title: Efficient Degradation and Expression Prioritization with Small RNAs

abstract:
  We build a simple model for feedback systems involving small RNA (sRNA)
molecules based on the iron metabolism system in the bacterium E. coli, and
compare it with the corresponding system in H. pylori which uses purely
transcriptional regulation. This reveals several unique features of sRNA based
regulation that could be exploited by cells. Firstly, we show that sRNA
regulation can maintain a smaller turnover of target mRNAs than transcriptional
regulation, without sacrificing the speed of response to external shocks.
Secondly, we propose that a single sRNA can prioritize the usage of different
target mRNAs. This suggests that sRNA regulation would be more common in more
complex systems which need to co-regulate many mRNAs efficiently.


title:
Asymptotic Evolution of Protein-Protein Interaction Networks for General
  Duplication-Divergence Models

abstract:
  Genomic duplication-divergence events, which are the primary source of new
protein functions, occur stochastically at a wide range of genomic scales, from
single gene to whole genome duplications. Clearly, this fundamental
evolutionary process must have largely conditioned the emerging structure of
protein-protein interaction (PPI) networks, that control many cellular
activities. We propose and asymptotically solve a general
duplication-divergence model of PPI network evolution based on the statistical
selection of duplication-derived interactions. We also introduce a conservation
index, that formally defines the statistical evolutionary conservation of PPI
networks. Distinct conditions on microscopic parameters are then shown to
control global conservation and topology of emerging PPI networks. In
particular, conserved, non-dense networks, which are the only ones of potential
biological relevance, are also shown to be necessary scale-free.


title: Encoding folding paths of RNA switches

abstract:
  RNA co-transcriptional folding has long been suspected to play an active role
in helping proper native folding of ribozymes and structured regulatory motifs
in mRNA untranslated regions. Yet, the underlying mechanisms and coding
requirements for efficient co-transcriptional folding remain unclear.
Traditional approaches have intrinsic limitations to dissect RNA folding paths,
as they rely on sequence mutations or circular permutations that typically
perturb both RNA folding paths and equilibrium structures. Here, we show that
exploiting sequence symmetries instead of mutations can circumvent this problem
by essentially decoupling folding paths from equilibrium structures of designed
RNA sequences. Using bistable RNA switches with symmetrical helices conserved
under sequence reversal, we demonstrate experimentally that native and
transiently formed helices can guide efficient co-transcriptional folding into
either long-lived structure of these RNA switches. Their folding path is
controlled by the order of helix nucleations and subsequent exchanges during
transcription, and may also be redirected by transient antisense interactions.
Hence, transient intra- and intermolecular base pair interactions can
effectively regulate the folding of nascent RNA molecules into different native
structures, provided limited coding requirements, as discussed from an
information theory perspective. This constitutive coupling between RNA
synthesis and RNA folding regulation may have enabled the early emergence of
autonomous RNA-based regulation networks.


title: Ising models for networks of real neurons

abstract:
  Ising models with pairwise interactions are the least structured, or
maximum-entropy, probability distributions that exactly reproduce measured
pairwise correlations between spins. Here we use this equivalence to construct
Ising models that describe the correlated spiking activity of populations of 40
neurons in the retina, and show that pairwise interactions account for observed
higher-order correlations. By first finding a representative ensemble for
observed networks we can create synthetic networks of 120 neurons, and find
that with increasing size the networks operate closer to a critical point and
start exhibiting collective behaviors reminiscent of spin glasses.


title: The folding mechanics of a knotted protein

abstract:
  An increasing number of proteins are being discovered with a remarkable and
somewhat surprising feature, a knot in their native structures. How the
polypeptide chain is able to knot itself during the folding process to form
these highly intricate protein topologies is not known. Here, we perform a
computational study on the 160-amino acid homodimeric protein YibK which, like
other proteins in the SpoU family of MTases, contains a deep trefoil knot in
its C-terminal region. In this study, we use a coarse-grained C-alpha-chain
representation and Langevin dynamics to study folding kinetics. We find that
specific, attractive nonnative interactions are critical for knot formation. In
the absence of these interactions, i.e. in an energetics driven entirely by
native interactions, knot formation is exceedingly unlikely. Further, we find,
in concert with recent experimental data on YibK, two parallel folding pathways
which we attribute to an early and a late formation of the trefoil knot,
respectively. For both pathways, knot formation occurs before dimerization. A
bioinformatics analysis of the SpoU family of proteins reveals further that the
critical nonnative interactions may originate from evolutionary conserved
hydrophobic segments around the knotted region.


title:
Percolation on fitness landscapes: effects of correlation, phenotype,
  and incompatibilities

abstract:
  We study how correlations in the random fitness assignment may affect the
structure of fitness landscapes. We consider three classes of fitness models.
The first is a continuous phenotype space in which individuals are
characterized by a large number of continuously varying traits such as size,
weight, color, or concentrations of gene products which directly affect
fitness. The second is a simple model that explicitly describes
genotype-to-phenotype and phenotype-to-fitness maps allowing for neutrality at
both phenotype and fitness levels and resulting in a fitness landscape with
tunable correlation length. The third is a class of models in which particular
combinations of alleles or values of phenotypic characters are "incompatible"
in the sense that the resulting genotypes or phenotypes have reduced (or zero)
fitness. This class of models can be viewed as a generalization of the
canonical Bateson-Dobzhansky-Muller model of speciation. We also demonstrate
that the discrete NK model shares some signature properties of models with high
correlations. Throughout the paper, our focus is on the percolation threshold,
on the number, size and structure of connected clusters, and on the number of
viable genotypes.


title: Progress in the definition of a reference human mitochondrial proteome

abstract:
  Owing to the complexity of higher eukaryotic cells, a complete proteome is
likely to be very difficult to achieve. However, advantage can be taken of the
cell compartmentalization to build organelle proteomes, which can moreover be
viewed as specialized tools to study specifically the biology and "physiology"
of the target organelle. Within this frame, we report here the construction of
the human mitochondrial proteome, using placenta as the source tissue. Protein
identification was carried out mainly by peptide mass fingerprinting. The
optimization steps in two-dimensional electrophoresis needed for proteome
research are discussed. However, the relative paucity of data concerning
mitochondrial proteins is still the major limiting factor in building the
corresponding proteome, which should be a useful tool for researchers working
on human mitochondria and their deficiencies.


title:
Evaluation of nonionic and zwitterionic detergents as membrane protein
  solubilizers in two-dimensional electrophoresis

abstract:
  The solubilizing power of various nonionic and zwitterionic detergents as
membrane protein solubilizers for two-dimensional electrophoresis was
investigated. Human red blood cell ghosts and Arabidopsis thaliana leaf
membrane proteins were used as model systems. Efficient detergents could be
found in each class, i.e. with oligooxyethylene, sugar or sulfobetaine polar
heads. Among the commercially available nonionic detergents, dodecyl maltoside
and decaethylene glycol mono hexadecyl ether proved most efficient. They
complement the more classical sulfobetaine detergents to widen the scope of
useful detergents for the solubilization of membrane proteins in proteomics.


title:
Detergents and Chaotropes for Protein Solubilization before
  Two-Dimensional Electrophoresis

abstract:
  Because of the outstanding separating capabilities of two-dimensional
electrophoresis for complete proteins, it would be advantageous to be able to
apply it to all types of proteins. Unfortunately, severe solubility problems
hamper the analysis of many classes of proteins, but especially membrane
proteins. These problems arise mainly in the extraction and isoelectric
focusing steps, and solutions are sought to improve protein solubility under
the conditions prevailing during isoelectric focusing. These solutions deal
mainly with chaotropes and new detergents, which are both able to enhance
protein solubility. The input of these compounds in proteomics analysis of
membrane proteins is discussed, as well as future directions.


title: Thermodynamics of the Hairpin Ribozyme from All-Atom Simulations

abstract:
  The structure of the self-cleaving hairpin ribozyme is well characterized,
and its folding has been examined in bulk and by single-molecule fluorescence,
establishing the importance of cations, especially magnesium in the stability
of the native fold. Here we describe the first all-atom folding simulations of
the hairpin ribozyme, using a version of a Go potential with separate secondary
and tertiary structure energetic contributions. The ratio of tertiary/secondary
interaction energies serves as a proxy for non-specific cation binding: a high
ratio corresponds to a high concentration, while a low one mimics low
concentration. By studying the unfolding behavior of the RNA over a range of
temperature and tertiary/secondary energies, a three-state phase diagram
emerges, with folded, unfolded (coil) and transient folding/unfolding tertiary
structure species. The thermodynamics were verified by paired folding
simulations in each region of the phase diagram. The three phase behaviors
correspond with experimentally observed states, so this simple model captures
the essential aspect of thermodynamics in RNA folding.


title:
About thiol derivatization and resolution of basic proteins in
  two-dimensional electrophoresis

abstract:
  The influence of thiol blocking on the resolution of basic proteins by
two-dimensional electrophoresis was investigated. Cysteine blocking greatly
increased resolution and decreased streaking, especially in the basic region of
the gels. Two strategies for cysteine blocking were found to be efficient:
classical alkylation with maleimide derivatives and mixed disulfide exchange
with an excess of a low molecular weight disulfide. The effect on resolution
was significant enough to allow correct resolution of basic proteins with
in-gel rehydration on wide gradients (e.g. 3-10 and 4-12), but anodic
cup-loading was still required for basic gradients (e.g. 6-12 or 8-12). These
results demonstrate that thiol-related problems are not solely responsible for
streaking of basic proteins on two-dimensional gels.


