Biology subjects

Hinczewski, M.

Publications and source records attributed to Hinczewski, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Drag racers and long haulers: the design space of myosin-V-like motor proteins

The ubiquitous myosin class-V molecular motors transport cellular cargo long distances at roughly 0.5 m/s. Plant-specific myosin class-XI motors share nearly identical structural features with myosin-V, yet display velocities from 0.25 to 70 m/s, the fastest transport motor in nature. Empirical observations suggest an inverse relationship between run length and velocity. These observations raise a central question: how can a single motor design support both highly processive "long-hauler" and extremely rapid "drag-racer" motors? We generalize a polymer-based model of force-dependent myosin-V dynamics to encompass the broader design space. The model quantitatively captures experimental data from fast myosin-XI and yields general analytical expressions for key observables. Notably, we derive a Pareto frontier that defines the trade-off between velocity and run distance, as well as the theoretical maximum velocity achievable by such motors. Our results establish the biological limits of natural and synthetic myosin-V-like motor design.

biophysics↗

bioRxiv2026

Can public good producing subclones invade a population of non-producers?

Cancer progression is increasingly understood as an evolutionary process shaped not only by competition but also by cooperative interactions including those mediated through diffusible "public goods" (PGs). Classical evolutionary game theory predicts that PG-producing (altruistic) subclones cannot invade well-mixed populations of non-producers, creating a paradox given their observed emergence in tumors. Here, we resolve this contradiction by combining stochastic spatial simulations with an analytically tractable Moran model to study the invasion dynamics of PG-producing cells in structured populations. Starting from a single producer cell, we explicitly model stochastic PG secretion, diffusion, binding/unbinding, and cell proliferation across biologically relevant parameter ranges. We demonstrate that spatial structure fundamentally alters invasion dynamics, enabling PG producers to invade and establish even when production incurs a fitness cost. Both numerical and analytical approaches converge on a key unifying parameter, a characteristic length scale{delta} , that captures the combined effects of diffusivity, binding kinetics, and degradation. This length scale determines the spatial extent of PG availability and thus the selective advantage of producers. We identify distinct regimes: when PGs are localized (small{delta} ), producers preferentially benefit and invasion is likely; when PGs are widely dispersed (large{delta} ), benefits are shared and invasion approaches neutrality or is suppressed by costs. Our results highlight that invasion of cooperative traits is governed by spatially mediated resource localization rather than intrinsic fitness alone. This framework provides a mechanistic basis for understanding the emergence of cooperative subclones in tumors and suggests that modulating biophysical transport properties of signaling molecules could influence tumor evolution, metastasis, and therapeutic resistance.

evolutionary biology↗

bioRxiv2026

Minding the gap: collective determinants of multiscale structure across interacting bacterial colonies

A bacterial colony rarely exists in isolation - in natural habitats, colonies interact to form spatially structured communities across length and time scales. Eco-evolutionary feedbacks link these scales, such that structure at one level can influence another, yet the interplay between single- and multi-colony organization remains poorly understood. As a step toward addressing this, we develop a high-throughput platform to track population dynamics across spatially extended networks of colonies. A common structural feature observed at the multi-colony scale is the formation of a stable gap region between colonies, even when they are isogenic. Numerous studies observe similar patterns of behavior across species, with few resolving the underlying mechanism. Here, we ask: what are the minimal ingredients shaping this multi-colony structure? We focus on colonies of the opportunistic pathogen Enterococcus faecalis, a model organism for which this behavior has yet to be reported. By combining modeling and experiments, we show that both nutrient competition and direct growth inhibition control colony morphology and expansion of interacting colonies. We identify distinct regimes of gap formation, relating intra- and inter-colony spatial patterns to ecological interactions mediated at the cellular scale. Together, our results suggest that antagonism, even between isogenic populations through self-inhibition, is likely a common behavior of bacterial species in general.

