title: Kernel methods in genomics and computational biology

abstract:
  Support vector machines and kernel methods are increasingly popular in
genomics and computational biology, due to their good performance in real-world
applications and strong modularity that makes them suitable to a wide range of
problems, from the classification of tumors to the automatic annotation of
proteins. Their ability to work in high dimension, to process non-vectorial
data, and the natural framework they provide to integrate heterogeneous data
are particularly relevant to various problems arising in computational biology.
In this chapter we survey some of the most prominent applications published so
far, highlighting the particular developments in kernel methods triggered by
problems in biology, and mention a few promising research directions likely to
expand in the future.


title:
Tropical rainforest bird community structure in relation to altitude,
  tree species composition, and null models in the Western Ghats, India

abstract:
  Studies of species distributions on elevational gradients are essential to
understand principles of community organisation as well as to conserve species
in montane regions. This study examined the patterns of species richness,
abundance, composition, range sizes, and distribution of rainforest birds at 14
sites along an elevational gradient (500-1400 m) in the Kalakad-Mundanthurai
Tiger Reserve (KMTR) of the Western Ghats, India. In contrast to theoretical
expectation, resident bird species richness did not change significantly with
elevation although the species composition changed substantially (<10%
similarity) between the lowest and highest elevation sites. Constancy in
species richness was possibly due to relative constancy in productivity and
lack of elevational trends in vegetation structure. Elevational range size of
birds, expected to increase with elevation according to Rapoport's rule, was
found to show a contrasting inverse U-shaped pattern because species with
narrow elevational distributions, including endemics, occurred at both ends of
the gradient (below 800 m and above 1,200 m). Bird species composition also did
not vary randomly along the gradient as assessed using a hierarchy of null
models of community assembly, from completely unconstrained models to ones with
species richness and range-size distribution restrictions. Instead, bird
community composition was significantly correlated with elevation and tree
species composition of sites, indicating the influence of deterministic factors
on bird community structure. Conservation of low- and high-elevation areas and
maintenance of tree species composition against habitat alteration are
important for bird conservation in the southern Western Ghats rainforests.


title: Bending a slab of neural tissue

abstract:
  In comparative and developmental neuroanatomy one encounters questions
regarding the deformation of neural tissue under stress. The motivation of this
note is an observation (Barbas {\it et al}) that at cortical folds or gyri, the
layers of neural tissue show relative thickening or thinning of upper or deep
layers. In general, the material properties of a slab of neural tissue are not
known, and even if known, would probably lead to a difficult problem in
elasticity theory. Here a simple argument is presented to show that bending an
elastic slab should produce a relative thickening of the layers on the inside
of the bend. The argument is based on the incompressibility of the material and
should therefore be fairly robust.


title:
Using the tangle: a consistent construction of phylogenetic distance
  matrices for quartets

abstract:
  Distance based algorithms are a common technique in the construction of
phylogenetic trees from taxonomic sequence data. The first step in the
implementation of these algorithms is the calculation of a pairwise distance
matrix to give a measure of the evolutionary change between any pair of the
extant taxa. A standard technique is to use the log det formula to construct
pairwise distances from aligned sequence data. We review a distance measure
valid for the most general models, and show how the log det formula can be used
as an estimator thereof. We then show that the foundation upon which the log
det formula is constructed can be generalized to produce a previously unknown
estimator which improves the consistency of the distance matrices constructed
from the log det formula. This distance estimator provides a consistent
technique for constructing quartets from phylogenetic sequence data under the
assumption of the most general Markov model of sequence evolution.


title:
When Response Variability Increases Neural Network Robustness to
  Synaptic Noise

abstract:
  Cortical sensory neurons are known to be highly variable, in the sense that
responses evoked by identical stimuli often change dramatically from trial to
trial. The origin of this variability is uncertain, but it is usually
interpreted as detrimental noise that reduces the computational accuracy of
neural circuits. Here we investigate the possibility that such response
variability might, in fact, be beneficial, because it may partially compensate
for a decrease in accuracy due to stochastic changes in the synaptic strengths
of a network. We study the interplay between two kinds of noise, response (or
neuronal) noise and synaptic noise, by analyzing their joint influence on the
accuracy of neural networks trained to perform various tasks. We find an
interesting, generic interaction: when fluctuations in the synaptic connections
are proportional to their strengths (multiplicative noise), a certain amount of
response noise in the input neurons can significantly improve network
performance, compared to the same network without response noise. Performance
is enhanced because response noise and multiplicative synaptic noise are in
some ways equivalent. These results are demonstrated analytically for the most
basic network consisting of two input neurons and one output neuron performing
a simple classification task, but computer simulations show that the phenomenon
persists in a wide range of architectures, including recurrent (attractor)
networks and sensory-motor networks that perform coordinate transformations.
The results suggest that response variability could play an important dynamic
role in networks that continuously learn.


title: Context-dependent selection of visuomotor maps

abstract:
  Behavior results from the integration of ongoing sensory signals and
contextual information in various forms, such as past experience, expectations,
current goals, etc. Thus, the response to a specific stimulus, say the ringing
of a doorbell, varies depending on whether you are at home or in someone else's
house. What is the neural basis of this flexibility? What mechanism is capable
of selecting, in a context-dependent way, an adequate response to a given
stimulus? One possibility is based on a nonlinear neural representation in
which context information regulates the gain of stimulus-evoked responses. Here
I explore the properties of this mechanism. By means of three hypothetical
visuomotor tasks, I study a class of neural network models in which any one of
several possible stimulus-response maps or rules can be selected according to
context. The underlying mechanism based on gain modulation has three key
features: (1) modulating the sensory responses is equivalent to switching on or
off different subpopulations of neurons, (2) context does not need to be
represented continuously, although this is advantageous for generalization, and
(3) context-dependent selection is independent of the discriminability of the
stimuli. In all cases, the contextual cues can quickly turn on or off a
sensory-motor map, effectively changing the functional connectivity between
inputs and outputs in the networks. The model predicts that sensory responses
that are nonlinearly modulated by arbitrary context signals should be found in
behavioral situations that involve choosing or switching between multiple
sensory-motor maps.


title:
Are structural biases at protein termini a signature of vectorial
  folding?

abstract:
  Experimental investigations of the biosynthesis of a number of proteins have
pointed out that part of the native structure can be acquired already during
translation. We carried out a comprehensive statistical analysis of some
average structural properties of proteins that have been put forward as
possible signatures of this progressive buildup process. Contrary to a
widespread belief, it is found that there is no major propensity of the amino
acids to form contacts with residues that are closer to the N terminus.
Moreover, it is found that the C terminus is significantly more compact and
locally-organized than the N one. Also this bias, though, is unlikely to be
related to vectorial effects, since it correlates with subtle differences in
the primary sequence. These findings indicate that even if proteins aquire
their structure vectorially no signature of this seems to be detectable in
their average structural properties.


title:
Biological nonlocality and the mind-brain interaction problem: comments
  on a new empirical approach

abstract:
  Up to now, we have been faced with an age old fundamental dilemma posed by
the mind-brain interaction problem, i.e. how is it that the mind which is
subjective and immaterial, can interact with the brain which is objective and
material? Analysis of recent experiments appears to indicate that quantum
mechanics may have a role to play in the resolution of the mind-brain
interaction problem in the form of biological entanglement and nonlocality.
This analysis, when coupled with ongoing and proposed experiments, may help us
to simultaneously resolve related issues such as whether mental events can
initiate neural events, the transference of conscious subjective experience,
the measurement problem and the binding problem.


title:
The role of inhibitory feedback for information processing in
  thalamocortical circuits

abstract:
  The information transfer in the thalamus is blocked dynamically during sleep,
in conjunction with the occurence of spindle waves. As the theoretical
understanding of the mechanism remains incomplete, we analyze two modeling
approaches for a recent experiment by Le Masson {\sl et al}. on the
thalamocortical loop. In a first step, we use a conductance-based neuron model
to reproduce the experiment computationally. In a second step, we model the
same system by using an extended Hindmarsh-Rose model, and compare the results
with the conductance-based model. In the framework of both models, we
investigate the influence of inhibitory feedback on the information transfer in
a typical thalamocortical oscillator. We find that our extended Hindmarsh-Rose
neuron model, which is computationally less costly and thus siutable for
large-scale simulations, reproduces the experiment better than the
conductance-based model. Further, in agreement with the experiment of Le Masson
{\sl et al}., inhibitory feedback leads to stable self-sustained oscillations
which mask the incoming input, and thereby reduce the information transfer
significantly.

categories: q-bio.NC cond-mat.dis-nn nlin.CD physics.bio-ph


title: A New Form-Factor Method for the Analysis of Tissue Fluorescence

abstract:
  The aim of this article is to present a developed method that decomposes the
autofluorescence spectrum into the spectra of naturally occurring biochemical
components of biotissue. It requires knowledge of detailed spectrum behaviour
of different endogenous fluorophores. We have studied the main bio-markers in
human tissue and proposed a simple modelling algorithm for their spectra
shapes. The empirical method was tested theoretically by quantum-mechanical
calculations of the spectra in the unharmonic Morse potential approach.


title: Impact of Tandem Repeats on the Scaling of Nucleotide Sequences

abstract:
  Techniques such as detrended fluctuation analysis (DFA) and its extensions
have been widely used to determine the nature of scaling in nucleotide
sequences. In this brief communication we show that tandem repeats which are
ubiquitous in nucleotide sequences can prevent reliable estimation of possible
long-range correlations. Therefore, it is important to investigate the presence
of tandem repeats prior to scaling exponent estimation.


