Quantitative Biology Seminars


Current Seminar


Monday, April 10, 2017: 335 West Hall, 12:00p - 1:00p

PresenterThomas Gregor, Department of Physics and Lewis-Sigler Institute for Integrative Genomics Princeton University

Discussion How the physics of enhancers shapes development

Abstract Enhancers are small regulatory pieces of DNA that control the activity of genes, which eventually determine cellular fates during the development of multicellular organisms. They need to measure the concentrations of various input effector molecules, called transcription factors, and then act over often very long distances along the DNA in order to activate a distantly located gene. In this talk I will present my laboratory’s progress on two fundamental physical properties of these enhancers: 1. How do enhancers operate at long distances to instruct gene activity? 2. How do enhancers decode the information of the input transcription factors and then transduce it into a precise output? We use a combination of genome editing, live imaging and statistical mechanics techniques to address these questions in the developing fly embryo.


Past Seminars

2016


Monday, November 7, 2016: 335 West Hall, 12:00p - 1:00p

PresenterDaniel Wójcik, PhD, Nencki Institute for Experimental Biology, Warsaw

Discussion Source reconstruction from extracellular potentials: from single cells to the whole brains

Abstract Extracellular recordings of electric potential remain a popular tool for investigations of brain activity on all scales in animals and humans, from single cells (spikes) to systems studied with depth electrodes (LFP, SEEG), subdural recordings (ECoG), and on the scalp (EEG). They are relatively easy to record but difficult to interpret: since electric field is long range one can observe neural activity several millimeters from its source. As a consequence, every recording reflects activity of many cells, populations and regions, depending on which level we focus. One way to overcome this problem is to reconstruct the distribution of current sources (CSD) underlying the measurement.

We recently proposed a kernel-based method of CSD estimation from multiple extracellular recordings from arbitrarily placed probes (i.e. not necessarily on a grid) which we called kernel Current Source Density method (kCSD). In my presentation, I will present the recent advances of this method, latest software implementations, and explain why it works. I will also show two recent developments, skCSD (single cell kCSD) and kESI (kernel Electrophysiological Source Imaging). skCSD assumes that we know which part of the recorded signal comes from a given cell and we have access to the morphology of the cell. This could be achieved by patching a cell, driving it externally while recording the potential on a multielectrode array, injecting a dye, and reconstructing the morphology. In this case we know that the sources must be located on the cell and this information can be successfully used in source estimation. In kESI we consider simultaneous recordings with subdural ECoG (strip and grid electrodes) and with depth electrodes (SEEG). Such recordings are taken on some epileptic patients prepared for surgical removal of epileptogenic zone. When MR scan of the patient head is taken and the positions of the electrodes are known as well as the brain’s shape, the idea of kCSD can be applied to constrain the possible distribution of sources facilitating localization of the foci.



Monday, October 10, 2016: 335 West Hall, 12:00p - 1:00p

PresenterSofia Piltz, PhD, Department of Mathematics, University of Michigan

Discussion A piecewise-smooth, two smooth, and a fast-slow system for plankton population dynamics

Abstract Phytoplankton, and the zooplankton that graze upon them, play a crucial role in the dynamics observed at higher levels of the aquatic ecosystem. There is a vast literature on differential equation models of plankton dynamics, and a recent trend in ecological models has considered plasticity in parameters and adaptation. In this talk, we interpret plasticity as prey switching, that is, predator’s adaptive change of diet in response to the abundance of prey. We first analyse a model constructed for one predator feeding on two different types of prey and inspired by plankton observations. This model has a discontinuity between two vector fields. We then discuss two different smooth formulations of the model and compare model predictions with data on freshwater plankton collected from Lake Constance on the German-Swiss-Austrian border. Finally, we discuss a 1 fast-3 slow system for one predator feeding adaptively on two different prey types and inspired by the first model.



Monday, September 12, 2016: 335 West Hall, 12:00p - 1:00p

PresenterIndika Rajapakse, Departments of Computational Medicine and Bioinformatics, and of Mathematics, University of Michigan

Discussion Mathematics of Cellular Reprogramming

Abstract In 2007, a remarkable discovery was made that with just 4 external inputs (transcription factors), it was possible to change differentiated cells into embryonic-like cells. This type of cellular reprogramming changes the fundamental nature of a cell. It invites the possibility of building a universal template for transcription factor guided reprogramming. I will discuss our initial work on this, using advanced genomics technologies + mathematics.



Monday, April 11, 2016: 335 West Hall, 12:00p - 1:00p

PresenterXueying Wang, Department of Mathematics, Washington State University

Discussion Mathematical Modeling of Cholera Epidemics

Abstract Cholera is a severe water-borne disease caused by the bacterium Vibrio cholerae. In this talk, I will present some recent investigations of cholera epidemics through mathematical modeling and analysis. The talk consists of two parts. In the first part, I will give a introduction to infectious disease modeling. In the second part, we will discuss disease threshold dynamics and cholera traveling waves by using ODE and PDE models.