title:
Refolding upon force quench and pathways of mechanical and thermal
  unfolding of ubiquitin

abstract:
  The refolding from stretched initial conformations of ubiquitin (PDB ID:
1ubq) under the quenched force is studied using the Go model and the Langevin
dynamics. It is shown that the refolding decouples the collapse and folding
kinetics. The force quench refolding times scale as tau_F ~ exp(f_q*x_F/k_B*T),
where f_q is the quench force and x_F = 0.96 nm is the location of the average
transition state along the reaction coordinate given by the end-to-end
distance. This value is close to x_F = 0.8 nm obtained from the force-clamp
experiments. The mechanical and thermal unfolding pathways are studied and
compared with the experimental and all-atom simulation results in detail. The
sequencing of thermal unfolding was found to be markedly different from the
mechanical one. It is found that fixing the N-terminus of ubiquitin changes its
mechanical unfolding pathways much more drastically compared to the case when
the C-end is anchored. We obtained the distance between the native state and
the transition state x_UF=0.24 nm which is in reasonable agreement with the
experimental data.


title:
Alterations of the mitochondrial proteome caused by the absence of
  mitochondrial DNA: A proteomic view

abstract:
  The proper functioning of mitochondria requires that both the mitochondrial
and the nuclear genome are functional. To investigate the importance of the
mitochondrial genome, which encodes only 13 subunits of the respiratory
complexes, the mitochondrial rRNAs and a few tRNAs, we performed a comparative
study on the 143B cell line and on its Rho-0 counterpart, i.e., devoid of
mitochondrial DNA. Quantitative differences were found, of course in the
respiratory complexes subunits, but also in the mitochondrial translation
apparatus, mainly mitochondrial ribosomal proteins, and in the ion and protein
import system, i.e., including membrane proteins. Various mitochondrial
metabolic processes were also altered, especially electron transfer proteins
and some dehydrogenases, but quite often on a few proteins for each pathway.
This study also showed variations in some hypothetical or poorly characterized
proteins, suggesting a mitochondrial localization for these proteins. Examples
include a stomatin-like protein and a protein sharing homologies with bacterial
proteins implicated in tyrosine catabolism. Proteins involved in apoptosis
control are also found modulated in Rho-0 mitochondria.


title:
High expression of antioxidant proteins in dendritic cells: possible
  implications in atherosclerosis

abstract:
  Dendritic cells (DCs) display the unique ability to activate naive T cells
and to initiate primary T cell responses revealed in DC-T cell alloreactions.
DCs frequently operate under stress conditions. Oxidative stress enhances the
production of inflammatory cytokines by DCs. We performed a proteomic analysis
to see which major changes occur, at the protein expression level, during DC
differentiation and maturation. Comparative two-dimensional gel analysis of the
monocyte, immature DC, and mature DC stages was performed. Manganese superoxide
dismutase (Mn-SOD) reached 0.7% of the gel-displayed proteins at the mature DC
stage. This important amount of Mn-SOD is a primary antioxidant defense system
against superoxide radicals, but its product, H(2)O(2), is also deleterious for
cells. Peroxiredoxin (Prx) enzymes play an important role in eliminating such
peroxide. Prx1 expression level continuously increased during DC
differentiation and maturation, whereas Prx6 continuously decreased, and Prx2
peaked at the immature DC stage. As a consequence, DCs were more resistant than
monocytes to apoptosis induced by high amounts of oxidized low density
lipoproteins containing toxic organic peroxides and hydrogen peroxide.
Furthermore DC-stimulated T cells produced high levels of receptor activator of
nuclear factor kappaB ligand, a chemotactic and survival factor for monocytes
and DCs. This study provides insights into the original ability of DCs to
express very high levels of antioxidant enzymes such as Mn-SOD and Prx1, to
detoxify oxidized low density lipoproteins, and to induce high levels of
receptor activator of nuclear factor kappaB ligand by the T cells they activate
and further emphasizes the role that DCs might play in atherosclerosis, a
pathology recognized as a chronic inflammatory disorder.


title:
Improved mass spectrometry compatibility is afforded by ammoniacal
  silver staining

abstract:
  Sequence coverage in MS analysis of protein digestion-derived peptides is a
key issue for detailed characterization of proteins or identification at low
quantities. In gel-based proteomics studies, the sequence coverage greatly
depends on the protein detection method. It is shown here that ammoniacal
silver detection methods offer improved sequence coverage over standard silver
nitrate methods, while keeping the high sensitivity of silver staining. With
the development of 2D-PAGE-based proteomics, another burden is placed on the
detection methods used for protein detection on 2-D-gels. Besides the classical
requirements of linearity, sensitivity, and homogeneity from one protein to
another, detection methods must now take into account another aspect, namely
their compatibility with MS. This compatibility is evidenced by two different
and complementary aspects, which are (i) the absence of adducts and artefactual
modifications on the peptides obtained after protease digestion of a protein
detected and digested in - gel, and (ii) the quantitative yield of peptides
recovered after digestion and analyzed by the mass spectrometer. While this
quantitative yield is not very important per se, it is however a crucial
parameter as it strongly influences the S/N of the mass spectrum and thus the
number of peptides that can be detected from a given protein input, especially
at low protein amounts. This influences in turn the sequence coverage and thus
the detail of the analysis provided by the mass spectrometer.


title:
A Novel Test for Host-Symbiont Codivergence Indicates Ancient Origin of
  Fungal Endophytes in Grasses

abstract:
  Significant phylogenetic codivergence between plant or animal hosts ($H$) and
their symbionts or parasites ($P$) indicate the importance of their
interactions on evolutionary time scales. However, valid and realistic methods
to test for codivergence are not fully developed. One of the systems where
possible codivergence has been of interest involves the large subfamily of
temperate grasses (Pooideae) and their endophytic fungi (epichloae). These
widespread symbioses often help protect host plants from herbivory and
stresses, and affect species diversity and food web structures. Here we
introduce the MRCALink (most-recent-common-ancestor link) method and use it to
investigate the possibility of grass-epichlo\"e codivergence. MRCALink applied
to ultrametric $H$ and $P$ trees identifies all corresponding nodes for
pairwise comparisons of MRCA ages. The result is compared to the space of
random $H$ and $P$ tree pairs estimated by a Monte Carlo method. Compared to
tree reconciliation the method is less dependent on tree topologies (which
often can be misleading), and it crucially improves on phylogeny-independent
methods such as {\tt ParaFit} or the Mantel test by eliminating an extreme (but
previously unrecognized) distortion of node-pair sampling. Analysis of 26 grass
species-epichlo\"e species symbioses did not reject random association of $H$
and $P$ MRCA ages. However, when five obvious host jumps were removed the
analysis significantly rejected random association and supported
grass-endophyte codivergence. Interestingly, early cladogenesis events in the
Pooideae corresponded to early cladogenesis events in epichloae, suggesting
concomitant origins of this grass subfamily and its remarkable group of
symbionts. We also applied our method to the well-known gopher-louse data set.


title: Finite-size domains in membranes with active two-state inclusions

abstract:
  The distribution of inclusion-rich domains in membranes with active two-state
inclusions is studied by simulations. Our study shows that typical size of
inclusion-rich domains ($L$) can be controlled by inclusion activities in
several ways. When there is effective attraction between state-1 inclusions, we
find: (i) Small domains with only several inclusions are observed for
inclusions with time scales ($\sim 10^{-3} {\rm s}$) and interaction energy
[$\sim \mathcal{O}({\rm k_BT})$] comparable to motor proteins. (ii) $L$ scales
as 1/3 power of the lifetime of state-1 for a wide range of parameters. (iii)
$L$ shows a switch-like dependence on state-2 lifetime $k_{12}^{-1}$. That is,
$L$ depends weakly on $k_{12}$ when $k_{12} < k_{12}^*$ but increases rapidly
with $k_{12}$ when $k_{12} > k_{12}^*$, the crossover $k_{12}^*$ occurs when
the diffusion length of a typical state-2 inclusion within its lifetime is
comparable to $L$. (iv) Inclusion-curvature coupling provides another length
scale that competes with the effects of transition rates.


title: All-atom ab initio folding of a diverse set of proteins

abstract:
  Natural proteins fold to a unique, thermodynamically dominant state. Modeling
of the folding process and prediction of the native fold of proteins are two
major unsolved problems in biophysics. Here, we show successful all-atom ab
initio folding of a representative diverse set of proteins, using a minimalist
transferable energy model that consists of two-body atom-atom interactions,
hydrogen-bonding, and a local sequence energy term that models
sequence-specific chain stiffness. Starting from a random coil, the native-like
structure was observed during replica exchange Monte Carlo (REMC) simulation
for most proteins regardless of their structural classes; the lowest energy
structure was close to native- in the range of 2-6 A root-mean-square deviation
(RMSD). Our results demonstrate that the successful all-atom folding of a
protein chain to its native state is governed by only a few crucial energetic
terms.


title: Microcolony and Biofilm Formation as a Survival Strategy for Bacteria

abstract:
  Bacterial communities such as biofilms are widely recognised as being
important for survival and persistence of bacteria in harsh environments.
Mechanistic models of biofilm growth indicate that the way in which the surface
is seeded can effect the morphology of simulated biofilms. Experimental studies
indicate that genes which are important for chemotaxis also influence biofilm
formation, perhaps by influencing aggregation on a surface. Understanding
aggregation and microcolony formation could therefore help clarify factors
influencing biofilm formation as well as understanding how groups may influence
the fitness of bacteria. In this paper I develop an Individual Based Model to
examine how different behaviours involved in microcolony formation on a surface
determines patterns of group sizes, and link patterns to bacterial fitness. I
also provide a method for comparing data with model hypotheses to identify
bacterial behaviours in experimental systems.


