title:
Scenarios for protein aggregation: Molecular Dynamics simulations and
  Bioinformatic Analysis

abstract:
  The need to understand the assembly kinetics of fibril formation has become
urgent because of the realization that soluble oligomers of amyloidogenic
peptides may be even more neurotoxic than the end product, namely, the amyloid
fibrils. In order to fully understand the routes to fibril formation one has to
characterize the major species in the assembly pathways. The characterization
of the energetics and dynamics of oligomers (dimers, trimers etc) is difficult
using experiments alone because they undergo large conformational fluctuations.
In this context, carefully planned molecular dynamics simulation studies,
computations using coarse-grained models, and bioinformatic analysis have given
considerable insights into the early events in the route to fibril formation.
Here, we describe progress along this direction using examples taken largely
from our own work. In this chapter, we focus on aspects of protein aggregation
using Abeta-peptides and prion proteins as examples.


title:
Nearest neighbour spacing distribution of basis in some intron-less and
  intron-containing DNA sequences

abstract:
  We show that the nearest neighbour distribution of distances between basis
pairs of some intron-less and intron-containing coding regions are the same
when a procedure, called {\em unfolding}, is applied. Such a procedure consists
in separating the secular variations from the oscillatory terms. The form of
the distribution obtained is quite similar to that of a random, i.e.
Poissonian, sequence. This is done for the HUMBMYH7CD, DROMYONMA, HUMBMYH7 and
DROMHC sequences. The first two correspond to highly coding regions while the
last two correspond to non-coding regions. We also show that the distributions
before the unfolding procedure depend on the secular part but, after the
unfolding procedure we obtain an striking result: all distributions are similar
to each other. The result becomes independent of the content of introns or the
species we have chosen. This is in contradiction with the results obtained with
the detrended fluctuation analysis in which the correlations yield different
results for intron-less and intron-containing regions.


title:
Lagrangian Approaches for a class of Matching Problems in Computational
  Biology

abstract:
  This paper presents efficient algorithms for solving the problem of aligning
a protein structure template to a query amino-acid sequence, known as protein
threading problem. We consider the problem as a special case of graph matching
problem. We give formal graph and integer programming models of the problem.
After studying the properties of these models, we propose two kinds of
Lagrangian relaxation for solving them. We present experimental results on real
life instances showing the efficiency of our approaches.


title:
Functional Integration of Ecological Networks through Pathway
  Proliferation

abstract:
  Large-scale structural patterns commonly occur in network models of complex
systems including a skewed node degree distribution and small-world topology.
These patterns suggest common organizational constraints and similar functional
consequences. Here, we investigate a structural pattern termed pathway
proliferation. Previous research enumerating pathways that link species
determined that as pathway length increases, the number of pathways tends to
increase without bound. We hypothesize that this pathway proliferation
influences the flow of energy, matter, and information in ecosystems. In this
paper, we clarify the pathway proliferation concept, introduce a measure of the
node--node proliferation rate, describe factors influencing the rate, and
characterize it in 17 large empirical food-webs. During this investigation, we
uncovered a modular organization within these systems. Over half of the
food-webs were composed of one or more subgroups that were strongly connected
internally, but weakly connected to the rest of the system. Further, these
modules had distinct proliferation rates. We conclude that pathway
proliferation in ecological networks reveals subgroups of species that will be
functionally integrated through cyclic indirect effects.


title:
Maximum-frequency gene tree: a simplified genome-scale approach to
  overcoming incongruence in molecular phylogenies

abstract:
  Genomes and genes diversify during evolution; however, it is unclear to what
extent genes still retain the relationship among species. Model species for
molecular phylogenetic studies include yeasts and viruses whose genomes were
sequenced as well as plants that have the fossil-supported true phylogenetic
trees available. In this study, we generated single gene trees of seven yeast
species as well as single gene trees of nine baculovirus species using all the
orthologous genes among the species compared. Homologous genes among seven
known plants were used for validation of the fi nding. Four algorithms: maximum
parsimony, minimum evolution, maximum likelihood, and neighbor-joining, were
used. Trees were reconstructed before and after weighting the DNA and protein
sequence lengths among genes. Rarely a gene can always generate the "true tree"
by all the four algorithms. However, the most frequent gene tree, termed
"maximum gene-support tree" (MGS tree, or WMGS tree for the weighted one), in
yeasts, baculoviruses, or plants was consistently found to be the "true tree"
among the species. The results provide insights into the overall degree of
divergence of orthologous genes of the genomes analyzed and suggest the
following: 1) The true tree relationship among the species studied is still
maintained by the largest group of orthologous genes; 2) There are usually more
orthologous genes with higher similarities between genetically closer species
than between genetically more distant ones; and 3) The maximum gene-support
tree refl ects the phylogenetic relationship among species in comparison.
  Keywords: genome, gene evolution, molecular phylogeny, true tree


title:
Genome-wide EST data mining approaches to resolving incongruence of
  molecular phylogenies

abstract:
  36 single genes of six plants inferred 18 unique trees using maximum
parsimony. Such incongruence is an important issue and how to reconstruct the
congruent tree still is one of the most challenges in molecular phylogenetics.
For resolving this problem, a genome-wide EST data mining approach was
systematically investigated by retrieving a large size of EST data of 144
shared genes of six green plants from GenBank. The results show that the
concatenated alignments approach overcame incongruence among single-gene
phylogenies and successfully reconstructed the congruent tree of six species
with 100% jackknife support across each branch when 144 genes was used.
Jackknife supports of correct branches increased with number of genes linearly,
but those of wrong branches also increased linearly. For inferring the
congruent tree, the minimum 30 genes were required. This approach may provide
potential power in resolving conflictions of phylogenies.
  Keywords: Genome-wide; Data mining; EST; Phylogeny; Congruent tree; Jackknife
support; Plants.


title: Why Nestedness in Mutualistic Networks?

abstract:
  We investigate the relationship between the nested organization of
mutualistic systems and their robustness against the extinction of species. We
establish that a nested pattern of contacts is the best possible one as far as
robustness is concerned, but only when the least linked species have the
greater probability of becoming extinct. We introduce a coefficient that
provides a quantitative measure of the robustness of a mutualistic system.


title:
Searching for memories, Sudoku, implicit check-bits, and the iterative
  use of not-always-correct rapid neural computation

abstract:
  The algorithms that simple feedback neural circuits representing a brain area
can rapidly carry out are often adequate to solve only easy problems, and for
more difficult problems can return incorrect answers. A new
excitatory-inhibitory circuit model of associative memory displays the common
human problem of failing to rapidly find a memory when only a small clue is
present. The memory model and a related computational network for solving
Sudoku puzzles produce answers that contain implicit check-bits in the
representation of information across neurons, allowing a rapid evaluation of
whether the putative answer is correct or incorrect through a computation
related to visual 'pop-out'. This fact may account for our strong psychological
feeling of right or wrong when we retrieve a nominal memory from a minimal
clue. This information allows more difficult computations or memory retrievals
to be done in a serial fashion by using the fast but limited capabilities of a
computational module multiple times. The mathematics of the
excitatory-inhibitory circuits for associative memory and for Sudoku, both of
which are understood in terms of 'energy' or Lyapunov functions, is described
in detail.


title: A simple model of energy expenditure in human locomotion

abstract:
  A simple harmonic oscillator model is proposed to describe the mechanical
power involved in human locomotion. In this framework, by taking into account
the anthropometric parameters of a standard individual, we are able to
calculate the speed-power curves in human walking. The proposed model accounts
for the well known Margaria's law in which the cost ofthe human running
(independent from the speed) is fixed to 1 Kcal/(Kg Km). The model includes the
effects of a gentle slope (either positive or negative) and the effect due to
the mechanical response of the walking surface. The model results obtained in
the presence of a slope are in qualitative agreement with the experimental data
obtained by A. Leonardi et al.


title: Discovering Functional Communities in Dynamical Networks

abstract:
  Many networks are important because they are substrates for dynamical
systems, and their pattern of functional connectivity can itself be dynamic --
they can functionally reorganize, even if their underlying anatomical structure
remains fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering_functional
communities_, and describe an approach to doing so. This method combines recent
work on measuring information sharing across stochastic networks with an
existing and successful community-discovery algorithm for weighted networks. We
illustrate it with an application to a large biophysical model of the
transition from beta to gamma rhythms in the hippocampus.

categories: q-bio.NC nlin.AO physics.data-an q-bio.QM


title:
Neutral Evolution as Diffusion in phenotype space: reproduction with
  mutation but without selection

abstract:
  The process of `Evolutionary Diffusion', i.e. reproduction with local
mutation but without selection in a biological population, resembles standard
Diffusion in many ways. However, Evolutionary Diffusion allows the formation of
local peaks with a characteristic width that undergo drift, even in the
infinite population limit. We analytically calculate the mean peak width and
the effective random walk step size, and obtain the distribution of the peak
width which has a power law tail. We find that independent local mutations act
as a diffusion of interacting particles with increased stepsize.


title: Analytic Behaviour of Competition among Three Species

abstract:
  We analyse the classical model of competition between three species studied
by May and Leonard ({\it SIAM J Appl Math} \textbf{29} (1975) 243-256) with the
approaches of singularity analysis and symmetry analysis to identify values of
the parameters for which the system is integrable. We observe some striking
relations between critical values arising from the approach of dynamical
systems and the singularity and symmetry analyses.


