Open Access

Highlights • We define a stochastic agent-based model for cancer cell dormancy and chemotherapy. • The main subject of our simulation study is the ODE system arising from this model. • Even a small fraction of dormant cancer cells can prevent treatment success. • We analyse several multidrug regimes, targeting also dormant cells in different ways. • Depending on the parameters, we provide basic rules for treatment protocol design. Abstract Therapy evasion – and subsequent disease progression – is a major challenge in current oncology. An important role in this context seems to be played by various forms of cancer cell dormancy. For example, therapy-induced dormancy, over short timescales, can create serious obstacles to aggressive treatment approaches such as chemotherapy, and long-term dormancy may lead to relapses and metastases even many years after an initially successful treatment. In this paper, we focus on individual cancer cells switching into and out of a dormant state both spontaneously as well as in response to treatment. We introduce an idealized mathematical model, based on stochastic agent-based interactions, for the dynamics of cancer cell populations involving individual short-term dormancy, and allow for a range of (multi-drug) therapy protocols. Our analysis – based on simulations of the many-particle limit – shows that in our model, depending on the specific underlying dormancy mechanism, even a small initial population (of explicitly quantifiable size) of dormant cells can lead to therapy failure under classical single-drug treatments that would successfully eradicate the tumour in the absence of dormancy. We further investigate and quantify the effectiveness of several multi-drug regimes (manipulating dormant cancer cells in specific ways, including increasing or decreasing resuscitation rates or targeting dormant cells directly). Relying on quantitative results for concrete simulation parameters, we provide some general basic rules for the design of (multi-)drug treatment protocols depending on the types and processes of dormancy mechanisms present in the population.

Biological movement patterns can sometimes be quasi linear with abrupt changes in direction and speed, as in plastids in root cells investigated here. For the analysis of such changes we propose a new stochastic model for movement along linear structures. Maximum likelihood estimators are provided, and due to serial dependencies of increments, the classical MOSUM statistic is replaced by a moving kernel estimator. Convergence of the resulting difference process and strong consistency of the variance estimator are shown. We estimate the change points and propose a graphical technique to distinguish between change points in movement direction and speed.

The FIND algorithm (also called Quickselect) is a fundamental algorithm to select ranks or quantiles within a set of data. It was shown by Grübel and Rösler that the number of key comparisons required by Find as a process of the quantiles α∈[0,1] in a natural probabilistic model converges after normalization in distribution within the càdlàg space D[0,1] endowed with the Skorokhod metric. We show that the process of the residuals in the latter convergence after normalization converges in distribution to a mixture of Gaussian processes in D[0,1] and identify the limit's conditional covariance functions. A similar result holds for the related algorithm QuickVal. Our method extends to other cost measures such as the number of swaps (key exchanges) required by Find or cost measures which are based on key comparisons but take into account that the cost of a comparison between two keys may depend on their values, an example being the number of bit comparisons needed to compare keys given by their bit expansions.

We provide a simplified proof of the random k-XORSAT satisfiability threshold theorem. As an extension we also determine the full rank threshold for sparse random matrices over finite fields with precisely k non-zero entries per row. This complements a result from [Ayre, Coja-Oghlan, Gao, Müller: Combinatorica 2020]. The proof combines physics-inspired message passing arguments with a surgical moment computation.

We derive a sufficient condition for a sparse random matrix with given numbers of non-zero entries in the rows and columns having full row rank. The result covers both matrices over finite fields with independent non-zero entries and {0,1}-matrices over the rationals. The sufficient condition is generally necessary as well.