title:
A versatile electrophoresis system for the analysis of high- and
  low-molecular-weight proteins

abstract:
  A new, versatile, multiphasic buffer system for high-resolution sodium
dodecyl sulfate-polyacrylamide gel electrophoresis of proteins in the relative
molecular weight range of 300 000-3000 Da is described. The system, based on
the theory of multiphasic zone electrophoresis, allows complete stacking and
destacking of proteins in the above M(r) range. The buffer system uses taurine
and chloride as trailing and leading ion, respectively, and Tris, at a pH close
to its pK(a), as the buffering counterion. Coupled with limited variation in
the acrylamide concentration, this electrophoresis system allows to tailor the
resolution in the 6-200 kDa M(r) range, with minimal difficulties in the post
electrophoretic identification processes.


title:
Simulation of networks of spiking neurons: A review of tools and
  strategies

abstract:
  We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.


title: Breathing dynamics in heteropolymer DNA

abstract:
  While the statistical mechanical description of DNA has a long tradition,
renewed interest in DNA melting from a physics perspective is nourished by
measurements of the fluctuation dynamics of local denaturation bubbles by
single molecule spectroscopy. The dynamical opening of DNA bubbles (DNA
breathing) is supposedly crucial for biological functioning during, for
instance, transcription initiation and DNA's interaction with selectively
single-stranded DNA binding proteins. Motivated by this, we consider the bubble
breathing dynamics in a heteropolymer DNA based on a (2+1)-variable master
equation and complementary stochastic Gillespie simulations, providing the
bubble size and the position of the bubble along the sequence as a function of
time. We utilize new experimental data that independently obtain stacking and
hydrogen bonding contributions to DNA stability. We calculate the spectrum of
relaxation times and the experimentally measurable autocorrelation function of
a fluorophore-quencher tagged base-pair, and demonstrate good agreement with
fluorescence correlation experiments. A significant dependence of opening
probability and waiting time between bubble events on the local DNA sequence is
revealed and quantified for a promoter sequence of the T7 phage. The strong
dependence on sequence, temperature and salt concentration for the breathing
dynamics of DNA found here points at a good potential for nanosensing
applications by utilizing short fluorophore-quencher dressed DNA constructs.


title: Population models with singular equilibrium

abstract:
  A class of models of biological population and communities with a singular
equilibrium at the origin is analyzed; it is shown that these models can
possess a dynamical regime of deterministic extinction, which is crucially
important from the biological standpoint. This regime corresponds to the
presence of a family of homoclinics to the origin, so-called elliptic sector.
The complete analysis of possible topological structures in a neighborhood of
the origin, as well as asymptotics to orbits tending to this point, is given.
An algorithmic approach to analyze system behavior with parameter changes is
presented. The developed methods and algorithm are applied to existing
mathematical models of biological systems. In particular, we analyze a model of
anticancer treatment with oncolytic viruses, a parasite-host interaction model,
and a model of Chagas' disease.


title:
Genotype-based Case-Control Analysis, Violation of Hardy-Weinberg
  Equilibrium, and Phase Diagrams

abstract:
  We study in detail a particular statistical method in genetic case-control
analysis, labeled "genotype-based association", in which the two test results
from assuming dominant and recessive model are combined in one optimal output.
This method differs both from the allele-based association which artificially
doubles the sample size, and the direct chi-square test on 3-by-2 contingency
table which may overestimate the degree of freedom. We conclude that the
comparative advantage (or disadvantage) of the genotype-based test over the
allele-based test mainly depends on two parameters, the allele frequency
difference delta and the Hardy-Weinberg disequilibrium coefficient difference
delta_epsilon. Six different situations, called "phases", characterized by the
two X^2 test statistics in allele-based and genotype-based test, are well
separated in the phase diagram parameterized by delta and delta_epsilon. For
two major groups of phases, a single parameter theta = tan^-1
(delta/delta_epsilon) is able to achieves an almost perfect phase separation.
We also applied the analytic result to several types of disease models. It is
shown that for dominant and additive models, genotype-based tests are favored
over allele-based tests.


title:
Effective Sample Size: Quick Estimation of the Effect of Related Samples
  in Genetic Case-Control Association Analyses

abstract:
  Affected relatives are essential for pedigree linkage analysis, however, they
cause a violation of the independent sample assumption in case-control
association studies. To avoid the correlation between samples, a common
practice is to take only one affected sample per pedigree in association
analysis. Although several methods exist in handling correlated samples, they
are still not widely used in part because these are not easily implemented, or
because they are not widely known. We advocate the effective sample size method
as a simple and accessible approach for case-control association analysis with
correlated samples. This method modifies the chi-square test statistic,
p-value, and 95% confidence interval of the odds-ratio by replacing the
apparent number of allele or genotype counts with the effective ones in the
standard formula, without the need for specialized computer programs. We
present a simple formula for calculating effective sample size for many types
of relative pairs and relative sets. For allele frequency estimation, the
effective sample size method captures the variance inflation exactly. For
genotype frequency, simulations showed that effective sample size provides a
satisfactory approximation. A gene which is previously identified as a type 1
diabetes susceptibility locus, the interferon-induced helicase gene (IFIH1), is
shown to be significantly associated with rheumatoid arthritis when the
effective sample size method is applied. This significant association is not
established if only one affected sib per pedigree were used in the association
analysis. Relationship between the effective sample size method and other
methods -- the generalized estimation equation, variance of eigenvalues for
correlation matrices, and genomic controls -- are discussed.


title: Dynamics of allosteric transitions in GroEL

abstract:
  The chaperonin GroEL-GroES, a machine which helps some proteins to fold,
cycles through a number of allosteric states, the $T$ state, with high affinity
for substrate proteins (SPs), the ATP-bound $R$ state, and the
$R^{\prime\prime}$ ($GroEL-ADP-GroES$) complex. Structures are known for each
of these states. Here, we use a self-organized polymer (SOP) model for the
GroEL allosteric states and a general structure-based technique to simulate the
dynamics of allosteric transitions in two subunits of GroEL and the heptamer.
The $T \to R$ transition, in which the apical domains undergo counter-clockwise
motion, is mediated by a multiple salt-bridge switch mechanism, in which a
series of salt-bridges break and form. The initial event in the $R \to
R^{\prime\prime}$ transition, during which GroEL rotates clockwise, involves a
spectacular outside-in movement of helices K and L that results in K80-D359
salt-bridge formation. In both the transitions there is considerable
heterogeneity in the transition pathways. The transition state ensembles (TSEs)
connecting the $T$, $R$, and $R^{\prime\prime}$ states are broad with the the
TSE for the $T \to R$ transition being more plastic than the $R\to
R^{\prime\prime}$ TSE. The results suggest that GroEL functions as a
force-transmitting device in which forces of about (5-30) pN may act on the SP
during the reaction cycle.


title: Energetics of Protein-DNA Interactions

abstract:
  Protein-DNA interactions are vital for many processes in living cells,
especially transcriptional regulation and DNA modification. To further our
understanding of these important processes on the microscopic level, it is
necessary that theoretical models describe the macromolecular interaction
energetics accurately. While several methods have been proposed, there has not
been a careful comparison of how well the different methods are able to predict
biologically important quantities such as the correct DNA binding sequence,
total binding free energy, and free energy changes caused by DNA mutation. In
addition to carrying out the comparison, we present two important theoretical
models developed initially in protein folding that have not yet been tried on
protein-DNA interactions. In the process, we find that the results of these
knowledge-based potentials show a strong dependence on the interaction distance
and the derivation method. Finally, we present a knowledge-based potential that
gives comparable or superior results to the best of the other methods,
including the molecular mechanics force field AMBER99.


title:
Face Recognition in the Machine Reveals Properties of Human Face
  Recognition

abstract:
  Psychophysical studies suggest that face recognition takes place in a narrow
band of low spatial frequencies (``critical band''). Here, we examined the
recognition performance of an artificial face recognition system as a function
of the size of the input images. Recognition performance was quantified with
three discriminability measures: Fisher Linear Discriminant Analysis, non
Parametric Discriminant Analysis, and mutual information. All of the three
measures revealed a maximum at the same image sizes. Since spatial frequency
content is a function of image size, our data consistently predict the range of
psychophysical found frequencies. Our results therefore support the notion that
the critical band of spatial frequencies for face recognition in humans and
machines follows from inherent properties of face images.


title:
Improvement of the solubilization of proteins in two-dimensional
  electrophoresis with immobilized pH gradients

abstract:
  Membrane and nuclear proteins of poor solubility have been separated by high
resolution two-dimensional (2-D) gel electrophoresis. Isoelectric focusing with
immobilized pH gradients leads to severe quantitative losses of proteins in the
resulting 2-D map, although the resolution is usually high. Protein solubility
could be improved by using denaturing solutions containing various detergents
and chaotropes. Best results were obtained with a denaturing solution
containing urea, thiourea, and detergents (both nonionic and zwitterionic). The
usefulness of thiourea-containing denaturing mixtures is shown for microsomal
and nuclear proteins as well as for tubulin, a protein highly prone to
aggregation.


title: Cooperative transport by small teams of molecular motors

abstract:
  Molecular motors power directed transport of cargoes within cells. Even if a
single motor is sufficient to transport a cargo, motors often cooperate in
small teams. We discuss the cooperative cargo transport by several motors
theoretically and explore some of its properties. In particular we emphasize
how motor teams can drag cargoes through a viscous environment.


title: An FFT based measure of phase synchronization

abstract:
  In this paper phase of a signal has been viewed from a different angle.
According to this view a signal can have countably infinitely many phases, one
associated with each Fourier component. In other words each frequency has a
phase associated with it. It has been shown that if two signals are phase
synchronous then the difference between phases at a given component changes
very slowly across the subsequent components. This leads to an FFT based phase
synchronization measuring algorithm between any two signals. The algorithm does
not take any more time than the FFT itself. Mathematical motivations as well as
some results of implementation of the algorithm on artificially generated
signals and real EEG signals have been presented.