title: Morphogen Transport in Epithelia

abstract:
  We present a general theoretical framework to discuss mechanisms of morphogen
transport and gradient formation in a cell layer. Trafficking events on the
cellular scale lead to transport on larger scales. We discuss in particular the
case of transcytosis where morphogens undergo repeated rounds of
internalization into cells and recycling. Based on a description on the
cellular scale, we derive effective nonlinear transport equations in one and
two dimensions which are valid on larger scales. We derive analytic expressions
for the concentration dependence of the effective diffusion coefficient and the
effective degradation rate. We discuss the effects of a directional bias on
morphogen transport and those of the coupling of the morphogen and receptor
kinetics. Furthermore, we discuss general properties of cellular transport
processes such as the robustness of gradients and relate our results to recent
experiments on the morphogen Decapentaplegic (Dpp) that acts in the fruit fly
Drosophila.


title: Species-area relationship for power-law species abundance distribution

abstract:
  We studied the mathematical relations between species abundance distributions
(SADs) and species-area relationships (SARs) and found that a power-law SAR can
be generally derived from a power-law SAD without a special assumption such as
the ``canonical hypothesis''. In the present analysis, an SAR-exponent is
obtained as a function of an SAD-exponent for a finite number of species. We
also studied the inverse problem, from SARs to SADs, and found that a power-SAD
can be derived from a power-SAR under the condition that the functional form of
the corresponding SAD is invariant for changes in the number of species. We
also discuss general relationships among lognormal SADs, the broken-stick model
(exponential SADs), linear SARs and logarithmic SARs. These results suggest the
existence of a common mechanism for SADs and SARs, which could prove a useful
tool for theoretical and experimental studies on biodiversity and species
coexistence.


title: Complex Behavior in Simple Models of Biological Coevolution

abstract:
  We explore the complex dynamical behavior of simple predator-prey models of
biological coevolution that account for interspecific and intraspecific
competition for resources, as well as adaptive foraging behavior. In long
kinetic Monte Carlo simulations of these models we find quite robust 1/f-like
noise in species diversity and population sizes, as well as power-law
distributions for the lifetimes of individual species and the durations of
quiet periods of relative evolutionary stasis. In one model, based on the
Holling Type II functional response, adaptive foraging produces a metastable
low-diversity phase and a stable high-diversity phase.


title: Dynamic re-wiring of protein interaction: The case of transactivation

abstract:
  We are looking at local protein interaction networks from the perspective of
directed, labeled graphs with quantitative values for monotonic changes in
concentrations. These systems can be used to perform stability analysis for a
stable attractor, given initial values. They can also show re-configuration of
whole system states by dynamic insertion of links, given specific patterns of
input. The latter issue seems particularly relevant for the concept of
multistability in cellular memory. We attempt to show that this level of
analysis is well-suited for a number of relevant biological subsystems, such as
transactivation in cardiac myocytes or G-protein coupling to adrenergic
receptors. In particular, we analyse the 'motif' of an "overflow gate" as a
concentration-dependent system reconfiguration.


title: Dynamic Studies of Scaffold-dependent Mating Pathway in Yeast

abstract:
  The mating pathway in \emph{Saccharomyces cerevisiae} is one of the best
understood signal transduction pathways in eukaryotes. It transmits the mating
signal from plasma membrane into the nucleus through the G-protein coupled
receptor and the mitogen-activated protein kinase (MAPK) cascade. According to
the current understandings of the mating pathway, we construct a system of
ordinary differential equations to describe the process. Our model is
consistent with a wide range of experiments, indicating that it captures some
main characteristics of the signal transduction along the pathway.
Investigation with the model reveals that the shuttling of the scaffold protein
and the dephosphorylation of kinases involved in the MAPK cascade cooperate to
regulate the response upon pheromone induction and to help preserving the
fidelity of the mating signaling. We explored factors affecting the
dose-response curves of this pathway and found that both negative feedback and
concentrations of the proteins involved in the MAPK cascade play crucial role.
Contrary to some other MAPK systems where signaling sensitivity is being
amplified successively along the cascade, here the mating signal is transmitted
through the cascade in an almost linear fashion.


title: Dynamic behaviors in directed networks

abstract:
  Motivated by the abundance of directed synaptic couplings in a real
biological neuronal network, we investigate the synchronization behavior of the
Hodgkin-Huxley model in a directed network. We start from the standard model of
the Watts-Strogatz undirected network and then change undirected edges to
directed arcs with a given probability, still preserving the connectivity of
the network. A generalized clustering coefficient for directed networks is
defined and used to investigate the interplay between the synchronization
behavior and underlying structural properties of directed networks. We observe
that the directedness of complex networks plays an important role in emerging
dynamical behaviors, which is also confirmed by a numerical study of the
sociological game theoretic voter model on directed networks.


title: Expression of MHC II genes

abstract:
  Innate and adaptive immunity are connected via antigen processing and
presentation (APP), which results in the presentation of antigenic peptides to
T cells in the complex with the major histocompatibility (MHC) determinants.
MHC class II (MHC II) determinants present antigens to CD4+ T cells, which are
the main regulators of the immune response. Their genes are transcribed from
compact promoters that form first the MHC II enhanceosome, which contains
DNA-bound activators and then the MHC II transcriptosome with the addition of
the class II transactivator (CIITA). CIITA is the master regulator of MHC II
transcription. It is expressed constitutively in dendritic cells (DC) and
mature B cells and is inducible in most other cell types. Three isoforms of
CIITA exist, depending on cell type and inducing signals. CIITA is regulated at
the levels of transcription and post-translational modifications, which are
still not very clear. Inappropriate immune responses are found in several
diseases, including cancer and autoimmunity. Since CIITA regulates the
expression of MHC II genes, it is involved directly in the regulation of the
immune response. The knowledge of CIITA will facilitate the manipulation of the
immune response and might contribute to the treatment of these diseases.


title:
Gene cluster analysis method reliably identifies horizontally
  transferred genes and reveals their involvement in operon formation

abstract:
  The formation mechanism of operons remains controversial despite the proposal
of many models. Although acquisition of genes from other species, horizontal
gene transfer, is considered to occur, definitive concrete cases have been
unavailable. It is desirable to select horizontally transferred genes reliably
and examine their relationship to operons. We here developed a method to
identify candidates of horizontally transferred genes based on minimization of
gene cluster insertions/deletions. To select a benchmark set of positively
horizontally transferred genes against which the candidate set can be
appraised, we devised another procedure using intergenetic alignments.
Comparison with the benchmark set of horizontally transferred genes
demonstrated the absence of a significant number of false positives in the
candidates, showing that the method identifies horizontally transferred genes
with a high degree of confidence. Horizontally transferred genes constitute at
least 5.5% of the genes in Escherichia, Shigella, and Salmonella and ~46% of
which originate from other gamma-proteobacteria. Not only informational genes,
but also operational genes (those involved in housekeeping) are horizontally
transferred less frequently than expected. A gene-cluster analysis of
Escherichia coli K-12 operons revealed that horizontal transfer produced four
entire operons and expanded two operons, but deletion of intervening genes
accounts for the formation of no operons. We propose that operons generally
form by horizontal gene transfer. We further suggest that genes with related
essential functions tend to reside in conserved operons, while genes in
nonconserved operons generally confer slight advantage to the organisms and
frequently undergo horizontal transfer and decay.


title: A Multiscale Model of Biofilm as a Senescence-Structured Fluid

abstract:
  We derive a physiologically structured multiscale model for biofilm
development. The model has components on two spatial scales, which induce
different time scales into the problem. The macroscopic behavior of the system
is modeled using growth-induced flow in a domain with a moving boundary.
Cell-level processes are incorporated into the model using a so-called
physiologically structured variable to represent cell senescence, which in turn
affects cell division and mortality. We present computational results for our
models which shed light on modeling the combined role senescence and the
biofilm state play in the defense strategy of bacteria.


title: Stochastic Dynamics of Invasion and Fixation

abstract:
  We study evolutionary game dynamics in finite populations. We analyze an
evolutionary process, which we call pairwise comparison, for which we adopt the
ubiquitous Fermi distribution function from statistical mechanics. The inverse
temperature in this process controls the intensity of selection, leading to a
unified framework for evolutionary dynamics at all intensities of selection,
from random drift to imitation dynamics. We derive, for the first time, a
simple closed formula which determines the feasibility of cooperation in finite
populations, whenever cooperation is modeled in terms of any symmetric
two-person game. In contrast with previous results, the present formula is
valid at all intensities of selection and for any initial condition. We
investigate the evolutionary dynamics of cooperators in finite populations, and
study the interplay between intensity of selection and the remnants of interior
fixed points in infinite populations, as a function of a given initial number
of cooperators, showing how this interplay strongly affects the approach to
fixation of a given trait in finite populations, leading to counter-intuitive
results at different intensities of selection.


title: Stochasticity and evolutionary stability

abstract:
  In stochastic dynamical systems, different concepts of stability can be
obtained in different limits. A particularly interesting example is
evolutionary game theory, which is traditionally based on infinite populations,
where strict Nash equilibria correspond to stable fixed points that are always
evolutionarily stable. However, in finite populations stochastic effects can
drive the system away from strict Nash equilibria, which gives rise to a new
concept for evolutionary stability. The conventional and the new stability
concepts may apparently contradict each other leading to conflicting
predictions in large yet finite populations. We show that the two concepts can
be derived from the frequency dependent Moran process in different limits. Our
results help to determine the appropriate stability concept in large finite
populations. The general validity of our findings is demonstrated showing that
the same results are valid employing vastly different co-evolutionary
processes.


title: Reconsidering the significance of genomic word frequency

abstract:
  We propose that the distribution of DNA words in genomic sequences can be
primarily characterized by a double Pareto-lognormal distribution, which
explains lognormal and power-law features found across all known genomes. Such
a distribution may be the result of completely random sequence evolution by
duplication processes. The parametrization of genomic word frequencies allows
for an assessment of significance for frequent or rare sequence motifs.