title:
How do proteins search for their specific sites on coiled or globular
  DNA

abstract:
  It is known since the early days of molecular biology that proteins locate
their specific targets on DNA up to two orders of magnitude faster than the
Smoluchowski 3D diffusion rate. It was the idea due to Delbruck that they are
non-specifically adsorbed on DNA, and sliding along DNA provides for the faster
1D search. Surprisingly, the role of DNA conformation was never considered in
this context. In this article, we explicitly address the relative role of 3D
diffusion and 1D sliding along coiled or globular DNA and the possibility of
correlated re-adsorbtion of desorbed proteins. We have identified a wealth of
new different scaling regimes. We also found the maximal possible acceleration
of the reaction due to sliding, we found that the maximum on the
rate-versus-ionic strength curve is asymmetric, and that sliding can lead not
only to acceleration, but in some regimes to dramatic deceleration of the
reaction.


title: Long-range dependencies in heart rate signals- revisited

abstract:
  The RR series extracted from human electrocardiogram signal (ECG) is
considered as a fractal stochastic process. The manifestation of long-range
dependencies is the presence of power laws in scale dependent process
characteristics. Exponents of these laws: $\beta$ - describing power spectrum
decay, $\alpha$ - responsible for decay of detrended fluctuations or $H$
related to, so-called, roughness of a signal, are known to differentiate hearts
of healthy people from hearts with congestive heart failure. There is a strong
expectation that resolution spectrum of exponents, so-called, local exponents
in place of global exponents allows to study differences between hearts in
details. The arguments are given that local exponents obtained in multifractal
analysis by the two methods: wavelet transform modulus maxima (WTMM) and
multifractal detrended fluctuation analysis (MDFA), allow to recognize the
following four stages of the heart: healthy and young, healthy and advance in
years, subjects with left ventricle systolic dysfunction (NYHA I--III class)
and characterized by severe congestive heart failure (NYHA III-IV class).


title: Dominant Pathways in Protein Folding

abstract:
  We present a method to investigate the kinetics of protein folding on a long
time-scale and the dynamics underlying the formation of secondary and tertiary
structures during the entire reaction. The approach is based on the formal
analogy between thermal and quantum diffusion: by writing the solution of the
Fokker-Planck equation for the time-evolution of a protein in a viscous
heat-bath in terms of a path integral, we derive a Hamilton-Jacobi variational
principle from which we are able to compute the most probable pathway of
folding. The method is applied to the folding of the Villin Headpiece
Subdomain, in the framework of a Go-model. We have found that, in this model,
the transition occurs through an initial collapsing phase driven by the
starting coil configuration and a later rearrangement phase, in which secondary
structures are formed and all computed paths display strong similarities. This
method is completely general, does not require the prior knowledge of any
reaction coordinate and represents an efficient tool to perfom ab-initio
simulations of the entire folding process with available computers.


title:
Speed of synchronization in complex networks of neural oscillators
  Analytic results based on Random Matrix Theory

abstract:
  We analyze the dynamics of networks of spiking neural oscillators. First, we
present an exact linear stability theory of the synchronous state for networks
of arbitrary connectivity. For general neuron rise functions, stability is
determined by multiple operators, for which standard analysis is not suitable.
We describe a general non-standard solution to the multi-operator problem.
Subsequently, we derive a class of rise functions for which all stability
operators become degenerate and standard eigenvalue analysis becomes a suitable
tool. Interestingly, this class is found to consist of networks of leaky
integrate and fire neurons. For random networks of inhibitory
integrate-and-fire neurons, we then develop an analytical approach, based on
the theory of random matrices, to precisely determine the eigenvalue
distribution. This yields the asymptotic relaxation time for perturbations to
the synchronous state which provides the characteristic time scale on which
neurons can coordinate their activity in such networks. For networks with
finite in-degree, i.e. finite number of presynaptic inputs per neuron, we find
a speed limit to coordinating spiking activity: Even with arbitrarily strong
interaction strengths neurons cannot synchronize faster than at a certain
maximal speed determined by the typical in-degree.


title: An Algorithm for Missing Value Estimation for DNA Microarray Data

abstract:
  Gene expression data matrices often contain missing expression values. In
this paper, we describe a new algorithm, named improved fixed rank
approximation algorithm (IFRAA), for missing values estimations of the large
gene expression data matrices. We compare the present algorithm with the two
existing and widely used methods for reconstructing missing entries for DNA
microarray gene expression data: the Bayesian principal component analysis
(BPCA) and the local least squares imputation method (LLS). The three
algorithms were applied to four microarray data sets and two synthetic low-rank
data matrices. Certain percentages of the elements of these data sets were
randomly deleted, and the three algorithms were used to recover them. In
conclusion IFRAA appears to be the most reliable and accurate approach for
recovering missing DNA microarray gene expression data, or any other noisy data
matrices that are effectively low rank.


title:
Solvent and mutation effects on the nucleation of amyloid
  $\beta$-protein folding

abstract:
  Experimental evidence suggests that the folding and aggregation of the
amyloid $\beta$-protein (A$\beta$) into oligomers is a key pathogenetic event
in Alzheimer's disease (AD). Inhibiting the pathologic folding and
oligomerization of A$\beta$ could be effective in the prevention and treatment
of AD. Here, using all-atom molecular dynamics simulations in explicit solvent,
we probe the initial stages of folding of a decapeptide segment of A$\beta$,
A$\beta_{21-30}$, shown experimentally to nucleate the folding process. In
addition, we examine the folding of a homologous decapeptide containing an
amino acid substitution linked to hereditary cerebral hemorrhage with
amyloidosis--Dutch type, [Gln22]A$\beta_{21-30}$. We find that: (i) when the
decapeptide is in water, hydrophobic interactions and transient salt bridges
between Lys28 and either Glu22 or Asp23 are important in the formation of a
loop in the Val24--Lys28 region of the wild type decapeptide; (ii) in the
presence of salt ions, salt bridges play a more prominent role in the
stabilization of the loop; (iii) in water with a reduced density, the
decapeptide forms a helix, indicating the sensitivity of folding to different
aqueous environments; (iv) the ``Dutch'' peptide in water, in contrast to the
wild type peptide, fails to form a long-lived Val24--Lys28 loop, suggesting
that loop stability is a critical factor in determining whether A$\beta$ folds
into pathologic structures. Our results are relevant to understand the
mechanism of A$\beta$ peptide folding in different environments, such as intra-
and extracellular milieus or cell membranes, and how amino acid substitutions
linked to familial forms of amyloidosis cause disease.


title: Stretching short biopolymers by fields and forces

abstract:
  We study the mechanical properties of semiflexible polymers when the contour
length of the polymer is comparable to its persistence length. We compute the
exact average end-to-end distance and shape of the polymer for different
boundary conditions, and show that boundary effects can lead to significant
deviations from the well-known long-polymer results. We also consider the case
of stretching a uniformly charged biopolymer by an electric field, for which we
compute the average extension and the average shape, which is shown to be
trumpetlike. Our results also apply to long biopolymers when thermal
fluctuations have been smoothed out by a large applied field or force.


title:
Continuum Description of the Cytoskeleton: Ring Formation in the Cell
  Cortex

abstract:
  Motivated by the formation of ring-like filament structures in the cortex of
plant and animal cells, we study the dynamics of a two-dimensional layer of
cytoskeletal filaments and motor proteins near a surface by a general continuum
theory. As a result of active processes, dynamic patterns of filament
orientation and density emerge via instabilities. We show that
self-organization phenomena can lead to the formation of stationary and
oscillating rings. We present state diagrams which reveal a rich scenario of
asymptotic behaviors and discuss the role of boundary conditions.


title: Stable Concurrent Synchronization in Dynamic System Networks

abstract:
  In a network of dynamical systems, concurrent synchronization is a regime
where multiple groups of fully synchronized elements coexist. In the brain,
concurrent synchronization may occur at several scales, with multiple
``rhythms'' interacting and functional assemblies combining neural oscillators
of many different types. Mathematically, stable concurrent synchronization
corresponds to convergence to a flow-invariant linear subspace of the global
state space. We derive a general condition for such convergence to occur
globally and exponentially. We also show that, under mild conditions, global
convergence to a concurrently synchronized regime is preserved under basic
system combinations such as negative feedback or hierarchies, so that stable
concurrently synchronized aggregates of arbitrary size can be constructed.
Robustnesss of stable concurrent synchronization to variations in individual
dynamics is also quantified. Simple applications of these results to classical
questions in systems neuroscience and robotics are discussed.


title: Alignment Metric Accuracy

abstract:
  We propose a metric for the space of multiple sequence alignments that can be
used to compare two alignments to each other. In the case where one of the
alignments is a reference alignment, the resulting accuracy measure improves
upon previous approaches, and provides a balanced assessment of the fidelity of
both matches and gaps. Furthermore, in the case where a reference alignment is
not available, we provide empirical evidence that the distance from an
alignment produced by one program to predicted alignments from other programs
can be used as a control for multiple alignment experiments. In particular, we
show that low accuracy alignments can be effectively identified and discarded.
We also show that in the case of pairwise sequence alignment, it is possible to
find an alignment that maximizes the expected value of our accuracy measure.
Unlike previous approaches based on expected accuracy alignment that tend to
maximize sensitivity at the expense of specificity, our method is able to
identify unalignable sequence, thereby increasing overall accuracy. In
addition, the algorithm allows for control of the sensitivity/specificity
tradeoff via the adjustment of a single parameter. These results are confirmed
with simulation studies that show that unalignable regions can be distinguished
from homologous, conserved sequences. Finally, we propose an extension of the
pairwise alignment method to multiple alignment. Our method, which we call
AMAP, outperforms existing protein sequence multiple alignment programs on
benchmark datasets. A webserver and software downloads are available at
http://bio.math.berkeley.edu/amap/ .


title: tRNA-isoleucine-tryptophan Composite Gene

abstract:
  Transfer-RNA genes in archaea often have introns intervening between exon
sequences. The structural motif at the boundary between exon and intron is the
bulge-helix-bulge. Computational investigations of these boundary structures in
H. marismortui lead us to propose that tRNA-isoleucine and tRNA-tryptophan
genes are co-located. Precise insilico identification of the splice-sites on
the bulges at the exon-intron boundaries conduce us to infer that a single
intron-containing composite tRNA-gene can give rise to more than one gene
produc.