ecology↗

bioRxiv2026

Stochastic Evolutionary Control in Heterogeneous Populations

Therapeutic resistance, which poses a central challenge in cancer and infectious disease treatment, arises from the evolutionary dynamics of heterogeneous populations as a natural consequence of the evolutionary tendency towards higher fitness. Here we introduce SHEPHERD (Stochastic Heterogeneity-informed Evolutionary Policy Hampering the Expansion of Resistance to Drugs), which integrates Wright-Fisher (WF) population genetics modeling with Markov Decision Processes (MDPs) to design optimal adaptive drug policies which reduce the fitness of evolving disease, mitigating resistance. While MDPs have been applied to evolutionary control under the strong-selection, weak-mutation (SSWM) assumption--where populations are effectively homogeneous--SHEPHERD operates beyond this regime, effectively capturing the full stochastic dynamics of genetically heterogeneous populations experiencing mutation and selection of a wide array of strengths. Using synthetic multi-drug fitness landscapes, we show the benefit of the optimized SHEPHERD protocol for in silico disease systems with three, four, and eight genotypes: the long-term mean fitness of the disease is reduced compared to any single-drug or two-drug periodic regimen. Although these examples specifically model drug resistance, we discuss the potential broad applicability of our approach to calculating strategies for controlling a wide class of evolving populations. We further analyze the sensitivity of SHEPHERD strategies to different levels of genotypic frequency discretization and temporal resolution, demonstrating robustness once moderate resolution is achieved, but strong dependence on the timing of drug updates. Although current results rely on synthetic landscapes due to the lack of empirical multi-genotype, multi-drug measurements outside the SSWM regime, our study establishes a foundation for applying MDP optimization to broad classes of evolutionary dynamics, highlighting both the opportunities and the computational challenges of controlling evolution.

evolutionary biology↗

bioRxiv2025

Ultra-deep sequencing of somatic mutations induced by a maize transposon

Cells accumulate mutations throughout development, contributing to cancer, aging, and evolution. Quantitative data on the abundance of de novo mutations within plants or animals are limited, as new mutations are often rare within a tissue and fall below the limits of current sequencing depths and error rates. Here, we show that mutations induced by the maize Mutator (Mu) transposon can be reliably quantified down to a detection limit of 1 part in 16,000. We measured the abundance of millions of de novo Mu insertions across four tissue types. Within a tissue, the distribution of de novo Mu allele frequencies was highly reproducible between plants, showing that, despite the stochastic nature of mutation, repeated statistical patterns of mutation abundance emerge. In contrast, there were significant differences in the allele frequency distribution between tissues. At the extremes, root was dominated by a small number of highly abundant de novo insertions, while endosperm was characterized by thousands of insertions at low allele frequencies. Finally, we used the measured pollen allele frequencies to reinterpret a classic genetic experiment, showing that evidence for late Mu activity in pollen are better explained by cell division statistics. These results provide insight into the complexity of mutation accumulation in multicellular organisms and a system to interrogate the factors that shape mutation abundance. SignificanceNew mutations provide the raw material for evolution and contribute to cancer, aging, and genetic diseases. It has been challenging to follow the origin and spread of new mutations because they can be exceptionally rare and difficult to detect. By focusing on a class of mutation that can be detected more readily - Mu transposon insertions - we followed the abundance of new mutations in multiple maize tissues. We find that the Mu has broad activity across tissues, but with significant tissue-specific differences in how abundant individual new mutations become. Most mutations were below the detection limit available for other classes of mutation. These results provide a glimpse into the complexity of mutation within multicellular organisms.

