Biology subjects

Sivak, D.

Publications and source records attributed to Sivak, D..

3 recordsLinked to original sources

bioRxiv2025

How to quantify immigration from community abundance data using the Neutral Community Model

Biological communities are connected through dispersal, which regulates diversity across local and regional scales. However, dispersal is difficult to measure directly, limiting what is known about dispersals impact on species composition in complex communities. One method to measure dispersal employs the Neutral Community Model (NCM) to quantify how a local community is influenced by the immigration of individuals from a larger source community. Conveniently, the immigration rate NTm of the NCM can be fit from biological sequence abundance datasets, which are plentiful. Yet it is neither known if these estimated values reflect the ground truth, nor what sampling effort is required to yield accurate estimates. In this study we introduce two inference methods, a variance-based and a Dirichlet-Multinomial Log-Likelihood (DM-LL) method, to complement the established occupancy-based inference method. In simulations of communities that resemble activated sludge microbiomes, all inference methods were capable of estimating NTm within 10% of ground-truth, with the variance-based and DM-LL methods requiring less sampling effort. Accurate inferences require read depths greater than NTm in each sample. The three methods agree in their inferred NTm in simulations of communities experiencing weak non-neutral effects (e.g., selection), and in applications to an empirical dataset from wastewater activated sludge. Based on these findings, we propose practical sampling and methodological guidelines for quantifying immigration between highly diverse, complex communities using the NCM.

ecology↗

bioRxiv2018

Using a System’s Equilibrium Behavior to Reduce Its Energy Dissipation in Non-Equilibrium Processes

Cells must operate far from equilibrium1, utilizing and dissipating energy continuously to maintain their organization and to avoid stasis and death. However, they must also avoid unnecessary waste of energy2. Recent studies have revealed that molecular machines are extremely efficient thermodynamically when compared to their macroscopic counterparts3,4. There are also tantalizing hints of molecular machines conserving energy while operating out of equilibrium5,6. However, the principles governing the efficient out-of-equilibrium operation of molecular machines remain a mystery. A theoretical framework has been recently formulated in which a generalized friction coefficient quantifies the energetic efficiency in non-equilibrium processes7,8. Moreover, it posits that to minimize energy dissipation, external control should drive the system along the reaction coordinate with a speed inversely proportional to the square root of that friction coefficient. Here, we test and validate the predictions of this theory by probing the non-equilibrium energetic efficiency of a single DNA hairpin subjected to unfolding and refolding protocols using a dual-trap optical tweezers.

biophysics↗

bioRxiv2018

Quantifying configuration-sampling error in Langevin simulations of complex molecular systems

While Langevin integrators are popular in the study of equilibrium properties of complex systems, it is challenging to estimate the timestep-induced discretization error: the degree to which the sampled phase-space or configuration-space probability density departs from the desired target density due to the use of a finite integration timestep. In [1], Sivak et al. introduced a convenient approach to approximating a natural measure of error between the sampled density and the target equilibrium density, the KL divergence, in phase space, but did not specifically address the issue of configuration-space properties, which are much more commonly of interest in molecular simulations. Here, we introduce a variant of this near-equilibrium estimator capable of measuring the error in the configuration-space marginal density, validating it against a complex but exact nested Monte Carlo estimator to show that it reproduces the KL divergence with high fidelity. To illustrate its utility, we employ this new near-equilibrium estimator to assess a claim that a recently proposed Langevin integrator introduces extremely small configuration-space density errors up to the stability limit at no extra computational expense. Finally, we show how this approach to quantifying sampling bias can be applied to a wide variety of stochastic integrators by following a straightforward procedure to compute the appropriate shadow work, and describe how it can be extended to quantify the error in arbitrary marginal or conditional distributions of interest.

biophysics↗