title: Evolutionary Analysis of Biological Excitability

abstract:
  Excitability is an attribute of life, and is a driving force in the descent
of complexity. Cellular electrical activity as realized by membrane proteins
that act as either channels or transporters is the basis of excitability.
Electrical signaling is mediated by a wave of action potentials, which consist
of synchronous redistribution of ionic gradients down ion channels. Ion
channels select for the passage of a particular ion species. Potassium ion
channels are gated by a variety of stimuli, including membrane voltage. Sodium
and calcium channels are gated only by membrane voltage, suggesting the
conservative argument that voltage-gated potassium channels are the founding
members of the voltage-gated ion channel superfamily. The principal focus of
this work is the investigation of the complement of potassium ion channels in
our genome and its generalizabilty. An array of issues relevant to excitability
is addressed, and a range of engagement in questions regarding the unity of
life is proffered.


title: Graph kernels based on tree patterns for molecules

abstract:
  Motivated by chemical applications, we revisit and extend a family of
positive definite kernels for graphs based on the detection of common subtrees,
initially proposed by Ramon et al. (2003). We propose new kernels with a
parameter to control the complexity of the subtrees used as features to
represent the graphs. This parameter allows to smoothly interpolate between
classical graph kernels based on the count of common walks, on the one hand,
and kernels that emphasize the detection of large common subtrees, on the other
hand. We also propose two modular extensions to this formulation. The first
extension increases the number of subtrees that define the feature space, and
the second one removes noisy features from the graph representations. We
validate experimentally these new kernels on binary classification tasks
consisting in discriminating toxic and non-toxic molecules with support vector
machines.


title: Convergent dynamics in the protease enzymatic superfamily

abstract:
  Proteases regulate various aspects of the life cycle in all organisms by
cleaving specific peptide bonds. Their action is so central for biochemical
processes that at least 2% of any known genome encodes for proteolytic enzymes.
Here we show that selected proteases pairs, despite differences in oligomeric
state, catalytic residues and fold, share a common structural organization of
functionally relevant regions which are further shown to undergo similar
concerted movements. The structural and dynamical similarities found
pervasively across evolutionarily distant clans point to common mechanisms for
peptide hydrolysis.


title:
Mechanisms of B cell Synapse Formation Predicted by Stochastic
  Simulation

abstract:
  The clustering of B cell receptor (BCR) molecules and the formation of the
protein segregation structure known as the immunological synapse appears to
precede antigen (Ag) uptake by B cells. The mature B cell synapse is
characterized by a central cluster of BCR/Ag molecular complexes surrounded by
a ring of LFA-1/ICAM-1 complexes. Recent experimental evidence shows receptor
clustering in B cells can occur via mechanical or signaling-driven processes.
An alternative mechanism of diffusion and affinity-dependent binding has been
proposed to explain synapse formation in the absence of signaling-driven
processes. In this work, we investigated the biophysical mechanisms that drive
immunological synapse formation in B cells across the physiological range of
BCR affinity (KA~10^6-10^10 M-1) through computational modeling. Our
computational approach is based on stochastic simulation of diffusion and
reaction events with a clearly defined mapping between probabilistic parameters
of our model and their physical equivalents. We show that a
diffusion-and-binding mechanism is sufficient to drive synapse formation only
at low BCR affinity and for a relatively stiff B cell membrane that undergoes
little deformation. We thus predict the need for alternative mechanisms: a
difference in the mechanical properties of BCR/Ag and LFA-1/ICAM-1 bonds and/or
signaling driven processes.


title: Coevolution. Extending Prigogine Theorem

abstract:
  The formal consideration of the concept of interaction in thermodynamic
analysis makes it possible to deduce, in the broadest terms, new results
related to the coevolution of interacting systems, irrespective of their
distance from thermodynamic equilibrium. In this paper I prove the existence of
privileged coevolution trajectories characterized by the minimum joint
production of internal entropy, a conclusion that extends Prigogine theorem to
systems evolving far from thermodynamic equilibrium. Along these trajectories
of minimum internal entropy production one of the system goes always ahead of
the other with respect to equilibrium.


title:
Living beings as informed systems: towards a physical theory of
  information

abstract:
  I propose here a new concept of information based on two relevant aspects of
its expression. The first related to the undeniable fact that the expression of
information modifies the physical state of its receiver. The second to the
arbitrariness of such physical changes. In fact, these changes are not
deducible from physical laws but from a code established arbitrarily. Thus,
physical information is proposed here as the capacity of producing arbitrary
changes. Once defined physical information from this physical point of view, I
deduce some basic properties of informed systems. These properties (renewal,
self-reproducing, evolution, diversification) are immediately recognized as the
attributes most characteristic of living beings, the only natural informed
systems we know. I also propose here a double evaluation of information. The
former is an absolute measure of the physical effects of its expression based
on Einstein's probability. The second is a functional measure based on the
probability that an informed systems attain a given objective as consequence of
the expression of its information.


title:
Mechanical unfolding of RNA: From hairpins to structures with internal
  multiloops

abstract:
  Mechanical unfolding of RNA structures, ranging from hairpins to ribozymes,
using laser optical tweezer (LOT) experiments have begun to reveal the features
of the energy landscape that cannot be easily explored using conventional
experiments. Upon application of constant force ($f$), RNA hairpins undergo
cooperative transitions from folded to unfolded states whereas subdomains of
ribozymes unravel one at a time. Here, we use a self-organized polymer (SOP)
model and Brownian dynamics simulations to probe mechanical unfolding at
constant force and constant-loading rate of four RNA structures of varying
complexity. Our work shows (i) the response of RNA to force is largely
determined by the native structure; (ii) only by probing mechanical unfolding
over a wide range of forces can the underlying energy landscape be fully
explored.

categories: q-bio.BM cond-mat.soft physics.bio-ph q-bio.QM


title:
Calculating event-triggered average synaptic conductances from the
  membrane potential

abstract:
  The optimal patterns of synaptic conductances for spike generation in central
neurons is a subject of considerable interest. Ideally, such conductance time
courses should be extracted from membrane potential (Vm) activity, but this is
difficult because the nonlinear contribution of conductances to the Vm renders
their estimation from the membrane equation extremely sensitive. We outline
here a solution to this problem based on a discretization of the time axis.
This procedure can extract the time course of excitatory and inhibitory
conductances solely from the analysis of Vm activity. We test this method by
calculating spike-triggered averages of synaptic conductances using numerical
simulations of the integrate-and-fire model subject to colored conductance
noise. The procedure was also tested successfully in biological cortical
neurons using conductance noise injected with dynamic-clamp. This method should
allow the extraction of synaptic conductances from Vm recordings in vivo.


title: Network Models of Phage-Bacteria Coevolution

abstract:
  Bacteria and their bacteriophages are the most abundant, widespread and
diverse groups of biological entities on the planet. In an attempt to
understand how the interactions between bacteria, virulent phages and temperate
phages might affect the diversity of these groups, we developed a novel
stochastic network model for examining the co-evolution of these ecologies. In
our approach, nodes represent whole species or strains of bacteria or phages,
rather than individuals, with "speciation" and extinction modelled by
duplication and removal of nodes. Phage-bacteria links represent host-parasite
relationships and temperate-virulent phage links denote prophage-encoded
resistance. The effect of horizontal transfer of genetic information between
strains was also included in the dynamical rules. The observed networks evolved
in a highly dynamic fashion but the ecosystems were prone to collapse (one or
more entire groups going extinct). Diversity could be stably maintained in the
model only if the probability of speciation was independent of the diversity.
Such an effect could be achieved in real ecosystems if the speciation rate is
primarily set by the availability of ecological niches.


title: Effects of rapid prey evolution on predator-prey cycles

abstract:
  We study the qualitative properties of population cycles in a predator-prey
system where genetic variability allows contemporary rapid evolution of the
prey. Previous numerical studies have found that prey evolution in response to
changing predation risk can have major quantitative and qualitative effects on
predator-prey cycles, including: (i) large increases in cycle period, (ii)
changes in phase relations (so that predator and prey are cycling exactly out
of phase, rather than the classical quarter-period phase lag), and (iii)
"cryptic" cycles in which total prey density remains nearly constant while
predator density and prey traits cycle. Here we focus on a chemostat model
motivated by our experimental system [Fussmann et al. 2000,Yoshida et al. 2003]
with algae (prey) and rotifers (predators), in which the prey exhibit rapid
evolution in their level of defense against predation. We show that the effects
of rapid prey evolution are robust and general, and furthermore that they occur
in a specific but biologically relevant region of parameter space: when traits
that greatly reduce predation risk are relatively cheap (in terms of reductions
in other fitness components), when there is coexistence between the two prey
types and the predator, and when the interaction between predators and
undefended prey alone would produce cycles. Because defense has been shown to
be inexpensive, even cost-free, in a number of systems [Andersson and Levin
1999, Gagneux et al. 2006,Yoshida et al. 2004], our discoveries may well be
reproduced in other model systems, and in nature. Finally, some of our key
results are extended to a general model in which functional forms for the
predation rate and prey birth rate are not specified.


title:
Comparative study of the transcriptional regulatory networks of E. coli
  and yeast: Structural characteristics leading to marginal dynamic stability

abstract:
  Dynamical properties of the transcriptional regulatory network of {\it
Escherichia coli} and {\it Saccharomyces cerevisiae} are studied within the
framework of random Boolean functions. The dynamical response of these networks
to a single point mutation is characterized by the number of mutated elements
as a function of time and the distribution of the relaxation time to a new
stationary state, which turn out to be different in both networks. Comparison
with the behavior of randomized networks reveals relevant structural
characteristics other than the mean connectivity, namely the organization of
circuits and the functional form of the in-degree distribution. The abundance
of single-element circuits in {\it E. coli} and the power-law in-degree
distribution of {\it S. cerevisiae} shift their dynamics towards marginal
stability overcoming the restrictions imposed by their mean connectivities,
which is argued to be related to the simultaneous presence of robustness and
adaptivity in living organisms.


