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Events for April 10, 2008
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Analysis of complex heterogeneous networks: scalability, robustness and fundamental limitations.
Thu, Apr 10, 2008 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Workshops & Infosessions
Abstract:
Complex networks are receiving an increasing attention by various scientific communities, as a result of their
significance and enormous impact in both natural and man made systems. Such examples could range from
Internet protocols, power networks, vehicle platoons to flocking phenomena and gene regulatory networks. On the
one hand it is important to understand the fundamental principles and theories behind the success of networks
present in nature, but, at the same time, there is an urgent need to develop methodologies that enable the design of
networkswhere robustness and scalability can be guaranteed. This talk is going to address such challenges by
presenting recently developed tools that can lead to scalable network designs, and by deriving fundamental
limitations in biological networks.
By scalability we refer to the requirement that robust stability is guaranteed for an arbitrary interconnection by
conditions on only local interactions, without having to redesign the whole network whenever a new
heterogeneous agent is added or removed. It would be, for example unrealistic to carry out a centralized analysis,
whenever a computer/router enters the Internet or a generator becomes part of a power network. We show in the
talk, how the new notion of an S-hull, a relaxed convexification in the complex plane, can lead to stability
certificates that are both decentralized and scalable. This creates an abstraction that is relevant in diverse
applications such as data network protocols and group coordination problems.
For the case of biological networks we focus on regulatory processes at the molecular level. These are well known
to be inherently stochastic, with a substantial part of the noise being intrinsic, arising from the random births and
deaths of individual molecules. We establish in the talk fundamental limitations for the suppression of intrinsic
noise in gene regulatory networks. These are hard bounds that hold for arbitrary feedback, being a result of simple
generic features of the underlying jump processes. Such features include causality in conjunction with limited
information, arising from the inevitable presence of delays and feedback channels with finite Shannon capacity.
Bio:
Ioannis Lestas graduated with a BA (Starred First) and an MEng (Distinction) in Electrical and Information
Sciences from the University of Cambridge (Trinity College). Prior to that he was chosen to participate in
International Mathematics, Physics, and Chemistry Olympiads (two bronze medals in International Chemistry
Olympiads and first prizes in several national mathematics and physics competitions). From 2002 to 2006 he was a
PhD student in the Control Group at the University of Cambridge as a Gates Scholar and Trinity College Research
Scholar. Since October 2006 he is a class A Fellow of Clare College, Cambridge (and a member of the University
governing body). These are prestigious appointments at Cambridge previously held by a number of Cambridge's
distinguished academics and Nobel laureates. His research interests lie within the area of complex networks,
focusing on the development of theoretical frameworks for addressing issues of scalability, robustness and
fundamental limitations, with applications in data networks, multiagent systems and biological networks.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Shane Goodoff
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Development of a biomimetic lung surfactant
Thu, Apr 10, 2008 @ 12:45 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Lyman Handy Colloquium SeriesPresentsAnnelise E. BarronAssociate Professor, Dept. of Bioengineering, Stanford University, Stanford, CA 94305AbstractWe are developing a new family of amphipathic peptide mimics for a synthetic lung surfactant (LS) replacement. Presently used exogenous LS replacements are extracted from animal lungs and used to treat respiratory distress sydrome in premature infants. The hydrophobic lung surfactant proteins SP-B and SP-C are necessary constituents of an effective surfactant replacement for the treatment of respiratory distress. As there are concerns and limitations associated with animal-derived surfactants, much recent work has focused on synthetic peptide analogues of SP-B and SP-C. However, creating an accurate peptide mimic of SP-C that retains good biophysical surface activity is challenging, given this lipopeptide's extreme hydrophobicity and propensity to misfold and aggregate. One approach that overcomes these difficulties is the use of helical poly-/N/-substituted glycines, or "peptoids," to mimic SP-C. We discuss advances in the design and characterization of peptoid-based SP-C mimics, which recently have led to the creation of our most biomimetic surfactant replacements to date.
Peptoid sequences were systemically varied in order to study surface activity effects of varying peptoid helicity,/ N/-terminal side chain chemistry and sequence length, as well as the side chain structures used within the hydrophobic C-terminal helix. The secondary structures of the peptoid SP-C mimics are analyzed in organic solution by CD spectroscopy. Langmuir-Wilhelmy surface balance experiments, epifluorescence videomicroscopy studies, and pulsating bubble surfactometry are used to characterize the surface activity and surface film morphology of the mimics in combination with a biomimetic phospholipid formulation. These results provide us with the first comprehensive structure-function relationships for peptoid-based analogues of surfactant protein C, as well as strong evidence that they offer significant promise for use in a synthetic replacement for animal-derived surfactants. There are several other potential applications for a safe and non-immunogenic surfactant formulation with these properties, other than treating respiratory distress, including protection against ventilator-induced lung injury, drug delivery to the lungs, and treatment of ear infections.Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Petra Pearce Sapir
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CS Colloq: Dynamics of Real-World Networks
Thu, Apr 10, 2008 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Title: Dynamics of Real-World NetworksSpeaker: Jure Leskovec (CMU)Abstract:
Emergence of the web and cyberspace gave rise to detailed traces of human social activity. This offers great opportunities to analyze and model behaviors of millions of people. For example, we examined ''planetary scale'' dynamics of a full Microsoft Instant Messenger network that contains 240 million people, with more than 255 billion exchanged messages per month (4.5TB of data), which makes it the largest social network analyzed to date. In this talk I will focus on two aspects of the dynamics of large real- world networks: (a) dynamics of information diffusion and cascading behavior in networks, and (b) dynamics of the structure of time evolving networks. First, I will consider network cascades that are created by the diffusion process where behavior cascades from node to node like an epidemic. We study two related scenarios: information diffusion among blogs, and a viral marketing setting of 16 million product recommendations among 4 million people. Motivated by our empirical observations we develop algorithms for detecting disease outbreaks and finding influential bloggers that create large cascades. We exploit the ''submodularity'' principle to develop an efficient algorithm that finds near optimal solutions, while scaling to large problems and being 700 times faster than a simple greedy solution. Second, in our recent work we found counter intuitive patterns that change some of the basic assumptions about fundamental structural properties of networks varying over time. Leveraging our observations we developed a Kronecker graph generator model that explains processes governing network evolution. Moreover, we can fit the model to large networks, and then use it to generate realistic graphs and give formal statements about their properties. Estimating the model naively takes O(N!N^2) while we develop a linear time O(E) algorithm.Biography:
Jure Leskovec (www.cs.cmu.edu/~jure) is a PhD candidate in Machine Learning Departmen at Carnegie Mellon University. He is also a Microsoft Research Graduate Fellow. He received the ACM KDD 2005 and ACM KDD 2007 best paper awards, won the ACM KDD cup in 2003 and topped the Battle of the Sensor Networks 2007 competition. Jure holds three patents. His research interests include applied machine learning and large-scale data mining focusing on the analysis and modeling of large real-world networks as the study of phenomena across the social, technological, and natural worlds.Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Colloquia
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Precision Castparts Corp. Information Session
Thu, Apr 10, 2008 @ 05:00 PM - 07:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join representatives of this company as they share general company information and available opportunities.
Location: Grace Ford Salvatori (GFS) 106
Audiences: All Viterbi Students
Contact: RTH 218 Viterbi Career Services
