circuitry and various cortical modification rules per models, some significant
factors determine the typical phenomena in cortical dynamics. The FBM
representation method reveals them plainly and gives profit in building or
analyzing the cortical dynamic models. Not only the similarity in formulas, the
cortical dynamics can share the statistical properties with other physical
systems, which validated in primary visual maps. We apply our method to
proposed models in visual map formations, in addition our suggestion using the
lateral interaction scheme. In this paper, we will show that the neural dynamic
procedures can be treated through conventional physics expressions and
theories.


title: The Heart of Protein-Protein Interaction Networks

abstract:
  Recent developments in complex networks have paved the way to a series of
important biological insights, such as the fact that many of the essential
proteins of S. cerevisae corresponds to the so-called hubs of the respective
protein-protein interaction networks. Despite the special importance of hubs,
other types of nodes such as those corresponding to the network border, as well
as the innermost nodes, also deserve special attention. This work reports on
how the application of the concept of distance transform to networks showed
that a great deal of the innermost nodes correspond to essential proteins, with
interesting biological implications.


title: On tumor development: fractional transport approach

abstract:
  A growth of malignant neoplasm is considered as a fractional transport
approach. We suggested that the main process of the tumor development through a
lymphatic net is fractional transport of cells. In the framework of this
fractional kinetics we were able to show that the mean size of main growth is
due to subdiffusion, while the appearance of metaphases is determined by
superdiffusion.


title: Open Mass Spectrometry Search Algorithm

abstract:
  Large numbers of MS/MS peptide spectra generated in proteomics experiments
require efficient, sensitive and specific algorithms for peptide
identification. In the Open Mass Spectrometry Search Algorithm [OMSSA],
specificity is calculated by a classic probability score using an explicit
model for matching experimental spectra to sequences. At default thresholds,
OMSSA matches more spectra from a standard protein cocktail than a comparable
algorithm. OMSSA is designed to be faster than published algorithms in
searching large MS/MS datasets.


title:
The principal eigenvector of contact matrices and hydrophobicity
  profiles in proteins

abstract:
  With the aim to study the relationship between protein sequences and their
native structures, we adopt vectorial representations for both sequence and
structure. The structural representation is based on the Principal Eigenvector
of the fold's contact matrix (PE). As recently shown, the latter encodes
sufficient information for reconstructing the whole contact matrix. The
sequence is represented through a Hydrophobicity Profile (HP), using a
generalized hydrophobicity scale that we obtain from the principal eigenvector
of a residue-residue interaction matrix and denote it as interactivity scale.
Using this novel scale, we define the optimal HP of a protein fold, and
predict, by means of stability arguments, that it is strongly correlated with
the PE of the fold's contact matrix. This prediction is confirmed through an
evolutionary analysis, which shows that the PE correlates with the HP of each
individual sequence adopting the same fold and, even more strongly, with the
average HP of this set of sequences. Thus, protein sequences evolve in such a
way that their average HP is close to the optimal one, implying that neutral
evolution can be viewed as a kind of motion in sequence space around the
optimal HP. Our results indicate that the correlation coefficient between
N-dimensional vectors constitutes a natural metric in the vectorial space in
which we represent both protein sequences and protein structures, which we call
Vectorial Protein Space. In this way, we define a unified framework for
sequence to sequence, sequence to structure, and structure to structure
alignments. We show that the interactivity scale is nearly optimal both for the
comparison of sequences with sequences and sequences with structures.


title:
Ligand Rebinding: Self-consistent Mean-field Theory and Numerical
  Simulations Applied to SPR Studies

abstract:
  Rebinding of dissociated ligands from cell surface proteins can confound
quantitative measurements of dissociation rates important for characterizing
the affinity of binding interactions. This can be true also for in vitro
techniques such as surface plasmon resonance (SPR). We present experimental
results using SPR for the interaction of insulin-like growth factor-I (IGF-I)
with one of its binding proteins, IGF binding protein-3 (IGFBP-3), and show
that rebinding, even with the addition of soluble heparin in the dissociation
phase, does not exhibit the expected exponential decay characteristic of a 1:1
binding reaction. We thus consider the effect of (multiple) rebinding events
and, within a self-consistent mean-field approximation, we derive the complete
mathematical form for the fraction of bound ligand as a function of time. We
show that, except for very low surface coverage/association rate, this function
is non-exponential at all times, indicating that multiple rebinding events
strongly influence dissociation even at early times. We compare the mean-field
results with numerical simulations and find good agreement, although deviations
are measurable in certain cases. Our analysis of the IGF-I-IGFBP-3 data
indicates that rebinding is prominent for this system and that the theoretical
predictions fit the experimental data well. Our results provide a means for
analyzing SPR biosensor data where rebinding is problematic and a methodology
to do so is presented.


title: War of attrition with implicit time cost

abstract:
  In the game-theoretic model war of attrition, players are subject to an
explicit cost proportional to the duration of contests. We construct a model
where the time cost is not explicitly given, but instead depends implicitly on
the strategies of the whole population. We identify and analyse the underlying
mechanisms responsible for the implicit time cost. Each player participates in
a series of games, where those prepared to wait longer win with higher
certainty but play less frequently. The model is characterised by the ratio of
the winner's score to the loser's score, in a single game. The fitness of a
player is determined by the accumulated score from the games played during a
generation. We derive the stationary distribution of strategies under the
replicator dynamics. When the score ratio is high, we find that the stationary
distribution is unstable, with respect to both evolutionary and dynamical
stability, and the dynamics converge to a limit cycle. When the ratio is low,
the dynamics converge to the stationary distribution. For an intermediate
interval of the ratio, the distribution is dynamically but not evolutionarily
stable. Finally, the implications of our results for previous models based on
the war of attrition are discussed.


title: Sampling rare switching events in biochemical networks

abstract:
  Bistable biochemical switches are ubiquitous in gene regulatory networks and
signal transduction pathways. Their switching dynamics, however, are difficult
to study directly in experiments or conventional computer simulations, because
switching events are rapid, yet infrequent. We present a simulation technique
that makes it possible to predict the rate and mechanism of flipping of
biochemical switches. The method uses a series of interfaces in phase space
between the two stable steady states of the switch to generate transition
trajectories in a ratchet-like manner. We demonstrate its use by calculating
the spontaneous flipping rate of a symmetric model of a genetic switch
consisting of two mutually repressing genes. The rate constant can be obtained
orders of magnitude more efficiently than using brute-force simulations. For
this model switch, we show that the switching mechanism, and consequently the
switching rate, depends crucially on whether the binding of one regulatory
protein to the DNA excludes the binding of the other one. Our technique could
also be used to study rare events and non-equilibrium processes in soft
condensed matter systems.