plant biology↗

bioRxiv2024

Inverted topologies in sequential fitness landscapes enable evolutionary control

Adaptive populations rarely evolve in a static environment. Therefore, understanding and ultimately controlling the evolution of a population requires consideration of fluctuating selective pressures. The fitness landscape metaphor has long been used as a tool for representing the selective pressures a given environment imposes on a population. Much work has already been done to understand the dynamics of evolution on a single fitness landscape. More recently, evolution on fluctuating or sequentially applied landscapes has come to the fore of evolutionary biology. As more empirical landscapes are described, metrics for describing salient features of paired landscapes will have uses for understanding likely evolutionary dynamics. Currently, Pearson correlation coefficient and collateral sensitivity likelihoods are used to quantify topographical relatedness or dissimilarity of a pair of landscapes. Here, we introduce the edge flip fraction, a new metric for comparing landscapes, which quantifies changes in the directionality of evolution between pairs of fitness landscapes. We demonstrate that the edge flip fraction captures topographical differences in landscapes that traditional metrics may overlook which have important consequences for the trajectories of populations evolving on them. By applying this metric to both empirical and synthetic fitness landscapes, we show that it partially predicts the collateral sensitivity likelihoods and can inform the optimality of drug sequences. We show that optimal drug sequences that keep populations within lower fitness regions require shifts in evolutionary directions, which are quantified by the edge flip fraction. Edge flip fraction complements existing measures and may help researchers understand how populations evolve under changing environmental conditions, and could yield clues in the pursuit of evolutionary control.

evolutionary biology↗

bioRxiv2024

Asymmetric Interactions Shape Survival During Population Range Expansions

An organism that is newly introduced into an existing population has a survival probability that is dependent on both the population density of its environment and the competition it experiences with the members of that population. Expanding populations naturally form regions of high and low density, and simultaneously experience ecological interactions both internally and at the boundary of their range. For this reason, systems of expanding populations are ideal for studying the combination of density and ecological effects. Conservation ecologists have been studying the ability of an invasive species to establish for some time, attributing success to both ecological and spatial factors. Similar behaviors have been observed in spatially structured cell populations, such as those found in cancerous tumors and bacterial biofilms. In these scenarios, invasion may be the introduction of a new mutation or bacterial species with some form of drug resistance, leading to the possibility of treatment failure. In order to gain insight into the relationship between population density and ecological interactions, we study an expanding population of interacting wild-type cells and mutant cells. We simulate these interactions in time and study the spatially dependent probability for a mutant to survive or to take over the front of the population wave (gene surfing). Additionally, we develop a mathematical model that describes this survival probability and find agreement when the payoff for the mutant is positive (corresponding to cooperation, exploitation, or commensalism). Given the types of interactions, our model provides insight into the spatial distribution of survival probability. Conversely, given a spatial distribution of survival probabilities, our model provides insight into the types of interactions that were involved to generate it.

evolutionary biology↗

bioRxiv2024

Pathological C-terminal phosphomimetic substitutions alter the mechanism of liquid-liquid phase separation of TDP-43 low complexity domain

C-terminally phosphorylated TAR DNA-binding protein of 43 kDa (TDP-43) marks the proteinaceous inclusions that characterize a number of age-related neurodegenerative diseases, including amyotrophic lateral sclerosis, frontotemporal lobar degeneration and Alzheimers disease. TDP-43 phosphorylation at S403/S404, and especially at S409/S410, is in fact accepted as a biomarker of proteinopathy. These residues are located within the low complexity domain (LCD), which also drives the proteins liquid-liquid phase separation (LLPS). The impact of phosphorylation at these LCD sites on phase separation of the protein is a topic of great interest, as these post-translational modifications and LLPS are both implicated in proteinopathies. Here, we employed a combination of experimental and simulation-based approaches to explore this question on a phosphomimetic model of the TDP-43 LCD. Our turbidity and fluorescence microscopy data show that Ser-to-Asp substitutions at residues S403, S404, S409 and S410 alter the LLPS behavior of TDP-43 LCD. In particular, in contrast to the unmodified protein, the phosphomimetic variants display a biphasic dependence on salt concentration. Through coarse-grained modeling, we find that this biphasic salt dependence is derived from an altered mechanism of phase separation, in which LLPS-driving short-range intermolecular hydrophobic interactions are modulated by long-range attractive electrostatic interactions. Overall, this in vitro and in silico study provides a physiochemical foundation for understanding the impact of pathologically-relevant C-terminal phosphorylation on the LLPS of the TDP-43 in a more complex cellular environment. Statement of SignificanceProteinaceous inclusions composed of phosphorylated, C-terminal TDP-43 fragments have long been recognized as hallmarks of several neurodegenerative diseases, in particular amyotrophic lateral sclerosis and frontotemporal dementia. A rapidly growing number of studies indicate that these proteinopathies may be closely related to liquid-liquid phased separation (LLPS) of TDP-43, but the impact of phosphorylation on TDP-43 LLPS remains largely unexplored. In this study we used a combination of experimental methods and coarse-grained simulations to ascertain, in mechanistic terms, how phosphorylation at pathologically-critical C-terminal sites impacts liquid-liquid phase separation of the low complexity domain of TDP-43. Our results broaden our understanding of the mechanisms driving pathogenic process in these neurodegenerative diseases.

