The stability and number of nucleating interactions determine DNA hybridization rates in the absence of secondary structure.

The kinetics of DNA hybridization are fundamental to biological processes and DNA-based technologies. However, the precise physical mechanisms that determine why different DNA sequences hybridize at different rates are not well understood. Secondary structure is one predictable factor that influences hybridization rates but is not sufficient on its own to fully explain the observed sequence-dependent variance. In this context, we measured hybridization rates of 43 different DNA sequences that are not predicted to form secondary structure and present a parsimonious physically justified model to quantify our observations. Accounting only for the combinatorics of complementary nucleating interactions and their sequence-dependent stability, the model achieves good correlation with experiment with only two free parameters. Our results indicate that greater repetition of Watson-Crick pairs increases the number of initial states able to proceed to full hybridization, with the stability of those pairings dictating the likelihood of such progression, thus providing new insight into the physical factors underpinning DNA hybridization rates.

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data.

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.

Nonequilibrium thermodynamics of stochastic systems with odd and even variables.

The total entropy production of stochastic systems can be divided into three quantities. The first corresponds to the excess heat, while the second two comprise the housekeeping heat. We denote these two components the transient and generalized housekeeping heat and we obtain an integral fluctuation theorem for the latter, valid for all Markovian stochastic dynamics. A previously reported formalism is obtained when the stationary probability distribution is symmetric for all variables that are odd under time reversal, which restricts consideration of directional variables such as velocity.

Rapid Exchange of Stably Bound Protein and DNA Cargo on a DNA Origami Receptor.

Biomolecular complexes can form stable assemblies yet can also rapidly exchange their subunits to adapt to environmental changes. Simultaneously allowing for both stability and rapid exchange expands the functional capacity of biomolecular machines and enables continuous function while navigating a complex molecular world. Inspired by biology, we design and synthesize a DNA origami receptor that exploits multivalent interactions to form stable complexes that are also capable of rapid subunit exchange. The system utilizes a mechanism first outlined in the context of the DNA replisome, known as multisite competitive exchange, and achieves a large separation of time scales between spontaneous subunit dissociation, which requires days, and rapid subunit exchange, which occurs in minutes. In addition, we use the DNA origami receptor to demonstrate stable interactions with rapid exchange of both DNA and protein subunits, thus highlighting the applicability of our approach to arbitrary molecular cargo, an important distinction with canonical toehold exchange between single-stranded DNA. We expect this study to benefit future studies that use DNA origami structures to exploit multivalent interactions for the design and synthesis of a wide range of possible kinetic behaviors.

Population mobility induced phase separation in SIS epidemic and social dynamics.

Understanding the impact of behavior dependent mobility in the spread of epidemics and social disorders is an outstanding problem in computational epidemiology. We present a modelling approach for the study of mobility that adapts dynamically according to individual state, epidemic/social-contagion state and network topology in accordance with limited data and/or common behavioral models. We demonstrate that even for simple compartmental network processes, our approach leads to complex spatial patterns of infection in the endemic state dependent on individual behavior. Specifically, we characterize the resulting phenomena in terms of phase separation, highlighting phase transitions between distinct spatial states and determining the systems' phase diagram. The existence of such phases implies that small changes in the populations' perceptions could lead to drastic changes in the spatial extent and morphology of the epidemic/social phenomena.

Irreversibility and emergent structure in active matter.

Active matter is rapidly becoming a key paradigm of out-of-equilibrium soft matter exhibiting complex collective phenomena, yet the thermodynamics of such systems remain poorly understood. In this article we study the dynamical irreversibility of large-scale active systems capable of motility-induced phase separation and polar alignment. We use a model with momenta in both translational and rotational degrees of freedom, revealing a hidden component not previously reported in the literature. Steady-state irreversibility is quantified at each point in the phase diagram which exhibits sharp discontinuities at phase transitions. Identification of the irreversibility in individual particles lays the groundwork for discussion of the thermodynamics of microfeatures, such as defects in the emergent structure. The interpretation of the time reversal symmetry in the dynamics of the particles is found to be crucial.

