Biology subjects

Vladimirsky, A.

Publications and source records attributed to Vladimirsky, A..

5 recordsLinked to original sources

bioRxiv2026

Shielded by the dead: how killed bacteria shape the dynamics and evolution of innate immunity

The outcome of an infection is determined by the dynamic interplay between microbial growth and host immunity. During a bacterial infection, bacteria killed by innate immune effectors can accumulate as corpses in the extracelluar space, where they can continue to bind (and thus sequester) immune effectors. The impacts on infection outcomes of continued biochemical activity ("sponginess") by the dead have been generally overlooked in theoretical and empirical studies of within-host disease dynamics. We develop a mechanism-based mathematical model of within-host dynamics that incorporates host microbial sensing, the production of immune effectors, the interaction of those effectors with microbes, and shutdown of the immune response after an infection has been controlled. Corpse sponginess impedes the hosts ability to control infection, but at the same time, the rapid mopping up of effectors by bacterial corpses also protects host tissue against autoimmune self-harm from immune effectors still circulating after the infection has been resolved. This dual impact of bacterial sponginess alters the trade-off between damage done by infecting bacteria versus autoimmune damage, consequently shifting the evolutionarily optimal immune activation and shutdown kinetics. Thus, the sponginess of bacterial corpses likely shapes both short-term infection dynamics and the long-term evolution of immune systems.

immunology↗

bioRxiv2024

Navigating the Landscape of Fear

O_LIAnimals searching for food must navigate complex landscapes with varying terrain, food availability, predator activity, and shelter. Where and when should they gather food? To what extent should they engage in anti-predator behaviors such as vigilance or seeking refuge if a predator is detected? Optimal foraging theory (OFT) posits that animals balance potentially conflicting goals (such as feeding versus escaping predation) by making decisions that maximize some expected utility or reward. However, OFT models have generally considered highly simplified landscapes, either ignoring spatial variability or assuming that the habitat consists of discrete, internally uniform habitat patches. As a result, OFT has largely avoided the question of how animals should move from one potential feeding area to another, or between feeding areas and refuges. C_LIO_LIWe develop methods based on stochastic dynamic programming to find optimal foraging strategies, including optimal movement paths, in a continuous landscape with spatially varying predation risk. Our approach accounts for switching from foraging to escape behavior when pursued by a predator. Because contingent escape paths are considered for all visited locations, they influence the optimal foraging path even before threats are encountered. The optimal strategy thus depends on the animals level of hunger, the distribution of food, and the perceived threat distribution. The realized path is further influenced by actual predator encounters. C_LIO_LIWe illustrate our approach with two numerical examples: the first hypothetical with two food-abundant regions accessible only via high-risk areas, the second based on empirical studies on foraging Samango monkeys, Cercopithecus albogularis schwarzi. We find that the shape of the foragers utility function (risk-averse, risk-neutral, or with state-dependent risk sensitivity) affects not only its choices of where to feed, but also the optimal paths to and from each feeding ground. C_LIO_LIOur methods make it possible to compare properties of observed foraging trajectories with those predicted for different goal functions. Foraging trajectories can then provide additional information, along with other behavioral choices, about what quantity, if any, animals aim to optimize while foraging. C_LI

ecology↗

bioRxiv2024

Overcoming toxicity: why boom-and-bust cycles are good for non-antagonistic microbes

Antagonistic interactions are critical determinants of microbial community stability and composition, offering host benefits such as pathogen protection and providing avenues for antimicrobial control. While the ability to eliminate competitors confers an advantage to antagonistic microbes, it often incurs a fitness cost. Consequently, many microbes only produce toxins or engage in antagonistic behavior in response to specific cues like quorum sensing molecules or environmental stress. In laboratory settings, antagonistic microbes typically dominate over sensitive ones, raising the question of why both antagonistic and non-antagonistic microbes are found in natural environments and host microbiomes. Here, using both theoretical models and experiments with killer strains of Saccharomyces cerevisiae, we show that boom-and-bust dynamics caused by temporal environmental fluctuations can favor non-antagonistic microbes that do not incur the growth rate cost of toxin production. Additionally, using control theory, we derive bounds on the competitive performance and identify optimal regulatory toxin-production strategies in various boom- and-bust environments where population dilutions occur either deterministically or stochastically over time. Our findings offer a new perspective on how both antagonistic and non-antagonistic microbes can thrive under varying environmental conditions.

microbiology↗

bioRxiv2022

Stochastic optimal control to guide adaptive cancer therapy

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. Author SummaryTumor heterogeneities provide an opportunity to improve therapies by leveraging complex (often competitive) interactions of different types of cancer cells. These interactions are usually stochastic due to both individual cell differences and random events affecting the patient as a whole. The new generation of cancer models strive to account for this inherent stochasticity, and adaptive treatment plans need to reflect it as well. In optimizing such treatment, the most common approach is to maximize the probability of eventually stabilizing or eradicating the tumor. In this paper, we consider a more nuanced version of success, maximizing the probability of reaching these therapy goals before the cumulative burden from the disease and treatment exceed a chosen threshold. Importantly, our method allows computing such optimal treatment plans efficiently and for a range of thresholds at once. If used on a high-fidelity personalized model, our general approach could potentially be used by clinicians to choose the most suitable threshold after a detailed discussion of a specific patients goals (e.g., to include the trade-offs between toxicity and quality of life).

cancer biology↗

bioRxiv2018

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory

BACKGROUNDRecent clinical trials have shown that the adaptive drug therapy can be more efficient than a standard MTD-based policy in treatment of cancer patients. The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumor. But the adaptive treatment policies examined so far have been largely ad hoc. In this paper we propose a method for systematically optimizing the rules of adaptive policies based on an Evolutionary Game Theory model of cancer dynamics.\n\nMETHODSGiven a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation based on a mathematical model of tumor evolution.\n\nRESULTSWe compare adaptive/optimal treatment strategy with MTD-based treatment policy. We show that optimal treatment strategies can dramatically decrease the total amount of drugs prescribed as well as increase the fraction of initial tumour states from which the recovery is possible. We also examine the optimization tradeoffs between the total administered drugs and recovery time.\n\nCONCLUSIONSThe adaptive therapy combined with optimal control theory is a promising concept in the cancer treatment and should be integrated into clinical trial design.

cancer biology↗