BaMBA 2015 Presentation Abstracts
Steven Brenner, UC Berkeley
Carina Curto, Pennsylvania State University
Title: What can topology tells us about the neural code?
Abstract: Cracking the neural code is one of the central challenges of neuroscience. Neural codes allow the brain to represent, process, and store information about the outside world. Unlike other types of codes, they must also reflect relationships between stimuli, such as proximity between locations in an environment. In this talk, I will explain why algebraic topology and commutative algebra provide natural tools for understanding the structure and function of neural codes.
Haiyan Huang, UC Berkeley
Title: Inferring gene pairwise and group interactions beyond standard statistical models
Abstract: With the advent of high-throughput technologies making large-scale gene expression
data readily available, developing appropriate computational tools to infer gene interactions
has been a major challenge in systems biology. I will discuss two methods of finding
gene-gene associations that differ in their considerations of how genes behave across
the given samples. The first method applies to the case of large heterogenous samples,
where the patterns of gene association may change or only exist in a subset of all
the samples. We propose two new gene coexpression statistics based on counting local patterns
of gene expression ranks to take into account the potentially diverse nature of gene
interactions. In particular, one of our statistics is designed for time-course data
with local dependence structures, such as time series coupled over a subregion of
the time domain. In comparison, the second method goes beyond pairwise gene relationships
to higher level group interactions, but requiring similar gene behaviours across all
the samples. The estimation procedure relies on sparse canonical correlation analysis
(SCCA) coupled with repeated random partition and subsampling of the expression dataset.
By considering different subsets of genes and ways of grouping them, our interaction
measure can be viewed as an aggregated estimate of partial correlations of different
orders. We compare both methods to other popular approaches using simulated and real
data, and demonstrate they lead to better general performance and capture important
biological features that are missed by the other methods.
Folarin Erogbogbo, San Jose State University
Title: Assessing Clinical Prospects of Silicon Quantum Dots
Abstract: Silicon nanocrystals can provide the outstanding imaging capabilities of toxic heavy-metal-based quantum dots without employing heavy metals and have potential for rapid progression to the clinic. Understanding the toxicity of silicon quantum dots (SiQDs) is essential to realizing this potential. However, existing studies of SiQD biocompatibility are limited, with no systematic progression from small-animal to large-animal studies that are more clinically relevant. Here, we test the response of both mice and monkeys to high intravenous doses of a nanoconstruct created using only SiQDs and FDA-approved materials. We show that (1) neither mice nor monkeys show overt signs of toxicity reflected in their behavior, body mass, or blood chemistry, even at a dose of 200 mg/kg. (2) This formulation did not biodegrade as expected. Elevated levels of silicon were present in the liver and spleen of mice three months post-treatment. (3) Histopathology three months after treatment showed adverse effects of the nanoformulation in the livers of mice, but showed no such effects in monkeys. This investigation reveals that the systemic reactions of the two animal models may have some differences and there are no signs of toxicity clearly attributable to silicon quantum dots.
Nicolette Meshkat, Santa Clara University
Title: Parameter identifiability of biological models
Abstract: Parameter identifiability analysis addresses the question of which unknown parameters of a model can be determined from given input-output data. In this talk, we discuss structural identifiability analysis, which addresses whether or not the model parameters can be determined from perfect input-output data (noise-free and of any duration required) and is an important step in the parameter estimation problem. Many linear ODE models used in systems biology are unidentifiable, which means that parameters can take on an infinite number of values and yet yield the same input-output data. We study a particular class of unidentifiable models and find conditions to obtain identifiable reparametrizations of these models. In particular, we use a graph-theoretic approach to analyze the models and show that graphs with certain properties allow a monomial scaling reparametrization over identifiable functions of the parameters. We also examine conditions to obtain identifiability for this class of models, and in particular, show how identifiability can be determined by simply looking at the graphical structure of these linear compartment models.