2015


Monday, April 27th, 2015: 335 West Hall, 12:00p - 1:00p

PresenterPaul Francois
Department of Physics McGill University

Discussion Immunology ‡ la physicienne

Abstract T Cells have to make quick, sensitive and specific decisions to recognize not self from self. I will describe a coarse-grained modelling approach aimed at uncovering the basic principles of such immune recognition, that led us to a new "adaptive sorting" model. Adaptive sorting relies on a combination of kinetic proofreading and biochemical adaptation. A more refined model of immune recognition explains many puzzling features of early immune detection, such as antagonism and "digital" response when varying phosphatase concentrations, and was validated experimentally. Finally, I will recast this problem using decision theory and show how this mechanism is especially useful for detection in a fluctuating background of biochemically similar ligands. Adaptive sorting thus constitutes a generic model for "qualitative" sensing of biological signals.
Additional seminar
information
http://www.math.lsa.umich.edu/seminars_events
under Mathematical Biology



Monday, April 20th, 2015

PresenterBard Ermentrout, PhD
Department of Mathematics, University of Pittsburgh

Discussion Keeping the beat: Homeostatic frequency control in coupled oscillators

Abstract When nonlinear oscillators are forced or coupled they will generally lock if the frequency is in a narrow enough range. However, humans and other animals such as fireflies and Snowball the dancing cockatoo are able to adjust the intrinsic frequency of their oscillators to widen the range of locking and even zeroing the phase-lag. In this talk, I will start with some simple abstract circle maps and show that when the frequency is modulated by the coupling there are many possible final states and fractal basin boundaries between them. I will then turn to continuous time oscillators. Using averaging I will derive a new class of phase models and analyze their properties. I apply this to some neural models and show how the homeostatic control of the frequency greatly expands the ability to lock. Finally, I show that traveling periodic wave trains can be destabilized when there is frequency adjustment in rings of coupled oscillators



Monday, April 6th, 2015

PresenterUnCheol Lee, PhD
Department of Anesthesiology, University of Michigan

Discussion Identification of a general relationship between network topology and directed connectivity provides an explanation for how a brain network topology shapes information flow pattern in conscious and unconscious states

Abstract Background: Recent simulation and empirical studies suggest that network topology shapes directed connectivity (sometimes referred to as information flow) in brain networks. However, no general relationship has been systematically studied or identified.

Methods: In simple network models, neural mass models and empirical brain networks, hub nodes of higher degree were consistently targets for directed connectivity. After computational perturbation of networks or administration of anesthetics in humans, monkey and mouse, major hub nodes lost degree and directed connectivity was altered accordingly. Furthermore, the theoretical predictions for the information flow patterns before and after reorganizations of network topology in the conscious and unconscious states were confirmed by the experimental data.

Results: A general relationship can describe directed connectivity in brain networks and perturbations of topology predictably alter the direction of information flow. This relationship may have implications for the neurobiology of consciousness across species as well as state transitions in humans.




Monday, March 9th, 2015

PresenterLawrence Cohen, PhD
Department of Cellular and Molecular Physiology
Yale University

Discussion Determining the function of the mammalian olfactory bulb

Abstract We measured the input from the nose to the olfactory bulb in the mouse using calcium sensitive dye in the olfactory receptor cell nerve terminals in the glomeruli. We are now trying to measure the output of the bulb, carried by the mitral/tufted neurons, with the same glomerular level spatial resolution. The comparison of input and output would define the function(s) carried out by the bulb. Understanding the roles of different neuron types requires fluorescent protein activity indicators that can be expressed in specific cell types. The genetically encoded voltage indicator (GEVI) ArcLight consists of the voltage sensing domain of the Ciona voltage-sensitive phosphatase and the fluorescent protein super ecliptic pHluorin (A227D). The fluorescence of ArcLight changes by ~40% in response to a 100mV depolarization; about five times larger than previously reported signals. ArcLight can be specifically expressed in mitral/tufted neurons and reports odor-evoked electrical activity. The experiments comparing the input and output of the bulb are just beginning.



Monday, January 26th, 2015

PresenterMark Reimers, PhD
Neuroscience Program, Michigan State University

Discussion Two Vignettes in Computational Neuroscience: from data to models

Abstract This talk will describe two models for brain dynamics and their relation to data.

The first is a model for slow (delta-band) activity over the surface of mouse cortex. We develop a linear model for both intrinsic regional dynamics and communication between cortical regions, and use this model to estimate some of the effective connectivity between different cortical regions. Our estimates from dynamical data correspond well to known anatomical connections. Short-term predictions from this model are correlated 65% with observed data. The model is being extended to non-linear dynamics.

The second model reproduces the gamma rhythm found in active regions of cortex. There have been several models that generate plausible rhythms in the gamma range (30-50 Hz); but the parameters of these models are not realistic. Furthermore recent genomic data from healthy human subjects indicates very high variability in key parameters of these models, and current models are not robust to this variability. We propose a more realistic model drawing on data; the distinctive feature is high diversity among connection strengths. This model gives much more realistic gamma rhythms, on all measures, and is also more robust to inter-individual variation.


2014


Monday, December 8th, 2014

PresenterTibin John, PhD
Kalamazoo College, Center for Complex Systems Studies

Discussion Cell and Network Level Changes Related to Overproduction of Alzheimers Amyloid Cause Altered Synchronized Activity in Model of Hippocampal Theta Rhythm Generation



Monday, November 24th, 2014

PresenterAngela Violi, PhD
Associate Professor of Mechanical Engineering, Chemical Engineering, Biomedical Engineering, Macromolecular Science and Engineering, and Applied Physics, at the University of Michigan

Discussion Simulating biological membranes