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Terence Hwa

Publications and source records attributed to Terence Hwa.

At least 37 records · Page 2Linked to original sources

Analytical study of the effect of recombination on evolution via DNA shuffling

We investigate a multi-locus evolutionary model which is based on the DNA shuffling protocol widely applied in \textit{in vitro} directed evolution. This model incorporates selection, recombination and point mutations. The simplicity of the model allows us to obtain a full analytical treatment of both its dynamical and equilibrium properties, for the case of an infinite population. We also briefly discuss finite population size corrections.

physics.bio-ph↗

Localization of Denaturation Bubbles in Random DNA Sequences

We study the thermodynamic and dynamic behaviors of twist-induced denaturation bubbles in a long, stretched random sequence of DNA. The small bubbles associated with weak twist are delocalized. Above a threshold torque, the bubbles of several tens of bases or larger become preferentially localized to \AT-rich segments. In the localized regime, the bubbles exhibit ``aging'' and move around sub-diffusively with continuously varying dynamic exponents. These properties are derived using results of large-deviation theory together with scaling arguments, and are verified by Monte-Carlo simulations.

cond-mat.stat-mech↗

The style of genetic computing

Cells receive a wide variety of cellular and environmental signals, which must be processed combinatorially to generate specific and timely genetic responses. We present here a theoretical study on the combinatorial control and integration of transcription signals, with the finding that cis-regulatory systems with specific protein-DNA interaction and glue-like protein-protein interactions, supplemented by distal activation or repression mechanisms, have the capability to execute a wide range of control functions encoded in the regulatory DNA sequences. Using a quantitative model based on the well-characterized bacterial transcription system, we show explicitly how various regulatory logic functions can be implemented, by selecting the strengths and relative positions of the relevant protein-binding DNA sequences in the cis-regulatory region. The architecture that emerges is naturally modular and highly evolvable. Our findings suggest that the transcription regulatory apparatus is a "programmable" computing machine, belonging formally to the class of Boltzmann machines. We also expose critical shortcomings of the bacterial transcription systems, which limit the genome-wide adoption of schemes for complex transcription control, and discuss how they may be overcome by the eukaryotic transcription systems.

cond-mat.soft↗

Dynamics of Competitive Evolution on a Smooth Landscape

We study competitive DNA sequence evolution directed by {\it in vitro} protein binding. The steady-state dynamics of this process is well described by a shape-preserving pulse which decelerates and eventually reaches equilibrium. We explain this dynamical behavior within a continuum mean-field framework. Analytical results obtained on the motion of the pulse agree with simulations. Furthermore, finite population correction to the mean-field results are found to be insignificant.

cond-mat.stat-mech↗

On the Selection and Evolution of Regulatory DNA Motifs

The mutation and selection of regulatory DNA sequences is presented as an ideal model system of molecular evolution where genotype, phenotype, and fitness can be explicitly and independently characterized. In this theoretical study, we construct an explicit model for the evolution of regulatory sequences, making use of the known biophysics of the binding of regulatory proteins to DNA sequences, under the assumption that fitness of a sequence depends only on its binding affinity to the regulatory protein. The model is confined to the mean field (i.e., infinite population size) limit. Using realistic values for all parameters, we determine the minimum fitness advantage needed to maintain a binding sequence, demonstrating explicitly the ``error threshold'' below which a binding sequence cannot survive the accumulated effect of mutation over long time. The commonly observed ``fuzziness'' in binding motifs arises naturally as a consequence of the balance between selection and mutation in our model. In addition, we devise a simple model for the evolution of multiple binding sequences in a given regulatory region. We find the number of evolutionarily stable binding sequences to increase in a step-like fashion with increasing fitness advantage, if multiple regulatory proteins can synergistically enhance gene transcription. We discuss possible experimental approaches to resolve open questions raised by our study.

