MS07 - NEUR-2
Brutus Buckeye Room (#3044) in The Ohio Union

Biological Networks Across Scales

Thursday, July 20 at 04:00pm

SMB2023 SMB2023 Follow Thursday during the "MS07" time block.
Room assignment: Brutus Buckeye Room (#3044) in The Ohio Union.
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Richard Bertram


Biological systems are characterized by network interactions among components. This is true with gene transcription, protein-protein interactions, cellular interactions, whole body interactions, and at the level of populations of individuals. This mini-symposium presentations research on biological networks that covers the whole range from genes to social networks. There will be discussion of a new approach to clustering of gene transcription data, discussions of network dynamics in electrically-active pancreatic beta-cells and neurons, and of collective behavior in decision making. The overall goal of the mini-symposium is to illustrate the use of network approaches to the study of biological networks.

Wilfredo Blanco Figuerola

State University of Rio Grande do Norte (Department of Computer Science)
"Population Bursting in Modular Neural Networks"
Population bursts are observed in developing neural systems and in some fully developed neural systems. These can be achieved in networks in which synaptic connections are fully excitable, with no inhibitory connections. We have previously shown mechanisms and properties of such population bursts in purely excitatory neural systems, but only in unstructured networks, as would be expected in developing neural systems. In this presentation, we explore emergent dynamics in modular networks, focusing on how both intra- and inter-cluster connectivity impacts the behavior of the full population of cells.

Mehran Fazli

Henry M. Jackson Foundation for the Advancement of Military > Medicine, Inc., Bethesda, MD, USA (Austere Environments Consortium for Enhanced Sepsis Outcomes (ACESO))
"Gene bundling: a new approach to clustering and reversed engineering of gene expression network"
Sepsis, responsible for one in five deaths globally, results from the body's response to inflammation caused by infection and can lead to life-threatening tissue damage. Detecting gene expression patterns that signify infection severity or type may improve patient diagnosis and care. The Austere environments Consortium for Enhanced Sepsis Outcomes (ACESO) conducts an international observational study to boost sepsis patient survival rates. In this research, we develop a new parameter-free algorithm for gene correlation-based clustering, founded on graph-theoretic concepts and spectral clustering. Blood samples collected upon hospital admission from 505 participants at ACESO sites in the United States, Ghana, and Cambodia are used for this algorithm. The subsequent transcriptomic data is employed to create and evaluate this innovative clustering approach. A single dataset can yield various clustering configurations, each highlighting distinct aspects of the data. Our primary aim is to build the necessary mechanisms to capture these aspects and achieve optimal gene clusters composed of genes that co-cluster in the most prevalent clustering schemes using spectral theory. This method, referred to as gene bundling, is both straightforward and versatile, permitting the analysis of diverse clustering scales to determine the optimal gene clustering. Using our sepsis dataset, we found that gene bundles have a strong connection to known biological pathways. Furthermore, by utilizing 28-day mortality data and a scoring system, we identified gene bundles that distinguish survivors and non-survivors within the entire population. Employing a multi-layered bundling scheme allows us to reverse-engineer the bundle-bundle interaction network. This algorithm holds promise for deepening our understanding of biological pathway interaction networks in sepsis patients, ultimately contributing to progress in sepsis diagnosis, prognosis, and therapy.
Additional authors: Chris Oppong, Komfo Anokye Teaching Hospital, Kumasi, Ghana;Tin Som, Takeo Provincial Referral Hospital, Takeo, Cambodia;Emily R. Ko, Hospital Medicine, Division of General Internal Medicine, Duke University School of Medicine, Durham, NC, USA;Ephraim L. Tsalik, Duke University Division of Infectious Diseases, Duke University School of Medicine, Durham, NC, USA;Josh Chenoweth, ACESO, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA;Joost Brandsma, ACESO, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA;Chris Woods, Duke University Division of Infectious Diseases, Duke University School of Medicine, Durham, NC, USA;Andrew Letizia, Naval Medical Research Center Unit 2, Singapore;Anne Fox, Naval Medical Research Unit-3 Ghana Detachment, Accra, Ghana, 9Naval Health Research Center- San Diego;Dennis Faix, Naval Health Research Center- San Diego;Te Vantha, Takeo Provincial Referral Hospital, Takeo, Cambodia;George Oduro, Komfo Anokye Teaching Hospital, Kumasi, Ghana;Kevin L. Schully, ACESO, Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, USA;Richard Bertram, Department of Mathematics, Florida State University, Tallahassee, FL, USA;Danielle V. Clark, ACESO, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA;Deborah A. Striegel, ACESO, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA

Bhargav Karamched

Florida State University (Mathematics)
"How do Heterogeneity and Correlated Information Affect Decision-Making in Social Networks?"
Normative models are often used to describe how humans and animals make decisions. These models treat deliberation as the accumulation of uncertain evidence that terminates with a commitment to a choice. Such models exhibit two major limitations: (1) they model decision-making by individuals in isolation; (2) they assume observations are conditionally independent. Humans and animals often make decisions based on their own observations in conjunction with information provided by their peers. How should classical drift-diffusion models of decision-making be generalized to situations where decisions are made by networks of individuals? How does heterogeneity in network makeup affect collective decisions? We find that heterogeneous networks collectively make faster and more accurate decisions than homogeneous networks of identical observers. Moreover, individuals rarely observe independent data in making a decision. How does correlated information affect decision-making in networks? Surprisingly, we find that early decisions are less accurate than later decisions even in networks of identical agents who have the same criteria to make a decision! Our models are for idealized situations but can provide insight into strategies for optimizing individual and collective decision-making.
Additional authors: Megan Stickler, University of Houston; William Ott, University of Houston; Benjamin Lindner, Humboldt University Berlin; Zachary P. Kilpatrick, University of Colorado Boulder; Krešimir Josić, University of Houston

Brad Peercy

University of Maryland, Baltimore County (UMBC) (Mathematics and Statistics)
"Loss of Synchrony to Silencing in Networks of Excitable Cells: Impact of Cell and Coupling Heterogeneity in Small Network Examples"
Experiments on pancreatic islets have raised a question about the potential unitary impact of certain cells in islet synchrony. Previous modeling to corroborate these findings under the suggested conditions proved unfruitful, but wide parameter searches did identify cases where silencing or ablating individual beta cells could completely or nearly completely silence islet behavior. We term such islets as 'switch' islets and such critical cells as 'switch' cells. We describe our efforts to create minimal examples representative of 'switch' behavior. This includes three cell beta cell networks and a small 2D grid network of simpler two-variable excitable cells. We find examples of 'switch' behavior in each case.
Additional authors: Zainab Almutawa (UMBC); Yelena Dubovaya (UMBC)

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