title: Scale- free networks in cell biology

abstract:
  A cell's behavior is a consequence of the complex interactions between its
numerous constituents, such as DNA, RNA, proteins and small molecules. Cells
use signaling pathways and regulatory mechanisms to coordinate multiple
processes, allowing them to respond to and adapt to an ever-changing
environment. The large number of components, the degree of interconnectivity
and the complex control of cellular networks are becoming evident in the
integrated genomic and proteomic analyses that are emerging. It is increasingly
recognized that the understanding of properties that arise from whole-cell
function require integrated, theoretical descriptions of the relationships
between different cellular components. Recent theoretical advances allow us to
describe cellular network structure with graph concepts, and have revealed
organizational features shared with numerous non-biological networks. How do we
quantitatively describe a network of hundreds or thousands of interacting
components? Does the observed topology of cellular networks give us clues about
their evolution? How does cellular networks' organization influence their
function and dynamical responses? This article will review the recent advances
in addressing these questions.


title: Signal Propagation in Nonlinear Stochastic Gene Regulatory Networks

abstract:
  The structure of a stochastic nonlinear gene regulatory network is uncovered
by studying its response to input signal generators. Four applications are
studied in detail: a nonlinear connection of two linear systems, the design of
a logic pulse, a molecular amplifier and the interference of three signal
generators in E2F1 regulatory element. The gene interactions are presented
using molecular diagrams that have a precise mathematical structure and retain
the biological meaning of the processes.


title: Impact of Climate Change on Forests in India

abstract:
  Global assessments have shown that future climate change is likely to
significantly impact forest ecosystems. The present study makes an assessment
of the impact of projected climate change on forest ecosystems in India. This
assessment is based on climate projections of Regional Climate Model of the
Hadley Centre (HadRM3) using the A2 (740 ppm CO2) and B2 (575 ppm CO2)
scenarios of Special Report on Emissions Scenarios and the BIOME4 vegetation
response model. The main conclusion is that under the climate projection for
the year 2085, 77% and 68% of the forested grids in India are likely to
experience shift in forest types under A2 and B2 scenario, respectively.
Indications are a shift towards wetter forest types in the northeastern region
and drier forest types in the northwestern region in the absence of human
influence. Increasing atmospheric CO2 concentration and climate warming could
also result in a doubling of net primary productivity under the A2 scenario and
nearly 70% increase under the B2 scenario. The trends of impacts could be
considered as robust but the magnitudes should be viewed with caution, due to
the uncertainty in climate projections. Given the projected trends of likely
impacts of climate change on forest ecosystems, it is important to incorporate
climate change consideration in forest sector long-term planning process.


title: Species lifetime distribution for simple models of ecologies

abstract:
  Interpretation of empirical results based on a taxa's lifetime distribution
shows apparently conflicting results. Species' lifetime is reported to be
exponentially distributed, whereas higher order taxa, such as families or
genera, follow a broader distribution, compatible with power law decay. We show
that both these evidences are consistent with a simple evolutionary model that
does not require specific assumptions on species interaction. The model
provides a zero-order description of the dynamics of ecological communities and
its species lifetime distribution can be computed exactly. Different behaviors
are found: an initial $t^{-3/2}$ power law, emerging from a random walk type of
dynamics, which crosses over to a steeper $t^{-2}$ branching process-like
regime and finally is cutoff by an exponential decay which becomes weaker and
weaker as the total population increases. Sampling effects can also be taken
into account and shown to be relevant: if species in the fossil record were
sampled according to the Fisher log-series distribution, lifetime should be
distributed according to a $t^{-1}$ power law. Such variability of behaviors in
a simple model, combined with the scarcity of data available, cast serious
doubts on the possibility to validate theories of evolution on the basis of
species lifetime data.


title:
Novel gene cluster analysis method identifies horizontally transferred
  genes with high reliability and reveals their involvement in operon formation

abstract: This paper was withdrawn by the authors.


title:
Definition of Systematic, Approximately Separable and Modular Internal
  Coordinates (SASMIC) for macromolecular simulation

abstract:
  A set of rules is defined to systematically number the groups and the atoms
of organic molecules and, particularly, of polypeptides in a modular manner.
Supported by this numeration, a set of internal coordinates is defined. These
coordinates (termed Systematic, Approximately Separable and Modular Internal
Coordinates, SASMIC) are straightforwardly written in Z-matrix form and may be
directly implemented in typical Quantum Chemistry packages. A number of Perl
scripts that automatically generate the Z-matrix files for polypeptides are
provided as supplementary material. The main difference with other
Z-matrix-like coordinates normally used in the literature is that normal
dihedral angles (``principal dihedrals'' in this work) are only used to fix the
orientation of whole groups and a somewhat non-standard type of dihedrals,
termed ``phase dihedrals'', are used to describe the covalent structure inside
the groups. This physical approach allows to approximately separate soft and
hard movements of the molecule using only topological information and to
directly implement constraints. As an application, we use the coordinates
defined and ab initio quantum mechanical calculations to assess the commonly
assumed approximation of the free energy, obtained from ``integrating out'' the
side chain degree of freedom chi, by the Potential Energy Surface (PES) in the
protected dipeptide HCO-L-Ala-NH2. We also present a sub-box of the Hessian
matrix in two different sets of coordinates to illustrate the approximate
separation of soft and hard movements when the coordinates defined in this work
are used.


title:
Metabolic Futile Cycles and Their Functions: A Systems Analysis of
  Energy and Control

abstract:
  It has long been hypothesized that futile cycles in cellular metabolism are
involved in the regulation of biochemical pathways. Following the work of
Newsholme and Crabtree, we develop a quantitative theory for this idea based on
open-system thermodynamics and metabolic control analysis. It is shown that the
{\it stoichiometric sensitivity} of an intermediary metabolite concentration
with respect to changes in steady-state flux is governed by the effective
equilibrium constant of the intermediate formation, and the equilibrium can be
regulated by a futile cycle. The direction of the shift in the effective
equilibrium constant depends on the direction of operation of the futile cycle.
High stoichiometric sensitivity corresponds to ultrasensitivity of an
intermediate concentration to net flow through a pathway; low stoichiometric
sensitivity corresponds to super-robustness of concentration with respect to
changes in flux. Both cases potentially play important roles in metabolic
regulation. Futile cycles actively shift the effective equilibrium by expending
energy; the magnitude of changes in effective equilibria and sensitivities is a
function of the amount of energy used by a futile cycle. This proposed
mechanism for control by futile cycles works remarkably similarly to kinetic
proofreading in biosynthesis. The sensitivity of the system is also intimately
related to the rate of concentration fluctuations of intermediate metabolites.
The possibly different roles of the two major mechanisms for cellular
biochemical regulation, namely reversible chemical modifications via futile
cycles and shifting equilibrium by macromolecular binding, are discussed.


title: Dynamic Pathways for Viral Capsid Assembly

abstract:
  We develop a class of models with which we simulate the assembly of particles
into T1 capsid-like objects using Newtonian dynamics. By simulating assembly
for many different values of system parameters, we vary the forces that drive
assembly. For some ranges of parameters, assembly is facile, while for others,
assembly is dynamically frustrated by kinetic traps corresponding to malformed
or incompletely formed capsids. Our simulations sample many independent
trajectories at various capsomer concentrations, allowing for statistically
meaningful conclusions. Depending on subunit (i.e., capsomer) geometries,
successful assembly proceeds by several mechanisms involving binding of
intermediates of various sizes. We discuss the relationship between these
mechanisms and experimental evaluations of capsid assembly processes.


title:
Evolution as context-driven actualization of potential: Toward an
  interdisciplinary theory of change of state

abstract:
  It is increasingly evident that there is more to biological evolution than
natural selection; moreover, the concept of evolution is not limited to
biology. We propose an integrative framework for characterizing how entities
evolve, in which evolution is viewed as a process of context-driven
actualization of potential (CAP). Processes of change differ according to the
degree of nondeterminism, and the degree to which they are sensitive to,
internalize, and depend upon a particular context. The approach enables us to
embed phenomena across disciplines into a broad conceptual framework. We give
examples of insights into physics, biology, culture and cognition that derive
from this unifying framework.


title:
Modelling and simulation of polycomb-dependent chromosomal interactions
  in drosophila

abstract:
  The conditions of the chromosomes inside the nucleus in the Rabl
configuration have been modelled as self-avoiding polymer chains under
restraining conditions. To ensure that the chromosomes remain stretched out and
lined up, we fixed their end points to two opposing walls. The numbers of
segments $N$, the distances $d_1$ and $d_2$ between the fixpoints, and the
wall-to-wall distance $z$ (as measured in segment lengths) determine an
approximate value for the Kuhn segment length $k_l$. We have simulated the
movement of the chromosomes using molecular dynamics to obtain the expected
distance distribution between the genetic loci in the absence of further
attractive or repulsive forces. A comparison to biological experiments on
\textit{Drosophila Melanogaster} yields information on the parameters for our
model. With the correct parameters it is possible to draw conclusions on the
strength and range of the attraction that leads to pairing.


title:
The identifiability of tree topology for phylogenetic models, including
  covarion and mixture models

abstract:
  For a model of molecular evolution to be useful for phylogenetic inference,
the topology of evolutionary trees must be identifiable. That is, from a joint
distribution the model predicts, it must be possible to recover the tree
parameter. We establish tree identifiability for a number of phylogenetic
models, including a covarion model and a variety of mixture models with a
limited number of classes. The proof is based on the introduction of a more
general model, allowing more states at internal nodes of the tree than at
leaves, and the study of the algebraic variety formed by the joint
distributions to which it gives rise. Tree identifiability is first established
for this general model through the use of certain phylogenetic invariants.