title: Subthreshold oscillations in a map-based neuron model

abstract:
  Self-sustained subthreshold oscillations in a discrete-time model of neuronal
behavior are considered. We discuss bifurcation scenarios explaining the birth
of these oscillations and their transformation into tonic spikes. Specific
features of these transitions caused by the discrete-time dynamics of the model
and the influence of external noise are discussed.


title: Optimal Path to Epigenetic Switching

abstract:
  We use large deviation methods to calculate rates of noise-induced
transitions between states in multistable genetic networks. We analyze a
synthetic biochemical circuit, the toggle switch, and compare the results to
those obtained from a numerical solution of the master equation.


title: Finite Width Model Sequence Comparison

abstract:
  Sequence comparison is a widely used computational technique in modern
molecular biology. In spite of the frequent use of sequence comparisons the
important problem of assigning statistical significance to a given degree of
similarity is still outstanding. Analytical approaches to filling this gap
usually make use of an approximation that neglects certain correlations in the
disorder underlying the sequence comparison algorithm. Here, we use the longest
common subsequence problem, a prototype sequence comparison problem, to
analytically establish that this approximation does make a difference to
certain sequence comparison statistics. In the course of establishing this
difference we develop a method that can systematically deal with these disorder
correlations.


title:
Role of calcium and noise in the persistent activity of an isolated
  neuron

abstract:
  The activity of an isolated and auto-connected neuron is studied using
Hodgkin--Huxley and Integrate-and-Fire frameworks. Main ingredients of the
modeling are the auto-stimulating autaptic current observed in experiments,
with a spontaneous synaptic liberation noise and a calcium--dependent negative
feedback mechanism. The distributions of inter-spikes intervals and burst
durations are analytically calculated, and show a good agreement with
experimental data.


title: Imperfect DNA Repair and the Error Catastrophe

abstract:
  In this Letter, we extend the semiconservative quasispecies equations to
incorporate imperfect DNA lesion repair. We study the equilibrium behavior of
this model in the limit of infinite sequence length and population size, using
a single-fitness-peak landscape for which the master genome can sustain a
finite number of lesions and remain viable. We provide a full analytical
treatment of the problem, providing a general mathematical framework as well as
the full solution for a particular class of fitness landscapes. Stochastic
simulations using finite sequence lengths and populations agree well with the
analytical results. Applications to biological systems are briefly discussed.


title:
A computational study of the statistical mechanics of antibody-antigen
  conformations

abstract:
  We describe the representation of the chemical affinity between the
antigen-combining site of the immunoglobulin molecule and the antigen molecule
as the probability of the two molecules existing in a bound state. Our model is
based on the identification of shape attractors in the configuration space for
the joint antibody / antigen combining site sequence. We parameterize
configuration space in terms of Ramachandran angles. The shape attractors allow
us to construct a Markov chain whose steady state distribution gives rise to
the desired attachment probability. As a result we are able to delineate the
enthalpic, entropic and kinetic components of affinity and study their
interactions.


title: Polynomial epidemics and clustering in contact networks

abstract:
  It is widely known that the spread of the HIV virus was slower than
exponential in several populations, even at the very beginning of the epidemic.
We show that this implies a significant reduction in the effective reproductive
rate of the epidemic, and describe a general mechanism, related to the
clustering properties of the disease transmission network, capable of
explaining this reduction. Our considerations provide a new angle on polynomial
epidemic processes, and may have implications for the choice of strategy
against such epidemics.


title:
Evolution at the Edge of Chaos: A Paradigm for the Maturation of the
  Humoral Immune Response

abstract:
  We study the maturation of the antibody population following primary antigen
presentation as a global optimization problem. Emphasis is placed on the
trade-off between the safety of mutations that lead to local improvements to
the antibody's affinity and the necessity of eventual mutations that result in
global reconfigurations in the antibody's shape. The model described herein
gives evidence of the underlying optimization process from which the rapidity
and consistency of the biologic response could be derived.


title:
Information theory, multivariate dependence, and genetic network
  inference

abstract:
  We define the concept of dependence among multiple variables using maximum
entropy techniques and introduce a graphical notation to denote the
dependencies. Direct inference of information theoretic quantities from data
uncovers dependencies even in undersampled regimes when the joint probability
distribution cannot be reliably estimated. The method is tested on synthetic
data. We anticipate it to be useful for inference of genetic circuits and other
biological signaling networks.

categories: q-bio.QM cs.IT math.IT math.ST physics.data-an q-bio.GN stat.TH


title:
Predicting Genetic Regulatory Response using Classification: Yeast
  Stress Response

abstract:
  We present a novel classification-based algorithm called GeneClass for
learning to predict gene regulatory response. Our approach is motivated by the
hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can
learn a decision rule for predicting whether a gene is up- or down-regulated in
a particular experiment based on (1) the presence of binding site subsequences
(``motifs'') in the gene's regulatory region and (2) the expression levels of
regulators such as transcription factors in the experiment (``parents''). Thus
our learning task integrates two qualitatively different data sources:
genome-wide cDNA microarray data across multiple perturbation and mutant
experiments along with motif profile data from regulatory sequences. Rather
than focusing on the regression task of predicting real-valued gene expression
measurements, GeneClass performs the classification task of predicting +1 and
-1 labels, corresponding to up- and down-regulation beyond the levels of
biological and measurement noise in microarray measurements. GeneClass uses the
Adaboost learning algorithm with a margin-based generalization of decision
trees called alternating decision trees. In computational experiments based on
the Gasch S. cerevisiae dataset, we show that the GeneClass method predicts up-
and down-regulation on held-out experiments with high accuracy. We explore a
range of experimental setups related to environmental stress response, and we
retrieve important regulators, binding site motifs, and relationships between
regulators and binding sites that are known to be associated to specific stress
response pathways. Our method thus provides predictive hypotheses, suggests
biological experiments, and provides interpretable insight into the structure
of genetic regulatory networks.


title: The emergence of prime numbers as the result of evolutionary strategy

abstract:
  We investigate by means of a simple theoretical model the emergence of prime
numbers as life cycles, as those seen for some species of cicadas. The cicadas,
more precisely, the Magicicadas spend most of their lives below the ground and
then emerge and die in a short period of time. The Magicicadas display an
uncommon behavior: their emergence is synchronized and these periods are
usually prime numbers. In the current work, we develop a spatially extended
model at which preys and predators coexist and can change their evolutionary
dynamics through the occurrence of mutations. We verified that prime numbers as
life cycles emerge as a result of the evolution of the population. Our results
seem to be a first step in order to prove that the development of such strategy
is selectively advantageous, especially for those organisms that are highly
vulnerable to attacks of predators.