biophysics↗

bioRxiv2023

Motion Blur Microscopy

Imaging and characterizing the dynamics of cellular adhesion in blood samples is of fundamental importance in understanding biological function. In vitro microscopy methods are widely used for this task, but typically require diluting the blood with a buffer to allow for transmission of light. However whole blood provides crucial mechanical and chemical signaling cues that influence adhesion dynamics, which means that conventional approaches lack the full physiological complexity of living microvasculature. We propose to overcome this challenge by a new in vitro imaging method which we call motion blur microscopy (MBM). By decreasing the source light intensity and increasing the integration time during imaging, flowing cells are blurred, allowing us to identify adhered cells. Combined with an automated analysis using machine learning, we can for the first time reliably image cell interactions in microfluidic channels during whole blood flow. MBM provides a low cost, easy to implement alternative to intravital microscopy, the in vivo approach for studying how the whole blood environment shapes adhesion dynamics. We demonstrate the methods reproducibility and accuracy in two example systems where understanding cell interactions, adhesion, and motility is crucial--sickle red blood cells adhering to laminin, and CAR-T cells adhering to E-selectin. We illustrate the wide range of data types that can be extracted from this approach, including distributions of cell size and eccentricity, adhesion durations, trajectories and velocities of adhered cells moving on a functionalized surface, as well as correlations among these different features at the single cell level. In all cases MBM allows for rapid collection and processing of large data sets, ranging from thousands to hundreds of thousands of individual adhesion events. The method is generalizable to study adhesion mechanisms in a variety of diseases, including cancer, blood disorders, thrombosis, inflammatory and autoimmune diseases, as well as providing rich datasets for theoretical modeling of adhesion dynamics.

biophysics↗

bioRxiv2023

Bioenergetic costs and the evolution of noise regulation by microRNAs

Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of mRNAs, but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant KM describing miRNA-mRNA affinity, and show supporting evidence from analysis of experimental data. KM is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6-7 nucleotides in length, the most commonly observed types. Moreover, at optimality the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.

biophysics↗

bioRxiv2023

Masking, maintenance and mimicry: the interplay of cell-intrinsic and cell-extrinsic effects in evolutionary games

