The Joint FEA/FM Biomedical Engineering Program and the Center for Advanced Mathematical Sciences (CAMS) at AUB cordially invite you to the Third AUB Biomedical Engineering Winter School. The event will take place over two days and will feature five international distinguished speakers giving lectures on emerging topics in Biomedical Engineering. The lectures will highlight the importance of mathematical and computational modeling in biomedical research at the molecular and physiological levels with medical and clinical applications. Each lecture will be divided into two parts: the first part will cover basics and fundamentals, whereas the second part will cover state-of-the-art research findings and open research directions. The School is technically sponsored by the IEEE EMBS Lebanon Chapter and supported by the AUB Biomedical Engineering Student Society.
The 21st Century provides us with grand scientific challenges to understand complex nonlinear dynamical systems, and it gives us huge data sets and substantial computing capability with which to meet these challenges.
Data assimilation is the process of transferring information from observed data to models of the processes producing those data then testing and validating those models. A systematic, statistical Physics formulation of data assimilation will be discussed. Implementation of data assimilation in the common situation where the noisy data, though huge, may be sparse compared to the demands of the models, and where the models themselves are uncertain, will be the focus of the discussion.
Examples will be taken from quite different, but related, areas of scientific inquiry: numerical weather prediction and the quantitative study of functional networks of neurons.
Henry Abarbanel earned his PhD in Physics from Princeton University in 1966. He currently serves as Distinguished Professor of Physics and as Research Physicist (Scripps Institution of Oceanography) at the University of California, San Diego. He is a member of the UCSD Graduate Program in Neuroscience and Associate Director of the Center for Engineered Natural Intelligence. His research has long been in the properties of nonlinear dynamics systems as used in the understanding of physical and biological systems. He served on the Del Mar, CA City Council for 12 years, including one as Mayor. He is the Chair of the State of California’s San Diego Regional Water Quality Control Board.
Behavioral science allows reading out the computations performed by the brain (whereas neuroscience reveals how they are implemented). In my talk, I will reveal the algorithm songbirds use to match their songs to an auditory template. We tested how zebra finches cope with the computational complexity of song learning, by prompting juveniles to modify their song to correct conflicting phonological and sequential mismatches in song syllables. Birds matched each syllable to the most acoustically similar sound in the target, regardless of its temporal position, resulting in unnecessary sequence errors that they later corrected. Thus, birds prioritized efficient learning of syllable vocabulary, at the cost of inefficient syntax learning. Overall, we find that birds learn their songs by solving a linear assignment problem. This is the type of problem that taxi companies solve to optimally dispatch their taxis to waiting customers.
We also study how insights obtained from observation compare with insights gained from trial-and-error. We find that birds can learn to discriminate auditory stimuli by observing expert performers. These findings agree with social learning theories showing that to copy others’ behavior is a successful strategy. However, our results indicate that the benefit of rapid learning from observation comes at the cost of poor generalization, revealing a sensory analogue to the common view that the best means to learn a (motor) skill is rigorous practice.
Dr. Richard Hahnloser graduated with a PhD in Natural Sciences from the Institute of Neuroinformatics at ETH Zuric in 1999, and was a postdoctoral fellow in the Department of Brain and Cognitive Sciences (Seung Lab) at MIT and the Biological Computation Research group at Lucent Technologies' Bell Labs. Professor Richard Hahnloser heads the Birdsong Research Group at the University of Zurich's Institute of Neuroinformatics. He is also Dean of the joint master's degree program in Neural Systems and Computation, offered in collaboration with the Mathematics and Natural Sciences Faculty at the University of Zurich and by the Department of Physics at the Swiss Federal Institute of Technology in Zurich (ETH Zurich).
Dr. Hahnloser is interested in brain functions that can be characterized by a computational goal encompassing sensory inputs and motor outputs. He studies vocal production and vocal learning in songbirds using reductionist experimental and theoretical approaches. His research group have started to investigate the structure of the nervous system in birds using a combination of electron and light microscopy, termed correlative array tomography (CAT); they perform electrophysiological recordings to read out the neural code in singing birds, and design behavioral experiments to study vocal communication and social learning in bird groups. Currently they are trying to identify the simplest possible mechanistic equations to describe song learning trajectories.
Self-assembling peptides which are stimuli-responsive can serve as inks for bio-printing and building blocks for bio-scaffolds, enabling the development of high-throughput cell screening, and printing of organotypic biological constructs. We discovered a subclass of amphiphilic ultrashort peptides containing lysine or lysine-mimetic residues that demonstrate salt and pH-enhanced self-assembly into nanofibrous hydrogels. Consisting of only 3 to 7 aliphatic amino acids, their characteristic motif stimulates self-assembly into helical fibers which further aggregate into three-dimensional nanofibrous networks that entrap water. These peptides are intrinsically biocompatible and non-immunogenic, and are of interest as implantable scaffolds for tissue engineering.