title: Monte Carlo simulations of the inside-intron recombination

abstract:
  Biological genomes are divided into coding and non-coding regions. Introns
are non-coding parts within genes, while the remaining non-coding parts are
intergenic sequences. To study the evolutionary significance of recombination
inside introns we have used two models based on the Monte Carlo method. In our
computer simulations we have implemented the internal structure of genes by
declaring the probability of recombination between exons. One situation when
inside-intron recombination is advantageous is recovering functional genes by
combining proper exons dispersed in the genetic pool of the population after a
long period without selection for the function of the gene. Populations have to
pass through the bottleneck, then. These events are rather rare and we have
expected that there should be other phenomena giving profits from the
inside-intron recombination. In fact we have found that inside-intron
recombination is advantageous only in the case when after recombination,
besides the recombinant forms, parental haplotypes are available and selection
is set already on gametes.


title:
Finding the center reliably: robust patterns of developmental gene
  expression

abstract:
  We investigate a mechanism for the robust identification of the center of a
developing biological system. We assume the existence of two morphogen
gradients, an activator emanating from the anterior, and a co-repressor from
the posterior. The co-repressor inhibits the action of the activator in
switching on target genes. We apply this system to Drosophila embryos, where we
predict the existence of a hitherto undetected posterior co-repressor. Using
mathematical modelling, we show that a symmetric activator-co-repressor model
can quantitatively explain the precise mid-embryo expression boundary of the
hunchback gene, and the scaling of this pattern with embryo size.

categories: q-bio.TO cond-mat.other cond-mat.stat-mech q-bio.OT


title: Temporal correlation based learning in neuron models

abstract:
  We study a learning rule based upon the temporal correlation (weighted by a
learning kernel) between incoming spikes and the internal state of the
postsynaptic neuron, building upon previous studies of spike timing dependent
synaptic plasticity (\cite{KGvHW,KGvH1,vH}). Our learning rule for the synaptic
weight $w_{ij}$ is $$ \dot w_{ij}(t)= \epsilon \int_{-\infty}^\infty
\frac{1}{T_l} \int_{t-T_l}^t \sum_\mu \delta(\tau+s-t_{j,\mu}) u(\tau) d\tau\
\Gamma(s)ds $$ where the $t_{j,\mu}$ are the arrival times of spikes from the
presynaptic neuron $j$ and the function $u(t)$ describes the state of the
postsynaptic neuron $i$. Thus, the spike-triggered average contained in the
inner integral is weighted by a kernel $\Gamma(s)$, the learning window,
positive for negative, negative for positive values of the time diffence $s$
between post- and presynaptic activity. An antisymmetry assumption for the
learning window enables us to derive analytical expressions for a general class
of neuron models and to study the changes in input-output relationships
following from synaptic weight changes. This is a genuinely non-linear effect
(\cite{SMA}).


title:
Incorporating expression data in metabolic modeling: a case study of
  lactate dehydrogenase

abstract:
  Integrating biological information from different sources to understand
cellular processes is an important problem in systems biology. We use data from
mRNA expression arrays and chemical kinetics to formulate a metabolic model
relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters
the expression of metabolic enzymes in K562 cells. Our array data show changes
in expression of lactate dehydrogenase (LDH) isoforms after treatment with
phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We
model the change in lactate production which occurs when the MAP kinase pathway
is activated, using a non-equilibrium, chemical-kinetic model of homolactic
fermentation. In particular, we examine the role of LDH isoforms, which
catalyze the conversion of pyruvate to lactate. Changes in the isoform ratio
are not the primary determinant of the production of lactate. Rather, the total
concentration of LDH controls the lactate concentration.


title:
Conservation of statistical results under the reduction of pair-contact
  interactions to solvation interactions

abstract:
  We show that the hydrophobicity of sequences is the leading term in
Miyazawa-Jernigan interactions. Being the source of additive (solvation) terms
in pair-contact interactions, they were used to reduce the energy parameters
while resulting in a clear vector manipulation of energy. The reduced
(additive) potential performs considerably successful in predicting the
statistical properties of arbitrary structures. The evaluated designabilities
of the structures by both models are highly correlated. Suggesting
geometrically non-degenerate vectors (structures) as protein-like structures,
the additive model is a powerful tool for protein design. Moreover, a crossing
point in the log-linear diagram of designability-ranking shows that about 1/e
of the structures have designabilities above the average, independent on the
used model.


title:
Genetically engineered cardiac pacemaker: stem cells transfected with
  HCN2 gene and myocytes - a model

abstract:
  Artificial biological pacemakers were developed and tested in canine
ventricles. Next steps will require obtaining oscillations sensitive to
external regulations, and robust with respect to long term drifts of expression
levels of pacemaker currents and gap junctions. We introduce mathematical
models intended to be used in parallel with the experiments.
  The models describe human mesenchymal stem cells ({\it hMSC}) transfected
with HCN2 genes and connected to myocytes. They are intended to mimic
experiments with oscillation induction in a cell pair, in cell culture and in
the cardiac tissue. We give examples of oscillations in a cell pair, in a 1 dim
cell culture, and oscillation dependence on number of pacemaker channels per
cell and number of gap junctions. The models permit to mimic experiments with
levels of gene expressions not achieved yet, and to predict if the work to
achieve this levels will significantly increase the quality of oscillations.
This give arguments for selecting the directions of the experimental work.


title:
Expression profiles of acute lymphoblastic and myeloblastic leukemias
  with ALL-1 rearrangements

abstract:
  The ALL-1 gene is directly involved in 5-10% of ALLs and AMLs by fusion to
other genes or through internal rearrangements. DNA microarrays were utilized
to determine expression profiles of ALLs and AMLs with ALL-1 rearrangements.
These profiles distinguish those tumors from other ALLs and AMLs. The
expression patterns of ALL-1-associated tumors, in particular ALLs, involve
oncogenes, tumor suppressors, anti apoptotic genes, drug resistance genes etc.,
and correlate with the aggressive nature of the tumors. The genes whose
expression differentiates between ALLs with and without ALL-1 rearrangement
were further divided into several groups enabling separation of ALL-1-
associated ALLs into two subclasses. Further, AMLs with partial duplication of
ALL-1 vary in their expression pattern from AMLs in which ALL-1 had undergone
fusion to other genes. The extensive analysis described here draws attention to
genes which might have a direct role in pathogenesis.


title:
Gene expression analysis reveals a strong signature of an interferon
  induced pathway in childhood lymphoblastic leukemia as well as in breast and
  ovarian cancer

abstract:
  On the basis of epidemiological studies, infection was suggested to play a
role in the etiology of human cancer. While for some cancers such a role was
indeed demonstrated, there is no direct biological support for the role of
viral pathogens in the pathogenesis of childhood leukemia. Using a novel
bioinformatic tool, that alternates between clustering and standard statistical
methods of analysis, we performed a "double blind" search of published gene
expression data of subjects with different childhood ALL subtypes, looking for
unanticipated partitions of patients, induced by unexpected groups of genes
with correlated expression. We discovered a group of about thirty genes,
related to the interferon response pathway, whose expression levels divide the
ALL samples into two subgroups; high in 50, low in 285 patients. Leukemic
subclasses prevalent in early childhood (the age most susceptible to infection)
are over-represented in the high expression subgroup. Similar partitions,
induced by the same genes, were found also in breast and ovarian cancer but not
in lung cancer, prostate cancer and lymphoma. About 40% of breast cancer
samples expressed the "interferon- related" signature. It is of interested that
several studies demonstrated MMTV-like sequences in about 40% of breast cancer
samples. Our discovery of an unanticipated strong signature of an interferon
induced pathway provides molecular support for a role for either inflammation
or viral infection in the pathogenesis of childhood leukemia as well as breast
and ovarian cancer.


title:
Another Way to Calculate Fitness from Life History Variables: Solution
  of the Age-Structured Logistic Equation

abstract:
  r-selection refers to evolutionary competition in the rate of a population's
exponential increase. This is contrasted with K-selection, in which populations
in steady-state compete in efficiency of resource conversion. Evolution in
nature is thought to combine these two in various proportions. But in modeling
the evolution of life histories, theorists have used r-selection exclusively;
up until now, there has not been a practical algorithm for computing the target
function of K-selection. The Malthusian parameter, as computed from the
Euler-Lotka equation, is a quantitative rendering of the r in r-selection,
computed from the fundamental life history variables mortality and fertility.
Herein, a quantitative formulation of K is derived in similar terms. The basis
for our model is the logistic equation which, we argue, applies more generally
than is commonly appreciated. Support is offered for the utility of this
paradigm, and one example computation is exhibited, in which K-selection
appears to support pleiotropic explanations for senescence only one fourth as
well as r-selection.


title:
Identification of a Proliferation Gene Cluster Associated with HPV E6/E7
  Expression Level and Viral DNA Load in Invasive Cervical Carcinoma

abstract:
  Specific HPV DNA sequences are associated with more than 90% of invasive
carcinomas of the uterine cervix. Viral E6 and E7 oncogenes are key mediators
in cell transformation by disrupting TP53 and RB pathways. To investigate
molecular mechanisms involved in the progression of invasive cervical
carcinoma, we performed a gene expression study on cases selected according to
viral and clinical parameters. Using Coupled Two-Way Clustering and Sorting
Points Into Neighbourhoods methods, we identified a Cervical Cancer
Proliferation Cluster composed of 163 highly correlated transcripts, many of
which corresponded to E2F pathway genes controlling cell proliferation, whereas
no primary TP53 targets were present in this cluster. The average expression
level of the genes of this cluster was higher in tumours with an early relapse
than in tumours with a favourable course (P=0.026). Moreover, we found that
E6/E7 mRNA expression level was positively correlated with the expression level
of the cluster genes and with viral DNA load. These findings suggest that HPV
E6/E7 expression level plays a key role in the progression of invasive
carcinoma of the uterine cervix via the deregulation of cellular genes
controlling tumour cell proliferation. HPV expression level may thus correspond
to a biological marker useful for prognosis assessment and specific therapy of
the disease.