title:
Fluctuations of Complex Networks: Electrical Properties of Single
  Protein Nanodevices

abstract:
  We present for the first time a complex network approach to the study of the
electrical properties of single protein devices. In particular, we consider an
electronic nanobiosensor based on a G-protein coupled receptor. By adopting a
coarse grain description, the protein is modeled as a complex network of
elementary impedances. The positions of the alpha-carbon atoms of each amino
acid are taken as the nodes of the network. The amino acids are assumed to
interact electrically among them. Consequently, a link is drawn between any
pair of nodes neighboring in space within a given distance and an elementary
impedance is associated with each link. The value of this impedance can be
related to the physical and chemical properties of the amino acid pair and to
their relative distance. Accordingly, the conformational changes of the
receptor induced by the capture of the ligand, are translated into a variation
of its electrical properties. Stochastic fluctuations in the value of the
elementary impedances of the network, which mimic different physical effects,
have also been considered. Preliminary results concerning the impedance
spectrum of the network and its fluctuations are presented and discussed for
different values of the model parameters.

categories: q-bio.MN cond-mat.other physics.bio-ph q-bio.OT


title:
Untangling influences of hydrophobicity on protein sequences and
  structures

abstract:
  We fit the Fourier transforms of solvent accessibility and hydrophobicity
profiles of a representative set of proteins to a joint multi-variable
Gaussian. This allows us to separate the intrinsic tendencies of sequence and
structure profiles from the interactions that correlate them; for example, the
$\alpha$-helix periodicity in sequence hydrophobicity is dictated by the
solvent accessibility of structures. The distinct intrinsic tendencies of
sequence and structure profiles are most pronounced at long periods, where
sequence hydrophobicity fluctuates more, while solvent accessibility
fluctuations are less than average. Interestingly, correlations between the two
profiles can be interpreted as the Boltzmann weight of the solvation energy at
room temperature.


title: Organization of Ecosystems in the Vicinity of a Novel Phase Transition

abstract:
  It is shown that an ecosystem in equilibrium is generally organized in a
state which is poised in the vicinity of a novel phase transition.


title: Membrane-protein interactions in mechanosensitive channels

abstract:
  In this paper, we examine the mechanical role of the lipid bilayer in ion
channel conformation and function with specific reference to the case of the
mechanosensitive channel of large conductance (MscL). In a recent paper
(Wiggins and Phillips, 2004), we argued that mechanotransduction very naturally
arises from lipid-protein interactions by invoking a simple analytic model of
the MscL channel and the surrounding lipid bilayer. In this paper, we focus on
improving and expanding this analytic framework for studying lipid-protein
interactions with special attention to MscL. Our goal is to generate simple
scaling relations which can be used to provide qualitative understanding of the
role of membrane mechanics in protein function and to quantitatively interpret
experimental results. For the MscL channel, we find that the free energies
induced by lipid-protein interaction are of the same order as the free energy
differences between conductance states measured by Sukharev et al. (1999). We
therefore conclude that the mechanics of the bilayer plays an essential role in
determining the conformation and function of the channel. Finally, we compare
the predictions of our model to experimental results from the recent
investigations of the MscL channel by Perozo et al. (2002), Powl et al. (2003),
Yoshimura et al. (2004), and others and suggest a suite of new experiments.


title: Forces During Bacteriophage DNA Packaging and Ejection

abstract:
  The conjunction of insights from structural biology, solution biochemistry,
genetics and single molecule biophysics has provided a renewed impetus for the
construction of quantitative models of biological processes. One area that has
been a beneficiary of these experimental techniques is the study of viruses. In
this paper we describe how the insights obtained from such experiments can be
utilized to construct physical models of processes in the viral life cycle. We
focus on dsDNA bacteriophages and show that the bending elasticity of DNA and
its electrostatics in solution can be combined to determine the forces
experienced during packaging and ejection of the viral genome. Furthermore, we
quantitatively analyze the effect of fluid viscosity and capsid expansion on
the forces experienced during packaging. Finally, we present a model for DNA
ejection from bacteriophages based on the hypothesis that the energy stored in
the tightly packed genome within the capsid leads to its forceful ejection. The
predictions of our model can be tested through experiments in vitro where DNA
ejection is inhibited by the application of external osmotic pressure.


title:
Community Structure and Metacommunity Dynamics of Aquatic Invertebrates:
  a Test of the Neutral Theory

abstract:
  We used a metacommunity of 49 discrete communities of aquatic invertebrates
to analyze the dynamical relationship between community and metacommunity
species distributions as a test of the neutral theory of biodiversity and
biogeography. At the community scale, observed variation in species richness
and relative abundance was greater than predicted by neutral models, and
revealed important differences among species in competitive ability and
tolerance for predation. At the metacommunity scale, species with metacommunity
proportions of less than 0.01% (38% of the observed metacommunity) were
consistently more abundant than predicted by models. Our results are at
variance with the neutral theory, and suggest that the use of an identical
survival probability for all species in neutral models misrepresents
substantial aspects of community assembly. Nevertheless, building and testing
neutral models can provide valuable insights into the processes that determine
species distributions.


title: Projective geometry for human motion, with an application to injury risk

abstract:
  We give an exposition of Plucker vectors for a system of joint axes in
projective 3-space. We use Plucker vectors to analyse dependencies among joint
axes, and in particular show that two rotational joints rigidly joined by a bar
and each with 3 degrees of freedom always forms a 5-dimensional system. We
introduce the concept of reduced redundancy in a dependent set of projective
Lines, and argue that reduced redundancy in the axes of a body position
increases injury risk. We apply this to a simple two-joint model of bowling in
cricket, and show by analysis of some experimental data that reduced redundancy
around ball release is observed in some cases.


title:
Unbound Protein-Protein Docking Selections by the DFIRE-based
  Statistical Pair Potential

abstract:
  A newly developed statistical pair potential based on Distance-scaled Finite
Ideal-gas REference (DFIRE) state is applied to unbound protein-protein docking
structure selections. The performance of the DFIRE energy function is compared
to those of the well-established ZDOCK energy scores and RosettaDock energy
function using the comprehensive decoy sets generated by ZDOCK and RosettaDock.
Despite significant difference in the functional forms and complexities of the
three energy scores, the differences in overall performance for docking
structure selections are small between DFIRE and ZDOCK2.3 and between DFIRE and
RosettaDock. This result is remarkable considering that a single-term DFIRE
energy function was originally designed for monomer proteins while
multiple-term energy functions of ZDOCK and RosettaDock were specifically
optimized for docking. This provides hope that the accuracy of the existing
energy functions for docking can be improved.