title:
Evolution of cooperation and communication skills as a consequence of
  environment fluctuations

abstract:
  Dynamics of a social population is analyzed taking into account some physical
constraints on individual behavior and decision making abilities. The model,
based on Evolutionary Game Theory, predicts that a population has to pass
through a series of different games, e.g as a consequence of environmental
fluctuations, in order to develop social cooperation and communication skills.
It differs from the general assumption that evolution of cooperation, the so
called Cooperation Paradox, can be explained by a single set of rules for
intra-population competitions. The developed methods, potentially, have a
practical value for some learning optimization problems in multiagent, e.g.
corporate, environment.


title: Size, shape, and flexibility of RNA structures

abstract:
  Determination of sizes and flexibilities of RNA molecules is important in
understanding the nature of packing in folded structures and in elucidating
interactions between RNA and DNA or proteins. Using the coordinates of the
structures of RNA in the Protein Data Bank we find that the size of the folded
RNA structures, measured using the radius of gyration, $R_G$, follows the Flory
scaling law, namely, $R_G =5.5 N^{1/3}$ \AA where N is the number of
nucleotides. The shape of RNA molecules is characterized by the asphericity
$\Delta$ and the shape $S$ parameters that are computed using the eigenvalues
of the moment of inertia tensor. From the distribution of $\Delta$, we find
that a large fraction of folded RNA structures are aspherical and the
distribution of $S$ values shows that RNA molecules are prolate ($S>0$). The
flexibility of folded structures is characterized by the persistence length
$l_p$. By fitting the distance distribution function $P(r)$ to the worm-like
chain model we extracted the persistence length $l_p$. We find that $l_p\approx
1.5 N^{0.33}$ \AA. The dependence of $l_p$ on $N$ implies the average length of
helices should increases as the size of RNA grows. We also analyze packing in
the structures of ribosomes (30S, 50S, and 70S) in terms of $R_G$, $\Delta$,
$S$, and $l_p$. The 70S and the 50S subunits are more spherical compared to
most RNA molecules. The globularity in 50S is due to the presence of an
unusually large number (compared to 30S subunit) of small helices that are
stitched together by bulges and loops. Comparison of the shapes of the intact
70S ribosome and the constituent particles suggests that folding of the
individual molecules might occur prior to assembly.


title: Using graph concepts to understand the organization of complex systems

abstract:
  Complex networks are universal, arising in fields as disparate as sociology,
physics, and biology. In the past decade, extensive research into the
properties and behaviors of complex systems has uncovered surprising
commonalities among the topologies of different systems. Attempts to explain
these similarities have led to the ongoing development and refinement of
network models and graph-theoretical analysis techniques with which to
characterize and understand complexity. In this tutorial, we demonstrate
through illustrative examples, how network measures and models have contributed
to the elucidation of the organization of complex systems.


title:
Comparison of amino acid occurrence and composition for predicting
  protein folds

abstract:
  Background:Prediction of protein three-dimensional structures from amino acid
sequences is a long-standing goal in computational/molecular biology. The
successful discrimination of protein folds would help to improve the accuracy
of protein 3D structure prediction. Results: In this work, we propose a method
based on linear discriminant analysis (LDA) for recognizing proteins belonging
to 30 different folds using the occurrence of amino acid residues in a set of
1612 proteins. The present method could discriminate the globular proteins from
30 major folding types with the sensitivity of 37%, which is comparable to or
better than other methods in the literature. A web server has been developed
for predicting the folding type of the protein from amino acid sequence and it
is available at http://granular.com/PROLDA/. Conclusions:Linear discriminant
analysis based on amino acid occurrence could successfully recognize protein
folds. The present method has several advantages such as, (i) it directly
predicts the folding type of a protein without performing pair-wise
comparisons, (ii) it can discriminate folds among large number of proteins and
(iii) it is very fast to obtain the results. This is a simple method, which can
be easily incorporated in any other structure prediction algorithms.


title:
Phylogeny of Mixture Models: Robustness of Maximum Likelihood and
  Non-identifiable Distributions

abstract:
  We address phylogenetic reconstruction when the data is generated from a
mixture distribution. Such topics have gained considerable attention in the
biological community with the clear evidence of heterogeneity of mutation
rates. In our work, we consider data coming from a mixture of trees which share
a common topology, but differ in their edge weights (i.e., branch lengths). We
first show the pitfalls of popular methods, including maximum likelihood and
Markov chain Monte Carlo algorithms. We then determine in which evolutionary
models, reconstructing the tree topology, under a mixture distribution, is
(im)possible. We prove that every model whose transition matrices can be
parameterized by an open set of multi-linear polynomials, either has
non-identifiable mixture distributions, in which case reconstruction is
impossible in general, or there exist linear tests which identify the topology.
This duality theorem, relies on our notion of linear tests and uses ideas from
convex programming duality. Linear tests are closely related to linear
invariants, which were first introduced by Lake, and are natural from an
algebraic geometry perspective.


title: Spatial stochastic predator-prey models

abstract:
  We consider a broad class of stochastic lattice predator-prey models, whose
main features are overviewed. In particular, this article aims at drawing a
picture of the influence of spatial fluctuations, which are not accounted for
by the deterministic rate equations, on the properties of the stochastic
models. Here, we outline the robust scenario obeyed by most of the lattice
predator-prey models with an interaction "a' la Lotka-Volterra". We also show
how a drastically different behavior can emerge as the result of a subtle
interplay between long-range interactions and a nearest-neighbor exchange
process.


title: Aging and Immortality in a Cell Proliferation Model

abstract:
  We investigate a model of cell division in which the length of telomeres
within the cell regulate their proliferative potential. At each cell division
the ends of linear chromosomes change and a cell becomes senescent when one or
more of its telomeres become shorter than a critical length. In addition to
this systematic shortening, exchange of telomere DNA between the two daughter
cells can occur at each cell division. We map this telomere dynamics onto a
biased branching diffusion process with an absorbing boundary condition
whenever any telomere reaches the critical length. As the relative effects of
telomere shortening and cell division are varied, there is a phase transition
between finite lifetime and infinite proliferation of the cell population.
Using simple first-passage ideas, we quantify the nature of this transition.


title:
Hydrophobic, hydrophilic and charged amino acids' networks within
  Protein

abstract:
  The native three dimensional structure of a single protein is determined by
the physico chemical nature of its constituent amino acids. The twenty
different types of amino acids, depending on their physico chemical properties,
can be grouped into three major classes - hydrophobic, hydrophilic and charged.
We have studied the anatomy of the weighted and unweighted networks of
hydrophobic, hydrophilic and charged residues separately for a large number of
proteins. Our results show that the average degree of the hydrophobic networks
has significantly larger value than that of hydrophilic and charged networks.
The average degree of the hydrophilic networks is slightly higher than that of
charged networks. The average strength of the nodes of hydrophobic networks is
nearly equal to that of the charged network; whereas that of hydrophilic
networks has smaller value than that of hydrophobic and charged networks. The
average strength for each of the three types of networks varies with its
degree. The average strength of a node in charged networks increases more
sharply than that of the hydrophobic and hydrophilic networks. Each of the
three types of networks exhibits the 'small-world' property. Our results
further indicate that the all amino acids' networks and hydrophobic networks
are of assortative type. While maximum of the hydrophilic and charged networks
are of assortative type, few others have the characteristics of disassortative
mixing of the nodes. We have further observed that all amino acids' networks
and hydrophobic networks bear the signature of hierarchy; whereas the
hydrophilic and charged networks do not have any hierarchical signature.


title: Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli

abstract:
  Iron is an essential trace-element for most organisms. However, because high
concentration of free intracellular iron is cytotoxic, cells have developed
complex regulatory networks that keep free intracellular iron concentration at
optimal range, allowing the incorporation of the metal into iron-using enzymes
and minimizing damage to the cell. We built a mathematical model of the network
that controls iron uptake and usage in the bacterium Escherichia coli to
explore the dynamics of iron flow. We simulate the effect of sudden decrease or
increase in the extracellular iron level on intracellular iron distribution.
Based on the results of simulations we discuss the possible roles of the small
RNA RyhB and the Fe-S cluster assembly systems in the optimal redistribution of
iron flows. We suggest that Fe-S cluster assembly is crucial to prevent the
accumulation of toxic levels of free intracellular iron when the environment
suddenly becomes iron rich.


title:
Efficiency, selectivity and robustness of the nuclear pore complex
  transport

abstract:
  All materials enter or exit the cell nucleus through nuclear pore complexes
(NPCs), efficient transport devices that combine high selectivity and
throughput. A central feature of this transport is the binding of
cargo-carrying soluble transport factors to flexible, unstructured
proteinaceous filaments called FG-nups that line the NPC. We have modeled the
dynamics of transport factors and their interaction with the flexible FG-nups
as diffusion in an effective potential, using both analytical theory and
computer simulations. We show that specific binding of transport factors to the
FG-nups facilitates transport and provides the mechanism of selectivity. We
show that the high selectivity of transport can be accounted for by competition
for both binding sites and space inside the NPC, which selects for transport
factors over other macromolecules that interact only non-specifically with the
NPC. We also show that transport is relatively insensitive to changes in the
number and distribution of FG-nups in the NPC, due mainly to their flexibility;
this accounts for recent experiments where up to half of the total mass of the
NPC has been deleted, without abolishing the transport. Notably, we demonstrate
that previously established physical and structural properties of the NPC can
account for observed features of nucleocytoplasmic transport. Finally, our
results suggest strategies for creation of artificial nano-molecular sorting
devices.