title:
Induction in myeloid leukemic cells of genes that are expressed in
  different normal tissues

abstract:
  Using DNA microarray and cluster analysis of expressed genes in a cloned line
(M1-t-p53) of myeloid leukemic cells, we have analyzed the expression of genes
that are preferentially expressed in different normal tissues. Clustering of
547 highly expressed genes in these leukemic cells showed 38 genes
preferentially expressed in normal hematopoietic tissues and 122 other genes
preferentially expressed in different normal non-hematopoietic tissues
including neuronal tissues, muscle, liver and testis. We have also analyzed the
genes whose expression in the leukemic cells changed after activation of
wild-type p53 and treatment with the cytokine interleukin 6 (IL-6) or the
calcium mobilizer thapsigargin (TG). Out of 620 such genes in the leukemic
cells that were differentially expressed in normal tissues, clustering showed
80 genes that were preferentially expressed in hematopoietic tissues and 132
genes in different normal non-hematopietic tissues that also included neuronal
tissues, muscle, liver and testis. Activation of p53 and treatment with IL-6 or
TG induced different changes in the genes preferentially expressed in these
normal tissues. These myeloid leukemic cells thus express genes that are
expressed in normal non-hematopoietic tissues, and various treatments can
reprogram these cells to induce other such non-hematopoietic genes. The results
indicate that these leukemic cells share with normal hematopoietic stem cells
the plasticity of differentiation to different cell types. It is suggested that
this reprogramming to induce in malignant cells genes that are expressed in
different normal tissues may be of clinical value in therapy.


title:
Human cancers over express genes that are specific to a variety of
  normal human tissues

abstract:
  We have analyzed gene expression data from 3 different kinds of samples:
normal human tissues, human cancer cell lines and leukemic cells from lymphoid
and myeloid leukemia pediatric patients. We have searched for genes that are
over expressed in human cancer and also show specific patterns of
tissue-dependent expression in normal tissues. Using the expression data of the
normal tissues we identified 4346 genes with a high variability of expression,
and clustered these genes according to their relative expression level. Of 91
stable clusters obtained, 24 clusters included genes preferentially expressed
either only in hematopoietic tissues or in hematopoietic and 1-2 other tissues;
28 clusters included genes preferentially expressed in various
non-hematopoietic tissues such as neuronal, testis, liver, kidney, muscle,
lung, pancreas and placenta. Analysis of the expression levels of these 2
groups of genes in the human cancer cell lines and leukemias, identified genes
that were highly expressed in cancer cells but not in their normal
counterparts, and were thus over expressed in the cancers. The different cancer
cell lines and leukemias varied in the number and identity of these over
expressed genes. The results indicate that many genes that are over expressed
in human cancer cells are specific to a variety of normal tissues, including
normal tissues other than those from which the cancer originated. It is
suggested that this general property of cancer cells plays a major role in
determining the behavior of the cancers, including their metastatic potential.


title:
The promoters of human cell cycle genes integrate signals from two tumor
  suppressive pathways during cellular transformation

abstract:
  Deciphering regulatory events that drive malignant transformation represents
a major challenge for systems biology. Here we analyzed genome-wide
transcription profiling of an in-vitro transformation process. We focused on a
cluster of genes whose expression levels increased as a function of p53 and
p16INK4A tumor suppressors inactivation. This cluster predominantly consists of
cell cycle genes and constitutes a signature of a diversity of cancers. By
linking expression profiles of the genes in the cluster with the dynamic
behavior of p53 and p16INK4A, we identified a promoter architecture that
integrates signals from the two tumor suppressive channels and that maps their
activity onto distinct levels of expression of the cell cycle genes, which in
turn, correspond to different cellular proliferation rates. Taking components
of the mitotic spindle as an example, we experimentally verified our
predictions that p53-mediated transcriptional repression of several of these
novel targets is dependent on the activities of p21, NFY and E2F. Our study
demonstrates how a well-controlled transformation process allows linking
between gene expression, promoter architecture and activity of upstream
signaling molecules.


title: Mathematical modeling of the GnRH pulse and surge generator

abstract:
  We propose a mathematical model allowing for the alternating pulse and surge
pattern of GnRH (Gonadotropin Releasing Hormone) secretion. The model is based
on the coupling between two systems running on different time scales. The
faster system corresponds to the average activity of GnRH neurons, while the
slower one corresponds to the average activity of regulatory neurons. The
analysis of the slow/fast dynamics exhibited within and between both systems
allows to explain the different patterns (slow oscillations, fast oscillations
and periodical surge) of GnRH secretion. Specifications on the model parameter
values are derived from physiological knowledge in terms of amplitude,
frequency and plateau length of oscillations. The behavior of the model is
finally illustrated by numerical simulations reproducing natural ovarian cycles
and either direct or indirect actions of ovarian steroids on GnRH secretion.


title:
Theory of Hantavirus Infection Spread Incorporating Localized Adult and
  Itinerant Juvenile Mice

abstract:
  A generalized model of the spread of the Hantavirus in mice populations is
presented on the basis of recent observational findings concerning the movement
characteristics of the mice that carry the infection. The factual information
behind the generalization is based on mark-recapture observations reported in
Giuggioli et al. [Bull. Math. Biol. 67, 1135 (2005)] that have necessitated the
introduction of home ranges in the simple model of Hantavirus spread presented
by Abramson and Kenkre [Phys. Rev. E 66, 11912 (2002)]. The essential feature
of the model presented here is the existence of adult mice that remain largely
confined to locations near their home ranges, and itinerant juvenile mice that
are not so confined, and, during their search for their own homes, move and
infect both other juveniles and adults that they meet during their movement.
The model is presented at three levels of description: mean field, kinetic and
configuration. Results of calculations are shown explicitly from the mean field
equations and the simulation rules, and are found to agree in some respects and
to differ in others. The origin of the differences is shown to lie in spatial
correlations. It is indicated how mark-recapture observations in the field may
be employed to verify the applicability of the theory.


title:
Diffusion and Home Range Parameters from Rodent Population Measurements
  in Panama

abstract:
  Simple random walk considerations are used to interpret rodent population
data collected in Hantavirus-related investigations in Panama regarding the
short-tailed cane mouse, \emph{Zygodontomys brevicauda}. The diffusion constant
of mice is evaluated to be of the order of (and larger than) 200 meters squared
per day. The investigation also shows that the rodent mean square displacement
saturates in time, indicating the existence of a spatial scale which could, in
principle, be the home range of the rodents. This home range is concluded to be
of the order of 70 meters. Theoretical analysis is provided for interpreting
animal movement data in terms of an interplay of the home ranges, the diffusion
constant, and the size of the grid used to monitor the movement. The study
gives impetus to a substantial modification of existing theory of the spread of
the Hantavirus epidemic which has been based on simple diffusive motion of the
rodents, and additionally emphasizes the importance for developing more
accurate techniques for the measurement of rodent movement.


title:
Theory of Home Range Estimation from Mark-Recapture Measurements of
  Animal Populations

abstract:
  A theory is provided for the estimation of home ranges of animals from the
standard mark-recapture technique in which data are collected by capturing,
tagging and recapturing the animals. The theoretical tool used is the
Fokker-Planck equation, its characteristic quantities being the diffusion
constant which describes the motion of the animals, and the attractive
potential which addresses their tendency to live near their burrows. The
measurement technique is shown to correspond to the calculation of a certain
kind of mean square displacement of the animals relevant to the specific
probing window in space corresponding to the trapping region. The output of the
theory is a sigmoid curve of the observable mean square displacement as a
function of the ratio of distances characteristic of the home range and the
trapping region, along with an explicit prescription to extract the home range
form observations. Applications of the theory to rodent movement in Panama and
New Mexico are pointed out. An analysis is given of the sensitivity of our
theory to the choice of the confining potential via the use of various
representative cases. A comparison is provided between home range size inferred
from our method and from other procedures employed in the literature.
Consequences of home range overlap are also discussed.


title: Persistence Length Changes Dramatically as RNA Folds

abstract:
  We determine the persistence length, $l_p$, for a bacterial group I ribozyme
as a function of concentration of monovalent and divalent cations by fitting
the distance distribution functions $P(r)$ obtained from small angle X-ray
scattering intensity data to the asymptotic form of the calculated $P_{WLC}(r)$
for a worm-like chain (WLC). The $l_p$ values change dramatically over a narrow
range of \Mg concentration from $\sim$21 \AA in the unfolded state (\textbf{U})
to $\sim$10 \AA in the compact ($\mathrm{I_C}$) and native states. Variations
in $l_p$ with increasing \Na concentration are more gradual. In accord with the
predictions of polyelectrolyte theory we find $l_p \propto 1/ \kappa^2$ where
$\kappa$ is the inverse Debye-screening length.


title:
Diffusion and Home Range Parameters for Rodents: Peromyscus maniculatus
  in New Mexico

abstract:
  We analyze data from a long term field project in New Mexico, consisting of
repeated sessions of mark-recaptures of Peromyscus maniculatus (Rodentia:
Muridae), the host and reservoir of Sin Nombre Virus (Bunyaviridae:
Hantavirus). The displacements of the recaptured animals provide a means to
study their movement from a statistical point of view. We extract two
parameters from the data with the help of a simple model: the diffusion
constant of the rodents, and the size of their home range. The short time
behavior shows the motion to be approximately diffusive and the diffusion
constant to be 470+/-50m^2/day. The long time behavior provides an estimation
of the diameter of the rodent home ranges, with an average value of 100+/-25m.
As in previous investigations directed at Zygodontomys brevicauda observations
in Panama, we use a box model for home range estimation. We also use a harmonic
model in the present investigation to study the sensitivity of the conclusions
to the model used and find that both models lead to similar estimates.


