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Megan C. Engel

Publications and source records attributed to Megan C. Engel.

9 recordsLinked to original sources
arXiv2025

Iterative improvement of free energy landscape reconstructions with optimal protocols derived from differentiable simulations

Free energy landscapes encode the kinetics, intermediates, and transition states that govern molecular processes and are thus a key target of single biomolecule research. Typical approaches to deriving optimal, error-minimizing, non-equilibrium driving protocols for estimating these landscapes require a priori knowledge of the landscape. Here, we present an alternative: an iterative algorithm for optimizing full free energy landscape reconstructions which can be used alongside experiments on unknown landscapes. Our approach (i) takes experimental or simulated trajectory data; (ii) reconstructs an `approximate' energy landscape; (iii) derives optimal control protocols from low-dimensional differentiable Brownian dynamics simulations on the candidate landscape using automatic differentiation; (iv) re-runs the experiment or simulation using the updated protocol; and (v) iterates until convergence. Using this approach, we recover known benchmarks from the literature and probe far-from-equilibrium regimes for symmetric, asymmetric, and triple-well energy landscapes under both 1- and 2-dimensional control. Our control protocols -- derived with no a priori knowledge of the energy landscape -- yield substantially reduced variance and bias in free energy landscape reconstructions compared to naive linear protocols.

physics.bio-ph↗
arXiv2025

Fitting Coarse-Grained Models to Macroscopic Experimental Data via Automatic Differentiation

Developing physics-based models for molecular simulation requires fitting many unknown parameters to diverse experimental datasets. Traditionally, this process is piecemeal and difficult to reproduce, leading to a fragmented landscape of models. Here, we establish a systematic, extensible framework for fitting coarse-grained molecular models to macroscopic experimental data by leveraging recently developed methods for computing low-variance gradient estimates with automatic differentiation. Using a widely validated DNA force field as an exemplar, we develop methods for optimizing structural, mechanical, and thermodynamic properties across a range of simulation techniques, including enhanced sampling and external forcing, spanning micro- and millisecond timescales. We highlight how gradients enable efficient sensitivity analyses that yield physical insight. We then demonstrate the broad applicability of these techniques by optimizing diverse biomolecular systems, including RNA and DNA-protein hybrid models. We show how conflict-free gradient methods from multi-task learning can be adapted to impose multiple constraints simultaneously without compromising accuracy. This approach provides a foundation for transparent, reproducible, community-driven force field development, accelerating progress in molecular modeling.

physics.bio-ph↗
arXiv2024

Inferring interaction potentials from stochastic particle trajectories

Accurate interaction potentials between microscopic components such as colloidal particles or cells are crucial to understanding a range of processes, including colloidal crystallization, bacterial colony formation, and cancer metastasis. Even in systems where the precise interaction mechanisms are unknown, effective interactions can be measured to inform simulation and design. However, these measurements are difficult and time-intensive, and often require conditions that are drastically different from in situ conditions of the system of interest. Moreover, existing methods of measuring interparticle potentials rely on constraining a small number of particles at equilibrium, placing limits on which interactions can be measured. We introduce a method for inferring interaction potentials directly from trajectory data of interacting particles. We explicitly solve the equations of motion to find a form of the potential that maximizes the probability of observing a known trajectory. Our method is valid for systems both in and out of equilibrium, is well-suited to large numbers of particles interacting in typical system conditions, and does not assume a functional form of the interaction potential. We apply our method to infer the interactions of colloidal spheres from experimental data, successfully extracting the range and strength of a depletion interaction from the motion of the particles.