Information ratchets exploiting spatially structured information reservoirs.

Fully mechanized Maxwell's demons, also called information ratchets, are an important conceptual link between computation, information theory, and statistical physics. They exploit low-entropy information reservoirs to extract work from a heat reservoir. Previous models of such demons have either ignored the cost of delivering bits to the demon from the information reservoir or assumed random access or infinite-dimensional information reservoirs to avoid such an issue. In this work we account for this cost when exploiting information reservoirs with physical structure and show that the dimensionality of the reservoir has a significant impact on the performance and phase diagram of the demon. We find that for conventional one-dimensional tapes the scope for work extraction is greatly reduced. An expression for the net-extracted work by demons exploring information reservoirs by means of biased random walks on d-dimensional, Z^{d}, information reservoirs is presented. Furthermore, we derive exact probabilities of recurrence in these systems, generalizing previously known results. We find that the demon is characterized by two critical dimensions. First, to extract work at zero bias the dimensionality of the information reservoir must be larger than d=2, corresponding to the dimensions where a simple random walker is transient. Second, for integer dimensions d>4 the unbiased random walk optimizes work extraction corresponding to the dimensions where a simple random walker is strongly transient.

Characterizing information-theoretic storage and transfer in continuous time processes.

The characterization of information processing is an important task in complex systems science. Information dynamics is a quantitative methodology for modeling the intrinsic information processing conducted by a process represented as a time series, but to date has only been formulated in discrete time. Building on previous work which demonstrated how to formulate transfer entropy in continuous time, we give a total account of information processing in this setting, incorporating information storage. We find that a convergent rate of predictive capacity, comprising the transfer entropy and active information storage, does not exist, arising through divergent rates of active information storage. We identify that active information storage can be decomposed into two separate quantities that characterize predictive capacity stored in a process: active memory utilization and instantaneous predictive capacity. The latter involves prediction related to path regularity and so solely inherits the divergent properties of the active information storage, while the former permits definitions of pathwise and rate quantities. We formulate measures of memory utilization for jump and neural spiking processes and illustrate measures of information processing in synthetic neural spiking models and coupled Ornstein-Uhlenbeck models. The application to synthetic neural spiking models demonstrates that active memory utilization for point processes consists of discontinuous jump contributions (at spikes) interrupting a continuously varying contribution (relating to waiting times between spikes), complementing the behavior previously demonstrated for transfer entropy in these processes.

A thermodynamically consistent model of finite-state machines.

Finite-state machines (FSMs) are a theoretically and practically important model of computation. We propose a general, thermodynamically consistent model of FSMs and characterize the resource requirements of these machines. We model FSMs as time-inhomogeneous Markov chains. The computation is driven by instantaneous manipulations of the energy levels of the states. We calculate the entropy production of the machine, its error probability, and the time required to complete one update step. We find that a sequence of generalized bit-setting operations is sufficient to implement any FSM.

Thermodynamics and computation during collective motion near criticality.

We study self-organization of collective motion as a thermodynamic phenomenon in the context of the first law of thermodynamics. It is expected that the coherent ordered motion typically self-organises in the presence of changes in the (generalized) internal energy and of (generalized) work done on, or extracted from, the system. We aim to explicitly quantify changes in these two quantities in a system of simulated self-propelled particles and contrast them with changes in the system's configuration entropy. In doing so, we adapt a thermodynamic formulation of the curvatures of the internal energy and the work, with respect to two parameters that control the particles' alignment. This allows us to systematically investigate the behavior of the system by varying the two control parameters to drive the system across a kinetic phase transition. Our results identify critical regimes and show that during the phase transition, where the configuration entropy of the system decreases, the rates of change of the work and of the internal energy also decrease, while their curvatures diverge. Importantly, the reduction of entropy achieved through expenditure of work is shown to peak at criticality. We relate this both to a thermodynamic efficiency and the significance of the increased order with respect to a computational path. Additionally, this study provides an information-geometric interpretation of the curvature of the internal energy as the difference between two curvatures: the curvature of the free entropy, captured by the Fisher information, and the curvature of the configuration entropy.

