Lecture

Design Optimization in ChemBio

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In course

BIO-212: Biological chemistry I

Biochemistry is a key discipline for the Life Sciences. Biological Chemistry I and II are two tightly interconnected courses that aim to describe and understand in molecular terms the processes that make life possible. ...

Description

This lecture covers the design optimization of Snif-it's sensors, focusing on geometry, sequence of domains, spacing, and circular permutation, aiming to understand the molecular basis for changes in FRET ratio and the effective concentration of intra-molecular ligands. It also introduces semi-synthetic sensors for visualizing metabolite concentrations and LUCID's bioluminescent sensor proteins for drug monitoring, including the principles behind them and their applications in point-of-care therapeutic drug monitoring.

Instructors (4)

Matteo Dal Peraro

Matteo Dal Peraro graduated in Physics at the University of Padua in 2000. He obtained his Ph.D. in Biophysics at the International School for Advanced Studies (SISSA, Trieste) in 2004. He then received postdoctoral training at the University of Pennsylvania (Philadelphia) under the guidance of Prof. M. L. Klein. He was nominated Tenure Track Assistant Professor at the EPFL School of Life Sciences in late 2007. His research at the Laboratory for Biomolecular Modeling (LBM), within the Interfaculty Institute of Bioengineering (IBI), focuses on the multiscale modeling of large macromolecular systems.

Bruno Emanuel Ferreira De Sousa Correia

Throughout my PhD and postdoctoral studies I was trained in world-renowned laboratories and institutions in the United States of America (University of Washington and The Scripps Research Institute). Very early in my scientific career I found out my fascination about protein structure and function. My PhD studies evolved in the direction of immunogen design and vaccine engineering which sparked my interest in the many needs and opportunities in vaccinology and translational research. My efforts resulted in an enlightening piece of work where for the first time, computationally designed immunogens elicited potent neutralizing antibodies. During my postdoctoral studies I joined a chemical biology laboratory at the Scripps Research Institute. In this stage I developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In March 2015, I joined the École Polytechnique Fédérale de Lausanne (EPFL) – Switzerland as a tenure track assistant professor. The focus of my research group is to develop computational tools for protein design with particular emphasis in applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). The activities in my laboratory focus on computational design methods development and experimental characterization of the designed proteins. Our laboratory has been awarded with 2 prestigious research grants from the European Research Council. Lastly, I have been awarded the prize for best teacher of Life sciences in 2019.

Giovanni D'Angelo

Teaching & PhD PhD Students Mélissa Ruiz, Gary Domeniconi, Irmak Kaysudu, Alessandro Valente, Surayyo Akramkhanova, Jean Andrea Maillat, Linda Wedemann, Manel Leila Nadia Boussouf, Jiayan Lin, Nika Gorsek, Christina Ulrich, William Daniel Chan-Ha Verdier Past EPFL PhD Students Laura Capolupo, Jaipreet Singh Loomba, Halima Hannah Emilie Schede Past EPFL PhD Students as codirector Muhammad Umair Anwar Courses Biological chemistry II BIO-213 Biochemistry is a key discipline in the Life Scienc

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Related concepts (26)

Multidisciplinary design optimization

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO). MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

Bioluminescence

Bioluminescence is the production and emission of light by living organisms. It is a form of chemiluminescence. Bioluminescence occurs widely in marine vertebrates and invertebrates, as well as in some fungi, microorganisms including some bioluminescent bacteria, and terrestrial arthropods such as fireflies. In some animals, the light is bacteriogenic, produced by symbiotic bacteria such as those from the genus Vibrio; in others, it is autogenic, produced by the animals themselves.

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Global optimization

Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. It is usually described as a minimization problem because the maximization of the real-valued function is equivalent to the minimization of the function . Given a possibly nonlinear and non-convex continuous function with the global minima and the set of all global minimizers in , the standard minimization problem can be given as that is, finding and a global minimizer in ; where is a (not necessarily convex) compact set defined by inequalities .

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

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