title:
Elucidation of Directionality for Co-Expressed Genes: Predicting
  Intra-Operon Termination Sites

abstract:
  We present a novel framework for inferring regulatory and sequence-level
information from gene co-expression networks. The key idea of our methodology
is the systematic integration of network inference and network topological
analysis approaches for uncovering biological insights. We determine the gene
co-expression network of Bacillus subtilis using Affymetrix GeneChip time
series data and show how the inferred network topology can be linked to
sequence-level information hard-wired in the organism's genome. We propose a
systematic way for determining the correlation threshold at which two genes are
assessed to be co-expressed by using the clustering coefficient and we expand
the scope of the gene co-expression network by proposing the slope ratio metric
as a means for incorporating directionality on the edges. We show through
specific examples for B. subtilis that by incorporating expression level
information in addition to the temporal expression patterns, we can uncover
sequence-level biological insights. In particular, we are able to identify a
number of cases where (i) the co-expressed genes are part of a single
transcriptional unit or operon and (ii) the inferred directionality arises due
to the presence of intra-operon transcription termination sites.


title: Correlation Statistics for cDNA Microarray Image Analysis

abstract:
  In this report, correlation of the pixels comprising a microarray spot is
investigated. Subsequently, correlation statistics namely: Pearson correlation
and Spearman rank correlation are used to segment the foreground and background
intensity of microarray spots. The performance of correlation-based
segmentation is compared to clustering-based (PAM, k-means) and seeded-region
growing techniques (SPOT). It is shown that correlation-based segmentation is
useful in flagging poorly hybridized spots, thus minimizes false-positives. The
present study also raises the intriguing question of whether a change in
correlation can be an indicator of differential gene expression.


title:
Divergent evolution paths of different genetic families in the Penna
  model

abstract:
  We present some results of simulations of population growth and evolution,
using the standard asexual Penna model, with individuals characterized by a
string of bits representing a genome containing some possible mutations. After
about 20000 simulation steps, when only a few genetic families are still
present from among rich variety of families at the beginning of the simulation
game, strong peaks in mutation distribution functions are observed. This known
effect is due to evolution rules with hereditary mechanism. The birth and death
balance in the simulation game also leads to elimination of families specified
by different genomes. Number of families $G(t)$ versus time $t$ follow the
power law, $G \propto t^n$. Our results show the power coefficient exponent $n$
is changing as the time goes. Starting from about --1, smoothly achieves about
--2 after hundreds of steps, and finally has semi-smooth transition to 0, when
only one family exists in the environment. This is in contrast with constant
$n$ about --1 as found, for example, in \cite{bib:evolution}. We suspect that
this discrepancy may be due to two different time scales in simulations -
initial stages follow the $n\approx-1$ law, yet for large number of simulation
steps we get $n\approx-2$, providing random initial population was sufficiently
big to allow for still reliable statistical analysis. The $n\approx-1$
evolution stage seems to be associated with the Verhulst mechanism of
population elimination due to the limited environmental capacity - when the
standard evolution rules were modified, we observed a plateau ($n=0$) in the
power law in short time scale, again followed by $n\approx -2$ law for longer
times. The modified model uses birth rate controlled by the current population
instead of the standard Verhulst death factor.


title: The Freezing of Random RNA

abstract:
  We study secondary structures of random RNA molecules by means of a
renormalized field theory based on an expansion in the sequence disorder. We
show that there is a continuous phase transition from a molten phase at higher
temperatures to a low-temperature glass phase. The primary freezing occurs
above the critical temperature, with local islands of stable folds forming
within the molten phase. The size of these islands defines the correlation
length of the transition. Our results include critical exponents at the
transition and in the glass phase.


title: Wave->Diffusion Transition in Microtubules

abstract:
  In this paper the heat transport in microtubules (MT) is investigated. When
the dimension of the structure is of the order of the de Broglie wave length
the transport phenomena must be analyzed within quantum mechanics. In this
paper we developed the Dirac type thermal equation for MT .The solution of the
equation-the temperature fields for electrons can be wave type or diffusion
type depending on the dynamics of the scattering. Key words: Microtubules
ultrashort laser pulses, Dirac thermal equation, temperature fields.


title:
Maximum likelihood estimation of phylogenetic tree and substitution
  rates via generalized neighbor-joining and the EM algorithm

abstract:
  A central task in the study of molecular sequence data from present-day
species is the reconstruction of the ancestral relationships. The most
established approach to tree reconstruction is the maximum likelihood (ML)
method. In this method, evolution is described in terms of a discrete-state
continuous-time Markov process on a phylogenetic tree. The substitution rate
matrix, that determines the Markov process, can be estimated using the
expectation maximization (EM) algorithm. Unfortunately, an exhaustive search
for the ML phylogenetic tree is computationally prohibitive for large data
sets. In such situations, the neighbor-joining (NJ) method is frequently used
because of its computational speed. The NJ method reconstructs trees by
clustering neighboring sequences recursively, based on pairwise comparisons
between the sequences. The NJ method can be generalized such that
reconstruction is based on comparisons of subtrees rather than pairwise
distances. In this paper, we present an algorithm for simultaneous substitution
rate estimation and phylogenetic tree reconstruction. The algorithm iterates
between the EM algorithm for estimating substitution rates and the generalized
NJ method for tree reconstruction. Preliminary results of the approach are
encouraging.


title:
On the Fibril Elongation Mechanism of the Prion Protein Fragment
  PrP106-126

abstract:
  Mouse prion protein PrP106-126 is a peptide corresponding to the residues
107-127 of human prion protein. It has been shown that PrP106-126 can reproduce
the main neuropathological features of prionrelated transmissible spongiform
encephalopathies and can form amyloid-like fibrils in vitro. The conformational
characteristics of PrP106-126 fibril have been investigated by electron
microscopy, CD spectroscopy, NMR and molecular dynamics simulations. Recent
researches have found out that PrP106-126 in water assumes a stable structure
consisting of two parallel beta-sheets that are tightly packed against each
other. In this work we perform molecular dynamics simulation to reveal the
elongation mechanism of PrP106-126 fibril. Influenced by the edge strands of
the fibril which already adopt beta-sheets conformation, single PrP106-126
peptide forms beta-structure and becomes a new element of the fibril. Under
acidic condition, single PrP106-126 peptide adopts a much larger variety of
conformations than it does under neural condition, which makes a peptide easier
to be influenced by the edge strands of the fibril. However, acidic condition
dose not largely affect the stability of PrP106-126 peptide fibril. Thus, the
speed of fibril elongation can be dramatically increased by lowering the pH
value of the solution. The pH value was adjusted by either changing the
protonation state of the residues or adding hydronium ions (acidic solution) or
hydroxyl ions (alkaline solution). The differences between these two approaches
are analyzed here.


title:
On the discrete Peyrard-Bishop model of DNA: stationary solutions and
  stability

abstract:
  As a first step in the search of an analytical study of mechanical
denaturation of DNA in terms of the sequence, we study stable, stationary
solutions in the discrete, finite and homogeneous Peyrard-Bishop DNA model. We
find and classify all the stationary solutions of the model, as well as
analytic approximations of them, both in the continuum and in the discrete
limits. Our results explain the structure of the solutions reported by
Theodorakopoulos {\em et al.} [Phys. Rev. Lett. {\bf 93}, 258101 (2004)] and
provide a way to proceed to the analysis of the generalized version of the
model incorporating the genetic information.


title:
Nonlinear brain dynamics as macroscopic manifestation of underlying
  many-body field dynamics

abstract:
  Neural activity patterns related to behavior occur at many scales in time and
space from the atomic and molecular to the whole brain. Here we explore the
feasibility of interpreting neurophysiological data in the context of many-body
physics by using tools that physicists have devised to analyze comparable
hierarchies in other fields of science. We focus on a mesoscopic level that
offers a multi-step pathway between the microscopic functions of neurons and
the macroscopic functions of brain systems revealed by hemodynamic imaging. We
use electroencephalographic (EEG) records collected from high-density electrode
arrays fixed on the epidural surfaces of primary sensory and limbic areas in
rabbits and cats trained to discriminate conditioned stimuli (CS) in the
various modalities. High temporal resolution of EEG signals with the Hilbert
transform gives evidence for diverse intermittent spatial patterns of amplitude
(AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize
in the beta and gamma ranges at near zero time lags over long distances. The
dominant mechanism for neural interactions by axodendritic synaptic
transmission should impose distance-dependent delays on the EEG oscillations
owing to finite propagation velocities. It does not. EEGs instead show evidence
for anomalous dispersion: the existence in neural populations of a low velocity
range of information and energy transfers, and a high velocity range of the
spread of phase transitions. This distinction labels the phenomenon but does
not explain it. In this report we explore the analysis of these phenomena using
concepts of energy dissipation, the maintenance by cortex of multiple ground
states corresponding to AM patterns, and the exclusive selection by spontaneous
breakdown of symmetry (SBS) of single states in sequences.


title: Absolute Rate Theories of Epigenetic Stability

abstract:
  Spontaneous switching events in most characterized genetic switches are rare,
resulting in extremely stable epigenetic properties. We show how simple
arguments lead to theories of the rate of such events much like the absolute
rate theory of chemical reactions corrected by a transmission factor. Both the
probability of the rare cellular states that allow epigenetic escape, and the
transmission factor, depend on the rates of DNA binding and unbinding events
and on the rates of protein synthesis and degradation. Different mechanisms of
escape from the stable attractors occur in the nonadiabatic, weakly adiabatic
and strictly adiabatic regimes, characterized by the relative values of those
input rates.


title: Evolution on distributive lattices

abstract:
  We consider the directed evolution of a population after an intervention that
has significantly altered the underlying fitness landscape. We model the space
of genotypes as a distributive lattice; the fitness landscape is a real-valued
function on that lattice. The risk of escape from intervention, i.e., the
probability that the population develops an escape mutant before extinction, is
encoded in the risk polynomial. Tools from algebraic combinatorics are applied
to compute the risk polynomial in terms of the fitness landscape. In an
application to the development of drug resistance in HIV, we study the risk of
viral escape from treatment with the protease inhibitors ritonavir and
indinavir.