title: Phase synchronization in cerebral hemodynamics

abstract:
  A healthy human brain is perfused with blood flowing laminarly through
cerebral vessels, providing brain tissue with substrates such as oxygen and
glucose. Under normal conditions, cerebral blood flow is controlled by
autoregulation as well as metabolic, chemical and neurogenic regulation.
Physiological complexity of these mechanisms invariably leads to a question as
to what are the relations between the statistical properties of arterial and
intracranial pressure fluctuations. To shed new light on cerebral hemodynamics,
we employ a complex continuous wavelet transform to determine the instantaneous
phase difference between the arterial blood pressure (ABP) and intracranial
pressure (ICP) in patients with traumatic brain injuries or spontaneous
cerebral hemorrhage. For patients with mild to moderate injury, the phase
difference slowly evolves in time. However, severe neurological injury with
elevated ICP are herein associated with synchronization of arterial and
intracranial pressure. We use Shannon entropy to quantify the stability of
ABP-ICP phase difference and discuss the clinical applicability of such measure
to assessment of cerebrovascular reactivity and autoregulation integrity.


title: Altruism and Antagonistic Pleiotropy in Penna Ageing Model

abstract:
  The Penna ageing model is based on mutation accumulation theory. We show that
it also allows for self-organization of antagonistic pleiotropy which helps at
young age at the expense of old age. This can be interpreted as emergence of
altruism.


title: Aggregation of foraging swarms

abstract:
  In this paper we consider a continuous-time anisotropic swarm model with an
attraction/repulsion function and study its aggregation properties. It is shown
that the swarm members will aggregate and eventually form a cohesive cluster of
finite size around the swarm center. We also study the swarm cohesiveness when
the motion of each agent is a combination of the inter-individual interactions
and the interaction of the agent with external environment. Moreover, we extend
our results to more general attraction/repulsion functions. The model in this
paper is more general than isotropic swarms and our results provide further
insight into the effect of the interaction pattern on individual motion in a
swarm system.


title: Thermodynamics of beta-amyloid fibril formation

abstract:
  Amyloid fibers are aggregates of proteins. They are built out of a peptide
called $\beta$--amyloid (A$\beta$) containing between 41 and 43 residues,
produced by the action of an enzyme which cleaves a much larger protein known
as the Amyloid Precursor Protein (APP). X-ray diffraction experiments have
shown that these fibrils are rich in $\beta$--structures, whereas the shape of
the peptide displays an $\alpha$--helix structure within the APP in its
biologically active conformation. A realistic model of fibril formation is
developed based on the seventeen residues A$\beta$12--28 amyloid peptide, which
has been shown to form fibrils structurally similar to those of the whole
A$\beta$ peptide. With the help of physical arguments and in keeping with
experimental findings, the A$\beta$12--28 monomer is assumed to be in four
possible states (i.e., native helix conformation, $\beta$--hairpin, globular
low--energy state and unfolded state). Making use of these monomeric states,
oligomers (dimers, tertramers and octamers) were constructed. With the help of
short, detailed Molecular Dynamics (MD) calculations of the three monomers and
of a variety of oligomers, energies for these structures were obtained. Making
use of these results within the framework of a simple yet realistic model to
describe the entropic terms associated with the variety of amyloid
conformations, a phase diagram can be calculated of the whole many--body
system, leading to a thermodynamical picture in overall agreement with the
experimental findings. In particular, the existence of micellar metastable
states seem to be a key issue to determine the thermodynamical properties of
the system.


title:
Deriving amino acid contact potentials from their frequencies of
  occurence in proteins: a lattice model study

abstract:
  The possibility of deriving the contact potentials between amino acids from
their frequencies of occurence in proteins is discussed in evolutionary terms.
This approach allows the use of traditional thermodynamics to describe such
frequencies and, consequently, to develop a strategy to include in the
calculations correlations due to the spatial proximity of the amino acids and
to their overall tendency of being conserved in proteins. Making use of a
lattice model to describe protein chains and defining a "true" potential, we
test these strategies by selecting a database of folding model sequences,
deriving the contact potentials from such sequences and comparing them with the
"true" potential. Taking into account correlations allows for a markedly better
prediction of the interaction potentials.


title: Simple models of protein folding and of non--conventional drug design

abstract:
  While all the information required for the folding of a protein is contained
in its amino acid sequence, one has not yet learned how to extract this
information to predict the three--dimensional, biologically active, native
conformation of a protein whose sequence is known. Using insight obtained from
simple model simulations of the folding of proteins, in particular of the fact
that this phenomenon is essentially controlled by conserved (native) contacts
among (few) strongly interacting ("hot"), as a rule hydrophobic, amino acids,
which also stabilize local elementary structures (LES, hidden, incipient
secondary structures like $\alpha$--helices and $\beta$--sheets) formed early
in the folding process and leading to the postcritical folding nucleus (i.e.,
the minimum set of native contacts which bring the system pass beyond the
highest free--energy barrier found in the whole folding process) it is possible
to work out a succesful strategy for reading the native structure of designed
proteins from the knowledge of only their amino acid sequence and of the
contact energies among the amino acids. Because LES have undergone millions of
years of evolution to selectively dock to their complementary structures, small
peptides made out of the same amino acids as the LES are expected to
selectively attach to the newly expressed (unfolded) protein and inhibit its
folding, or to the native (fluctuating) native conformation and denaturate it.
These peptides, or their mimetic molecules, can thus be used as effective
non--conventional drugs to those already existing (and directed at neutralizing
the active site of enzymes), displaying the advantage of not suffering from the
uprise of resistance.


title: Amino acid substitution matrices for protein conformation identification

abstract:
  Methods for alignment of protein sequences typically measure similarity by
using substitution matrix with scores for all possible exchanges of one amino
acid with another. Although widely used, the matrices derived from homologous
sequence segments, such as Dayhoff's PAM matrices and Henikoff's BLOSUM
matrices, are not specific for protein conformation identification. Using a
different approach, we got many amino acid segment blocks. For each of them,
the protein secondary structure is identical. Based on these blocks, we have
derived new amino acid substitution matrices. The application of these matrices
led to marked improvements in conformation segment search and homologues
detection in twilight zone.