title: Allee Effects and Extinction in a Lattice Model

abstract:
  In the interest of conservation, the importance of having a large habitat
available for a species is widely known. Here, we introduce a lattice-based
model for a population and look at the importance of fluctuations as well as
that of the population density, particularly with respect to Allee effects. We
examine the model analytically and by Monte Carlo simulations and find that,
while the size of the habitat is important, there exists a critical population
density below which extinction is assured. This has large consequences with
respect to conservation, especially in the design of habitats and for
populations whose density has become small. In particular, we find that the
probability of survival for small populations can be increased by a reduction
in the size of the habitat and show that there exists an optimal size
reduction.


title:
Gene induction during differentiation of human monocytes into dendritic
  cells: an integrated study at the RNA and protein levels

abstract:
  Changes in gene expression occurring during differentiation of human
monocytes into dendritic cells were studied at the RNA and protein levels.
These studies showed the induction of several gene classes corresponding to
various biological functions. These functions encompass antigen processing and
presentation, cytoskeleton, cell signalling and signal transduction, but also
an increase in mitochondrial function and in the protein synthesis machinery,
including some, but not all, chaperones. These changes put in perspective the
events occurring during this differentiation process. On a more technical
point, it appears that the studies carried out at the RNA and protein levels
are highly complementary.


title:
Utilizing of protein splicing phenomenon for optimization of obtaining
  and purification of the recombinant human growth hormone

abstract:
  Protein splicing is a post-translational autocatalystic excision of internal
protein sequence (intein) with the subsequent ligation of the flanking
polypeptides (exteins). The high specificity of excision ensured by intein
makes it possible to use a phenomenon of protein splicing for the biotechnology
purposes. The aim of this work was optimization of obtaining and purification
of the recombinant human growth hormone using the protein splicing. It was
experimentally demonstrated that the use of modified intein as auto-removal
affine marker makes it possible to perform the rapid and cheap isolation of the
recombinant protein Hgh. Furthermore, this approach allows to obtain the human
growth hormone with native N-terminus, without formyl-metionine. Key words:
intein, human growth hormone, protein splicing


title:
Mechanistic home range models and resource selection analysis: a
  reconciliation and unification

abstract:
  In the three decades since its introduction, resource selection analysis
(RSA) has become a widespread method for analyzing spatial patterns of animal
relocations obtained from telemetry studies. Recently, mechanistic home range
models have been proposed as an alternative framework for studying patterns of
animal space use. In contrast to RSA models, mechanistic home range models are
derived from underlying mechanistic descriptions of individual movement
behavior and yield spatially-explicit predictions for patterns of animal
space-use. In addition, their mechanistic underpinning means that, unlike RSA,
mechanistic home range models can also be used to predict changes in space-use
following perturbation. In this paper, we develop a formal reconciliation
between these two methods of home range analysis, showing how differences in
the habitat preferences of individuals give rise to spatially-explicit patterns
of space-use. The resulting unified framework combines the simplicity of
resource selection analysis with the spatially-explicit and predictive
capabilities of mechanistic home range models.


abstract:
  The Neurobiology Of Thinking, Identity, And Geniality
  Abstract: Mathematically the axioms of representation are subtle, and
critical. The CNS expresses its function via its internal neuronal networks in
multidimensional, intrinsic frames. CNS operation can be represented by tensor
transformations within and among general frames of reference. So-called
metaorganization principle is transforming the dual representations of
intention from one to another. The property of intentionality refers to the
fact that consciousness is about objects or events, and is modulated by
attention. To construct a new neural framework we must outgoing basicly from
the competitive interactions among widely distributed groups of neurons. A
major system in this regard is the thalamocortical system. The integrative
dynamics of conscious experience suggests that the thalamocortical system
behaves as a functional cluster that interacts mainly with itself.


title:
Linguistic mechanism of the evolution of amino acid frequencies and
  genomic GC content

abstract:
  Much information is stored in amino acid composition of protein and base
composition of DNA. We simulated the evolution of amino acid frequencies and
genomic GC content by a linguistic model. It is showed that the evolution of
genetic code determines the evolution of amino acid frequencies and genomic GC
content. We explained the relationships among amino acid frequencies, genomic
GC content and protein length distribution in a unified theoretical framework.
Especially, the simulations of the evolution of amino acid frequencies and the
codon position GC content agree dramatically with the results based on the data
of all known genomes so far. Furthermore, we found that the space of average
protein length in proteome and ratio of amino acid frequencies is useful to
describe the phylogeny and evolution. Amazingly, the dots of all the species in
this space form an evolutionary flow. We believe that the amino acid gain and
loss is motivated by the established pattern of the variation of amino acid
frequencies. The linguistic mechanism is helpful to unveil the origin of the
genetic code.


title:
Stochastic approach to molecular interactions and computational theory
  of metabolic and genetic regulations

abstract:
  Binding and unbinding of ligands to specific sites of a macromolecule are one
of the most elementary molecular interactions inside the cell that embody the
computational processes of biological regulations. The interaction between
transcription factors and the operators of genes and that between ligands and
binding sites of allosteric enzymes are typical examples of such molecular
interactions. In order to obtain the general mathematical framework of
biological regulations, we formulate these interactions as finite Markov
processes and establish a computational theory of regulatory activities of
macromolecules based mainly on graphical analysis of their state transition
diagrams. The contribution is summarized as follows: (1) Stochastic
interpretation of Michaelis-Menten equation is given. (2) Notion of
\textit{probability flow} is introduced in relation to detailed balance. (3) A
stochastic analogy of \textit{Wegscheider condition} is given in relation to
loops in the state transition diagram. (4) A simple graphical method of
computing the regulatory activity in terms of ligands' concentrations is
obtained for Wegscheider cases.


title: Habitat width along a latitudinal gradient

abstract:
  We use the Chowdhury ecosystem model, one of the most complex agent-based
ecological models, to test the latitude-niche breadth hypothesis, with regard
to habitat width, i.e., whether tropical species generally have narrower
habitats than high latitude ones. Application of the model has given realistic
results in previous studies on latitudinal gradients in species diversity and
Rapoport's rule. Here we show that tropical species with sufficient vagility
and time to spread into adjacent habitats, tend to have wider habitats than
high latitude ones, contradicting the latitude-niche breadth hypothesis.


title: Clustering fetal heart rate tracings by compression

abstract:
  Fetal heart rate (FHR) monitoring, before and during labor, is a very
important medical practice in the detection of fetuses in danger. We clustered
FHR tracings by compression in order to identify abnormal ones. We use a
recently introduced approach based on algorithmic information theory, a
theoretical, rigorous and well-studied notion of information content in
individual objects. The new method can mine patterns in completely different
areas, there are no domain-specific parameters to set, and it does not require
specific background knowledge. At the highest level the FHR tracings were
clustered according to an unanticipated feature, namely the technology used in
signal acquisition. At the lower levels all tracings with abnormal or
suspicious patterns were clustered together, independent of the technology
used. Moreover, FHR tracings with future poor neonatal outcomes were included
in the cluster with other suspicious patterns.


title: The astrocyte as a gatekeeper of synaptic information transfer

abstract:
  We present a simple biophysical model for the coupling between synaptic
transmission and the local calcium concentration on an enveloping astrocytic
domain. This interaction enables the astrocyte to modulate the information flow
from presynaptic to postsynaptic cells in a manner dependent on previous
activity at this and other nearby synapses. Our model suggests a novel,
testable hypothesis for the spike timing statistics measured for rapidly-firing
cells in culture experiments.


title: Effects of Selection Logging on Rainforest Productivity

abstract:
  An analysis of data from 212 permanent sample plots provided no evidence of
any decline in rainforest productivity after three cycles of selection logging
in the tropical rainforests of north Queensland. Relative productivity was
determined as the difference between observed diameter increments and
increments predicted from a diameter increment function which incorporated tree
size, stand density and site quality. Analyses of variance and regression
analyses revealed no significant decline in productivity after repeated
harvesting. There is evidence to support the assertion that if any permanent
productivity decline exists, it does not exceed six per cent per harvest.


title:
Beneficial mutation-selection balance and the effect of linkage on
  positive selection

abstract:
  When beneficial mutations are rare, they accumulate by a series of selective
sweeps. But when they are common, many beneficial mutations will occur before
any can fix, so there will be many different mutant lineages in the population
concurrently. In an asexual population, these different mutant lineages
interfere and not all can fix simultaneously. In addition, further beneficial
mutations can accumulate in mutant lineages while these are still a minority of
the population. In this paper, we analyze the dynamics of such multiple
mutations and the interplay between multiple mutations and interference between
clones. These result in substantial variation in fitness accumulating within a
single asexual population. The amount of variation is determined by a balance
between selection, which destroys variation, and beneficial mutations, which
create more. The behavior depends in a subtle way on the population parameters:
the population size, the beneficial mutation rate, and the distribution of the
fitness increments of the potential beneficial mutations. The
mutation-selection balance leads to a continually evolving population with a
steady-state fitness variation. This variation increases logarithmically with
both population size and mutation rate and sets the rate at which the
population accumulates beneficial mutations, which thus also grows only
logarithmically with population size and mutation rate. These results imply
that mutator phenotypes are less effective in larger asexual populations. They
also have consequences for the advantages (or disadvantages) of sex via the
Fisher-Muller effect; these are discussed briefly.