physics.bio-ph↗

Mechanically probing the folding pathway of single RNA molecules

We study theoretically the denaturation of single RNA molecules by mechanical stretching, focusing on signatures of the (un)folding pathway in molecular fluctuations. Our model describes the interactions between nucleotides by incorporating the experimentally determined free energy rules for RNA secondary structure, while exterior single stranded regions are modeled as freely jointed chains. For exemplary RNA sequences (hairpins and the Tetrahymena thermophila group I intron), we compute the quasi-equilibrium fluctuations in the end-to-end distance as the molecule is unfolded by pulling on opposite ends. Unlike the average quasi-equilibrium force-extension curves, these fluctuations reveal clear signatures from the unfolding of individual structural elements. We find that the resolution of these signatures depends on the spring constant of the force-measuring device, with an optimal value intermediate between very rigid and very soft. We compare and relate our results to recent experiments by Liphardt et al. [Science 292, 733-737 (2001)].

cond-mat.soft↗

Physical Constraints and Functional Characteristics of Transcription Factor-DNA Interaction

We study theoretical ``design principles'' for transcription factor-DNA interaction in bacteria, focusing particularly on the statistical interaction of the transcription factors (TF's) with the genomic background (i.e., the genome without the target sites). We introduce and motivate the concept of `programmability', i.e. the ability to set the threshold concentration for TF binding over a wide range merely by mutating the binding sequence of a target site. This functional demand, together with physical constraints arising from the thermodynamics and kinetics of TF-DNA interaction, leads us to a narrow range of ``optimal'' interaction parameters. We find that this parameter set agrees well with experimental data for the interaction parameters of a few exemplary prokaryotic TF's. This indicates that TF-DNA interaction is indeed programmable. We suggest further experiments to test whether this is a general feature for a large class of TF's.

physics.bio-ph↗

Force-induced denaturation of RNA

We describe quantitatively a RNA molecule under the influence of an external force exerted at its two ends as in a typical single-molecule experiment. Our calculation incorporates the interactions between nucleotides by using the experimentally-determined free energy rules for RNA secondary structure and models the polymeric properties of the exterior single-stranded regions explicitly as elastic freely-jointed chains. We find that in spite of complicated secondary structures, force-extension curves are typically smooth in quasi-equilibrium. We identify and characterize two sequence/structure-dependent mechanisms that, in addition to the sequence-independent entropic elasticity of the exterior single-stranded regions, are responsible for the smoothness. These involve compensation between different structural elements on which the external force acts simultaneously, and contribution of suboptimal structures, respectively. We estimate how many features a force-extension curve recorded in non-equilibrium, where the pulling proceeds faster than rearrangements in the secondary structure of the molecule, could show in principle. Our software is available to the public through a `RNA-pulling server'.

cond-mat.soft↗

Hydrodynamics of the Kuramoto-Sivashinsky Equation in Two Dimensions

The large scale properties of spatiotemporal chaos in the 2d Kuramoto-Sivashinsky equation are studied using an explicit coarse graining scheme. A set of intermediate equations are obtained. They describe interactions between the small scale (e.g., cellular) structures and the hydrodynamic degrees of freedom. Possible forms of the effective large scale hydrodynamics are constructed and examined. Although a number of different universality classes are allowed by symmetry, numerical results support the simplest scenario, that being the KPZ universality class.

cond-mat.soft↗

Universal Susceptibility Variations in 1+1 Dimensional Vortex Glass

We model a planar array of fluxlines as a discrete solid-on-solid model with quenched disorder. Simulations at finite temperatures are made possible by a new algorithm which circumvents the slow glassy dynamics encountered by traditional Metropolis Monte Carlo algorithms. Numerical results on magnetic susceptibility variations support analytic predictions.

cond-mat.dis-nn↗

Thermodynamics of Mesoscopic Vortex Systems in 1+1 Dimensions

The thermodynamics of a disordered planar vortex array is studied numerically using a new polynomial algorithm which circumvents slow glassy dynamics. Close to the glass transition, the anomalous vortex displacement is found to agree well with the prediction of the renormalization-group theory. Interesting behaviors such as the universal statistics of magnetic susceptibility variations are observed in both the dense and dilute regimes of this mesoscopic vortex system.

cond-mat.dis-nn↗

Optimizing Smith-Waterman alignments

Mutual correlation between segments of DNA or protein sequences can be detected by Smith-Waterman local alignments. We present a statistical analysis of alignment of such sequences, based on a recent scaling theory. A new fidelity measure is introduced and shown to capture the significance of the local alignment, i.e., the extent to which the correlated subsequences are correctly identified. It is demonstrated how the fidelity may be optimized in the space of penalty parameters using only the alignment score data of a single sequence pair.

