What is the largest number $f(d)$ where every graph with average degree at least $d$ contains a subdivision of $K_{f(d)}$? Mader asked this question in 1967 and $f(d) = \Theta(\sqrt{d})$ was proved by Bollob\'as and Thomason and independently by Koml\'os and Szemer\'edi. This is best possible by considering a disjoint union of $K_{d,d}$. However, this example contains a much smaller subgraph with the almost same average degree, for example, one copy of $K_{d,d}$. In 2017, Liu and Montgomery proposed the study on the parameter $c_{\varepsilon}(G)$ which is the order of the smallest subgraph of $G$ with average degree at least $\varepsilon d(G)$. In fact, they conjectured that for small enough $\varepsilon>0$, every graph $G$ of average degree $d$ contains a clique subdivision of size $\Omega(\min\{d, \sqrt{\frac{c_{\varepsilon}(G)}{\log c_{\varepsilon}(G)}}\})$. We prove that this conjecture holds up to a multiplicative $\min\{(\log\log d)^6,(\log \log c_{\varepsilon}(G))^6\}$-term. As a corollary, for every graph $F$, we determine the minimum size of the largest clique subdivision in $F$-free graphs with average degree $d$ up to multiplicative polylog$(d)$-term. This is joint work with Jaehoon Kim, Youngjin Kim, and Hong Liu.

Simple mathematical models have had remarkable successes in biology, framing how we understand a host of mechanisms and processes. However, with the advent of a host of new experimental technologies, the last ten years has seen an explosion in the amount and types of quantitative data now being generated. This sets a new challenge for the field – to develop, calibrate and analyse new models to interpret these data. In this talk I will use examples relating to intracellular transport and cell motility to showcase how quantitative comparisons between models and data can help tease apart subtle details of biological mechanisms.

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This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)

The poset Ramsey number $R(Q_{m},Q_{n})$ is the smallest integer $N$ such that any blue-red coloring of the elements of the Boolean lattice $Q_{N}$ has a blue induced copy of~$Q_{m}$ or a red induced copy of $Q_{n}$. Axenovich and Walzer showed that $n+2\le R(Q_{2},Q_{n})\le2n+2$. Recently, Lu and Thompson improved the upper bound to $\frac{5}{3}n+2$. In this paper, we solve this problem asymptotically by showing that $R(Q_{2},Q_{n})=n+O(n/\log n)$. Joint work with Dániel Grósz and Abhishek Methuku.

TBA

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Abstract: From fertilization to birth, successful mammalian reproduction relies on interactions of elastic structures with a fluid environment. Sperm flagella must move through cervical mucus to the uterus and into the oviduct, where fertilization occurs. In fact, some sperm may adhere to oviductal epithelia, and must change their pattern of oscillation to escape. In addition, coordinated beating of oviductal cilia also drive the flow. Sperm-egg penetration, transport of the fertilized ovum from the oviduct to its implantation in the uterus and, indeed, birth itself are rich examples of elasto-hydrodynamic coupling. We will discuss successes and challenges in the mathematical and computational modeling of the biofluids of reproduction.

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This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)

A well-known theorem of Whitney states that a 3-connected planar graph admits an essentially unique embedding into the 2-sphere. We prove a 3-dimensional analogue: a simply-connected 2-complex every link graph of which is 3-connected admits an essentially unique locally flat embedding into the 3-sphere, if it admits one at all. This can be thought of as a generalisation of the 3-dimensional Schoenflies theorem. This is joint work with Agelos Georgakopoulos.

A family F of subsets of {1,2,…,n} is called maximal k-wise intersecting if every collection of at most k members from F has a common element, and moreover, no set can be added to F while preserving this property. In 1974, Erdős and Kleitman asked for the smallest possible size of a maximal k-wise intersecting family, for k≥3. We resolve this problem for k=3 and n even and sufficiently large. This is joint work with Kevin Hendrey, Casey Tompkins, and Tuan Tran.

Chronotherapeutics- that is administering drugs following the patient’s biological rhythms over the 24 h span- may largely impact on both drug toxicities and efficacy in various pathologies including cancer [1]. However, recent findings highlight the critical need of personalizing circadian delivery according to the patient sex, genetic background or chronotype. Chronotherapy personalization requires to reliably account for the temporal dynamics of molecular pathways of patient’s response to drug administration [2]. In a context where clinical molecular data is usually minimal in individual patients, multi-scale- from preclinical to clinical- systems pharmacology stands as an adapted solution to describe gene and protein networks driving circadian rhythms of treatment efficacy and side effects and allow for the design of personalized chronotherapies. Such a multiscale approach is being undertaken for personalizing the circadian administration of irinotecan, one of the cornerstones of chemotherapies against digestive cancers. Irinotecan molecular chronopharmacology was studied at the cellular level in an in vitro/in silico investigation. Large transcription rhythms of period T= 28 h 06 min (SD 1 h 41 min) moderated drug bioactivation, detoxification, transport, and target in synchronized Caco-2 colorectal cancer cell cultures. These molecular rhythms translated into statistically significant changes according to drug timing in irinotecan pharmacokinetics, pharmacodynamics, and drug-induced apoptosis. Clock silencing through siBMAL1 exposure ablated all the chronopharmacology mechanisms. Mathematical modeling highlighted circadian bioactivation and detoxification as the most critical determinants of irinotecan chronopharmacology [3]. The cellular model of irinotecan chronoPK-PD was further tested on SW480 and SW620 cell lines, and connected to a new clock model to investigate the feasibility of irinotecan timing personalization solely based on clock gene expression monitoring (Hesse, Martinelli et al., under review). To step towards the clinics, on one side, mathematical models of irinotecan, oxaliplatin and 5-fluorouracil pharmacokinetics were designed to precisely compute the exposure concentration of tissue over time after complex chronomodulated drug administration through programmable pumps [4]. On the other side, we aimed to design a model learning methodology predicting from non-invasively measured circadian biomarkers (e.g. rest-activity, body temperature, cortisol, food intake, melatonin), the patient peripheral circadian clocks and associated optimal drug timing [5]. We investigated at the molecular scale the influence of systemic regulators on peripheral clocks in four classes of mice (2 strains, 2 sexes). Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background. References 1. Ballesta, A., et al., Systems Chronotherapeutics. Pharmacol Rev, 2017. 69(2): p. 161-199. 2. Sancar, A. and R.N. Van Gelder, Clocks, cancer, and chronochemotherapy. Science, 2021. 371(6524). 3. Dulong, S., et al., Identification of Circadian Determinants of Cancer Chronotherapy through In Vitro Chronopharmacology and Mathematical Modeling. Mol Cancer Ther, 2015. 4. Hill, R.J.W., et al., Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy. PLoS Comput Biol, 2020. 16(1): p. e1007218. 5. Martinelli, J., et al., Model learning to identify systemic regulators of the peripheral circadian clock. 2021.

