Topology and Biology Symposium October 26th at UPenn
This full-day workshop is designed to bring together research from neuroscience, biology, and topology in order to foster a deeper understanding of large questions in the fields and encourage interdisciplinary communication and collaboration.

Schedule (titles and abstracts below):
9:00 - Welcome
9:15am - Sydney Shaffer (UPenn)
9:45am - Jeni Stiso (UPenn)
10:45am - Josh Pan (Harvard/DFCI)
11:15am - Josh Gold (UPenn, Keynote)
1:30pm - Rob Ghrist (UPenn, Keynote)
2:15pm - Darrick Lee (UPenn)
3:15pm - Carina Curto (Penn State, Keynote)
4:00pm - Ann Sizemore Blevins (UPenn)

The event is free but we would appreciate a head count for ordering snacks and coffee.

Event Address
Jordan Medical Education Center (JMEC) 3400 Civic Center Blvd, Philadelphia, PA 19104
Rooms 505 (afternoon) and 506 (morning)

For those of you who haven’t been to JMEC yet, it is the tall building that is constructed behind but attached to the Perelman Center for Advanced Medicine. Please refer to the map below.

To get there, enter Perelman and follow signs for the South Pavilion. Walk toward Dermatological Surgery and use the elevators closer to the center for Dermatologic Surgery. Take the elevators to the 5th floor (not all elevators access the 5th floor). Exit the elevator and you’ll see signs for the symposium.

Name *
Ann Sizemore
Will you be attending the symposium? *
Do you plan to join us for dinner after the symposium at a local restaurant?
Titles and abstracts
When does single-cell variability matter?

Individual cells can take on different phenotypes through both genetic and non-genetic mechanisms. A huge challenge in the field is determining which of the differences present in single cells dictates how a cell behaves. I will discuss a new approach based on Luria and Delbrück’s fluctuation analysis called MemorySeq for finding gene expression differences in single cells persisting for several cell divisions and thus having memory. We applied this method in melanoma and breast cancer, and MemorySeq revealed multiple gene modules that express together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors including the ability to proliferate in the face of anti-cancer therapeutics. More broadly, we anticipate that these non-genetic heritable fluctuations in gene expression are a form of memory at the single-cell level and may underlie many important processes in biology.

Using network control theory to model the effects of brain stimulation

Electrical brain stimulation is currently being investigated as a potential therapy for neurological
disease. However, opportunities to optimize and personalize such therapies are challenged by the fact
that the beneficial impact (and potential side-effects) of focal stimulation on both neighboring and
distant regions is not well understood. Here, we hypothesize that the effects of simulation will
propagate along white matter tracts, or connections in the brain to affect change in the activity of distal
brain regions. Specifically, we use network control theory to build a formal model of brain network
function that makes explicit predictions about how stimulation spreads through the brain's white matter
network and influences large-scale dynamics. We test these predictions using combined
electrocorticography (ECoG) measurements of brain activity, and diffusion weighted imaging (DWI)
measures of brain connectivity from people who volunteered to participate in an extensive stimulation
regimen. We posit a specific model-based manner in which white matter tracts constrain stimulation,
defining its capacity to drive the brain to new states, including states associated with successful memory
encoding (as defined by a previously trained and validated classifier). In a first validation of our model,
we find that the true pattern of white matter tracts can be used to more accurately predict the state
transitions induced by direct electrical stimulation than the artificial patterns of a topological or spatial
network null model. We then use a targeted optimal control framework to solve for the optimal energy
required to drive the brain to a given state, and make theoretical predictions about when and where to
stimulate. Our work demonstrates that individual white matter architecture plays a vital role in guiding
the dynamics of direct electrical stimulation, more generally offering empirical support for the utility of
network control-theoretic models of brain response to stimulation.

Systems approaches to characterizing genes of unknown function

15 years after the sequence of the human genome was completed, biologists have yet to assign molecular functions to about one-third of the protein coding genes. However, the rise of large-scale genetic profiling in humans have revealed disease associations to many of these uncharacterized genes. Given their potential importance to molecular and disease biology, why do so many genes remain uncharacterized?
I will describe the perceived challenges in characterizing genes of unknown function. I will then describe two breakthrough technologies – CRISPR-Cas9 genetic screening and mass spectrometry – that have enabled the creation of biological networks for gene functional inference at scale. Finally, I will give two examples of how we have taken these approaches in the laboratory to discover a novel protein complex that was hidden in plain sight.

What is optimal in adaptive decision-making? A complexity-based perspective.

Our brain uses past experiences, integrated over multiple timescales, to shape how it makes decisions in our uncertain and dynamic world. These adaptive processes can take many forms, across both conditions and individuals, with different combinations of costs (like processing time) and benefits (like flexibility) that can make them difficult to compare and benchmark. Here I will describe our recent efforts to characterize the effectiveness of decision processes with respect to the complexity of model they use to convert past observations into useful predictions that can guide choices. I will show that this approach: 1) has a solid theoretical foundation using concepts drawn from physics and other fields; 2) can account for substantial individual variability of human subjects performing certain decision tasks; and 3) leads to quantitative predictions about the most efficient and effective solutions to a host of decision problems according to a fundamental “law of diminishing returns” relating accuracy to complexity. I will then show that these notions of complexity can be encoded in pupil-linked arousal systems that, in turn, may reflect the influence of neuromodulatory systems like the locus coeruleus-norepinephrine system on coordinated neural dynamics that can affect how information is integrated over time.

Topological methods for data

This talk will be a general and visual introduction to recent topological methods in data analysis, including the use of simplicial complexes, homology, persistence, and a few other ideas. No background in algebraic topology will be assumed: just linear algebra.

Path Signatures and Topological Time Series Analysis

Path signatures are a sequence of numbers that characterize curves in Euclidean space, similar to how Fourier coefficients characterize periodic functions. The path signature has the additional property of being reparametrization invariant, capturing characteristics of curves that don't change under arbitrary monotone transformations of the time axis. Considering multi-dimensional time series as a curve in space, we describe how the path signature can be used to study relationships between time series in the absence of periodicity.

Topology in neuroscience: some examples from neural coding and neural networks

In this talk I will give a sampling of examples illustrating how topological ideas arise in neuroscience. First I'll tell you about some interesting neurons, such as place cells and grid cells, and the topology associated to their neural activity. Next I'll transition to the underlying networks, and how topological features of network motifs and their embeddings affect neural dynamics.

Topology in biological systems

Where do we see topology in biology? How might we use these concepts to understand biological systems? In this talk we will discuss recent examples from molecular biology and neuroscience as well as propose future avenues for research.

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