The temporal evolution of mutating pathogens in disease contexts arises from both the intrinsic properties of each subpopulation and the interactions among them, yet experimental inference often neglects the latter. Drug development studies commonly estimate selective advantages by comparing growth rates in monoculture, and in vitro monoculture dose-response curves are frequently used to justify or halt further investigation of a drug. Although many ecological models distinguish intrinsic from interaction-dependent growth rates, we show that simpler evolutionary game theory (EGT) frameworks can also be used to disentangle these contributions. We present a game-theoretic reparameterization of the replicator equation payoff matrix that separates intrinsic effects from interaction-driven contributions to frequency-dependent fitness. We also introduce an interaction-selection plot that facilitates the interpretation of the relative importance of between-population interactions compared with intrinsic evolutionary trade-offs. Using this framework, we map how interactions can mask, mirror, maintain, or mimic frequency-independent selection. We derive analytical conditions for these behaviors in both deterministic (replicator equation) and stochastic (Fokker-Planck-Kolmogorov) models, showing that simple conditions persist when mutation and noise are introduced. We validate these predictions using Wright-Fisher simulations. Applying our framework to published microbial and cancer co-culture data, we find that real systems span regimes dominated by either autonomous selection or interaction-driven effects, with interactions sometimes reversing or neutralizing frequency-independent fitness differences. Together, our results show that frequency-dependent effects can shape evolutionary dynamics in subtle and non-obvious ways, highlighting the importance of accounting for interactions when inferring fitness and predicting evolutionary outcomes.

evolutionary biology↗

bioRxiv2023

Frequency-dependent ecological interactions increase the prevalence and shape the distribution of pre-existing drug resistance

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of pre-existing resistance.

evolutionary biology↗

bioRxiv2023

Fitness seascapes promote genetic heterogeneity through spatiotemporally distinct mutant selection windows

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N *2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine. Author SummaryDrug resistance in infectious disease and cancer is a major driver of mortality. While undergoing treatment, the population of cells in a tumor or infection may evolve the ability to grow despite the use of previously effective drugs. Researchers hypothesize that the spatial organization of these disease populations may contribute to drug resistance. In this work, we analyze how spatial gradients of drug concentration impact the evolution of drug resistance. We consider a decades-old model called the mutant selection window (MSW), which describes the drug concentration range that selects for drug-resistant cells. We show how extending this model with continuous dose-response data, which describes how different types of cells respond to drug, improves the ability of MSWs to predict evolution. This work helps us understand how the spatial organization of cells, such as the organization of blood vessels within a tumor, may promote drug resistance. In the future, we may use these methods to optimize drug dosing to prevent resistance or leverage known vulnerabilities of drug-resistant cells.

evolutionary biology↗

bioRxiv2022

Catch bonds in sickle cell disease: shear-enhanced adhesion of red blood cells to laminin

Could the phenomenon of catch bonding--force-strengthened cellular adhesion--play a role in sickle cell disease, where abnormal red blood cell (RBC) adhesion obstructs blood flow? Here we investigate the dynamics of sickle RBCs adhering to a surface functionalized with the protein laminin (a component of the extracellular matrix around blood vessels) under physiologically relevant micro-scale flow. First, using total internal reflectance microscopy we characterize the spatial fluctuations of the RBC membrane above the laminin surface before detachment. The complex dynamics we observe suggest the possibility of catch bonding, where the mean detachment time of the cell from the surface initially increases to a maximum and then decreases as a function of shear force. We next conduct a series of shear-induced detachment experiments on blood samples from 25 sickle cell disease patients, quantifying the number and duration of adhered cells under both sudden force jumps and linear force ramps. The experiments reveal that a subset of patients does indeed exhibit catch bonding. By fitting the data to a theoretical model of the bond dynamics, we can extract the mean bond lifetime versus force for each patient. The results show a striking heterogeneity among patients, both in terms of the qualitative behavior (whether or not there is catch bonding) and in the magnitudes of the lifetimes. Patients with large bond lifetimes at physiological forces are more likely to have certain adverse clinical features, like a diagnosis of pulmonary arterial hypertension and intracardiac shunts. By introducing an in vitro platform for fully characterizing RBC-laminin adhesion dynamics, our approach could contribute to the development of patient-specific anti-adhesive therapies for sickle cell disease. The experimental setup is also easily generalizable to studying adhesion dynamics in other cell types, for example leukocytes or cancer cells, and can incorporate disease-relevant environmental conditions like oxygen deprivation. SIGNIFICANCEAbnormal red blood cell adhesion to the walls of blood vessels is a central feature of sickle cell disease. We study this adhesion by experimentally measuring how long on average red blood cells adhere to a protein-covered surface, and how the strength of the cell-protein bond depends on the force resulting from the surrounding fluid flow. The results vary widely from patient to patient, with some cases showing an unusual regime where the mean bond strength increases with force. We connect these measurements to clinical aspects of the disease, which may aid in the design of individualized therapies in the future.