title:
A model for African trypanosome cell motility and quantitative
  description of flagellar dynamics

abstract:
  A quantitative description of the flagellar dynamics in the procyclic T.
brucei is presented in terms of stationary oscillations and traveling waves. By
using digital video microscopy to quantify the kinematics of trypanosome
flagellar waveforms. A theoretical model is build starting from a
Bernoulli-Euler flexural-torsional model of an elastic string with internal
distribution of force and torque. The dynamics is internally driven by the
action of the molecular motors along the string, which is proportional to the
local shift and consequently to the local curvature. The model equation is a
nonlinear partial differential wave equation of order four, containing
nonlinear terms specific to the Korteweg-de Vries (KdV) equation and the
modified-KdV equation. For different ranges of parameters we obtained kink-like
solitons, breather solitons, and a new class of solutions constructed by
smoothly piece-wise connected conic functions arcs (e.g. ellipse). The
predicted amplitude and wavelengths are in good match with experiments. We also
present the hypotheses for a step-wise kinematical model of swimming of
procyclic African trypanosome.


title: Modeling the thermal evolution of enzyme-created bubbles in DNA

abstract:
  The formation of bubbles in nucleic acids (NAs) are fundamental in many
biological processes such as DNA replication, recombination, telomeres
formation, nucleotide excision repair, as well as RNA transcription and
splicing. These precesses are carried out by assembled complexes with enzymes
that separate selected regions of NAs. Within the frame of a nonlinear dynamics
approach we model the structure of the DNA duplex by a nonlinear network of
coupled oscillators. We show that in fact from certain local structural
distortions there originate oscillating localized patterns, that is radial and
torsional breathers, which are associated with localized H-bond deformations,
being reminiscent of the replication bubble. We further study the temperature
dependence of these oscillating bubbles. To this aim the underlying nonlinear
oscillator network of the DNA duplex is brought in contact with a heat bath
using the Nos$\rm{\acute{e}}$-Hoover-method. Special attention is paid to the
stability of the oscillating bubbles under the imposed thermal perturbations.
It is demonstrated that the radial and torsional breathers, sustain the impact
of thermal perturbations even at temperatures as high as room temperature.
Generally, for nonzero temperature the H-bond breathers move coherently along
the double chain whereas at T=0 standing radial and torsional breathers result.


title: Spatio-temporal Dynamics in the Origin of Genetic Information

abstract:
  We study evolutionary processes induced by spatio-temporal dynamics in
prebiotic evolution. Using numerical simulations we demonstrate that
hypercycles emerge from complex interaction structures in multispecies systems.
In this work we also find that `hypercycle hybrid' protects the hypercycle from
its environment during the growth process. There is little selective advantage
for one hypercycle to maintain coexistence with others. This brings the
possibility of the outcompetition between hypercycles resulting in the negative
effect on information diversity. To enrich the information in hypercycles,
symbiosis with parasites is suggested. It is shown that symbiosis with
parasites can play an important role in the prebiotic immunology.


title:
Nucleosome interactions in chromatin: fiber stiffening and hairpin
  formation

abstract:
  We use Monte Carlo simulations to study attractive and excluded volume
interactions between nucleosome core particles in 30 nm-chromatin fibers. The
nucleosomes are treated as disk-like objects having an excluded volume and
short range attraction modelled by a variant of the Gay-Berne potential. The
nucleosomes are connected via bendable and twistable linker DNA in the crossed
linker fashion. We investigate the influence of the nucleosomal excluded volume
on the stiffness of the fiber. For parameter values that correspond to chicken
erythrocyte chromatin we find that the persistence length is governed to a
large extent by that excluded volume whereas the soft linker backbone
elasticity plays only a minor role. We further find that internucleosomal
attraction can induce the formation of hairpin configurations. Tension-induced
opening of such configurations into straight fibers manifests itself in a
quasi-plateau in the force-extension curve that resembles results from recent
micromanipulation experiments. Such hairpins may play a role in the formation
of higher order structures in chromosomes like chromonema fibers.


title:
Theory of Nucleosome Corkscrew Sliding in the Presence of Synthetic DNA
  Ligands

abstract:
  Histone octamers show a heat-induced mobility along DNA. Recent theoretical
studies have established two mechanisms that are qualitatively and
quantitatively compatible with in vitro experiments on nucleosome sliding:
Octamer repositiong through one-basepair twist defects and through ten-basepair
bulge defects. A recent experiment demonstrated that the repositioning is
strongly suppressed in the presence of minor-groove binding DNA ligands. In the
present study we give a quantitative theory for nucleosome repositioning in the
presence of such ligands. We show that the experimentally observed octamer
mobilities are consistent with the picture of bound ligands blocking the
passage of twist defects through the nucleosome. This strongly supports the
model of twist defects inducing a corkscrew motion of the nucleosome as the
underlying mechanism of nucleosome sliding. We provide a theoretical estimate
of the nucleosomal mobility without adjustable parameters, as a function of
ligand concentration, binding affinity, binding site orientiation, temperature
and DNA anisotropy. Having this mobility at hand we speculate about the
interaction between a nucleosome and a transcribing RNA polymerase and suggest
a novel mechanism that might account for polymerase induced nucleosome
repositioning.


title: Sympatric speciation in an age-structured population living on a lattice

abstract:
  A square lattice is introduced into the Penna model for biological aging in
order to study the evolution of diploid sexual populations under certain
conditions when one single locus in the individual's genome is considered as
identifier of species. The simulation results show, after several generations,
the flourishing and coexistence of two separate species in the same
environment, i.e., one original species splits up into two on the same
territory (sympatric speciation). As well, the mortalities obtained are in a
good agreement with the Gompertz law of exponential increase of mortality with
age.


title: Modeling of microarray data with zippering

abstract:
  The ability of oligonucleotide microarrays to measure gene expression has
been hindered by an imperfect understanding of the relationship between input
RNA concentrations and output signals. We argue that this relationship can be
understood based on the underlying statistical mechanics of these devices. We
present a model that includes the relevant interactions between the molecules.
Our model for the first time accounts for partially zippered probe-target
hybrids in a physically realistic manner, and also includes target-target
binding in solution. Large segments of the target molecules are not bound to
the probes, often in an asymmetric pattern, emphasizing the importance of
modeling zippering properly. The resultant fit between the model and training
data using optimized parameters is excellent, and it also does well at
predicting test data.