title: The Regularizing Capacity of Metabolic Networks

abstract:
  Despite their topological complexity almost all functional properties of
metabolic networks can be derived from steady-state dynamics. Indeed, many
theoretical investigations (like flux-balance analysis) rely on extracting
function from steady states. This leads to the interesting question, how
metabolic networks avoid complex dynamics and maintain a steady-state behavior.
Here, we expose metabolic network topologies to binary dynamics generated by
simple local rules. We find that the networks' response is highly specific:
Complex dynamics are systematically reduced on metabolic networks compared to
randomized networks with identical degree sequences. Already small topological
modifications substantially enhance the capacity of a network to host complex
dynamic behavior and thus reduce its regularizing potential. This exceptionally
pronounced regularization of dynamics encoded in the topology may explain, why
steady-state behavior is ubiquitous in metabolism.


title: The Berry phase and the pump flux in stochastic chemical kinetics

abstract:
  We study a classical two-state stochastic system in a sea of substrates and
products (absorbing states), which can be interpreted as a single
Michaelis-Menten catalyzing enzyme or as a channel on a cell surface. We
introduce a novel general method and use it to derive the expression for the
full counting statistics of transitions among the absorbing states. For the
evolution of the system under a periodic perturbation of the kinetic rates, the
latter contains a term with a purely geometrical (the Berry phase)
interpretation. This term gives rise to a pump current between the absorbing
states, which is due entirely to the stochastic nature of the system. We
calculate the first two cumulants of this current, and we argue that it is
observable experimentally.


title:
Insects, Trees, and Climate: The Bioacoustic Ecology of Deforestation
  and Entomogenic Climate Change

abstract:
  Accumulating observational evidence suggests an intimate connection between
rapidly expanding insect populations, deforestation, and global climate change.
We review the evidence, emphasizing the vulnerability of key planetary carbon
pools, especially the Earth's forests that link the micro-ecology of insect
infestation to climate. We survey current research regimes and insect control
strategies, concluding that at present they are insufficient to cope with the
problem's present regional scale and its likely future global scale. We propose
novel bioacoustic interactions between insects and trees as key drivers of
infestation population dynamics and the resulting wide-scale deforestation. The
bioacoustic mechanisms suggest new, nontoxic control interventions and
detection strategies.


title:
Anharmonicity and self-similarity of the free energy landscape of
  protein G

abstract:
  The near-native free energy landscape of protein G is investigated through
0.4 microseconds-long atomistic molecular dynamics simulations in explicit
solvent. A theoretical and computational framework is used to assess the
time-dependence of salient thermodynamical features. While the quasi-harmonic
character of the free energy is found to degrade in a few ns, the slow modes
display a very mild dependence on the trajectory duration. This property
originates from a striking self-similarity of the free energy landscape
embodied by the consistency of the principal directions of the local minima,
where the system dwells for several ns, and of the virtual jumps connecting
them.


title:
Angular velocity variations and stability of spatially explicit
  prey-predator systems

abstract:
  The linear instability of Lotka-Volterra orbits in the homogenous manifold of
a two-patch system is analyzed. The origin of these orbits instability in the
absence of prey migration is revealed to be the dependence of the angular
velocity on the azimuthal angle; in particular, the system desynchronizes at
the exit from the slow part of the trajectory. Using this insight, an analogous
model of a two coupled oscillator is presented and shown to yield the same type
of linear instability. This unables one to incorporate the linear instability
within a recently presented general framework that allows for comparison of all
known stabilization mechanisms and for simple classification of observed
oscillations.


title:
A number theoretical observation about the degeneracy of the genetic
  code

abstract:
  We discuss the similarity of the degeneration structure of the genetic code
with a pure number theoretic -- ``divisors code.'' The most interesting thing
about our observation is not that there is a connection between number theory
and the genetic code, but the simplicity of the rule. We hope that the
observation and the naive model presented in this paper will serve for ideas to
other models of the degeneracy of the genetic code. Maybe, the ideas of this
article can also be used in the area of artificial life to syntesize artificial
genetic codes.


title: Quantifying the taxonomic diversity in real species communities

abstract:
  We analyze several florae (collections of plant species populating specific
areas) in different geographic and climatic regions. For every list of species
we produce a taxonomic classification tree and we consider its statistical
properties. We find that regardless of the geographical location, the climate
and the environment all species collections have universal statistical
properties that we show to be also robust in time. We then compare observed
data sets with simulated communities obtained by randomly sampling a large pool
of species from all over the world. We find differences in the behavior of the
statistical properties of the corresponding taxonomic trees. Our results
suggest that it is possible to distinguish quantitatively real species
assemblages from random collections and thus demonstrate the existence of
correlations between species.


title: Single-crossover dynamics: finite versus infinite populations

abstract:
  Populations evolving under the joint influence of recombination and
resampling (traditionally known as genetic drift) are investigated. First, we
summarise and adapt a deterministic approach, as valid for infinite
populations, which assumes continuous time and single crossover events. The
corresponding nonlinear system of differential equations permits a closed
solution, both in terms of the type frequencies and via linkage disequilibria
of all orders. To include stochastic effects, we then consider the
corresponding finite-population model, the Moran model with single crossovers,
and examine it both analytically and by means of simulations. Particular
emphasis is on the connection with the deterministic solution. If there is only
recombination and every pair of recombined offspring replaces their pair of
parents (i.e., there is no resampling), then the {\em expected} type
frequencies in the finite population, of arbitrary size, equal the type
frequencies in the infinite population. If resampling is included, the
stochastic process converges, in the infinite-population limit, to the
deterministic dynamics, which turns out to be a good approximation already for
populations of moderate size.


title: Single neuron computation: from dynamical system to feature detector

abstract:
  White noise methods are a powerful tool for characterizing the computation
performed by neural systems. These methods allow one to identify the feature or
features that a neural system extracts from a complex input, and to determine
how these features are combined to drive the system's spiking response. These
methods have also been applied to characterize the input/output relations of
single neurons driven by synaptic inputs, simulated by direct current
injection. To interpret the results of white noise analysis of single neurons,
we would like to understand how the obtained feature space of a single neuron
maps onto the biophysical properties of the membrane, in particular the
dynamics of ion channels. Here, through analysis of a simple dynamical model
neuron, we draw explicit connections between the output of a white noise
analysis and the underlying dynamical system. We find that under certain
assumptions, the form of the relevant features is well defined by the
parameters of the dynamical system. Further, we show that under some
conditions, the feature space is spanned by the spike-triggered average and its
successive order time derivatives.


title: Dwell time symmetry in random walks and molecular motors

abstract:
  The statistics of steps and dwell times in reversible molecular motors differ
from those of cycle completion in enzyme kinetics. The reason is that a step is
only one of several transitions in the mechanochemical cycle. As a result,
theoretical results for cycle completion in enzyme kinetics do not apply to
stepping data. To allow correct parameter estimation, and to guide data
analysis and experiment design, a theoretical treatment is needed that takes
this observation into account. In this paper, we model the distribution of
dwell times and number of forward and backward steps using first passage
processes, based on the assumption that forward and backward steps correspond
to different directions of the same transition. We extend recent results for
systems with a single cycle and consider the full dwell time distributions as
well as models with multiple pathways, detectable substeps, and detachments.
Our main results are a symmetry relation for the dwell time distributions in
reversible motors, and a relation between certain relative step frequencies and
the free energy per cycle. We demonstrate our results by analyzing recent
stepping data for a bacterial flagellar motor, and discuss the implications for
the efficiency and reversibility of the force-generating subunits. Key words:
motor proteins; single molecule kinetics; enzyme kinetics; flagellar motor;
Markov process; non-equilibrium fluctuations.


title:
Phase Diagrams of Quasispecies Theory with Recombination and Horizontal
  Gene Transfer

abstract:
  We consider how transfer of genetic information between individuals
influences the phase diagram and mean fitness of both the Eigen and the
parallel, or Crow-Kimura, models of evolution. In the absence of genetic
transfer, these physical models of evolution consider the replication and point
mutation of the genomes of independent individuals in a large population. A
phase transition occurs, such that below a critical mutation rate an
identifiable quasispecies forms. We generalize these models of quasispecies
evolution to include horizontal gene transfer. We show how transfer of genetic
information changes the phase diagram and mean fitness and introduces
metastability in quasispecies theory, via an analytic field theoretic mapping.


title:
New structural variation in evolutionary searches of RNA neutral
  networks

abstract:
  RNA secondary structure is an important computational model to understand how
genetic variation maps into phenotypic (structural) variation. Evolutionary
innovation in RNA structures is facilitated by neutral networks, large
connected sets of RNA sequences that fold into the same structure. Our work
extends and deepens previous studies on neutral networks. First, we show that
even the 1-mutant neighborhood of a given sequence (genotype) G0 with structure
(phenotype) P contains many structural variants that are not close to P. This
holds for biological and generic RNA sequences alike. Second, we analyze the
relation between new structures in the 1-neighborhoods of genotypes Gk that are
only a moderate Hamming distance k away from G0, and the structure of G0
itself, both for biological and for generic RNA structures. Third, we analyze
the relation between mutational robustness of a sequence and the distances of
structural variants near this sequence. Our findings underscore the role of
neutral networks in evolutionary innovation, and the role that high robustness
can play in diminishing the potential for such innovation.


title: Sensitivity dependent model of protein-protein interaction networks

abstract:
  The scale free structure p(k)~k^{-gamma} of protein-protein interaction
networks can be reproduced by a static physical model in simulation. We inspect
the model theoretically, and find the key reason for the model to generate
apparent scale free degree distributions. This explanation provides a generic
mechanism of "scale free" networks. Moreover, we predict the dependence of
gamma on experimental protein concentrations or other sensitivity factors in
detecting interactions, and find experimental evidence to support the
prediction.