Tuning the gelation kinetics and mechanical properties, we developed formulations that gel instantaneously upon exposure to physiologically-relevant salt solutions. Exploiting their stimuli-responsiveness, we encapsulated various human primary and pluripotent stem cells to create three-dimensional cell-scaffold arrays. These constructs were stable for more than 21 days under standard culture conditions, enabling long term culture of cells. To influence cell behavior, genes, small molecules and growth factors can be co-encapsulated. The resulting biological constructs can be used as organoid models for screening small molecules, studying cell behavior and disease progression, as well as tissue-engineered implants for regenerative medicine.
Dr. Charlotte A.E. Hauser is a Professor of Bioscience at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Before joining KAUST, she was Principal Investigator at the Institute of Bioengineering and Nanotechnology, A*Star, Singapore, and Adjunct Professor at Nanyang Technological University, Singapore. After her Ph.D. in Molecular Biology at the Massachusetts Institute of Technology, she joined INSERM in Paris, and the Max-Planck-Institute of Psychiatry in Munich, Germany. Furthermore, for almost ten years before returning to academia, she was founder and Managing Director of Octagene in Munich/Martinsried, Germany, where she developed the first truly human recombinant coagulation factor VIII (hFVIII) from a human cell line, approved in 2014 by FDA, EMEA and other regulatory authorities. This recombinant human clotting factor, available under the trade name of NUWIQ®, is the newest hFVIII replacement. Her honors and awards include awards from the German Federal Ministry of Science and Technology, France’s Société des Amis des Sciences, and Bavarian Research Foundation. She holds over 20 U.S. patents of which the majority is owned or licensed to Nestle Skin Health, Octapharma AG, 3-D Matrix Co. Ltd., and PepNano. She is a Fellow of the American Institute for Medical and Biological Engineers (AIMBE). In 2015, she was elected as a member of the National Academy of Inventors in Washington. Her research includes molecular self-assembly, synthetic peptide biomaterials, amyloidogenesis, and regenerative therapies.
Large volumes of heterogeneous data are now routinely collected and archived from patients in a variety of clinical environments, to support real-time decision-making, monitoring of disease progression, and titration of therapy. This rapid expansion of available physiological data has resulted in a data-rich – but often knowledge-poor – environment. Yet the abundance of clinical data also presents an opportunity to systematically fuse and analyze the available data streams, through appropriately chosen mathematical models, and to provide clinicians with insights that may not be readily extracted from visual review of the available data streams.
In this talk, I will highlight our work in model-based signal processing to derive additional and clinically useful information from routinely available data streams. In the first part of the talk, I will present our model-based approach to noninvasive, patient-specific and calibration free estimation of intracranial pressure, and will elaborate on the challenges of collecting high-quality clinical data for validation. In the second part of the talk, I will present our work on extracting clinically meaningful and actionable information from the shape of the capnogram, with applications to differentiating respiratory and cardiac causes of shortness of breath.
Prof. Thomas Heldt studied physics at Johannes Gutenberg University, Germany, at Yale University, and at MIT. He received the PhD degree in Medical Physics from MIT's Division of Health Sciences and Technology and undertook postdoctoral training at MIT's Laboratory for Electromagnetic and Electronic Systems and the Research Laboratory of Electronics. He currently holds the W.M. Keck Career Development Chair in Biomedical Engineering at MIT. He is a member of MIT’s Institute for Medical Engineering and Science and on the faculty of the Department of Electrical Engineering and Computer Science.
Dr. Heldt's research interests focus on signal processing, mathematical modeling and model identification in support of real-time clinical decision making, monitoring of disease progression, and titration of therapy, primarily in neurocritical and neonatal critical care. In particular, Dr. Heldt is interested in developing a mechanistic understanding of physiologic systems, and in formulating appropriately chosen computational physiologic models for improved patient care. His research is conducted in close collaboration with clinicians from Boston-area hospitals, where he is integrally involved in designing and deploying high-quality data-acquisition systems and collecting clinical data.
The basic goal of neuroscience is to understand the biological mechanisms underlying behavior. Songbirds offer the advantages of a complex sequenced behavior, together with dedicated neural substrates that are amenable to monitoring during behavior. Like humans, birds learn their song by imitating the song of another adult bird, often the bird’s father. Each song contains strings of acoustically distinct syllables organized into stereotyped sequences sung with precise timing. I will present a general introduction into some of the scientific questions that can be addressed by studying this model system, and discuss how models of action sequencing have been used to understand the incredible precision of song production. I will discuss recent studies combining behavior and modeling to understand the brain circuits that underly probabilistic sequencing of song syllables in Bengalese finches.
Dr. Todd Troyer received his B.A. in Mathematics and Physics from Washington University in St. Louis, a Ph.D in Mathematics from the University of California at Berkeley, and held consecutive postdoctoral appointments in computational neuroscience at the University of California at San Francisco. He is currently an Associate Professor in the Biology department at the University of Texas at San Antonio where he is the graduate advisor for neurobiology Ph.D. program. His current research combines theoretical, computational and behavioral approaches to investigate temporal processing in neural circuits. Specific interests include song learning in birds, sequence learning and oscillation, and the temporal dynamics of stochastic neuron models.