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Episode 10 15.03.2014
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Can you grow an organ inside a computer , and would it teach you something biology alone cannot? Computational biologist Yaki Setty describes how agent-based models of stem cell development can reconstruct the pancreas, the C. elegans gonad, and even parts of the brain from first principles, revealing emergent properties that no single experiment could predict. Subscribe for more from the Convergent Science Network podcast series. Yaki Setty joins Paul Verschure and Tony Prescott at the BCBT summer school to present his approach to synthetic organ development using autonomous agent-based modeling. Each cell in the simulation is defined by biologically justified state diagrams , covering differentiation, proliferation, movement, and environmental sensing , with every parameter traceable to published experimental data. The environment is modeled as a three-dimensional grid of voxels containing chemical gradients governed by differential equations, and cells interact with this environment and with each other to produce emergent organ structures. The discussion walks through three applications of increasing complexity. The pancreas model, with over 150 cell states, reproduces the characteristic cauliflower-like morphology of pancreatic tissue and demonstrates how blood vessel scaffolding guides cell aggregation. The C. elegans gonad model achieves quantitative predictions about cell numbers, zone lengths, and cell cycle ratios with far fewer states, validated against experimental measurements within weeks rather than years. The conversation also touches on extending these methods to neural development, where the same platform and principles apply but the complexity of cell types and connectivity presents new challenges. Key topics include how autonomous agent models differ from conventional computational approaches, why all available biological data should be incorporated rather than held back for testing, how mutations serve as the primary validation strategy for these models, what the relationship is between stem cell stemness and differentiation potential, why morphological benchmarks like cauliflower structure are difficult to quantify rigorously, and how these simulations could eventually model disease processes by tracing developmental history back to its origins. Part of the Convergent Science Network podcast series from the BCBT Summer School.
Tagged as:
agent-based modeling Stem Cell synthetic biology
About the author call_made
Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure
Exploring the convergence of neuroscience, robotics, and AI through conversations with leading researchers since 2010.
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