Jekyll2017-02-28T10:12:27-07:00http://www.buttenschoen.ca//Andreas ButtenschönA website featuring my research and related topicsAdhesion Random Walk Project2016-12-30T12:01:00-07:002016-12-30T12:01:00-07:00http://www.buttenschoen.ca/test-project<h2 id="introduction">Introduction</h2>
<script type="math/tex; mode=display">\begin{equation}
\frac{\partial u}{\partial t} = D \frac{\partial^2 u}{\partial x^2}
- \frac{\partial}{\partial x} \left( u \frac{\phi}{R}
\int_{-R}^{R} u(x + r) \Omega(r) \mathrm{d} r \right)
\end{equation}</script>
<h2 id="progress">Progress</h2>
<ul>
<li><a href="http://www.buttenschoen.ca/AdhesionRandomWalk/">Derivation</a> of the non-local adhesion model from a space-jump process</li>
</ul>adrsIntroductionAdhesion Random Walk Preprint2016-12-20T22:48:00-07:002016-12-20T22:48:00-07:00http://www.buttenschoen.ca/AdhesionRandomWalk<h2 id="abstract">Abstract</h2>
<p>Cellular adhesion provides one of the fundamental forms of biological
interaction between cells and their surroundings, yet the continuum modelling
of cellular adhesion has remained mathematically challenging. In 2006,
Armstrong et. al. proposed a mathematical model in the form of an
integro-partial differential equation. Although successful in applications, a
derivation from an underlying stochastic random walk has remained elusive. In
this work we develop a framework by which non-local models can be derived from
a space-jump process. We show how the notions of motility and a cell
polarization vector can be naturally included. With this derivation we are able
to include microscopic biological properties into the model. We show that
particular choices yield the original Armstrong model, while others lead to
more general models, including a doubly non-local adhesion model and non-local
chemotaxis models. Finally, we use random walk simulations to confirm that the
corresponding continuum model represents the mean field behaviour of the
stochastic random walk.</p>
<h2 id="where-to-get-it">Where to get it?</h2>
<p>A copy of preprint can be found at <a href="http://biorxiv.org/content/early/2016/12/13/093617">bioarxiv</a>.</p>adrsAbstractOptimizing Adoptive Cell Therapy2016-12-01T22:48:00-07:002016-12-01T22:48:00-07:00http://www.buttenschoen.ca/TIL-Metaphenotype<h2 id="abstract">Abstract</h2>
<p>There is an urgent need for reliable effective therapy for patients with
metastatic sarcoma. Approaches that manipulate the immune system have shown
promise for patients with advanced, widely disseminated malignancies. One of
these approaches is adoptive cell therapy (ACT), where tumor-infiltrating
lymphocytes (TIL) are isolated from the tumor, expanded ex vivo, and then
transferred back to the patient. This approach has shown great promise in
melanoma, leading to an objective response in approximately half of treated
patients [14]. Standard protocols involve characterization of TIL populations
with respect to adaptive CD4+ and CD8+ T-lymphocytes, but neglect the possible
role of the innate lymphoid repertoire. Due to toxicity and the high cost
associated with ACT, the IFN-γ release assay is currently used as a proxy to
identify suitable TIL isolates for ACT. Efforts in TIL-ACT for sarcoma, which
are pre-clinical and pioneered at Moffitt Cancer Center, have shown that only a
minority of the TIL cultures show tumor specific activity in ex vivo IFN-γ
assays. Surprisingly, internal melanoma trial data reveal a lack of correlation
between IFN-γ assay and clinical outcomes, highlighting the need for a more
reliable proxy. We hypothesize the existence of a predictable TIL
meta-phenotype that leads to optimal tumor response. Here, we describe
preliminary efforts to integrate prospective and existing patient data with
mathematical models to optimize the TIL meta-phenotype prior to re-injection.</p>
<h2 id="where-to-get-it">Where to get it?</h2>
<p>The write-up can be found at <a href="http://biorxiv.org/content/early/2016/11/05/085910">bioarxiv</a>.</p>adrsAbstract