title: Stochastic dynamics of macromolecular-assembly networks

abstract:
  The formation and regulation of macromolecular complexes provides the
backbone of most cellular processes, including gene regulation and signal
transduction. The inherent complexity of assembling macromolecular structures
makes current computational methods strongly limited for understanding how the
physical interactions between cellular components give rise to systemic
properties of cells. Here we present a stochastic approach to study the
dynamics of networks formed by macromolecular complexes in terms of the
molecular interactions of their components. Exploiting key thermodynamic
concepts, this approach makes it possible to both estimate reaction rates and
incorporate the resulting assembly dynamics into the stochastic kinetics of
cellular networks. As prototype systems, we consider the lac operon and phage
lambda induction switches, which rely on the formation of DNA loops by proteins
and on the integration of these protein-DNA complexes into intracellular
networks. This cross-scale approach offers an effective starting point to move
forward from network diagrams, such as those of protein-protein and DNA-protein
interaction networks, to the actual dynamics of cellular processes.

categories: q-bio.MN cond-mat.soft physics.bio-ph q-bio.SC


title: DNA looping: the consequences and its control

abstract:
  The formation of DNA loops by proteins and protein complexes is ubiquitous to
many fundamental cellular processes, including transcription, recombination,
and replication. Here we review recent advances in understanding the properties
of DNA looping in its natural context and how they propagate to the cellular
behavior through gene regulation. The results of connecting the molecular
properties with cellular physiology indicate that looping of DNA in vivo is
much more complex and easier than predicted from current models and reveals a
wealth of previously unappreciated details.

categories: q-bio.BM cond-mat.soft physics.bio-ph q-bio.SC


title: A mutation-selection model for general genotypes with recombination

abstract:
  We investigate a continuous time, probability measure-valued dynamical system
that describes the process of mutation-selection balance in a context where the
population is infinite, there may be infinitely many loci, and there are weak
assumptions on selective costs. Our model arises when we incorporate very
general recombination mechanisms into a previous model of mutation and
selection from Steinsaltz, Evans and Wachter (2005) and take the relative
strength of mutation and selection to be sufficiently small. The resulting
dynamical system is a flow of measures on the space of loci. Each such measure
is the intensity measure of a Poisson random measure on the space of loci: the
points of a realization of the random measure record the set of loci at which
the genotype of a uniformly chosen individual differs from a reference wild
type due to an accumulation of ancestral mutations. Our motivation for working
in such a general setting is to provide a basis for understanding
mutation-driven changes in age-specific demographic schedules that arise from
the complex interaction of many genes, and hence to develop a framework for
understanding the evolution of aging.
  We establish the existence and uniqueness of the dynamical system, provide
conditions for the existence and stability of equilibrium states, and prove
that our continuous-time dynamical system is the limit of a sequence of
discrete-time infinite population mutation-selection-recombination models in
the standard asymptotic regime where selection and mutation are weak relative
to recombination and both scale at the same infinitesimal rate in the limit.


title:
Formation of regular spatial patterns in ratio-dependent predator-prey
  model driven by spatial colored-noise

abstract:
  Results are reported concerning the formation of spatial patterns in the
two-species ratio-dependent predator-prey model driven by spatial
colored-noise. The results show that there is a critical value with respect to
the intensity of spatial noise for this system when the parameters are in the
Turing space, above which the regular spatial patterns appear in two
dimensions, but under which there are not regular spatial patterns produced. In
particular, we investigate in two-dimensional space the formation of regular
spatial patterns with the spatial noise added in the side and the center of the
simulation domain, respectively.


title: Structural similarity enhances interaction propensity of proteins

abstract:
  We study statistical properties of interacting protein-like surfaces and
predict two strong, related effects: (i) statistically enhanced self-attraction
of proteins; (ii) statistically enhanced attraction of proteins with similar
structures. The effects originate in the fact that the probability to find a
pattern self-match between two identical, even randomly organized interacting
protein surfaces is always higher compared with the probability for a pattern
match between two different, promiscuous protein surfaces. This theoretical
finding explains statistical prevalence of homodimers in protein-protein
interaction networks reported earlier. Further, our findings are confirmed by
the analysis of curated database of protein complexes that showed highly
statistically significant overrepresentation of dimers formed by structurally
similar proteins with highly divergent sequences (superfamily heterodimers). We
predict that significant fraction of heterodimers evolved from homodimers with
the negative design evolutionary pressure applied against promiscuous homodimer
formation. This is achieved through the formation of highly specific contacts
formed by charged residues as demonstrated both in model and real superfamily
heterodimers


title:
An algorithm for real-time estimation of Mezcal fermentation parameters
  based on redox potential measurements

abstract:
  We present an algorithm for the continuous monitoring of the biomass and
ethanol concentrations and moreover the kinetic rate in the Mezcal fermentation
process. This algorithm performs its task having only available the on-line
measurements of the redox potential. The procedure includes an artificial
neural network (ANN) that relates the redox potential to the ethanol and
biomass concentrations. Then a nonlinear-observer-based algorithm uses the
biomass estimations to infer the kinetic rate of this fermentation process. The
method shows that the redox potential is a valuable indicator of microorganism
metabolic activity during the Mezcal fermentation. In addition, the estimated
kinetic rate can be considered as a direct evidence of the presence of mixed
culture growth in the process. In this work, the detailed design of the
software-sensor is presented, as well as its experimental application at the
laboratory level


title:
Bayesian analysis of biological networks: clusters, motifs,
  cross-species correlations

abstract:
  An important part of the analysis of bio-molecular networks is to detect
different functional units. Different functions are reflected in a different
evolutionary dynamics, and hence in different statistical characteristics of
network parts. In this sense, the {\em global statistics} of a biological
network, e.g., its connectivity distribution, provides a background, and {\em
local deviations} from this background signal functional units. In the
computational analysis of biological networks, we thus typically have to
discriminate between different statistical models governing different parts of
the dataset. The nature of these models depends on the biological question
asked. We illustrate this rationale here with three examples: identification of
functional parts as highly connected \textit{network clusters}, finding
\textit{network motifs}, which occur in a similar form at different places in
the network, and the analysis of \textit{cross-species network correlations},
which reflect evolutionary dynamics between species.


title: Search for bottleneck effects in Penna ageing and Schulze language model

abstract:
  No influence was seen when in two models with memory effects the populations
were drastically decreased after equilibrium was established, and then allowed
to increase again.


title:
The maintenance of sex in bacteria is ensured by its potential to reload
  genes

abstract:
  Why sex is maintained in nature is a fundamental question in biology. Natural
genetic transformation (NGT) is a sexual process by which bacteria actively
take up exogenous DNA and use it to replace homologous chromosomal sequences.
As it has been demonstrated, the role of NGT in repairing deleterious mutations
under constant selection is insufficient for its survival, and the lack of
other viable explanations have left no alternative except that DNA uptake
provides nucleotides for food. Here we develop a novel simulation approach for
the long-term dynamics of genome organization (involving the loss and
acquisition of genes) in a bacterial species consisting of a large number of
spatially distinct populations subject to independently fluctuating ecological
conditions. Our results show that in the presence of weak inter-population
migration NGT is able to subsist as a mechanism to reload locally lost,
intermittently selected genes from the collective gene pool of the species
through DNA uptake from migrants. Reloading genes and combining them with those
in locally adapted genomes allow individual cells to re-adapt faster to
environmental changes. The machinery of transformation survives under a wide
range of model parameters readily encompassing real-world biological
conditions. These findings imply that the primary role of NGT is not to serve
the cell with food, but to provide homologous sequences for restoring genes
that have disappeared from or become degraded in the local population.


title:
Cooperation enhanced by inhomogeneous activity of teaching for
  evolutionary Prisoner's Dilemma games

abstract:
  Evolutionary Prisoner's Dilemma games with quenched inhomogeneities in the
spatial dynamical rules are considered. The players following one of the two
pure strategies (cooperation or defection) are distributed on a two-dimensional
lattice. The rate of strategy adoption from a randomly chosen neighbors are
controlled by the payoff difference and a two-value pre-factor $w$
characterizing the players whom the strategy learned from. The reduced teaching
activity of players is distributed randomly with concentrations $\nu$ at the
beginning and fixed further on. Numerical and analytical calculations are
performed to study the concentration of cooperators as a function of $w$ and
$\nu$ for different noise levels and connectivity structures. Significant
increase of cooperation is found within a wide range of parameters for this
dynamics. The results highlight the importance of asymmetry characterizing the
exchange of master-follower role during the strategy adoptions.


title: The dynamics of Machiavellian intelligence

abstract:
  The "Machiavellian intelligence" hypothesis (or the "social brain"
hypothesis) posits that large brains and distinctive cognitive abilities of
humans have evolved via intense social competition in which social competitors
developed increasingly sophisticated "Machiavellian" strategies as a means to
achieve higher social and reproductive success. Here we build a mathematical
model aiming to explore this hypothesis. In the model, genes control brains
which invent and learn strategies (memes) which are used by males to gain
advantage in competition for mates. We show that the dynamics of intelligence
has three distinct phases. During the dormant phase only newly invented memes
are present in the population. During the cognitive explosion phase the
population's meme count and the learning ability, cerebral capacity
(controlling the number of different memes that the brain can learn and use),
and Machiavellian fitness of individuals increase in a runaway fashion. During
the saturation phase natural selection resulting from the costs of having large
brains checks further increases in cognitive abilities. Overall, our results
suggest that the mechanisms underlying the "Machiavellian intelligence"
hypothesis can indeed result in the evolution of significant cognitive
abilities on the time scale of 10 to 20 thousand generations. We show that
cerebral capacity evolves faster and to a larger degree than learning ability.
Our model suggests that there may be a tendency toward a reduction in cognitive
abilities (driven by the costs of having a large brain) as the reproductive
advantage of having a large brain decreases and the exposure to memes increases
in modern societies.