title:
Cognitive Maps of Complex Systems Show Hierarchical Structure and
  Scale-Free Properties

abstract:
  Many networks in natural and human-made systems exhibit scale-free properties
and are small worlds. Now we show that people's understanding of complex
systems in their cognitive maps also follow a scale-free topology (P_k =
k^-lambda, lambda [1.24,3.03]; r^2 <= 0.95). People focus on a few attributes,
as indicated by a fat tail in the probability distribution of total degree.
These few attributes are related with many other variables in the system. Many
more attributes have very few connections. The scale-free properties in the
cognitive maps of people arise despite the fact that their average distances
are not different (Wilcoxon sign-rank test, W=78, p=0.75) than random networks
of the same size and connection density. The scale-free property manifests
itself in the higher hierarchical structure compared to random networks
(Wilcoxon sign-rank test, W=12, p=0.03). People use relatively short
explanations to describe systems. These findings may help us to better
understand people's perceptions, especially when it comes to decision-making,
conflict resolution, politics and management.


title: Folding of the Protein Domain hbSBD

abstract:
  The folding of the alpha-helice domain hbSBD of the mammalian mitochondrial
branched-chain alpha-ketoacid dehydrogenase (BCKD) complex is studied by the
circular dichroism technique in absence of urea. Thermal denaturation is used
to evaluate various thermodynamic parameters defining the equilibrium
unfolding, which is well described by the two-state model with the folding
temperature T_f = 317.8 K and the enthalpy change Delta H_g = 19.67 kcal/mol.
The folding is also studied numerically using the off-lattice coarse-grained Go
model and the Langevin dynamics. The obtained results, including the population
of the native basin, the free energy landscape as a function of the number of
native contacts and the folding kinetics, also suggest that the hbSBD domain is
a two-state folder. These results are consistent with the biological function
of hbSBD in BCKD.


title: Monotone and near-monotone network structure (part I)

abstract:
  This paper (parts I and II) provides an expository introduction to monotone
and near-monotone dynamical systems associated to biochemical networks, those
whose graphs are consistent or near-consistent. Many conclusions can be drawn
from signed network structure, associated to purely stoichiometric information
and ignoring fluxes. In particular, monotone systems respond in a predictable
fashion to perturbations and have robust and ordered dynamical characteristics,
making them reliable components of larger networks. Interconnections of
monotone systems may be fruitfully analyzed using tools from control theory, by
viewing larger systems as interconnections of monotone subsystems. This allows
one to obtain precise bifurcation diagrams without appeal to explicit knowledge
of fluxes or of kinetic constants and other parameters, using merely
"input/output characteristics" (steady-state responses or DC gains). The
procedure may be viewed as a "model reduction" approach in which monotone
subsystems are viewed as essentially one-dimensional objects. The possibility
of performing a decomposition into a small number of monotone components is
closely tied to the question of how "near" a system is to being monotone. We
argue that systems that are "near monotone" may be more biologically more
desirable than systems that are far from being monotone. Indeed, there are
indications that biological networks may be much closer to being monotone than
random networks that have the same numbers of vertices and of positive and
negative edges.


title: Monotone and near-monotone network structure (part II)


title: Principles of microRNA regulation of a human cellular signaling network

abstract:
  MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs, which suppress gene
expression by selectively binding to the 3-noncoding region of specific message
RNAs through base-pairing. Given the diversity and abundance of miRNA targets,
miRNAs appear to functionally interact with various components of many cellular
networks. By analyzing the interactions between miRNAs and a human cellular
signaling network, we found that miRNAs predominantly target positive
regulatory motifs, highly connected scaffolds and most downstream network
components such as signaling transcription factors, but less frequently target
negative regulatory motifs, common components of basic cellular machines and
most upstream network components such as ligands. In addition, when an adaptor
has potential to recruit more downstream components, these components are more
frequently targeted by miRNAs. This work uncovers the principles of miRNA
regulation of signal transduction networks and implies a potential function of
miRNAs for facilitating robust transitions of cellular response to
extracellular signals and maintaining cellular homeostasis.


title:
Network formation of tissue cells via preferential attraction to
  elongated structures

abstract:
  Vascular and non-vascular cells often form an interconnected network in
vitro, similar to the early vascular bed of warm blooded embryos. Our
time-lapse recordings show that the network forms by extending sprouts, i.e.,
multicellular linear segments. To explain the emergence of such structures, we
propose a simple model of preferential attraction to stretched cells. Numerical
simulations reveal that the model evolves into a quasi-stationary pattern
containing linear segments, which interconnect above the critical volume
fraction of 0.2. In the quasi-stationary state the generation of new branches
offset the coarsening driven by surface tension. In agreement with empirical
data, the characteristic size of the resulting polygonal pattern is
density-independent within a wide range of volume fractions.


title: How behavioral constraints may determine optimal sensory representations

abstract:
  The sensory-triggered activity of a neuron is typically characterized in
terms of a tuning curve, which describes the neuron's average response as a
function of a parameter that characterizes a physical stimulus. What determines
the shapes of tuning curves in a neuronal population? Previous theoretical
studies and related experiments suggest that many response characteristics of
sensory neurons are optimal for encoding stimulus-related information. This
notion, however, does not explain the two general types of tuning profiles that
are commonly observed: unimodal and monotonic. Here, I quantify the efficacy of
a set of tuning curves according to the possible downstream motor responses
that can be constructed from them. Curves that are optimal in this sense may
have monotonic or non-monotonic profiles, where the proportion of monotonic
curves and the optimal tuning curve width depend on the general properties of
the target downstream functions. This dependence explains intriguing features
of visual cells that are sensitive to binocular disparity and of neurons tuned
to echo delay in bats. The numerical results suggest that optimal sensory
tuning curves are shaped not only by stimulus statistics and signal-to-noise
properties, but also according to their impact on downstream neural circuits
and, ultimately, on behavior.


title:
Simulating Brain Tumor Heterogeneity with a Multiscale Agent-Based
  Model: Linking Molecular Signatures, Phenotypes and Expansion Rate

abstract:
  We have extended our previously developed 3D multi-scale agent-based brain
tumor model to simulate cancer heterogeneity and to analyze its impact across
the scales of interest. While our algorithm continues to employ an epidermal
growth factor receptor (EGFR) gene-protein interaction network to determine the
cells' phenotype, it now adds an explicit treatment of tumor cell adhesion
related to the model's biochemical microenvironment. We simulate a simplified
tumor progression pathway that leads to the emergence of five distinct glioma
cell clones with different EGFR density and cell 'search precisions'. The in
silico results show that microscopic tumor heterogeneity can impact the tumor
system's multicellular growth patterns. Our findings further confirm that EGFR
density results in the more aggressive clonal populations switching earlier
from proliferation-dominated to a more migratory phenotype. Moreover, analyzing
the dynamic molecular profile that triggers the phenotypic switch between
proliferation and migration, our in silico oncogenomics data display spatial
and temporal diversity in documenting the regional impact of tumorigenesis, and
thus support the added value of multi-site and repeated assessments in vitro
and in vivo. Potential implications from this in silico work for experimental
and computational studies are discussed.


title: Information Theory of Genomes

abstract:
  Relation of genome sizes to organisms complexity is still described rather
equivocally. Neither the number of genes (G-value), nor the total amount of DNA
(C-value) correlates consistently with phenotype complexity. Using information
theory considerations we developed a model that allows a quantative estimate
for the amount of functional information in a genomic sequence. This model
easily answers the long-standing question of why GC content is increased in
functional regions. The model allows consistent estimate of genome
complexities, resolving the major discrepancies of G- and C-values. For related
organisms with similarly complex phenotypes, this estimate provides biological
insights into their niches complexities. This theoretical framework suggests
that biological information can rapidly evolve on demand from environment,
mainly in non-coding genomic sequence and explains the role of duplications in
the evolution of biological information. Knowing the approximate amount of
functionality in a genomic sequence is useful for many applications such as
phylogenetics analyses, in-silico functional elements discovery or prioritising
targets for genotyping and sequencing.


title: Effect of finite size on cooperativity and rates of protein folding

abstract:
  We analyze the dependence of cooperativity of the thermal denaturation
transition and folding rates of globular proteins on the number of amino acid
residues, $N$, using lattice models with side chains,off-lattice Go models and
the available experimental data. A dimensionless measure of cooperativity,
$\Omega_c$ ($0 < \Omega_c < \infty$), scales as $\Omega_c \sim N^{\zeta}$. The
results of simulations and the analysis of experimental data further confirm
the earlier prediction that $\zeta$ is universal with $\zeta = 1 +\gamma$,
where exponent $\gamma$ characterizes the susceptibility of a self-avoiding
walk. This finding suggests that the structural characteristics in the
denaturated state are manifested in the folding cooperativity at the transition
temperature. The folding rates $k_F$ for the Go models and a dataset of 69
proteins can be fit using $k_F = k_F^0 \exp(-cN^\beta)$. Both $\beta = 1/2$ and
2/3 provide a good fit of the data. We find that $k_F = k_F^0
\exp(-cN^{{1/2}})$, with the average (over the dataset of proteins) $k_F^0
\approx (0.2\mu s)^{-1}$ and $c \approx 1.1$, can be used to estimate folding
rates to within an order of magnitude in most cases. The minimal models give
identical $N$ dependence with $c \approx 1$. The prefactor for off-lattice Go
models is nearly four orders of magnitude larger than the experimental value.


title:
Survey of the didemnins: A class of depsipeptide natural products with
  promising biomedical applications

abstract:
  The didemnins represent a versatile class of depsipeptides of marine origin
and hold a great deal of potential for biomedical application. The biological
and geographical origins of the didemnins are reviewed in addition to the
chemical structures of the major didemnins. The biological mechanisms behind
the antiviral and anticancer effects of selected didemnins are summarized and
the special case of dehydrodidemnin B (Aplidin) is expounded upon including
structural characteristics, synthesis, pharmacological mechanism and a
discussion of its current clinical trials as an anticancer agent.