cond-mat↗

Optimal Detection of Sequence Similarity by Local Alignment

The statistical properties of local alignment algorithms with gaps are analyzed theoretically for uncorrelated and correlated DNA sequences. In the vicinity of the log-linear phase transition, the statistics of alignment with gaps is shown to be characteristically different from that of gapless alignment. The optimal scores obtained for uncorrelated sequences obey certain robust scaling laws. Deviation from these scaling laws signals sequence homology, and can be used to guide the empirical selection of scoring parameters for the optimal detection of sequence similarities. This can be accomplished in a computationally efficient way by using a novel approach focusing on the score landscape. Furthermore, by assuming a few gross features characterizing the statistics of underlying sequence-sequence correlations, quantitative criteria are obtained for the choice of optimal scoring parameters: Optimal similarity detection is most likely to occur in a region close to the log side of the log-linear phase transition.

cond-mat.stat-mech↗

Scaling Laws and Similarity Detection in Sequence Alignment with Gaps

We study the problem of similarity detection by sequence alignment with gaps, using a recently established theoretical framework based on the morphology of alignment paths. Alignments of sequences without mutual correlations are found to have scale-invariant statistics. This is the basis for a scaling theory of alignments of correlated sequences. Using a simple Markov model of evolution, we generate sequences with well-defined mutual correlations and quantify the fidelity of an alignment in an unambiguous way. The scaling theory predicts the dependence of the fidelity on the alignment parameters and on the statistical evolution parameters characterizing the sequence correlations. Specific criteria for the optimal choice of alignment parameters emerge from this theory. The results are verified by extensive numerical simulations.

physics.bio-ph↗

Mode coupling and renormalization group results for the noisy Burgers equation

We investigate the noisy Burgers equation (Kardar--Parisi--Zhang equation in 1+1 dimensions) using the dynamical renormalization group (to two--loop order) and mode--coupling techniques. The roughness and dynamic exponent are fixed by Galilean invariance and a fluctuation--dissipation theorem. The fact that there are no singular two--loop contributions to the two--point vertex functions supports the mode--coupling approach, which can be understood as a self--consistent one--loop theory where vertex corrections are neglected. Therefore, the numerical solution of the mode coupling equations yields very accurate results for the scaling functions. In addition, finite--size effects can be studied. Furthermore, the results from exact Ward identities, as well as from second--order perturbation theory permit the quantitative evaluation of the vertex corrections, and thus provide a quantitative test for the mode--coupling approach. It is found that the vertex corrections themselves are of the order one. Surprisingly, however, their effect on the correlation function is substantially smaller.

cond-mat↗

Similarity-Detection and Localization

The detection of similarities between long DNA and protein sequences is studied using concepts of statistical physics. It is shown that mutual similarities can be detected by sequence alignment methods only if their amount exceeds a threshold value. The onset of detection is a continuous phase transition which can be viewed as a localization-delocalization transition. The ``fidelity'' of the alignment is the order parameter of that transition; it leads to criteria for the selection of optimal alignment parameters.

cond-mat↗

Defect-Mediated Stability: An Effective Hydrodynamic Theory of Spatio-Temporal Chaos

Spatiotemporal chaos (STC) exhibited by the Kuramoto-Sivashinsky (KS) equation is investigated analytically and numerically. An effective stochastic equation belonging to the KPZ universality class is constructed by incorporating the chaotic dynamics of the small KS system in a coarse-graining procedure. The bare parameters of the effective theory are computed approximately. Stability of the system is shown to be mediated by space-time defects that are accompanied by stochasticity. The method of analysis and the mechanism of stability may be relevant to a class of STC problems.

cond-mat↗

Disorder-Induced Depinning Transition

The competition in the pinning of a directed polymer by a columnar pin and a background of random point impurities is investigated systematically using the renormalization group method. With the aid of the mapping to the noisy-Burgers' equation and the use of the mode-coupling method, the directed polymer is shown to be marginally localized to an arbitrary weak columnar pin in 1+1 dimensions. This weak localization effect is attributed to the existence of large scale, nearly degenerate optimal paths of the randomly pinned directed polymer. The critical behavior of the depinning transition above 1+1 dimensions is obtained via an $ε$-expansion.

cond-mat↗