Mathematical modelling of the sleep-wake cycle: light, clocks and social rhythms

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: We’re all familiar with sleep, but how can we mathematically model it? And what determines how long and when we sleep? In this talk I’ll introduce the nonsmooth coupled oscillator systems that form the basis of current models of sleep-wake regulation and discuss their dynamical behaviour. I will describe how we are using models

Modeling cell-to-cell heterogeneity from a signaling network

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Cells make individual fate decisions through linear and nonlinear regulation of gene network, generating diverse dynamics from a single reaction pathway. In this colloquium, I will present two topics of our recent work on signaling dynamics at cellular and patient levels. The first example is about the initial value of the model, as a mechanism

Quantifying dynamical changes in sparse, noisy, high-dimensional data

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

The circadian clock orchestrates a vast array of behavioral and physiological processes with a 24-hour cycle, enabling nearly all organisms -- from bread mold to fruit-flies to humans -- to anticipate and adapt to the Earth's day. Entrainable by environmental cue, the rhythm itself is generated by a self-sustained molecular oscillator present in nearly every

Assessing the limits of control of Covid-19 outbreaks using agent-based modeling

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Transmission of SARS-CoV-2 relies on interactions between humans. Heterogeneity and stochasticity both in human-human interactions and in the transmission of the virus give rise to non-linear infection networks that gain complexity with time. We assessed the limits of control and the effect of pharmaceutical and non-pharmaceutical measures against COVID‐19 outbreaks with a detailed community‐specific agent-based model

Brain dynamics during shiftwork: from maths and codes to real-world applications

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Circadian clocks control the timing and 24-hour periodicity of virtually all physiological rhythms including sleep, cognition, and metabolism. There are optimal times for most behaviours; e.g., the best sleep is achieved during low circadian activity (night), while meals and physical exercise are best placed during high circadian activity (day) when metabolic rates, stress hormone

Mammalian synthetic biology by controller design

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: The ability to reliably engineer the mammalian cell will impact a variety of applications in a disruptive way, including cell fate control and reprogramming, targeted drug delivery, and regenerative medicine. However, our current ability to engineer mammalian genetic circuits that behave as predicted remains limited. These circuits depend on the intra and extra cellular

Taming Complexity in Data-Limited Nonlinear Nonequilibrium Settings

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Since before the time of Aristotle and the natural philosophers, reductionism has played a foundational role in western scientific thought. The premise of reductionism is that systems can be broken down into constituent pieces and studied independently, then reassembled to understand the behavior of the system as a whole. It embodies the classical linear

Shinya Kuroda, Systems Biology of Insulin Action

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: 1. The "temporal information code" of insulin action: a bottom-up approach One of the essential elements of signaling networks is to encode information from a wide variety of inputs into a limited set of molecules. We have proposed a "temporal information code" that regulates a variety of physiological functions by encoding input information in

Martin Nowak, Evolution of cooperation

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Cooperation means that one individual pays a cost for another to receive a benefit. Cooperation can be at variance with natural selection. Why should you help competitors? Yet cooperation is abundant in nature and is important component of evolutionary innovation. Cooperation can be seen as the master architect of evolution and as the third

Julio Saez-Rodriguez, Dynamic logic models complement machine learning for personalized medicine

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study the deregulation of intra- and inter-cellular signaling processes in disease. I will present recent methods and applications from our group toward this aim, focusing on computational approaches that combine data with biological knowledge within statistical and machine learning

(Rescheduled: 3/22 -> 3/24) Stefan Bauer, Neural Causal Models for Experimental Design

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Many questions in everyday life as well as in research are causal in nature: How would the climate change if we lower train prices or will my headache go away if I take an aspirin? Inherently, such questions need to specify the causal variables relevant to the question and their interactions. However, existing algorithms

George Karniadakis, BINNS: Biophysics-Informed Neural Networks

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: We will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of biophysical systems and for discovering hidden mechanisms and pathways from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and on generative adversarial networks (GANs). Unlike other approaches that rely on big data,

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