Transfer entropy in continuous time, with applications to jump and neural spiking processes.

Transfer entropy has been used to quantify the directed flow of information between source and target variables in many complex systems. While transfer entropy was originally formulated in discrete time, in this paper we provide a framework for considering transfer entropy in continuous time systems, based on Radon-Nikodym derivatives between measures of complete path realizations. To describe the information dynamics of individual path realizations, we introduce the pathwise transfer entropy, the expectation of which is the transfer entropy accumulated over a finite time interval. We demonstrate that this formalism permits an instantaneous transfer entropy rate. These properties are analogous to the behavior of physical quantities defined along paths such as work and heat. We use this approach to produce an explicit form for the transfer entropy for pure jump processes, and highlight the simplified form in the specific case of point processes (frequently used in neuroscience to model neural spike trains). Finally, we present two synthetic spiking neuron model examples to exhibit the pertinent features of our formalism, namely, that the information flow for point processes consists of discontinuous jump contributions (at spikes in the target) interrupting a continuously varying contribution (relating to waiting times between target spikes). Numerical schemes based on our formalism promise significant benefits over existing strategies based on discrete time formalisms.

Transfer entropy in physical systems and the arrow of time.

Recent developments have cemented the realization that many concepts and quantities in thermodynamics and information theory are shared. In this paper, we consider a highly relevant quantity in information theory and complex systems, the transfer entropy, and explore its thermodynamic role by considering the implications of time reversal upon it. By doing so we highlight the role of information dynamics on the nuanced question of observer perspective within thermodynamics by relating the temporal irreversibility in the information dynamics to the configurational (or spatial) resolution of the thermodynamics. We then highlight its role in perhaps the most enduring paradox in modern physics, the manifestation of a (thermodynamic) arrow of time. We find that for systems that process information such as those undergoing feedback, a robust arrow of time can be formulated by considering both the apparent physical behavior which leads to conventional entropy production and the information dynamics which leads to a quantity we call the information theoretic arrow of time. We also offer an interpretation in terms of optimal encoding of observed physical behavior.

Entropy production in full phase space for continuous stochastic dynamics.

Total entropy production and its three constituent components are described both as fluctuating trajectory-dependent quantities and as averaged contributions in the context of the continuous Markovian dynamics, described by stochastic differential equations with multiplicative noise, of systems with both odd and even coordinates with respect to time reversal, such as dynamics in full phase space. Two of these constituent quantities obey integral fluctuation theorems and are thus rigorously positive in the mean due to Jensen's inequality. The third, however, is not and furthermore cannot be uniquely associated with irreversibility arising from relaxation, nor with the breakage of detailed balance brought about by nonequilibrium constraints. The properties of the various contributions to total entropy production are explored through the consideration of two examples: steady-state heat conduction due to a temperature gradient, and transitions between stationary states of drift diffusion on a ring, both in the context of the full phase space dynamics of a single Brownian particle.

Entropy production from stochastic dynamics in discrete full phase space.

The stochastic entropy generated during the evolution of a system interacting with an environment may be separated into three components, but only two of these have a non-negative mean. The third component of entropy production is associated with the relaxation of the system probability distribution towards a stationary state and with nonequilibrium constraints within the dynamics that break detailed balance. It exists when at least some of the coordinates of the system phase space change sign under time reversal, and when the stationary state is asymmetric in these coordinates. We illustrate the various components of entropy production, both in detail for particular trajectories and in the mean, using simple systems defined on a discrete phase space of spatial and velocity coordinates. These models capture features of the drift and diffusion of a particle in a physical system, including the processes of injection and removal and the effect of a temperature gradient. The examples demonstrate how entropy production in stochastic thermodynamics depends on the detail that is included in a model of the dynamics of a process. Entropy production from such a perspective is a measure of the failure of such models to meet Loschmidt's expectation of dynamic reversibility.