title: Michaelis-Menten Dynamics in Complex Heterogeneous Networks

abstract:
  Biological networks have been recently found to exhibit many topological
properties of the so-called complex networks. It has been reported that they
are, in general, both highly skewed and directed. In this paper, we report on
the dynamics of a Michaelis-Menten like model when the topological features of
the underlying network resemble those of real biological networks.
Specifically, instead of using a random graph topology, we deal with a complex
heterogeneous network characterized by a power-law degree distribution coupled
to a continuous dynamics for each network's component. The dynamics of the
model is very rich and stationary, periodic and chaotic states are observed
upon variation of the model's parameters. We characterize these states
numerically and report on several quantities such as the system's phase diagram
and size distributions of clusters of stationary, periodic and chaotic nodes.
The results are discussed in view of recent debate about the ubiquity of
complex networks in nature and on the basis of several biological processes
that can be well described by the dynamics studied.


title:
Maximum principle and mutation thresholds for four-letter sequence
  evolution

abstract:
  A four-state mutation-selection model for the evolution of populations of
DNA-sequences is investigated with particular interest in the phenomenon of
error thresholds. The mutation model considered is the Kimura 3ST mutation
scheme, fitness functions, which determine the selection process, come from the
permutation-invariant class. Error thresholds can be found for various fitness
functions, the phase diagrams are more interesting than for equivalent
two-state models. Results for (small) finite sequence lengths are compared with
those for infinite sequence length, obtained via a maximum principle that is
equivalent to the principle of minimal free energy in physics.


title: Conserved network motifs allow protein-protein interaction prediction

abstract:
  High-throughput protein interaction detection methods are strongly affected
by false positive and false negative results. Focused experiments are needed to
complement the large-scale methods by validating previously detected
interactions but it is often difficult to decide which proteins to probe as
interaction partners. Developing reliable computational methods assisting this
decision process is a pressing need in bioinformatics. We show that we can use
the conserved properties of the protein network to identify and validate
interaction candidates. We apply a number of machine learning algorithms to the
protein connectivity information and achieve a surprisingly good overall
performance in predicting interacting proteins. Using a 'leave-one-out'
approach we find average success rates between 20-50% for predicting the
correct interaction partner of a protein. We demonstrate that the success of
these methods is based on the presence of conserved interaction motifs within
the network. A reference implementation and a table with candidate interacting
partners for each yeast protein are available at http://www.protsuggest.org


title:
The statistical mechanics of complex signaling networks : nerve growth
  factor signaling

abstract:
  It is becoming increasingly appreciated that the signal transduction systems
used by eukaryotic cells to achieve a variety of essential responses represent
highly complex networks rather than simple linear pathways. While significant
effort is being made to experimentally measure the rate constants for
individual steps in these signaling networks, many of the parameters required
to describe the behavior of these systems remain unknown, or at best,
estimates. With these goals and caveats in mind, we use methods of statistical
mechanics to extract useful predictions for complex cellular signaling
networks. To establish the usefulness of our approach, we have applied our
methods towards modeling the nerve growth factor (NGF)-induced differentiation
of neuronal cells. Using our approach, we are able to extract predictions that
are highly specific and accurate, thereby enabling us to predict the influence
of specific signaling modules in determining the integrated cellular response
to the two growth factors. We show that extracting biologically relevant
predictions from complex signaling models appears to be possible even in the
absence of measurements of all the individual rate constants. Our methods also
raise some interesting insights into the design and possible evolution of
cellular systems, highlighting an inherent property of these systems wherein
particular ''soft'' combinations of parameters can be varied over wide ranges
without impacting the final output and demonstrating that a few ''stiff''
parameter combinations center around the paramount regulatory steps of the
network. We refer to this property -- which is distinct from robustness -- as
''sloppiness.''


title: Random autocatalytic networks

abstract:
  We determine conditions under which a random biochemical system is likely to
contain a subsystem that is both autocatalytic and able to survive on some
ambient `food' source. Such systems have previously been investigated for their
relevance to origin-of-life models. In this paper we extend earlier work, by
finding precisely the order of catalysation required for the emergence of such
self-sustaining autocatalytic networks. This answers questions raised in
earlier papers, yet also allows for a more general class of models. We also
show that a recently-described polynomial time algorithm for determining
whether a catalytic reaction system contains an autocatalytic, self-sustaining
subsystem is unlikely to adapt to allow inhibitory catalysation - in this case
we show that the associated decision problem is NP-complete.


title: Complex Systems Analysis of Cell Cycling Models in Carcinogenesis

abstract:
  Carcinogenesis is a complex process that involves dynamically inter-connected
modular sub-networks that evolve under the influence of micro-environmentally
induced perturbations, in non-random, pseudo-Markov chain processes. An
appropriate n-stage model of carcinogenesis involves therefore n-valued Logic
treatments of nonlinear dynamic transformations of complex functional genomes
and cell interactomes. Lukasiewicz Algebraic Logic models of genetic networks
and signaling pathways in cells are formulated in terms of nonlinear dynamic
systems with n-state components that allow for the generalization of previous,
Boolean or "fuzzy", logic models of genetic activities in vivo. Such models are
then applied to cell transformations during carcinogenesis based on very
extensive genomic transcription and translation data from the CGAP databases
supported by NCI. Such models are represented in a Lukasiewicz-Topos with an
n-valued Lukasiewicz Algebraic Logics subobject classifier description that
represents non-random and nonlinear network activities as well as their
transformations in carcinogeness. Specific models for different types of cancer
are then derived from representations of the dynamic state-space of LT
non-random, pseudo-Markov chain process, network models in terms of cDNA and
proteomic, high throughput analyses by ultra-sensitive techniques. This novel
theoretical analysis is based on extensive CGAP genomic data for human tumors,
as well as recently published studies of cyclin signaling. Several such
specific models suggest novel clinical trials and rational therapies of cancer
through re-establishment of cell cycling inhibition in stage III cancers.


title: Cell Cycling Models of Carcinogenesis: A Complex Systems Analysis

abstract:
  A new approach to the modular, complex systems analysis of nonlinear dynamics
in cell cycling network transformations involved in carcinogenesis is proposed.
Carcinogenesis is a complex process that involves dynamically inter-connected
biomolecules in the intercellular, membrane, cytosolic, nuclear and nucleolar
compartments that form numerous inter-related pathways. One such family of
pathways contains the cell cyclins. Cyclins are proteins that link several
critical pro-apoptotic and other cell cycling/division components, including
the tumor suppressor gene TP53 and its product, the Thomsen-Friedenreich
antigen (T antigen), Rb, mdm2, c-Myc, p21, p27, Bax, Bad and Bcl-2, which all
play major roles in neoplastic transformation of many tissues. This novel
theoretical analysis based on recently published studies of cyclin signaling,
with special emphasis placed on the roles of cyclins D1 and E, suggests novel
clinical trials and rational therapies of cancer through reestablishment of
cell cycling inhibition in metastatic cancer cells.