title: Optimal signal processing in small stochastic biochemical networks

abstract:
  We quantify the influence of the topology of a transcriptional regulatory
network on its ability to process environmental signals. By posing the problem
in terms of information theory, we may do this without specifying the function
performed by the network. Specifically, we study the maximum mutual information
between the input (chemical) signal and the output (genetic) response
attainable by the network in the context of an analytic model of particle
number fluctuations. We perform this analysis for all biochemical circuits,
including various feedback loops, that can be built out of 3 chemical species,
each under the control of one regulator. We find that a generic network,
constrained to low molecule numbers and reasonable response times, can
transduce more information than a simple binary switch and, in fact, manages to
achieve close to the optimal information transmission fidelity. These
high-information solutions are robust to tenfold changes in most of the
networks' biochemical parameters; moreover they are easier to achieve in
networks containing cycles with an odd number of negative regulators (overall
negative feedback) due to their decreased molecular noise (a result which we
derive analytically). Finally, we demonstrate that a single circuit can support
multiple high-information solutions. These findings suggest a potential
resolution of the "cross-talk" dilemma as well as the previously unexplained
observation that transcription factors which undergo proteolysis are more
likely to be auto-repressive.


title:
A robust methodology for inferring physiology of a protein family:
  application to K+-ion channel family

abstract:
  We are interested in the subtle variations of function among the members of a
protein family. A protein family is usually subdivided into subfamilies based
on functional differences. Existence of this functional diversity is essential
for the successful performance of physiological roles expected of the family.
This presents a unique problem: there must be preservation of the active site;
simultaneously there should be specificity of protein action according to
subfamily. Though the classification into subfamilies is by no means a
formalized one, it is most times based on the character of regulation of the
primary function. The function of a subfamily is a modification of when the
protein performs its function, for example, by changing the protein's
sensitivity to regulatory factors. Rarely, a subfamily possesses a function
completely different for its family. A study of these details is necessary for
understanding the fine-tuning of protein function. I describe a theory for
studying subfamily-based functional specificity and then validate it with an
example application to deciphering the residue-level basis of fine functional
variations in the diverse set of K+-channel subfamilies. I provide specific
results that will be useful to channel physiologists, whereas the strategy
developed will be widely applicable to problems in comparative and functional
genomics.


title:
Hybridization to surface-bound oligonucleotide probes: Influence of
  point defects

abstract:
  Microarray-based genotyping is based on the high discrimination capability of
oligonucleotide probes. For detection of Single Nucleotide Polymorphisms (SNPs)
single-base discrimination is required. We investigate how various
point-mutations, comprising single base mismatches (MMs), insertions and
deletions, affect hybridization of DNA-DNA oligonucleotide duplexes. Employing
light-directed in situ synthesis we fabricate DNA microarrays with
comprehensive sets of cognate point-mutated probes, allowing us to
systematically investigate the influence of defect type, position and nearest
neighbor effects. Defect position has been identified as the dominating
influential factor. This positional effect which is almost identical for the
different point-mutation types, is biased from the local sequence environment.
The impact of the MM type is largely determined by the type of base pair
(either AT or CG) affected by the mismatch. We observe that single base
insertions next to like-bases result in considerably larger hybridization
signals than insertions next to nonidentical bases. The latter as well as the
distinct position dependence could be explained by a kinetic zipper model in
which point defects represent a barrier for the rapid closure of the DNA
duplex.


title: A complete classification of epistatic two-locus models

abstract:
  The study of epistasis is of great importance in statistical genetics in
fields such as linkage and association analysis and QTL mapping. In an effort
to classify the types of epistasis in the case of two biallelic loci Li and
Reich listed and described all models in the simplest case of 0/1 penetrance
values. However, they left open the problem of finding a classification of
two-locus models with continuous penetrance values. We provide a complete
classification of biallelic two-locus models. In addition to solving the
classification problem for dichotomous trait disease models, our results apply
to any instance where real numbers are assigned to genotypes, and provide a
complete framework for studying epistasis in QTL data. Our approach is
geometric and we show that there are 387 distinct types of two-locus models,
which can be reduced to 69 when symmetry between loci and alleles is accounted
for. The model types are defined by 86 circuits, which are linear combinations
of genotype values, each of which measures a fundamental unit of interaction.
The circuits provide information on epistasis beyond that contained in the
additive x add, add x dom, and dom x dom interaction terms. We discuss the
connection between our classification and standard epistatic models and
demonstrate its utility by analyzing a previously published dataset.


title:
Using Adaptive Dynamic Programming to Understand and Replicate Brain
  Intelligence: the Next Level Design

abstract:
  Since the 1960s I proposed that we could understand and replicate the highest
level of intelligence seen in the brain, by building ever more capable and
general systems for adaptive dynamic programming (ADP), which is like
reinforcement learning but based on approximating the Bellman equation and
allowing the controller to know its utility function. Growing empirical
evidence on the brain supports this approach. Adaptive critic systems now meet
tough engineering challenges and provide a kind of first-generation model of
the brain. Lewis, Prokhorov and myself have early second-generation work.
Mammal brains possess three core capabilities, creativity/imagination and ways
to manage spatial and temporal complexity, even beyond the second generation.
This paper reviews previous progress, and describes new tools and approaches to
overcome the spatial complexity gap.


title: An introduction to reconstructing ancestral genomes

abstract:
  Recent advances in high-throughput genomics technologies have resulted in the
sequencing of large numbers of (near) complete genomes. These genome sequences
are being mined for important functional elements, such as genes. They are also
being compared and contrasted in order to identify other functional sequences,
such as those involved in the regulation of genes. In cases where DNA sequences
from different organisms can be determined to have originated from a common
ancestor, it is natural to try to infer the an- cestral sequences. The
reconstruction of ancestral genomes can lead to insights about genome
evolution, and the origins and diversity of function. There are a number of
interesting foundational questions associated with reconstructing ancestral
genomes: Which statistical models for evolution should be used for making
inferences about ancestral sequences? How should extant genomes be compared in
order to facilitate ancestral reconstruction? Which portions of ancestral
genomes can be reconstructed reliably, and what are the limits of ancestral
reconstruction? We discuss recent progress on some of these questions, offer
some of our own opinions, and highlight interesting mathematics, statistics,
and computer science problems.


title: Preservation of information in a prebiotic package model

abstract:
  The coexistence between different informational molecules has been the
preferred mode to circumvent the limitation posed by imperfect replication on
the amount of information stored by each of these molecules. Here we reexamine
a classic package model in which distinct information carriers or templates are
forced to coexist within vesicles, which in turn can proliferate freely through
binary division. The combined dynamics of vesicles and templates is described
by a multitype branching process which allows us to write equations for the
average number of the different types of vesicles as well as for their
extinction probabilities. The threshold phenomenon associated to the extinction
of the vesicle population is studied quantitatively using finite-size scaling
techniques. We conclude that the resultant coexistence is too frail in the
presence of parasites and so confinement of templates in vesicles without an
explicit mechanism of cooperation does not resolve the information crisis of
prebiotic evolution.


title:
Locating the Long-range Contact Centers with B-factors and Hydrophobic
  Cores

abstract: TThe paper had many errors.


title: Neuronal networks and controlled symmetries, a generic framework

abstract:
  The extraordinary computational power of the brain may be related in part to
the fact that each of the smaller neural networks that compose it can behave
transiently in many different ways, depending on its inputs. Mathematically,
input continuity helps to show how a large network, constructed recursively
from smaller blocks, can exhibit robust specific properties according to its
input. By extending earlier work on synchrony and symmetry, we exploit input
continuity of contracting systems to ensure robust control of diverse spatial
and spatio-temporal symmetries of the output signal in such a network.


title:
Neural coding of a natural stimulus ensemble: Uncovering information at
  sub-millisecond resolution

abstract:
  Our knowledge of the sensory world is encoded by neurons in sequences of
discrete, identical pulses termed action potentials or spikes. There is
persistent controversy about the extent to which the precise timing of these
spikes is relevant to the function of the brain. We revisit this issue, using
the motion-sensitive neurons of the fly visual system as a test case. New
experimental methods allow us to deliver more nearly natural visual stimuli,
comparable to those which flies encounter in free, acrobatic flight, and new
mathematical methods allow us to draw more reliable conclusions about the
information content of neural responses even when the set of possible responses
is very large. We find that significant amounts of visual information are
represented by details of the spike train at millisecond and sub-millisecond
precision, even though the sensory input has a correlation time of ~60ms;
different patterns of spike timing represent distinct motion trajectories, and
the absolute timing of spikes points to particular features of these
trajectories with high precision. Under these naturalistic conditions, the
system continues to transmit more information at higher photon flux, even
though individual photoreceptors are counting more than one million photons per
second, and removes redundancy in the stimulus to generate a more efficient
neural code.


title:
MicroRNAs preferentially target the genes with high transcriptional
  regulation complexity

abstract:
  Over the past few years, microRNAs (miRNAs) have emerged as a new prominent
class of gene regulatory factors that negatively regulate expression of
approximately one-third of the genes in animal genomes at post-transcriptional
level. However, it is still unclear why some genes are regulated by miRNAs but
others are not, i.e. what principles govern miRNA regulation in animal genomes.
In this study, we systematically analyzed the relationship between
transcription factors (TFs) and miRNAs in gene regulation. We found that the
genes with more TF-binding sites have a higher probability of being targeted by
miRNAs and have more miRNA-binding sites on average. This observation reveals
that the genes with higher cis-regulation complexity are more coordinately
regulated by TFs at the transcriptional level and by miRNAs at the
post-transcriptional level. This is a potentially novel discovery of mechanism
for coordinated regulation of gene expression. Gene ontology analysis further
demonstrated that such coordinated regulation is more popular in the
developmental genes.