title: A family tree of Markov models in systems biology

abstract:
  Motivated by applications in systems biology, we seek a probabilistic
framework based on Markov processes to represent intracellular processes. We
review the formal relationships between different stochastic models referred to
in the systems biology literature. As part of this review, we present a novel
derivation of the differential Chapman-Kolmogorov equation for a general
multidimensional Markov process made up of both continuous and jump processes.
We start with the definition of a time-derivative for a probability density but
place no restrictions on the probability distribution, in particular, we do not
assume it to be confined to a region that has a surface (on which the
probability is zero). In our derivation, the master equation gives the jump
part of the Markov process while the Fokker-Planck equation gives the
continuous part. We thereby sketch a {}``family tree'' for stochastic models in
systems biology, providing explicit derivations of their formal relationship
and clarifying assumptions involved.


title: Sea urchin feeding fronts

abstract:
  Sea urchin feeding fronts are a striking example of spatial pattern formation
in an ecological system. If it is assumed that urchins are asocial, and that
they move randomly, then the formation of these dense fronts is an apparent
paradox. The key lies in observations that urchins move further in areas where
their algal food is less plentiful. This naturally leads to the accumulation of
urchins in areas with abundant algae. If urchin movement is represented as a
random walk, with a step size that depends on algal concentration, then their
movement may be described by a Fokker-Planck diffusion equation. For certain
combinations of algal growth and urchin grazing, travelling wave solutions are
obtained. Two dimensional simulations of urchin algal dynamics show that an
initially uniformly distributed urchin population, grazing on an alga with a
smoothly varying density, may form a propagating front separating two sharply
delineated regions. On one side of the front algal density is uniformly low,
and on the other side of the front algal density is uniformly high. Bounds on
when stable fronts will form are obtained in terms of urchin density and
grazing, and algal growth.


title:
Modulation of the reaction rate of regulating protein induces large
  morphological and motional change of amoebic cell

abstract:
  Morphologies of moving amoebae are categorized into two types. One is the
``neutrophil'' type in which the long axis of cell roughly coincides with its
moving direction. This type of cell extends a leading edge at the front and
retracts a narrow tail at the rear, whose shape has been often drawn as a
typical amoeba in textbooks. The other one is the ``keratocyte'' type with
widespread lamellipodia along the front side arc. Short axis of cell in this
type roughly coincides with its moving direction. In order to understand what
kind of molecular feature causes conversion between two types of morphologies,
and how two typical morphologies are maintained, a mathematical model of
amoebic cells is developed. This model describes movement of cell and
intracellular reactions of activator, inhibitor and actin filaments in a
unified way. It is found that the producing rate of activator is a key factor
of conversion between two types. This model also explains the observed data
that the keratocye type cells tend to rapidly move along a straight line. The
neutrophil type cells move along a straight line when the moving velocity is
small, but they show fluctuated motions deviating from a line when they move as
fast as the keratocye type cells. Efficient energy consumption in the
neutrophil type cells is predicted.


title:
Formation of spatial patterns in epidemic model with constant removal
  rate of the infectives

abstract:
  This paper addresses the question of how population diffusion affects the
formation of the spatial patterns in the spatial epidemic model by Turing
mechanisms. In particular, we present theoretical analysis to results of the
numerical simulations in two dimensions. Moreover, there is a critical value
for the system within the linear regime. Below the critical value the spatial
patterns are impermanent, whereas above it stationary spot and stripe patterns
can coexist over time. We have observed the striking formation of spatial
patterns during the evolution, but the isolated ordered spot patterns don't
emerge in the space.


title: Elementary simulation of tethered Brownian motion

abstract:
  We describe a simple numerical simulation, suitable for an undergraduate
project (or graduate problem set), of the Brownian motion of a particle in a
Hooke-law potential well. Understanding this physical situation is a practical
necessity in many experimental contexts, for instance in single molecule
biophysics; and its simulation helps the student to appreciate the dynamical
character of thermal equilibrium. We show that the simulation succeeds in
capturing behavior seen in experimental data on tethered particle motion.

categories: q-bio.BM cond-mat.stat-mech physics.bio-ph q-bio.QM


title:
Dynamical Structure Functions for the Estimation of LTI Networks with
  Limited Information

abstract:
  This research explores the role and representation of network structure for
LTI Systems. We demonstrate that transfer functions contain no structural
information without more assumptions being made about the system, assumptions
that we believe are unreasonable when dealing with truly complex systems. We
then introduce Dynamical Structure Functions as an alternative, graphical-model
based representation of LTI systems that contain both dynamical and structural
information of the system. We use Dynamical Structure to prove necessary and
sufficient conditions for estimating structure from data, and demonstrate, for
example, the danger of attempting to use steady-state information to estimate
network structure.


title: Electrostatic theory of viral self-assembly: a toy model

abstract:
  Viruses self-assemble from identical capsid proteins and their genome
consisting, for example, of a long single stranded (ss) RNA. For a big class of
T = 3 viruses capsid proteins have long positive N-terminal tails. We explore
the role played by the Coulomb interaction between the brush of positive
N-terminal tails rooted at the inner surface of the capsid and the negative ss
RNA molecule. We show that viruses are most stable when the total contour
length of ss RNA is close to the total length of the tails. For such a
structure the absolute value of the total RNA charge is approximately twice
larger than the charge of the capsid. This conclusion agrees with structural
data.


title:
The Hilbert transform of horizontal gaze position during natural image
  classification by saccades

abstract:
  Eye movements are a behavioral response that can be involved in tasks as
complicated as natural image classification. This report confirms that pro- and
anti-saccades can be used by a volunteer to designate target (animal) or
non-target images that were centered 16 degrees off the fixation point. With
more than 86% correct responses, 11 participants responded to targets in 470
milliseconds on average, starting as quick as 245 milliseconds. Furthermore,
tracking the gaze position is considered a powerful method in the studies of
recognition as the saccade response times, ocular dynamics and the events
around the response time can be calculated from the data sampled 240 times per
second. The Hilbert transform is applied to obtain the analytic signal from the
horizontal gaze position. Its amplitude and phase are used to describe
differences between saccades that may testify to the recognition process.


title:
Canalization and Symmetry in Boolean Models for Genetic Regulatory
  Networks

abstract:
  Canalization of genetic regulatory networks has been argued to be favored by
evolutionary processes due to the stability that it can confer to phenotype
expression. We explore whether a significant amount of canalization and partial
canalization can arise in purely random networks in the absence of evolutionary
pressures. We use a mapping of the Boolean functions in the Kauffman N-K model
for genetic regulatory networks onto a k-dimensional Ising hypercube to show
that the functions can be divided into different classes strictly due to
geometrical constraints. The classes can be counted and their properties
determined using results from group theory and isomer chemistry. We demonstrate
that partially canalized functions completely dominate all possible Boolean
functions, particularly for higher k. This indicates that partial canalization
is extremely common, even in randomly chosen networks, and has implications for
how much information can be obtained in experiments on native state genetic
regulatory networks.


title:
Predicting essential components of signal transduction networks: a
  dynamic model of guard cell abscisic acid signaling

abstract:
  Plants both lose water and take in carbon dioxide through microscopic
stomatal pores, each of which is regulated by a surrounding pair of guard
cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal
opening and promotes stomatal closure, thereby promoting water conservation.
Here we synthesize experimental results into a consistent guard cell signal
transduction network for ABA-induced stomatal closure, and develop a dynamic
model of this process. Our model captures the regulation of more than forty
identified network components, and accords well with previous experimental
results at both the pathway and whole cell physiological level. Our analysis
reveals the novel predictions that the disruption of membrane depolarizability,
anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the
phosphatidic acid pathway or of K+ efflux through slowly activating K+ channels
at the plasma membrane lead to the strongest reduction in ABA responsiveness.
Initial experimental analysis assessing ABA-induced stomatal closure in the
presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent
with model prediction. Our method can be readily applied to other biological
signaling networks to identify key regulatory components in systems where
quantitative information is limited.


title:
Toward an adequate mathematical model of mental space:
  conscious/unconscious dynamics on $m$-adic trees

abstract:
  We try to perform geometrization of cognitive science and psychology by
representing information states of cognitive systems by points of {\it mental
space} given by a hierarchic $m$-adic tree. Associations are represented by
balls and ideas by collections of balls. We consider dynamics of ideas based on
lifting of dynamics of mental points. We apply our dynamical model for modeling
of flows of unconscious and conscious information in the human brain. In series
of models, Models 1-3, we consider cognitive systems with increasing complexity
of psychological behavior determined by structure of flows of associations and
ideas.


title: Stochastic effects on biodiversity in cyclic coevolutionary dynamics

abstract:
  Finite-size fluctuations arising in the dynamics of competing populations may
have dramatic influence on their fate. As an example, in this article, we
investigate a model of three species which dominate each other in a cyclic
manner. Although the deterministic approach predicts (neutrally) stable
coexistence of all species, for any finite population size, the intrinsic
stochasticity unavoidably causes the eventual extinction of two of them.


title: The role of cell-cell adhesion in wound healing

abstract:
  We present a stochastic model which describes fronts of cells invading a
wound. In the model cells can move, proliferate, and experience cell-cell
adhesion. We find several qualitatively different regimes of front motion and
analyze the transitions between them. Above a critical value of adhesion and
for small proliferation large isolated clusters are formed ahead of the front.
This is mapped onto the well-known ferromagnetic phase transition in the Ising
model. For large adhesion, and larger proliferation the clusters become
connected (at some fixed time). For adhesion below the critical value the
results are similar to our previous work which neglected adhesion. The results
are compared with experiments, and possible directions of future work are
proposed.


