Talks

This is a summary of my 2022 paper "Understanding Metabolic Alterations in Cancer Cachexia through the Lens of Exercise Physiology" (PMID: 35954163; https://www.mdpi.com/2073-4409/11/15/2317) 

Here I propose that systemic cancer creates a chronic resource drain on the body that results in the same metabolic adaptations and patterns of nutrient consumption that are supposed to be transient during strenuous physical activity. 

I evaluate this hypothesis through modeling a very interesting set of data published by San Millan and Brooks (2018), where they describe patterns of nutrient metabolism as a function of increase in exercise intensity, and then I interpret it using the framework of ventilatory thresholds. Then I simulate the predicted pattern of relative nutrient metabolism if the simulated effort "got stuck" between the thresholds, and propose that this is consistent with what we observe in cachexia. 

This is a theoretical model, so if you have any experiment supporting or disproving this hypothesis, reach out, I'd love to know more! 

This is a summary of our paper with Jana Gevertz "Guiding model-driven combination dose selection using multi-objective synergy optimization" (PMID: 37415306; https://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.12997).

 Here we focus on the question of optimizing dose/schedule for two pre-selected drugs (for an amazing method on how to select those drugs in the first place, check the MuSyc method at musyc.lolab.xyz/about#overview)

We review existing metrics for quantifying synergy and show that they can be wildly inconsistent with what they predict as a synergistic vs additive vs antagonistic combination. 

We then propose that this can be overcome using multi-objective optimization and the concept of Pareto optimality, and apply this approach to a published combination of pembrolizumab and bevacizumab. 

We then show what you need to apply this to your own combinations. So if you run experiments where you chose doses using this approach, please, let us know! 

This is a summary of our paper with Jana Gevertz "Cytokine storm mitigation of exogenous immune agonists" (https://www.biorxiv.org/content/10.1101/2023.07.07.548130v1). 

Here we describe a conceptual mathematical model of a cytokine storm, apply it to an exogenous immune agonist, and show that 

1) you can have efficacy without storm and storm without efficacy for the same dose/schedule and 

2) you might be able to mitigate them with through changing dose/frequency of administration. 

This is a theoretical model, not based on data or real drugs but if you have experiments that may be relevant, reach out! 

This is a summary of our paper with Joel Brown, "Estrogen as an essential resource and the coexistence of ER+ and ER- cancer cells". (https://www.frontiersin.org/articles/10.3389/fevo.2021.673082/full) 

It provides a summary of the key ideas and results: 

- essential resources and Liebig's law of the minimum 

- evolutionary steering through resource manipulation (with applications of our HKV method for modeling evolution of heterogeneous populations)

- a discussion of some issues with how ER+ vs ER- tumors are classified 

This is a summary of the key ideas of this paper that we did with my dad Georgy Karev: "Linear rather than exponential decay: a mathematical model and underlying mechanisms" (https://arxiv.org/ftp/arxiv/papers/2004/2004.05726.pdf).

It talks about how a sub-exponential power law model can capture linear rather than exponential decay observed in red blood cells, and how this model in turn can be derived within the frameworks of frequency-dependent model of population extinction if there exists heterogeneity with respect to mortality rates such that their initial distribution is the Gamma distribution.

It's a lot of words and some math, but it's actually really cool :) And it allows capturing the decay of red blood cells using a simple ODE, which was really the goal of the whole exercise.

I originally planned this as another 15-minute summary of one of my research projects but ended up getting caught up in making sense of the Wakefield saga from the first half of Brian Deer's book "The doctor who fooled the world". It's truly fascinating!

So instead, here we have

1) a brief history of the autism diagnosis

2) origins of the "infectious cause of autism" theory

3) several landmark legal cases that set the backdrop to the MMR controversy

4) what actually causes autism (spoiler alert: it's a highly heritable very complex genetic disease)

5) a brief visualization of how at can actually manifest as a spectrum (the way that I understand it)

6) a short overview of the work that I did, based on serotonin signaling and its impact on enteric (gut) nervous system