title:
A complex social structure with fission-fusion properties can emerge
  from a simple foraging model

abstract:
  Precisely how ecological factors influence animal social structure is far
from clear. We explore this question using an agent-based model inspired by the
fission-fusion society of Spider monkeys (Ateles spp). Our model introduces a
realistic, complex foraging environment composed of many resource patches with
size varying according to an inverse power-law frequency distribution with
exponent $\beta$. Foragers do not interact among them and start from random
initial locations. They have either a complete or partial knowledge of the
environment and maximize the ratio between the size of the next visited patch
and the distance traveled to it, ignoring previously visited patches. At
intermediate values of $\beta$, when large patches are neither too scarce nor
too abundant, foragers form groups (coincide at the same patch) with a similar
size frequency distribution as the spider monkey's subgroups. Fission-fusion
events create a network of associations that contains weak bonds among foragers
that meet only rarely and strong bonds among those that repeat associations
more frequently than would be expected by chance. The latter form sub-networks
with the highest number of bonds and a high clustering coefficient at
intermediate values of $\beta$. The weak bonds enable the whole social network
to percolate. Some of our results are similar to those found in long-term field
studies of spider monkeys and other fission-fusion species. We conclude that
hypotheses about the ecological causes of fission-fusion and the origin of
complex social structures should consider the heterogeneity and complexity of
the environment in which social animals live.


title:
Protocell Self-Reproduction in a Spatially Extended Metabolism-Vesicle
  System

abstract:
  Cellular life requires the presence of a set of biochemical mechanisms in
order to maintain a predictable process of growth and division. Several
attempts have been made towards the building of minimal protocells from a
top-down approach, i.e. by using available biomolecules This type of synthetic
approach has so far been unsuccesful, and the lack of appropriate models of the
synthetic protocell cycle might be needed to guide future experiments. In this
paper we present a simple biochemically and physically feasible model of cell
replication involving a discrete semi-permeable vesicle with an internal
minimal metabolism involving two reactive centers. It is shown that such a
system can effectively undergo a whole cell replication cycle. The model can be
used as a basic framework to model whole protocell dynamics including more
complex sets of reactions. The possible implementation of our design in future
synthetic protocells is outlined.


title: Information based clustering: Supplementary material

abstract:
  This technical report provides the supplementary material for a paper
entitled "Information based clustering", to appear shortly in Proceedings of
the National Academy of Sciences (USA). In Section I we present in detail the
iterative clustering algorithm used in our experiments and in Section II we
describe the validation scheme used to determine the statistical significance
of our results. Then in subsequent sections we provide all the experimental
results for three very different applications: the response of gene expression
in yeast to different forms of environmental stress, the dynamics of stock
prices in the Standard and Poor's 500, and viewer ratings of popular movies. In
particular, we highlight some of the results that seem to deserve special
attention. All the experimental results and relevant code, including a freely
available web application, can be found at
http://www.genomics.princeton.edu/biophysics-theory .


title: Information based clustering

abstract:
  In an age of increasingly large data sets, investigators in many different
disciplines have turned to clustering as a tool for data analysis and
exploration. Existing clustering methods, however, typically depend on several
nontrivial assumptions about the structure of data. Here we reformulate the
clustering problem from an information theoretic perspective which avoids many
of these assumptions. In particular, our formulation obviates the need for
defining a cluster "prototype", does not require an a priori similarity metric,
is invariant to changes in the representation of the data, and naturally
captures non-linear relations. We apply this approach to different domains and
find that it consistently produces clusters that are more coherent than those
extracted by existing algorithms. Finally, our approach provides a way of
clustering based on collective notions of similarity rather than the
traditional pairwise measures.


title: Quasi-Species and Aggregate Dynamics

abstract:
  At an early stage in pre-biotic evolution, groups of replicating molecules
must coordinate their reproduction to form aggregated units of selection.
Mechanisms that enable this to occur are currently not well understood. In this
paper we introduce a deterministic model of primitive replicating aggregates,
proto-organisms, that host populations of replicating information carrying
molecules. Some of the molecules promote the reproduction of the proto-organism
at the cost of their individual replication rate. A situation resembling that
of group selection arises. We derive and analytically solve a partial
differential equation that describes the system. We find that the relative
prevalence of fast and slow replicators is determined by the relative strength
of selection at the aggregate level to the selection strength at the molecular
level. The analysis is concluded by a preliminary treatment of finite
population size effects.


title:
The use of the GARP genetic algorithm and internet grid computing in the
  Lifemapper world atlas of species biodiversity

abstract:
  Lifemapper (http://www.lifemapper.org) is a predictive electronic atlas of
the Earth's biological biodiversity. Using a screensaver version of the GARP
genetic algorithm for modeling species distributions, Lifemapper harnesses vast
computing resources through 'volunteers' PCs similar to SETI@home, to develop
models of the distribution of the worlds fauna and flora. The Lifemapper
project's primary goal is to provide an up to date and comprehensive database
of species maps and prediction models (i.e. a fauna and flora of the world)
using available data on species' locations. The models are developed using
specimen data from distributed museum collections and an archive of geospatial
environmental correlates. A central server maintains a dynamic archive of
species maps and models for research, outreach to the general community, and
feedback to museum data providers. This paper is a case study in the role, use
and justification of a genetic algorithm in development of large-scale
environmental informatics infrastructure.


title:
Improving ecological niche models by data mining large environmental
  datasets for surrogate models

abstract:
  WhyWhere is a new ecological niche modeling (ENM) algorithm for mapping and
explaining the distribution of species. The algorithm uses image processing
methods to efficiently sift through large amounts of data to find the few
variables that best predict species occurrence. The purpose of this paper is to
describe and justify the main parameterizations and to show preliminary success
at rapidly providing accurate, scalable, and simple ENMs. Preliminary results
for 6 species of plants and animals in different regions indicate a significant
(p<0.01) 14% increase in accuracy over the GARP algorithm using models with
few, typically two, variables. The increase is attributed to access to
additional data, particularly monthly vs. annual climate averages. WhyWhere is
also 6 times faster than GARP on large data sets. A data mining based approach
with transparent access to remote data archives is a new paradigm for ENM,
particularly suited to finding correlates in large databases of fine resolution
surfaces. Software for WhyWhere is freely available, both as a service and in a
desktop downloadable form from the web site http://biodi.sdsc.edu/ww_home.html.


title: Functional alignment of regulatory networks: A study of temperate phages

abstract:
  The relationship between the design and functionality of molecular networks
is now a key issue in biology. Comparison of regulatory networks performing
similar tasks can give insights into how network architecture is constrained by
the functions it directs. We here discuss methods of network comparison based
on network architecture and signaling logic. Introducing local and global
signaling scores for the difference between two networks we quantify
similarities between evolutionary closely and distantly related bacteriophages.
Despite the large evolutionary separation between phage $\lambda$ and 186 their
networks are found to be similar when difference is measured in terms of global
signaling. We finally discuss how network alignment can be used to to pinpoint
protein similarities viewed from the network perspective.

categories: q-bio.MN cond-mat.other q-bio.GN q-bio.OT


title: Gompertz mortality law and scaling behaviour of the Penna model

abstract:
  The Penna model is a model of evolutionary ageing through mutation
accumulation where traditionally time and the age of an organism are treated as
discrete variables and an organism's genome by a binary bit string. We
reformulate the asexual Penna model and show that, a universal scale invariance
emerges as we increase the number of discrete genome bits to the limit of a
continuum. The continuum model, introduced by Almeida and Thomas in
[Int.J.Mod.Phys.C, 11, 1209 (2000)] can be recovered from the discrete model in
the limit of infinite bits coupled with a vanishing mutation rate per bit.
Finally, we show that scale invariant properties may lead to the ubiquitous
Gompertz Law for mortality rates for early ages, which is generally regarded as
being empirical.


title:
A stochastic model of Min oscillations in Escherichia coli and Min
  protein segregation during cell division

abstract:
  The Min system in Escherichia coli directs division to the centre of the cell
through pole-to-pole oscillations of the MinCDE proteins. We present a one
dimensional stochastic model of these oscillations which incorporates membrane
polymerisation of MinD into linear chains. This model reproduces much of the
observed phenomenology of the Min system, including pole-to-pole oscillations
of the Min proteins. We then apply this model to investigate the Min system
during cell division. Oscillations continue initially unaffected by the closing
septum, before cutting off rapidly. The fractions of Min proteins in the
daughter cells vary widely, from 50%-50% up to 85%-15% of the total from the
parent cell, suggesting that there may be another mechanism for regulating
these levels in vivo.


title:
Response of electrically coupled spiking neurons: a cellular automaton
  approach

abstract:
  Experimental data suggest that some classes of spiking neurons in the first
layers of sensory systems are electrically coupled via gap junctions or
ephaptic interactions. When the electrical coupling is removed, the response
function (firing rate {\it vs.} stimulus intensity) of the uncoupled neurons
typically shows a decrease in dynamic range and sensitivity. In order to assess
the effect of electrical coupling in the sensory periphery, we calculate the
response to a Poisson stimulus of a chain of excitable neurons modeled by
$n$-state Greenberg-Hastings cellular automata in two approximation levels. The
single-site mean field approximation is shown to give poor results, failing to
predict the absorbing state of the lattice, while the results for the pair
approximation are in good agreement with computer simulations in the whole
stimulus range. In particular, the dynamic range is substantially enlarged due
to the propagation of excitable waves, which suggests a functional role for
lateral electrical coupling. For probabilistic spike propagation the Hill
exponent of the response function is $\alpha=1$, while for deterministic spike
propagation we obtain $\alpha=1/2$, which is close to the experimental values
of the psychophysical Stevens exponents for odor and light intensities. Our
calculations are in qualitative agreement with experimental response functions
of ganglion cells in the mammalian retina.

categories: q-bio.NC cond-mat.dis-nn nlin.CG physics.bio-ph


title: Quasispecies and recombination

abstract:
  Recombination is introduced into Eigen's theory of quasispecies evolution.
Comparing numerical simulations of the rate equations in the non-recombining
and recombining cases show that recombination has a strong effect on the error
threshold and, for a wide range of mutation rates, gives rise to two stable
fixed points in the dynamics. This bi-stability results in the existence of two
error thresholds. We prove that, under some assumptions on the fitness
landscape but for general crossover probability, a fixed point localized about
the sequence with superior fitness is globally stable for low mutation rates.