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Bouchet (1987) defined delta-matroids by relaxing the base exchange axiom of matroids. Oum (2009) introduced a graphic delta-matroid from a pair of a graph and its vertex subset. We define a $\Gamma$-graphic delta-matroid for an abelian group $\Gamma$, which generalizes a graphic delta-matroid. For an abelian group $\Gamma$, a $\Gamma$-labelled graph is a graph whose vertices are labelled by elements of $\Gamma$. We prove that a certain collection of edge sets of a $\Gamma$-labelled graph forms a delta-matroid, which we call a $\Gamma$-graphic delta-matroid, and provide a polynomial-time algorithm to solve the separation problem, which allows us to apply the symmetric greedy algorithm of Bouchet (1987) to find a maximum weight feasible set in such a delta-matroid. We also prove that a $\Gamma$-graphic delta-matroid is a graphic delta-matroid if and only if it is even. We prove that every $\mathbb{Z}_p^k$-graphic delta matroid is represented by some symmetric matrix over a field of characteristic of order $p^k$, and if every $\Gamma$-graphic delta-matroid is representable over a finite field $\mathbb{F}$, then $\Gamma$ is isomorphic to $\mathbb{Z}_p^k$ and $\mathbb{F}$ is a field of order $p^\ell$ for some prime $p$ and positive integers $k$ and $\ell$. This is joint work with Duksang Lee and Sang-il Oum.

Within a given species, fluctuations in egg or embryo size is unavoidable. Despite this, the gene expression pattern and hence the embryonic structure often scale in proportion with the body length. This scaling phenomenon is very common in development and regeneration and has long fascinated scientists. I will first discuss a generic theoretical framework to show how scaling gene expression pattern can emerge from non-scaling morphogen gradients. I will then demonstrate that the Drosophila gap gene system achieves scaling in a way that is entirely consistent with our theory. Remarkably, a parameter-free model based on the theory quantitatively accounts for the gap gene expression pattern in nearly all morphogen mutants. Furthermore, the regulation logic and the coding/decoding strategy of the gap gene system can be revealed. Our work provides a general theoretical framework on a large class of problems where scaling output is induced by non-scaling input, as well as a unified understanding of scaling, mutants’ behavior and regulation in the Drosophila gap gene and related systems.

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Majority dynamics on a graph $G$ is a deterministic process such that every vertex updates its $\pm 1$-assignment according to the majority assignment on its neighbor simultaneously at each step. Benjamini, Chan, O'Donnell, Tamuz and Tan conjectured that, in the Erd\H{o}s--R\'enyi random graph $G(n,p)$, the random initial $\pm 1$-assignment converges to a $99\%$-agreement with high probability whenever $p=\omega(1/n)$. This conjecture was first confirmed for $p\geq\lambda n^{-1/2}$ for a large constant $\lambda$ by Fountoulakis, Kang and Makai. Although this result has been reproved recently by Tran and Vu and by Berkowitz and Devlin, it was unknown whether the conjecture holds for $p< \lambda n^{-1/2}$. We break this $\Omega(n^{-1/2})$-barrier by proving the conjecture for sparser random graphs $G(n,p)$, where $\lambda' n^{-3/5}\log n \leq p \leq \lambda n^{-1/2}$ with a large constant $\lambda'>0$.

Immune sentinel cells must initiate the appropriate immune response upon sensing the presence of diverse pathogens or immune stimuli. To generate stimulus-specific gene expression responses, immune sentinel cells have evolved a temporal code in the dynamics of stimulus responsive transcription factors. I will present recent works 1) using an information theoretic approach to identify the codewords, termed “signaling codons”, 2) using a machine learning approach to characterize their reliability and points of confusion, and 3) dynamical systems modeling to characterize the molecular circuits that allow for their encoding. I will present progress on how the temporal code may be decoded to specify immune responses. Further, I will discuss to what extent such a code may be harnessed to achieve greater pharmacological specificity when therapeutically targeting pleiotropic signaling hubs. NFκB Signaling: information theory, signaling codons

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I am going to present an algorithm for computing a feedback vertex set of a unit disk graph of size k, if it exists, which runs in time $2^{O(\sqrt{k})}(n + m)$, where $n$ and $m$ denote the numbers of vertices and edges, respectively. This improves the $2^{O(\sqrt{k}\log k)}(n + m)$-time algorithm for this problem on unit disk graphs by Fomin et al. [ICALP 2017].