title:
Applications of Novel Techniques to Health Foods, Medical and
  Agricultural Biotechnology

abstract:
  Selected applications of novel techniques in Agricultural Biotechnology,
Health Food formulations and Medical Biotechnology are being reviewed with the
aim of unraveling future developments and policy changes that are likely to
open new niches for Biotechnology and prevent the shrinking or closing the
existing ones. Amongst the selected novel techniques with applications to both
Agricultural and Medical Biotechnology are: immobilized bacterial cells and
enzymes, microencapsulation and liposome production, genetic manipulation of
microorganisms, development of novel vaccines from plants, epigenomics of
mammalian cells and organisms, as well as biocomputational tools for molecular
modeling related to disease and Bioinformatics. Both fundamental and applied
aspects of the emerging new techniques are being discussed in relation to their
anticipated impact on future biotechnology applications together with policy
changes that are needed for continued success in both Agricultural and Medical
Biotechnology. Several novel techniques are illustrated in an attempt to convey
the most representative and powerful tools that are currently being developed
for both immediate and long term applications in Agriculture, Health Food
formulation and production, pharmaceuticals and Medicine. The research aspects
are naturally emphasized in our review as they are key to further developments
in Medical and Agricultural Biotechnology.


title:
How much can evolved characters tell us about the tree that generated
  them?

abstract:
  In this paper we review some recent results that shed light on a fundamental
question in molecular systematics: how much phylogenetic `signal' can we expect
from characters that have evolved under some Markov process? There are many
sides to this question and we begin by describing some explicit bounds on the
probability of correctly reconstructing an ancestral state from the states
observed at the tips. We show how this bound sets upper limits on the
probability of tree reconstruction from aligned sequences, and we provide some
new extensions that allow site-to-site rate variation or a covarion mechanism.
We then explore the relationship between the number of sites required for
accurate tree reconstruction and other model parameters - such as the number of
species, and substitution probabilities, and we describe a phase transition
that occurs when substitution probabilities exceed a critical value. In the
remainder of this paper we turn to models of character evolution where the
state space is assumed to be either infinite or very large. These models have
some relevance to certain types of genomic data (such as gene order) and here
we again investigate how many characters are required for accurate tree
reconstruction.


title: Analytical Solution of a Stochastic Content Based Network Model

abstract:
  We define and completely solve a content-based directed network whose nodes
consist of random words and an adjacency rule involving perfect or approximate
matches, for an alphabet with an arbitrary number of letters. The analytic
expression for the out-degree distribution shows a crossover from a leading
power law behavior to a log-periodic regime bounded by a different power law
decay. The leading exponents in the two regions have a weak dependence on the
mean word length, and an even weaker dependence on the alphabet size. The
in-degree distribution, on the other hand, is much narrower and does not show
scaling behavior. The results might be of interest for understanding the
emergence of genomic interaction networks, which rely, to a large extent, on
mechanisms based on sequence matching, and exhibit similar global features to
those found here.


title:
Cortical Dynamics and Awareness State: An Interpretation of Observed
  Interstimulus Interval Dependence in Apparent Motion

abstract:
  In a recent paper on Cortical Dynamics, Francis and Grossberg raise the
question how visual forms and motion information are integrated to generate a
coherent percept of moving forms? In their investigation of illusory contours
(which are, like Kanizsa squares, mental constructs rather than stimuli on the
retina) they quantify the subjective impression of apparent motion between
illusory contours that are formed by two subsequent stimuli with delay times of
about 0.2 second (called the interstimulus interval ISI). The impression of
apparent motion is due to a back referral of a later experience to an earlier
time in the conscious representation. A model is developed which describes the
state of awareness in the observer in terms of a time dependent Schroedinger
equation to which a second order time derivative is added. This addition
requires as boundary conditions the values of the solution both at the
beginning and after the process. Satisfactory quantitative agreement is found
between the results of the model and the experimental results. We recall that
in the von Neumann interpretation of the collapse of the quantum mechanical
wave-function, the collapse was associated with an observer's awareness. Some
questions of causality and determinism that arise from later-time boundary
conditions are touched upon.


title:
Statistics of lines of natural images and implications for visual
  detection

abstract:
  As borders between different regions, lines are an important element of
natural images. Already at the level of the mammalian primary visual cortex
(V1), neurons respond best to lines of a given orientation. We reduce a set of
images to linear segments and analyze their statistical properties. In
particular, appropriately defined Fourier spectra show more power in their
transverse component than in the longitudinal one. We then characterize filters
that are best suited for extracting information from such images, and find some
qualitative consistency with neural connections in V1. We also demonstrate that
such filters are efficient in reconstructing missing lines in an image.


title: A Constructive Approach to Gene Expression Dynamics

abstract:
  The advent of new experimental genomic technologies and the massive increase
of DNA sequence information is helping researchers better understand how our
genes work. Recently, experiments on mRNA abundance (gene expression) have
revealed that gene expression shows a stationary organization described by a
power-law distribution (scale-free organization) (i.e., gene expression $k$
decays as $k^{-\gamma}$), which is highly conserved in all the major five
kingdoms of life, from Bacteria to Human. An underlying gene expression
dynamics "rich-travel-more" was suggested to recover that evolutional
conservation of transcriptional organization. Here we propose a constructive
approach to gene expression dynamics with larger scope. Our gene expression
construction restores the stationary state, predicts the power-law exponent for
different organisms with natural explanation for small correction at high and
low expression levels, describes the intermediate state dynamics (time finite)
and elucidates the gene expression stability. This approach requires only one
assumption: Markov property.


title:
Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye
  Tracking of Moving Targets

abstract:
  A theoretical quantum brain model is proposed using a nonlinear Schroedinger
wave equation. The model proposes that there exists a quantum process that
mediates the collective response of a neural lattice (classical brain). The
model is used to explain eye movements when tracking moving targets. Using a
Recurrent Quantum Neural Network(RQNN) while simulating the quantum brain
model, two very interesting phenomena are observed. First, as eye sensor data
is processed in a classical brain, a wave packet is triggered in the quantum
brain. This wave packet moves like a particle. Second, when the eye tracks a
fixed target, this wave packet moves not in a continuous but rather in a
discrete mode. This result reminds one of the saccadic movements of the eye
consisting of 'jumps' and 'rests'. However, such a saccadic movement is
intertwined with smooth pursuit movements when the eye has to track a dynamic
trajectory. In a sense, this is the first theoretical model explaining the
experimental observation reported concerning eye movements in a static scene
situation. The resulting prediction is found to be very precise and efficient
in comparison to classical objective modeling schemes such as the Kalman
filter.