title: Quantitative modeling and data analysis of SELEX experiments

abstract:
  SELEX (Systematic Evolution of Ligands by Exponential Enrichment) is an
experimental procedure that allows extracting, from an initially random pool of
DNA, those oligomers with high affinity for a given DNA-binding protein. We
address what is a suitable experimental and computational procedure to infer
parameters of transcription factor-DNA interaction from SELEX experiments. To
answer this, we use a biophysical model of transcription factor-DNA
interactions to quantitatively model SELEX. We show that a standard procedure
is unsuitable for obtaining accurate interaction parameters. However, we
theoretically show that a modified experiment in which chemical potential is
fixed through different rounds of the experiment allows robust generation of an
appropriate data set. Based on our quantitative model, we propose a novel
bioinformatic method of data analysis for such modified experiment and apply it
to extract the interaction parameters for a mammalian transcription factor
CTF/NFI. From a practical point of view, our method results in a significantly
improved false positive/false negative trade-off, as compared to both the
standard information theory based method and a widely used empirically
formulated procedure.


title: Characterization of a Branch of the Phylogenetic Tree

abstract:
  We use a combination of analytic models and computer simulations to gain
insight into the dynamics of evolution. Our results suggest that certain
interesting phenomena should eventually emerge from the fossil record. For
example, there should be a ``tortoise and hare effect'': Those genera with the
smallest species death rate are likely to survive much longer than genera with
large species birth and death rates. A complete characterization of the
behavior of a branch of the phylogenetic tree corresponding to a genus and
accurate mathematical representations of the various stages are obtained. We
apply our results to address certain controversial issues that have arisen in
paleontology such as the importance of punctuated equilibrium and whether
unique Cambrian phyla have survived to the present.


title: Optimal strategies for fighting persistent bugs

abstract:
  Some microbial organisms are known to randomly slip into and out of
hibernation, irrespective of environmental conditions [1]. In a (genetically)
uniform population a typically very small subpopulation becomes metabolically
inactive whereas the majority subpopulation remains active and grows. Bacteria
such as E. coli, Staphylococcus aureus (MRSA-superbug), Mycobacterium
tuberculosis, and Pseudomonas aeruginosa [1-3] show persistence. It can render
bacteria less vulnerable in adverse environments [1, 4, 5] and their effective
eradication through medication more difficult [2, 3, 6]. Here we show that
medication treatment regimes may have to be modified when persistence is taken
into account and characterize optimal approaches assuming that the total
medication dose is constrained. The determining factors are cumulative
toxicity, eradication power of the medication and bacterial response
timescales. Persistent organisms have to be fought using tailored eradication
strategies which display two fundamental characteristics. Ideally, the
treatment time should be significantly longer than in the case of persistence
with the medication uniformly spread out over time; however, if treatment time
has to be limited, then the application of medication has to be concentrated
towards the beginning and end of the treatment. These findings deviate from
current clinical practice, and may therefore help to optimize and simplify
treatments. Our use of multi-objective optimization [7] to map out the optimal
strategies can be generalized to other related problems.


title: Stretching of proteins in a force-clamp

abstract:
  Mechanically induced protein unfolding in the force-clamp apparatus is shown,
in a coarse-grained model of ubiquitin, to have lognormal statistics above a
treshold force and exponential below it. Correspondingly, the mean unfolding
time is slowly varying and exponentially decreases as a function of the force.
The time dependencies of the end-to-end distances are also distinct. The time
sequence of unfolding events weakly depends on force and much of it resembles
that for stretching at constant speed. A more complicated time dependence
arises for integrin.


title:
An RNA foldability metric; implications for the design of rapidly
  foldable RNA sequences

abstract:
  Evidence is presented suggesting, for the first time, that the protein
foldability metric sigma=(T_theta - T_f)/T_theta, where T_theta and T_f are,
respectively, the collapse and folding transition temperatures, could be used
also to measure the foldability of RNA sequences. The importance of sigma is
discussed in the context of the in silico design of rapidly foldable RNA
sequences.


title: Transient pattern formation in a stochastic model of cancer growth

abstract:
  We study a spatially inhomogeneous model of cancer growth based on
Michaelis--Menten kinetics, subjected to additive Gaussian noise and
multiplicative dichotomous noise. In presence of the latter, we can observe a
transition between two stationary states of the system. The transient behaviour
generates a spatial pattern of two phases, where cancer cells or immune cells
predominate.


title:
Efficiency, Robustness and Stochasticity of Gene Regulatory Networks in
  Systems Biology: lambda Switch as a Working Example

abstract:
  Phage lambda is one of the most studied biological models in modern molecular
biology. Over the past 50 years quantitative experimental knowledge on this
biological model has been accumulated at all levels: physics, chemistry,
genomics, proteomics, functions, and more. All its components have been known
to a great detail. The theoretical task has been to integrate its components to
make the organism working quantitatively in a harmonic manner. This would test
our biological understanding and would lay a solid fundamental for further
explorations and applications, an obvious goal of systems biology. One of the
outstanding challenges in doing so has been the so-called stability puzzle of
lambda switch: the biologically observed robustness and its difficult
mathematical reconstruction based on known experimental values. In this chapter
we review the recent theoretical and experimental efforts on tackling this
problem. An emphasis is put on the minimum quantitative modeling where a
successful numerical agreement between experiments and modeling has been
achieved. A novel method tentatively named stochastic dynamical structure
analysis emerged from such study is also discussed within a broad modeling
perspective.

categories: q-bio.SC cond-mat.other nlin.AO q-bio.MN q-bio.OT


title: Parametric Alignment of Drosophila Genomes

abstract:
  The classic algorithms of Needleman--Wunsch and Smith--Waterman find a
maximum a posteriori probability alignment for a pair hidden Markov model
(PHMM). In order to process large genomes that have undergone complex genome
rearrangements, almost all existing whole genome alignment methods apply fast
heuristics to divide genomes into small pieces which are suitable for
Needleman--Wunsch alignment. In these alignment methods, it is standard
practice to fix the parameters and to produce a single alignment for subsequent
analysis by biologists.
  Our main result is the construction of a whole genome parametric alignment of
Drosophila melanogaster and Drosophila pseudoobscura. Parametric alignment
resolves the issue of robustness to changes in parameters by finding all
optimal alignments for all possible parameters in a PHMM. Our alignment draws
on existing heuristics for dividing whole genomes into small pieces for
alignment, and it relies on advances we have made in computing convex polytopes
that allow us to parametrically align non-coding regions using biologically
realistic models. We demonstrate the utility of our parametric alignment for
biological inference by showing that cis-regulatory elements are more conserved
between Drosophila melanogaster and Drosophila pseudoobscura than previously
thought. We also show how whole genome parametric alignment can be used to
quantitatively assess the dependence of branch length estimates on alignment
parameters.
  The alignment polytopes, software, and supplementary material can be
downloaded at http://bio.math.berkeley.edu/parametric/.


title: A geometric approach to tree shape statistics

abstract:
  This article presents a new way to understand the descriptive ability of tree
shape statistics. Where before tree shape statistics were chosen by their
ability to distinguish between macroevolutionary models, the ``resolution''
presented in this paper quantifies the ability of a statistic to differentiate
between similar and different trees. We term this a ``geometric'' approach to
differentiate it from the model-based approach previously explored. A distinct
advantage of this perspective is that it allows evaluation of multiple tree
shape statistics describing different aspects of tree shape. After developing
the methodology, it is applied here to make specific recommendations for a
suite of three statistics which will hopefully prove useful in applications.
The article ends with an application of the tree shape statistics to clarify
the impact of omission of taxa on tree shape.


title:
Ubiquity of synonymity: almost all large binary trees are not uniquely
  identified by their spectra or their immanantal polynomials

abstract:
  There are several common ways to encode a tree as a matrix, such as the
adjacency matrix, the Laplacian matrix (that is, the infinitesimal generator of
the natural random walk), and the matrix of pairwise distances between leaves.
Such representations involve a specific labeling of the vertices or at least
the leaves, and so it is natural to attempt to identify trees by some feature
of the associated matrices that is invariant under relabeling. An obvious
candidate is the spectrum of eigenvalues (or, equivalently, the characteristic
polynomial). We show for any of these choices of matrix that the fraction of
binary trees with a unique spectrum goes to zero as the number of leaves goes
to infinity. We investigate the rate of convergence of the above fraction to
zero using numerical methods. For the adjacency and Laplacian matrices, we show
that that the {\em a priori} more informative immanantal polynomials have no
greater power to distinguish between trees.


title: Cooperative Cargo Transport by Several Molecular Motors

abstract:
  The transport of cargo particles which are pulled by several molecular motors
in a cooperative manner is studied theoretically. The transport properties
depend primarily on the maximal number, $N$, of motor molecules that may pull
simultaneously on the cargo particle. Since each motor must unbind from the
filament after a finite number of steps but can also rebind to it again, the
actual number of pulling motors is not constant but varies with time between
zero and $N$. An increase in the maximal number $N$ leads to a strong increase
of the average walking distance (or run length) of the cargo particle. If the
cargo is pulled by up to $N$ kinesin motors, e.g., the walking distance is
estimated to be $5^{N-1}/N$ micrometers which implies that seven or eight
kinesin molecules are sufficient to attain an average walking distance in the
centimeter range. If the cargo particle is pulled against an external load
force, this force is shared between the motors which provides a nontrivial
motor-motor coupling and a generic mechanism for nonlinear force-velocity
relationships. With increasing load force, the probability distribution of the
instantenous velocity is shifted towards smaller values, becomes broader, and
develops several peaks. Our theory is consistent with available experimental
data and makes quantitative predictions that are accessible to systematic in
vitro experiments.

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