biophysics↗

bioRxiv2021

Cellular signaling beyond the Wiener-Kolmogorov limit

Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However, WK theory has one assumption that potentially limits its applicability: it relies on a linear, continuum description of the reaction dynamics. Despite this, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence the bound being broken. To accomplish this, we develop a theoretical framework for multi-level signaling cascades, including the possibility of feedback interactions between input and output. In the absence of feedback, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback, we rely on numerical solutions of the systems master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However the magnitude of the violation is biologically negligible, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds, its predictions for performance limits are excellent approximations, even for nonlinear systems.

systems biology↗

bioRxiv2021

Cheater suppression and spite through quorum sensing

The evolutionary consequences of quorum sensing in regulating bacterial cooperation are not fully understood. In this study, we reveal unexpected consequences of regulating public good production through quorum sensing on bacterial population dynamics, showing that quorum sensing can be a collectively harmful alternative to unregulated production. We analyze a birth-death model of bacterial population dynamics accounting for public good production and the presence of non-producing cheaters. Our model demonstrates that when demographic noise is a factor, the consequences of controlling public good production according to quorum sensing depend on the cost of public good production and the presence of non-public fitness benefits. When public good production is inexpensive, quorum sensing is a destructive alternative to unconditional production, in terms of the mean population extinction time. When costs are higher, quorum sensing becomes a constructive strategy for the producing strain, both stabilizing cooperation and decreasing the risk of population extinction.

biophysics↗

bioRxiv2021

Counterdiabatic control of biophysical processes

The biochemical reaction networks that regulate living systems are all stochastic to varying degrees. The resulting randomness affects biological outcomes at multiple scales, from the functional states of single proteins in a cell to the evolutionary trajectory of whole populations. Controlling how the distribution of these outcomes changes over time--via external interventions like time-varying concentrations of chemical species--is a complex challenge. In this work, we show how counterdiabatic (CD) driving, first developed to control quantum systems, provides a versatile tool for steering biological processes. We develop a practical graph-theoretic framework for CD driving in discrete-state continuous-time Markov networks. Though CD driving is limited to target trajectories that are instantaneous stationary states, we show how to generalize the approach to allow for non-stationary targets and local control--where only a subset of system states are targeted. The latter is particularly useful for biological implementations where there may be only a small number of available external control knobs, insufficient for global control. We derive simple graphical criteria for when local versus global control is possible. Finally, we illustrate the formalism with global control of a genetic regulatory switch and local control in chaperone-assisted protein folding. The derived control protocols in the chaperone system closely resemble natural control strategies seen in experimental measurements of heat shock response in yeast and E. coli.

biophysics↗

bioRxiv2020

Low force unfolding of a single-domain protein by parallel pathways

Deviations from linearity in the dependence of the logarithm of protein unfolding rates, log ku(f), as a function of mechanical force, f, measurable in single molecule experiments, can arise for many reasons. In particular, upward curvature in log ku(f) as a function of f implies that the underlying energy landscape must be multidimensional with the possibility that unfolding ensues by parallel pathways. Here, simulations using the SOP-SC model of a wild type {beta}-sandwich protein and several mutants, with immunoglobulin folds, show upward curvature in the unfolding kinetics. There are substantial changes in the structures of the transition state ensembles as force is increased, signaling a switch in the unfolding pathways. Our results, when combined with previous theoretical and experimental studies, show that parallel unfolding of structurally unrelated single domain proteins can be determined from the dependence of log ku(f) as a function of force (or log ku[C] where [C] is the denaturant concentration).

biophysics↗