cond-mat.soft↗
arXiv2022

Optimal control of nonequilibrium systems through automatic differentiation

Controlling the evolution of nonequilibrium systems to minimize dissipated heat or work is a key goal for designing nanodevices, both in nanotechnology and biology. Progress in computing optimal protocols has thus far been limited to either simple systems or near-equilibrium evolution. Here, we present an approach for computing optimal protocols based on automatic differentiation. Our methodology is applicable to complex systems and multidimensional protocols and is valid arbitrarily far from equilibrium. We validate our method by reproducing theoretical optimal protocols for a Brownian particle in a time-varying harmonic trap. We also compute departures from near-equilibrium behaviour for magnetization reversal on an Ising lattice and for barrier crossing driven by a harmonic trap, which has been used to represent a range of biological processes including biomolecular unfolding reactions. Algorithms based on automatic differentiation outperform the near-equilibrium theory for far-from-equilibrium magnetization reversal and driven barrier crossing. The optimal protocol for crossing an energy landscape barrier of 10kT is found to hasten the approach to, and slow the departure from, the barrier region compared to the near-equilibrium theoretical protocol.

physics.comp-ph↗
arXiv2021

Free-energy landscapes of DNA and its assemblies: Perspectives from coarse-grained modelling

This chapter will provide an overview of how characterizing free-energy landscapes can provide insights into the biophysical properties of DNA, as well as into the behaviour of the DNA assemblies used in the field of DNA nanotechnology. The landscapes for these complex systems are accessible through the use of accurate coarse-grained descriptions of DNA. Particular foci will be the landscapes associated with DNA self-assembly and mechanical deformation, where the latter can arise from either externally imposed forces or internal stresses.

cond-mat.soft↗
arXiv2020

Measuring internal forces in single-stranded DNA: Application to a DNA force clamp

We present a new method for calculating internal forces in DNA structures using coarse-grained models and demonstrate its utility with the oxDNA model. The instantaneous forces on individual nucleotides are explored and related to model potentials, and using our framework, internal forces are calculated for two simple DNA systems and for a recently-published nanoscopic force clamp. Our results highlight some pitfalls associated with conventional methods for estimating internal forces, which are based on elastic polymer models, and emphasise the importance of carefully considering secondary structure and ionic conditions when modelling the elastic behaviour of single-stranded DNA. Beyond its relevance to the DNA nanotechnological community, we expect our approach to be broadly applicable to calculations of internal force in a variety of structures -- from DNA to protein -- and across other coarse-grained simulation models.

cond-mat.soft↗
arXiv2016

Perspectives on astronomy: probing Norwegian pre-service teachers and middle school students

We report on ongoing work to gain insight into the astronomy knowledge and perspectives of pre-service teachers and middle school students in Norway. We carefully adapted and translated into Norwegian an existing instrument, the Introductory Astronomy Questionnaire (IAQ); we administered this adapted IAQ to (i) pre-service teachers at the largest teacher education institution in Norway, and (ii) students drawn from eight middle schools in Oslo, in both cases before and after astronomy instruction. Amongst our preliminary findings - based on an analysis of both free-response writing and multiple-choice responses - was that when prompted to provide responses to hypothetical students, the pre-service teachers exhibited a marked drop in pedagogical responses pre- to post-instruction, with corresponding shifts towards more authoritative responses. We also identified potentially serious issues relating to middle school students' conceptions of size and distances in the universe, with significant stratification along gender lines.

physics.ed-ph↗
arXiv2012

A 2.15 Hour Orbital Period for the Low Mass X-Ray Binary XB 1832-330 in the Globular Cluster NGC 6652

We present a candidate orbital period for the low mass X-ray binary XB 1832-330 in the globular cluster NGC 6652 using a 6.5 hour Gemini South observation of the optical counterpart of the system. Light curves in g' and r' for two LMXBs in the cluster, sources A and B in previous literature, were extracted and analyzed for periodicity using the ISIS image subtraction package. A clear sinusoidal modulation is evident in both of A's curves, of amplitude ~0.11 magnitudes in g' and ~0.065 magnitudes in r', while B's curves exhibit rapid flickering, of amplitude ~1 magnitude in g' and ~0.5 magnitudes in r'. A Lomb-Scargle test revealed a 2.15 hour periodic variation in the magnitude of A with a false alarm probability less than 10^-11, and no significant periodicity in the light curve for B. Though it is possible saturated stars in the vicinity of our sources partially contaminated our signal, the identification of A's binary period is nonetheless robust.

astro-ph.HE↗