251 | Rosemary Braun on Uncovering Patterns in Biological Complexity
Sep 25, 2023
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In this conversation, Rosemary Braun, an associate professor of molecular biosciences, unpacks the mysteries of biological complexity. She discusses how biological organisms, despite their seemingly chaotic parts, function seamlessly. Braun highlights the intersection of machine learning with biological research, showcasing how vast datasets shed light on gene expression and circadian rhythms. The dialogue also touches on the challenges of interpreting genetic information and the fascinating ways our internal clocks affect health and metabolism.
Circadian rhythms are maintained by molecular oscillators within each cell and their communication mechanisms are not fully understood.
Advancements in micro and molecular biology profiling technologies provide vast amounts of omics data to explore connections between genes, proteins, and pathways.
Understanding self-organized structures and processes in biology requires computational models and collaboration between theorists and experimentalists.
Deep dives
The Ubiquity of Circadian Rhythms
Circadian rhythms, which govern the internal clocks in our bodies, are present in nearly every cell. These rhythms are maintained by a molecular oscillator within each cell, which consists of genes that are transcribed and translated into proteins. These proteins then regulate the transcription of other genes in a negative feedback loop, sustaining oscillations. This ubiquitous presence of circadian rhythms raises questions about why every cell has its own clock and how they communicate with each other. While the exact mechanisms of communication are not fully understood, signaling molecules like melatonin and the nervous system play a role. Additionally, studies have shown that external cues like exercise can influence the phase of circadian rhythms in certain tissues. The complexity of these chemical networks and the multitude of genes controlled by the clock highlight the integrated nature of circadian rhythms within the larger biological system.
Advancements in Omics Data Collection
The field of biology has shifted towards a systems and network approach, driven by advancements in micro and molecular biology profiling technologies. These technologies allow for detailed analysis of gene expression and other molecular processes on a genome-wide scale. By assaying gene expression, researchers can gain insights into the interactions between microscopic elements that produce macroscopic features observed in organisms. The availability of omics data, such as genomics, transcriptomics, and proteomics, has provided researchers with vast amounts of data to explore and uncover connections between genes, proteins, and pathways. However, challenges remain in determining which data points are most relevant and informative for understanding the underlying biological processes.
The Intricacies of Biological Systems
Living systems are characterized by self-organized structures and processes at every scale, from molecular complexes to cells, tissues, organisms, and even ecological networks. Understanding the dynamics and mechanisms that give rise to these self-organized systems is a central focus in biology. Researchers approach this challenge from a computational standpoint, collaborating with experimentalists and utilizing computational models to explore the complex networks and interactions within biological systems. The integration of theoretical and experimental perspectives allows for a deeper understanding of fundamental questions in biology. While there is still much to discover, advances in data collection and computational power provide opportunities to unravel the mysteries of biological self-organization.
Understanding the complexity of biological networks
Biological networks, such as gene networks, exhibit complex interactions which are challenging to understand. These networks are dynamic and not fine-tuned, allowing for robustness and adaptation. The challenge lies in identifying the network's structure and behavior as a whole, rather than focusing on individual components. Machine learning techniques play an important role in analyzing and modeling biological networks, but it is essential to also consider the biochemical information and leverage existing knowledge. By integrating different data sets and statistically summarizing the network properties, researchers aim to identify key nodes and edges that can be targeted to gain insights and potentially develop therapeutic interventions.
Exploring the role of circadian rhythms in biological systems
Circadian rhythms play a crucial role in regulating various biological processes. These rhythms influence gene expression and other biochemical interactions, causing different behaviors and characteristics at different times of the day. Understanding the dynamics and patterns of circadian gene expression can provide insights into healthy aging and neurodegenerative diseases. By studying the statistical differences in circadian gene expression between normal and disease samples, researchers aim to identify potential therapeutic targets or develop improved diagnostics. Coarse-grained statistical properties and eigenvalues of networks can help uncover the underlying behaviors and dynamics of biological systems.
Biological organisms are paradigmatic emergent systems. That atoms of which they are made mindlessly obey the local laws of physics; even cells and organs do their individual jobs without explicitly understanding the larger whole of which they are a part. And yet the system as a whole functions beautifully, with apparent purpose and function. How do the small parts come together to form the greater whole? I talk with biophysicist Rosemary Braun about what we're learning about collective behavior within organisms from the modern era of huge biological datasets, especially crucial aspects like timekeeping (with bonus implications for dealing with jet lag).
Rosemary Braun received her Ph.D. in physics from the University of Illinois at Urbana-Champaign, and an M.P.H. in biostatistics from Johns Hopkins. She is currently an associate professor of molecular biosciences, applied math, and physics at Northwestern University and external faculty at the Santa Fe Institute.