title: Migration and proliferation dichotomy in tumor cell invasion

abstract:
  We propose a two-component reaction-transport model for the
migration-proliferation dichotomy in the spreading of tumor cells. By using a
continuous time random walk (CTRW) we formulate a system of the balance
equations for the cancer cells of two phenotypes with random switching between
cell proliferation and migration. The transport process is formulated in terms
of the CTRW with an arbitrary waiting time distribution law. Proliferation is
modeled by a standard logistic growth. We apply hyperbolic scaling and
Hamilton-Jacobi formalism to determine the overall rate of tumor cell invasion.
In particular, we take into account both normal diffusion and anomalous
transport (subdiffusion) in order to show that the standard diffusion
approximation for migration leads to overestimation of the overall cancer
spreading rate.


title:
Number sequence representation of protein structures based on the second
  derivative of a folded tetrahedron sequence

abstract:
  This paper proposes a new mathematical approach to characterize native
protein structures based on the discrete differential geometry of tetrahedron
tiles. In the approach, local structure of proteins is classified into finite
types according to shape. And one would obtain a number sequence representation
of protein structures automatically. As a result, it would become possible to
quantify structural preference of amino-acids objectively. And one could use
the wide variety of sequence alignment programs to study protein structures
since the number sequence has no internal structure.
  The programs and this paper with clear figures are available from
http://www.genocript.com.


title:
A Physical Theory of the Competition that Allows HIV to Escape from the
  Immune System

abstract:
  Competition within the immune system may degrade immune control of viral
infections. We formalize the evolution that occurs in both HIV-1 and the immune
system quasispecies. Inclusion of competition in the immune system leads to a
novel balance between the immune response and HIV-1, in which the eventual
outcome is HIV-1 escape rather than control. The analytical model reproduces
the three stages of HIV-1 infection. We propose a vaccine regimen that may be
able to reduce competition between T cells, potentially eliminating the third
stage of HIV-1.


title: Correlation entropy of synaptic input-output dynamics

abstract:
  The responses of synapses in the neocortex show highly stochastic and
nonlinear behavior. The microscopic dynamics underlying this behavior, and its
computational consequences during natural patterns of synaptic input, are not
explained by conventional macroscopic models of deterministic ensemble mean
dynamics. Here, we introduce the correlation entropy of the synaptic
input-output map as a measure of synaptic reliability which explicitly includes
the microscopic dynamics. Applying this to experimental data, we find that
cortical synapses show a low-dimensional chaos driven by the natural input
pattern.


title: Quartet consistency count method for reconstructing phylogenetic trees

abstract:
  Among the distance based algorithms in phylogenetic tree reconstruction, the
neighbor-joining algorithm has been a widely used and effective method. We
propose a new algorithm which counts the number of consistent quartets for
cherry picking with tie breaking. We show that the success rate of the new
algorithm is almost equal to that of neighbor-joining. This gives an
explanation of the qualitative nature of neighbor-joining and that of
dissimilarity maps from DNA sequence data. Moreover, the new algorithm always
reconstructs correct trees from quartet consistent dissimilarity maps.


title: Cell wall proteins: a new insight through proteomics

abstract:
  Cell wall proteins are essential constituents of plant cell walls; they are
involved in modifications of cell wall components, wall structure, signaling
and interactions with plasma membrane proteins at the cell surface. The
application of proteomic approaches to the cell wall compartment raises
important questions: are there technical problems specific to cell wall
proteomics? What kinds of proteins can be found in Arabidopsis walls? Are some
of them unexpected? What sort of post-translational modifications have been
characterized in cell wall proteins to date? The purpose of this review is to
discuss the experimental results obtained to date using proteomics, as well as
some of the new questions challenging future research.


title:
Evaluation of cell wall preparations for proteomics: a new procedure for
  purifying cell walls from Arabidopsis hypocotyls

abstract:
  The ultimate goal of proteomic analysis of a cell compartment should be the
exhaustive identification of resident proteins; excluding proteins from other
cell compartments. Plant cell walls possess specific difficulties. Several
reported procedures to isolate cell walls for proteomic analyses led to the
isolation of a high proportion (more than 50%) of predicted intracellular
proteins. The rationales of several published procedures to isolate cell walls
for proteomics were analyzed, with regard to the bioinformatic-predicted
subcellular localization of the identified proteins. A new procedure was
developed to prepare cell walls from etiolated hypocotyls of Arabidopsis
thaliana. After salt extraction, a high proportion of proteins predicted to be
secreted was released (73%), belonging to the same functional classes as
proteins identified using previously described protocols. The new cell wall
preparation described in this paper gives the lowest proportion of proteins
predicted to be intracellular when compared to available protocols. The
application of its principles should lead to a more realistic view of the cell
wall proteome, at least for the weakly bound CWP extractable by salts. In
addition, it offers a clean cell wall preparation for subsequent extraction of
strongly bound CWP.


title:
Transcriptional Interactions During Smallpox Infection and
  Identification of Early Infection Biomarkers

abstract:
  Smallpox is a deadly disease that can be intentionally reintroduced into the
human population as a bioweapon. While host gene expression microarray
profiling can be used to detect infection, the analysis of this information
using unsupervised and supervised classification techniques can produce
contradictory results. Here, we present a novel computational approach to
incorporate molecular genome annotation features that are key for identifying
early infection biomarkers (EIB). Our analysis identified 58 EIBs expressed in
peripheral blood mononuclear cells (PBMCs) collected from 21 cynomolgus
macaques (Macaca fascicularis) infected with two variola strains via aerosol
and intravenous exposure. The level of expression of these EIBs was correlated
with disease progression and severity. No overlap between the EIBs
co-expression and protein interaction data reported in public databases was
found. This suggests that a pathogen-specific re-organization of the gene
expression and protein interaction networks occurs during infection. To
identify potential genome-wide protein interactions between variola and humans,
we performed a protein domain analysis of all smallpox and human proteins. We
found that only 55 of the 161 protein domains in smallpox are also present in
the human genome. These co-occurring domains are mostly represented in proteins
involved in blood coagulation, complement activation, angiogenesis,
inflammation, and hormone transport. Several of these proteins are within the
EIBs category and suggest potential new targets for the development of
therapeutic countermeasures.


title:
Optimal experimental design in an EGFR signaling and down-regulation
  model

abstract:
  We apply the methods of optimal experimental design to a differential
equation model for epidermal growth factor receptor (EGFR) signaling,
trafficking, and down-regulation. The model incorporates the role of a recently
discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine
exchange factor (GEF), Cool-1 (Beta-Pix), and the Rho family G protein Cdc42.
The complex has been suggested to be important in disrupting receptor
down-regulation. We demonstrate that the model interactions can accurately
reproduce the experimental observations, that they can be used to make
predictions with accompanying uncertainties, and that we can apply ideas of
optimal experimental design to suggest new experiments that reduce the
uncertainty on unmeasurable components of the system.


title:
Function Constrains Network Architecture and Dynamics: A Case Study on
  the Yeast Cell Cycle Boolean Network

abstract:
  We develop a general method to explore how the function performed by a
biological network can constrain both its structural and dynamical network
properties. This approach is orthogonal to prior studies which examine the
functional consequences of a given structural feature, for example a scale free
architecture. A key step is to construct an algorithm that allows us to
efficiently sample from a maximum entropy distribution on the space of boolean
dynamical networks constrained to perform a specific function, or cascade of
gene expression. Such a distribution can act as a "functional null model" to
test the significance of any given network feature, and can aid in revealing
underlying evolutionary selection pressures on various network properties.
Although our methods are general, we illustrate them in an analysis of the
yeast cell cycle cascade. This analysis uncovers strong constraints on the
architecture of the cell cycle regulatory network as well as significant
selection pressures on this network to maintain ordered and convergent
dynamics, possibly at the expense of sacrificing robustness to structural
perturbations.


title: Senescence Can Explain Microbial Persistence

abstract:
  It has been known for many years that small fractions of persister cells
resist killing in many bacterial colony-antimicrobial confrontations. These
persisters are not believed to be mutants. Rather it has been hypothesized that
they are phenotypic variants. Current models allow cells to switch in and out
of the persister phenotype. Here we suggest a different explanation, namely
senescence, for persister formation. Using a mathematical model including age
structure, we show that senescence provides a natural explanation for
persister-related phenomena including the observations that persister fraction
depends on growth phase in batch culture and dilution rate in continuous
culture.


title: Cancer Dissemination: A Consequence of limited Carrying Capacity?

abstract:
  Assuming that there is feedback between an expanding cancer system and its
organ-typical microenvironment, we argue here that such local tumor growth is
guided by co-existence rather than competition with the surrounding tissue. We
then present a novel concept that understands cancer dissemination as a
biological mechanism to evade the specific carrying capacity limit of its host
organ. This conceptual framework allows us to relate the tumor system's
volumetric growth rate to the host organ's functionality-conveying composite
infrastructure, and, intriguingly, already provides useful insights into
several clinical findings.


title: Robustness and modular design of the Drosophila segment polarity network

abstract:
  Biomolecular networks have to perform their functions robustly. A robust
function may have preferences in the topological structures of the underlying
network. We carried out an exhaustive computational analysis on network
topologies in relation to a patterning function in Drosophila embryogenesis. We
found that while the vast majority of topologies can either not perform the
required function or only do so very fragilely, a small fraction of topologies
emerges as particularly robust for the function. The topology adopted by
Drosophila, that of the segment polarity network, is a top ranking one among
all topologies with no direct autoregulation. Furthermore, we found that all
robust topologies are modular--each being a combination of three kinds of
modules. These modules can be traced back to three sub-functions of the
patterning function and their combinations provide a combinatorial variability
for the robust topologies. Our results suggest that the requirement of
functional robustness drastically reduces the choices of viable topology to a
limited set of modular combinations among which nature optimizes its choice
under evolutionary and other biological constraints.