title: The role of input noise in transcriptional regulation

abstract:
  Even under constant external conditions, the expression levels of genes
fluctuate. Much emphasis has been placed on the components of this noise that
are due to randomness in transcription and translation; here we analyze the
role of noise associated with the inputs to transcriptional regulation, the
random arrival and binding of transcription factors to their target sites along
the genome. This noise sets a fundamental physical limit to the reliability of
genetic control, and has clear signatures, but we show that these are easily
obscured by experimental limitations and even by conventional methods for
plotting the variance vs. mean expression level. We argue that simple, global
models of noise dominated by transcription and translation are inconsistent
with the embedding of gene expression in a network of regulatory interactions.
Analysis of recent experiments on transcriptional control in the early
Drosophila embryo shows that these results are quantitatively consistent with
the predicted signatures of input noise, and we discuss the experiments needed
to test the importance of input noise more generally.


title:
The micromechanics of fluid-solid interactions during growth in porous
  soft biological tissue

abstract:
  In this paper we address some modelling issues related to biological growth.
Our treatment is based on a recently-proposed, general formulation for growth
within the context of Mixture Theory (Journal of the Mechanics and Physics of
Solids, 52, 2004, 1595--1625). We aim to enhance this treatment by making it
more appropriate for the biophysics of growth in porous soft tissue,
specifically tendon. This involves several modifications to the mathematical
formulation to represent the reactions, transport and mechanics, and their
interactions. We also reformulate the governing differential equations for
reaction-transport to represent the incompressibility constraint on the fluid
phase of the tissue. This revision enables a straightforward implementation of
numerical stabilisation for the hyperbolic, or advection-dominated, limit. A
finite element implementation employing an operator splitting scheme is used to
solve the coupled, non-linear partial differential equations that arise from
the theory. Motivated by our experimental model, an in vitro scaffold-free
engineered tendon formed by self-assembly of tendon fibroblasts (Tissue
Engineering, 10, 2004, 755--761), we solve several numerical examples
demonstrating biophysical aspects of tissue growth, and the improved numerical
performance of the models.


title: Directionality is an inherent property of biochemical networks

abstract:
  Thermodynamic constraints on reactions directions are inherent in the
structure of a given biochemical network. However, concrete procedures for
determining feasible reaction directions for large-scale metabolic networks are
not well established. This work introduces a systematic approach to compute
reaction directions, which are constrained by mass balance and thermodynamics,
for genome-scale networks. In addition, it is shown that the nonconvex solution
space constrained by physicochemical constraints can be approximated by a set
of linearized subspaces in which mass and thermodynamic balance are guaranteed.
The developed methodology can be used to {\it ab initio} predict reaction
directions of genome-scale networks based solely on the network stoichoimetry.


title: Reaction-noise induced homochirality

abstract:
  Starting from the chemical master equation, we employ field theoretic
techniques to derive Langevin-type equations that exactly describe the
stochastic dynamics of the Frank chiral amplification model with spatial
diffusion. The intrinsic multiplicative noise properties are completely and
rigorously derived by this procedure. We carry out numerical simulations in two
spatial dimensions. When the inherent spatio-temporal fluctuations are properly
included, then complete chiral amplification results from a purely racemic
initial configuration. Phase separation can also arise in which the enantiomers
coexist in spatially segregated domains separated by a sharp racemic interface
or boundary.

categories: q-bio.PE nlin.PS physics.chem-ph q-bio.QM


title: Elastica-based strain energy functions for soft biological tissue

abstract:
  Continuum strain energy functions are developed for soft biological tissues
that possess long fibrillar components. The treatment is based on the model of
an elastica, which is our fine scale model, and is homogenized in a simple
fashion to obtain a continuum strain energy function. Notably, we avoid solving
the full fourth-order, nonlinear, partial differential equation for the
elastica by resorting to other assumptions, kinematic and energetic, on the
response of the individual, elastica-like fibrils.


title: Genetic code on the dyadic plane

abstract:
  We introduce the simple parametrization for the space of codons (triples of
nucleotides) by 8\times 8 table. This table (which we call the dyadic plane)
possesses the natural 2-adic ultrametric. We show that after this
parametrization the genetic code will be a locally constant map of the simple
form. The local constancy of this map will describe degeneracy of the genetic
code.
  The map of the genetic code defines 2-adic ultrametric on the space of amino
acids. We show that hydrophobic amino acids will be clustered in two balls with
respect to this ultrametric. Therefore the introduced parametrization of space
of codons exhibits the hidden regularity of the genetic code.


title:
Co-evolution of strategy and structure in complex networks with
  dynamical linking

abstract:
  Here we introduce a model in which individuals differ in the rate at which
they seek new interactions with others, making rational decisions modeled as
general symmetric two-player games. Once a link between two individuals has
formed, the productivity of this link is evaluated. Links can be broken off at
different rates. We provide analytic results for the limiting cases where
linking dynamics is much faster than evolutionary dynamics and vice-versa, and
show how the individual capacity of forming new links or severing inconvenient
ones maps into the problem of strategy evolution in a well-mixed population
under a different game. For intermediate ranges, we investigate numerically the
detailed interplay determined by these two time-scales and show that the scope
of validity of the analytical results extends to a much wider ratio of time
scales than expected.


title: Attribute Exploration of Discrete Temporal Transitions

abstract:
  Discrete temporal transitions occur in a variety of domains, but this work is
mainly motivated by applications in molecular biology: explaining and analyzing
observed transcriptome and proteome time series by literature and database
knowledge. The starting point of a formal concept analysis model is presented.
The objects of a formal context are states of the interesting entities, and the
attributes are the variable properties defining the current state (e.g.
observed presence or absence of proteins). Temporal transitions assign a
relation to the objects, defined by deterministic or non-deterministic
transition rules between sets of pre- and postconditions. This relation can be
generalized to its transitive closure, i.e. states are related if one results
from the other by a transition sequence of arbitrary length. The focus of the
work is the adaptation of the attribute exploration algorithm to such a
relational context, so that questions concerning temporal dependencies can be
asked during the exploration process and be answered from the computed stem
base. Results are given for the abstract example of a game and a small gene
regulatory network relevant to a biomedical question.


title: Associative learning in biochemical networks

abstract:
  We develop a simple, chemostat-based model illustrating how a process
analogous to associative learning can occur in a biochemical network.
Associative learning is a form of learning whereby a system "learns" to
associate two stimuli with one another. In our model, two types of replicating
molecules, denoted A and B, are present in some initial concentration in the
chemostat. Molecules A and B are stimulated to replicate by some growth
factors, denoted GA and GB, respectively. It is also assumed that A and B can
covalently link, and that the conjugated molecule can be stimulated by either
the GA or GB growth factors (and can be degraded). We show that, if the
chemostat is stimulated by both growth factors for a certain time, followed by
a time gap during which the chemostat is not stimulated at all, and if the
chemostat is then stimulated again by only one of the growth factors, then
there will be a transient increase in the number of molecules activated by the
other growth factor. Therefore, the chemostat bears the imprint of earlier,
simultaneous stimulation with both growth factors, which is indicative of
associative learning. It is interesting to note that the dynamics of our model
is consistent with various aspects of Pavlov's original series of associative
learning experiments in dogs. We discuss how associative learning can
potentially be performed in vitro within RNA, DNA, or peptide networks. We also
highlight how such a mechanism could potentially be involved in genomic
evolution, and suggest bioinformatics studies that could be used to find
evidence for associative learning processes at work inside living cells.


title:
Designing sequential transcription logic: a simple genetic circuit for
  conditional memory

abstract:
  The ability to learn and respond to recurrent events depends on the capacity
to remember transient biological signals received in the past. Moreover, it may
be desirable to remember or ignore these transient signals conditioned upon
other signals that are active at specific points in time or in unique
environments. Here, we propose a simple genetic circuit in bacteria that is
capable of conditionally memorizing a signal in the form of a transcription
factor concentration. The circuit behaves similarly to a "data latch" in an
electronic circuit, i.e. it reads and stores an input signal only when
conditioned to do so by a "read command". Our circuit is of the same size as
the well-known genetic toggle switch (an unconditional latch) which consists of
two mutually repressing genes, but is complemented with a "regulatory front
end" involving protein heterodimerization as a simple way to implement
conditional control. Deterministic and stochastic analysis of the circuit
dynamics indicate that an experimental implementation is feasible based on
well-characterized genes and proteins. It is not known, to which extent
molecular networks are able to conditionally store information in natural
contexts for bacteria. However, our results suggest that such sequential logic
elements may be readily implemented by cells through the combination of
existing protein-protein interactions and simple transcriptional regulation.


title: Spatiotemporal complexity of a ratio-dependent predator-prey system

abstract:
  In this paper, we investigate the emergence of a ratio-dependent
predator-prey system with Michaelis-Menten-type functional response and
reaction-diffusion. We derive the conditions for Hopf, Turing and Wave
bifurcation on a spatial domain. Furthermore, we present a theoretical analysis
of evolutionary processes that involves organisms distribution and their
interaction of spatially distributed population with local diffusion. The
results of numerical simulations reveal that the typical dynamics of population
density variation is the formation of isolated groups, i.e., stripelike or
spotted or coexistence of both. Our study shows that the spatially extended
model has not only more complex dynamic patterns in the space, but also chaos
and spiral waves. It may help us better understand the dynamics of an aquatic
community in a real marine environment.