In this podcast, Rosemary Braun discusses studying complex biological networks, circadian rhythms, and using machine learning to target treatments based on individual circadian rhythm. The episode explores the search for undiscovered rules in biology, dynamics of gene expression networks, resilience in circadian clockkeeping, and the impact of genetic variations on gene regulation.
Uncovering new principles governing living systems beyond traditional rules of life through computational biology and whole system analytical approaches.
Studying how network configurations influence physiological states like sleep-wake cycles and disease development, emphasizing robustness and adaptability in networks.
Revolutionizing circadian rhythm assessments with the 'Time Machine' approach, providing personalized insights for optimal medication timing and disease management.
Deep dives
Rules of Life - Reevaluating the Known Rules of Life
The traditional rules of life emphasize fundamental concepts like cells composing living things, the use of DNA for inheritance, homeostasis, and evolution. Rosemary Brown challenges this view by suggesting the existence of additional undiscovered rules of life. Her computational biology expertise aims to uncover new principles governing living systems beyond the known paradigms. By leveraging vast data sets and whole system analytical approaches, Brown explores how living networks organize and respond to internal and environmental changes, potentially unveiling novel rules of life.
Networks and Biological Processes - Studying Living Systems Holistically
Rosemary Brown delves into the organization and self-organization of living systems through nested networks. By examining how specific network configurations influence healthy and diseased physiological states, Brown sheds light on critical aspects like sleep-wake cycles and disease development, such as cancer. Her innovative methodologies for studying temporal biological processes offer insights into circadian rhythms and disease-related genetic variants. Emphasizing the need for networks to exhibit both robustness and adaptability, Brown seeks a deeper understanding of living system behaviors and functions.
Time Machine - Personalized Circadian Rhythm Assessment
The development of 'Time Machine' revolutionizes circadian rhythm assessments by predicting an individual's circadian phase from a single blood sample. By leveraging machine learning algorithms and gene expression patterns, this novel approach offers personalized insights into optimal timing for medication administration and disease management based on an individual's internal body clock. 'Time Machine' signifies a step towards tailored healthcare interventions aligned with circadian biology, potentially enhancing treatment efficacy while minimizing side effects through optimal timing strategies.
Gene Surrounded - Pinpointing Network Epicenters of Gene Dysregulation
The concept of 'Gene Surrounded' unveils key network elements responsible for gene dysregulation within complex biological networks. By identifying central genes that instigate differential gene expression patterns, this method seeks to streamline therapeutic targeting efforts by pinpointing critical regulatory nodes instead of navigating the entire network landscape. Focusing on epicenters of gene dysregulation allows for more precise and efficient interventions, potentially transforming therapeutic approaches by targeting specific network nodes for optimal regulatory control.
Regulatory QTLs - Decrypting Genetic Variants' Influence on Gene Regulation
Regulatory Quantitative Trait Loci (QTLs) illuminate how genetic variants influence gene regulation by microRNAs. By exploring the interplay between genetic sequences, microRNA expression, and gene expression, this analysis uncovers regulatory variants that modulate the efficacy of microRNA-mediated gene regulation. Regulatory QTLs offer insights into the intricate gene regulatory landscape, shedding light on how specific genetic variants can dictate the interaction between microRNAs and target genes, potentially influencing broader regulatory processes within biological networks.
How should we study complex biological networks? How do cells keep time and stay in sync? What does it mean for a network to be resilient?
In this episode, we talk with Rosemary Braun, Associate Professor at Northwestern University in the Department of Molecular Biosciences and a member of the NSF-Simons Center for Quantitative Biology. Rosemary is broadly interested in learning whether “more is different” when it comes to complex molecular networks operating across different temporal and spatial scales. We talk with her about systems approaches to uncovering the “Rules of Life” and about circadian (daily) rhythms. She and her team use machine learning to understand emergent phenomena in networks, with the goal of helping medical professionals target treatments based on an individual patient’s circadian rhythm.
Cover art: Keating Shahmehri. Find a transcript of this episode on our website.
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