title:
Nonlinear excitations in DNA: Aperiodic models vs actual genome
  sequences

abstract:
  We study the effects of the sequence on the propagation of nonlinear
excitations in simple models of DNA in which we incorporate actual DNA
sequences obtained from human genome data. We show that kink propagation
requires forces over a certain threshold, a phenomenon already found for
aperiodic sequences [F. Dom\'\i nguez-Adame {\em et al.}, Phys. Rev. E {\bf
52}, 2183 (1995)]. For forces below threshold, the final stop positions are
highly dependent on the specific sequence. The results of our model are
consistent with the stick-slip dynamics of the unzipping process observed in
experiments. We also show that the effective potential, a collective coordinate
formalism introduced by Salerno and Kivshar [Phys. Lett. A {\bf 193}, 263
(1994)] is a useful tool to identify key regions in DNA that control the
dynamical behavior of large segments. Additionally, our results lead to further
insights in the phenomenology observed in aperiodic systems.

categories: q-bio.GN cond-mat.soft math-ph math.MP nlin.PS q-bio.BM


title: The criticality of the Hantavirus infected phase at Zuni

abstract:
  A preliminary analysis of the temporal evolution of a population of
\emph{Peromyscus maniculatus} infected with Hantavirus Sin Nombre is made.
Ecological and epidemiological parameters are derived from the data, and they
are used as inputs for the analytical model presented in [Abramson and Kenkre,
Phys. Rev. E \textbf{66}, 011912 (2002)]. A prediction of the critical carrying
capacity and its associated critical mouse density is made, and the time series
is analyzed under the light of these. It is found that the sporadic
disappearances and reappearances of the infected phase correspond to the
bifurcation predicted by the model.


title:
Semiconservative replication in the quasispecies model II:
  Generalization to arbitrary lesion repair probabilities

abstract:
  This paper extends the semiconservative quasispecies equations to account for
arbitrary post-replication lesion repair efficiency. Such an extension could be
an important tool for understanding processes such as cancer development and
stem cell growth. Starting from the quasispecies dynamics over the space of
genomes, we derive an equivalent dynamics over the space of ordered sequence
pairs. From this set of equations, we are able to derive the infinite sequence
length form of the dynamics for a class of ``master-genome''-based fitness
landscapes. We use these equations to solve for a ``generalized''
single-fitness-peak landscape, where the master genome can sustain a maximum
number of lesions and remain viable. The central pattern that emerges from our
studies is that imperfect lesion repair often leads to increased mutational
robustness over semiconservative replication with completely efficient lesion
repair. The reason for this is that imperfect lesion repair breaks some of the
correlation between the parent and daughter strands, thereby preventing
replication errors from destroying the information in the original genome. The
result is a delayed error catastrophe over that expected from the original
semiconservative quasispecies model. In particular, we show that when only of
the strands is necessary for conferring viability, then, when lesion repair is
turned off, a semiconservatively replicating system becomes an effectively
conservatively replicating one.


title: Vaccination pattern affects immunological response

abstract:
  The response of the immune system to different vaccination patterns is
studied with a simple model. It is argued that the history and characteristics
of the pattern defines very different secondary immune responses in the case of
infection. The memory function of the immune response can be set to work in
very different modes depending on the pattern followed during immunizations. It
is argued that the history and pattern of immunizations can be a decisive (and
experimentally accessible) factor to tailor the effectiveness of a specific
vaccine.


title:
Single Cancer Cell Detection by Near Infrared Microspectroscopy,
  Infrared Chemical Imaging and Fluorescence Microspectroscopy

abstract:
  Novel techniques are currently being developed and established for the
accurate chemical analysis and detection of single cancer cells, single embryos
and single seeds by Fourier Transform Near Infrared (FT-NIR) Microspectroscopy,
Fourier Transform Infrared (FT-IR), Fluorescence and High-Resolution NMR
(HR-NMR). The first FT-NIR chemical images of biological systems approaching
1micron resolution are here reported. 400 and 500 MHz, H-1 NMR analyses were
carried out that allowed the selection of mutagenized embryos. Detailed
chemical analyses are being demonstrated to be also possible by FT-NIR Chemical
Imaging/ Microspectroscopy of single cancer cells. FT-NIR Microspectroscopy and
Chemical Imaging are also shown to be potentially important in Functional
Genomics and Proteomics research through the rapid and accurate detection of
high-content microarrays (HCMA). Multi-photon (MP), pulsed femtosecond laser
NIR Fluorescence Excitation techniques were shown to be capable of Single
Molecule Detection (SMD. Thus, MP NIR excitation for Fluorescence Correlation
Spectroscopy (FCS) allowed not only single molecule detection, but also
molecular dynamics observations and high resolution, submicron imaging of
sub-femtoliter volumes inside living cells with 0.25 micron spatial resolution,
in both normal and cancer cells, as well as neoplastic tissues. These novel,
ultra-sensitive and rapid FT-NIR/FCS analyses have, therefore, substantial
potential for numerous applications in important research areas, such as:
medicine, medical/cancer research, pharmacology, agricultural biotechnology,
food safety, as well as clinical diagnosis of viral diseases and cancers.


title: Flexible Foraging of Ants under Unsteadily Varying Environment

abstract:
  Using a simple model for the trail formation of ants, the relation between
i)the schedule of feeding which represents the unsteady natural environment,
ii)emerging patterns of trails connecting a nest with food resources, and
iii)the foraging efficiency is studied. Simulations and a simple analysis show
that the emergent trail pattern flexibly varies depending on the feeding
schedule by which ants can make an efficient foraging according to the
underlying unsteady environment.


title: How Long can Left and Right Handed Life Forms Coexist?

abstract:
  Reaction-diffusion equations based on a polymerization model are solved to
simulate the spreading of hypothetic left and right handed life forms on the
Earth's surface. The equations exhibit front-like behavior as is familiar from
the theory of the spreading of epidemics. It is shown that the relevant time
scale for achieving global homochirality is not, however, the time scale of
front propagation, but the much longer global diffusion time. The process can
be sped up by turbulence and large scale flows. It is speculated that, if the
deep layers of the early ocean were sufficiently quiescent, there may have been
the possibility of competing early life forms with opposite handedness.