title:
Determination and Spectroscopy of Quantum Yields in
  Bio/Chemiluminescence via Novel Light-Collection-Efficiency Calibration:
  Reexamination of The Aqueous Luminol Chemiluminescence Standard

abstract:
  We have developed a luminescence-measurement system for liquid
bio/chemiluminescence that can obtain quantitative luminescence spectra as the
absolute total number of luminescence photons at each wavelength or photon
energy and quantum yields. Calibration of light-collection efficiency in the
system is performed with a reference double-plate cell. This method is
applicable to sample cells of any kind suitable for measurement, which is a
great advantage over previous techniques in practical experiments. Using this
system, the quantum yield of aqueous luminol chemiluminescence was obtained as
1.23-+0.20%, which is in good agreement with previously reported values.


title:
Performance of a new invariants method on homogeneous and
  non-homogeneous quartet trees

abstract:
  An attempt to use phylogenetic invariants for tree reconstruction was made at
the end of the 80s and the beginning of the 90s by several authors (the initial
idea due to Lake and Cavender and Felsenstein in 1987. However, the efficiency
of methods based on invariants is still in doubt, probably because these
methods only used few generators of the set of phylogenetic invariants. The
method studied in this paper was first introduced by Casanellas, Garcia and
Sullivant and it is the first method based on invariants that uses the whole
set of generators for DNA data. The simulation studies performed in this paper
prove that it is a very competitive and highly efficient phylogenetic
reconstruction method, especially for non-homogeneous models on phylogenetic
trees.


title: Directionality of glioblastoma invasion in a 3d in vitro experiment

abstract:
  Glioblastoma is the most malignant form of brain cancer. It is extremely
invasive; the mechanisms that govern invasion are not well understood. To
better understand the process of invasion, we conducted an in vitro experiment
in which a 3d tumour spheroid is implanted into a collagen gel. The paths of
individual invasive cells were tracked. These cells were modeled as radially
biased, persistent random walkers. The radial velocity bias was found to be 20
microns/hr on day one, but decayed significantly by day two. The cause of this
bias is thought to be due to chemotactic factors and contact guidance along
collagen fibers.


title: A domain wall model for spectral reflectance of plant leaves

abstract:
  We model a plant leaf by using two-dimensional domain walls with internal
structures. Such domain walls can be found as soliton solutions in field theory
describing magnetic materials. The radiation scattered by such domain walls
behaves quite similar to the spectral reflectance of plant leaves. The model
nicely simulates the spectral reflectance of a plant leaf as a function of the
wavelength.


title:
The Vaccinee's Dilemma: Individual-level Decisions, Self- Organization &
  Influenza Epidemics

abstract:
  Inspired by Minority Games, we constructed a novel individual-level game of
adaptive decision-making based on the dilemma of deciding whether to
participate in voluntary influenza vaccination programs. The proportion of the
population vaccinated (i.e., the vaccination coverage) determines epidemic
severity. Above a critical vaccination coverage, epidemics are prevented; hence
individuals find it unnecessary to vaccinate. The adaptive dynamics of the
decisions directly affect influenza epidemiology and, conversely, influenza
epidemiology strongly influences decision-making. This feedback mechanism
creates a unique self-organized state where epidemics are prevented. This state
is attracting, but unstable; thus epidemics are rarely prevented. This result
implies that vaccination will have to be mandatory if the public health
objective is to prevent influenza epidemics. We investigated how collective
behavior changes when public health programs are implemented. Surprisingly,
programs requiring advance payment for several years of vaccination prevents
severe epidemics, even with voluntary vaccination. Prevention is determined by
the individuals' adaptability, memory, and number of pre-paid vaccinations.
Notably, vaccinating families exacerbates and increases the frequency of severe
epidemics.


title:
Population size effects in evolutionary dynamics on neutral networks and
  toy landscapes

abstract:
  We study the dynamics of a population subject to selective pressures,
evolving either on RNA neutral networks or in toy fitness landscapes. We
discuss the spread and the neutrality of the population in the steady state.
Different limits arise depending on whether selection or random drift are
dominant. In the presence of strong drift we show that observables depend
mainly on $M \mu$, $M$ being the population size and $\mu$ the mutation rate,
while corrections to this scaling go as 1/M: such corrections can be quite
large in the presence of selection if there are barriers in the fitness
landscape. Also we find that the convergence to the large $M \mu$ limit is
linear in $1/M \mu$. Finally we introduce a protocol that minimizes drift; then
observables scale like 1/M rather than $1/(M\mu)$, allowing one to determine
the large $M$ limit faster when $\mu$ is small; furthermore the genotypic
diversity increases from $O(\ln M)$ to $O(M)$.


title: The riddle of the plant vacuolar sorting receptors

abstract:
  Proteins synthesized on membrane-bound ribosomes are sorted at the Golgi
apparatus level for delivery to various cellular destinations: the plasma
membrane or the extracellular space, and the lytic vacuole or lysosome. Sorting
involves the assembly of vesicles, which preferentially package soluble
proteins with a common destination. The selection of proteins for a particular
vesicle type involves the recognition of proteins by specific receptors, such
as the vacuolar sorting receptors for vacuolar targeting. Most eukaryotic
organisms have one or two receptors to target proteins to the lytic vacuole.
Surprisingly, plants have several members of the same family, seven in
Arabidopsis thaliana. Why do plants have so many proteins to sort soluble
proteins to their respective destinations? The presence of at least two types
of vacuoles, lytic and storage, seems to be a partial answer. In this review we
analyze the last experimental evidence supporting the presence of different
subfamilies of plant vacuolar sorting receptors.


title:
The di-flavoenzyme reductase directly activates oxygen for the
  metabolism of diverse drug molecules by liver microsomal Cytochrome P450s

abstract:
  This submission has been withdrawn by arXiv administration due to complaints
of misuse of institutional affiliation.


title: Brain, Music and non-Poisson Renewal Processes

abstract:
  In this paper we show that both music composition and brain function, as
revealed by the Electroencephalogram (EEG) analysis, are renewal non-Poisson
processes living in the non-ergodic dominion. To reach this important
conclusion we process the data with the minimum spanning tree method, so as to
detect significant events, thereby building a sequence of times, which is the
time series to analyze. Then we show that in both cases, EEG and music
composition, these significant events are the signature of a non-Poisson
renewal process. This conclusion is reached using a techniques of statistical
analysis recently developed by our group, the Aging Experiment (AE). First, we
find that in both cases the distances between two consecutive events are
described by non-exponential histograms, thereby proving the non-Poisson nature
of these processes. The corresponding survival probabilities $\Psi(t)$ are well
fitted by stretched exponentials ($\Psi(t) \propto exp(-(\gamma t)^\alpha$),
with $0.5 <\alpha <1$.) The second step rests on the adoption of the AE, which
shows that these are renewal processes. We show that the renewal stretched
exponential is the emerging tip of an iceberg, whose underwater part has slow
tails with an inverse power law structure with power index $\mu = 1 + \alpha$.
We find that both EEG and music composition yield $\mu < 2$. On the basis of
the recently discovered complexity matching effect, according to which a
complex system $S$ with $\mu_S < 2$ responds only to a complex driving signal
$P$ with $\mu_P = \mu_S$, we conclude that the results of our analysis may
explain the influence of music on human brain.


title: The minimal molecular surface

abstract:
  We introduce a novel concept, the minimal molecular surface (MMS), as a new
paradigm for the theoretical modeling of biomolecule-solvent interfaces. When a
less polar macromolecule is immersed in a polar environment, the surface free
energy minimization occurs naturally to stabilizes the system, and leads to an
MMS separating the macromolecule from the solvent. For a given set of atomic
constraints (as obstacles), the MMS is defined as one whose mean curvature
vanishes away from the obstacles. An iterative procedure is proposed to compute
the MMS. Extensive examples are given to validate the proposed algorithm and
illustrate the new concept. We show that the MMS provides an indication to
DNA-binding specificity. The proposed algorithm represents a major step forward
in minimal surface generation.


title:
Asterias: a parallelized web-based suite for the analysis of expression
  and aCGH data

abstract:
  Asterias (\url{http://www.asterias.info}) is an integrated collection of
freely-accessible web tools for the analysis of gene expression and aCGH data.
Most of the tools use parallel computing (via MPI). Most of our applications
allow the user to obtain additional information for user-selected genes by
using clickable links in tables and/or figures. Our tools include:
normalization of expression and aCGH data; converting between different types
of gene/clone and protein identifiers; filtering and imputation; finding
differentially expressed genes related to patient class and survival data;
searching for models of class prediction; using random forests to search for
minimal models for class prediction or for large subsets of genes with
predictive capacity; searching for molecular signatures and predictive genes
with survival data; detecting regions of genomic DNA gain or loss. The
capability to send results between different applications, access to additional
functional information, and parallelized computation make our suite unique and
exploit features only available to web-based applications.


title: Metric learning pairwise kernel for graph inference

abstract:
  Much recent work in bioinformatics has focused on the inference of various
types of biological networks, representing gene regulation, metabolic
processes, protein-protein interactions, etc. A common setting involves
inferring network edges in a supervised fashion from a set of high-confidence
edges, possibly characterized by multiple, heterogeneous data sets (protein
sequence, gene expression, etc.). Here, we distinguish between two modes of
inference in this setting: direct inference based upon similarities between
nodes joined by an edge, and indirect inference based upon similarities between
one pair of nodes and another pair of nodes. We propose a supervised approach
for the direct case by translating it into a distance metric learning problem.
A relaxation of the resulting convex optimization problem leads to the support
vector machine (SVM) algorithm with a particular kernel for pairs, which we
call the metric learning pairwise kernel (MLPK). We demonstrate, using several
real biological networks, that this direct approach often improves upon the
state-of-the-art SVM for indirect inference with the tensor product pairwise
kernel.