Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T4: Machine Learning and Model Inference for Biological Physicists |
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Chair: Ajay Gopinathan, University of California Merced Room: Room 129 |
Sunday, March 5, 2023 8:30AM - 9:30AM |
T4.00001: Machine Learning and Model Inference for Biological Physicists Chase P Broedersz Standard physics modeling approaches rely on symmetries and conservation laws to determine the relevant degrees of freedom and their dynamical rules. This method is challenged in biological physics where, for example, neither proteins nor genes can be identified through symmetries, but nonetheless play a significant role in an organism’s behavior. Instead, across biology, data-driven methods are now being used to unravel the physical laws underlying complex living systems. These approaches often take the form of inverse problems, aiming to infer underlying stochastic equations of motion or other statistical mechanics descriptions. Examples of systems where these approaches are applied range from chromosome organization and interacting proteins to cell motility, and from postural dynamics in animals, to the movement of interacting swarms of fish, insects or birds. These inverse problems are notoriously hard, posing exciting challenges for theory. Such data-driven theory can now capitalize on the recent surge in high-quality data coming from rapid advances in quantitative experiments across biology. An example is given by the explosion of available genome sequences, allowing data-driven modeling of biological sequence data. Other example is in the broad area of morphogenesis where a complex interplay of genes, protein and biomechanical fields can be elucidated with neural networks thanks to rapid advances in imaging and optogenetics techniques. |
Sunday, March 5, 2023 9:30AM - 10:30AM |
T4.00002: Machine Learning and Model Inference for Biological Physicists Steve Presse Standard physics modeling approaches rely on symmetries and conservation laws to determine the relevant degrees of freedom and their dynamical rules. This method is challenged in biological physics where, for example, neither proteins nor genes can be identified through symmetries, but nonetheless play a significant role in an organism’s behavior. Instead, across biology, data-driven methods are now being used to unravel the physical laws underlying complex living systems. These approaches often take the form of inverse problems, aiming to infer underlying stochastic equations of motion or other statistical mechanics descriptions. Examples of systems where these approaches are applied range from chromosome organization and interacting proteins to cell motility, and from postural dynamics in animals, to the movement of interacting swarms of fish, insects or birds. These inverse problems are notoriously hard, posing exciting challenges for theory. Such data-driven theory can now capitalize on the recent surge in high-quality data coming from rapid advances in quantitative experiments across biology. An example is given by the explosion of available genome sequences, allowing data-driven modeling of biological sequence data. Other example is in the broad area of morphogenesis where a complex interplay of genes, protein and biomechanical fields can be elucidated with neural networks thanks to rapid advances in imaging and optogenetics techniques. |
Sunday, March 5, 2023 10:30AM - 11:30AM |
T4.00003: Machine Learning and Model Inference for Biological Physicists Vincenzo Vitelli Standard physics modeling approaches rely on symmetries and conservation laws to determine the relevant degrees of freedom and their dynamical rules. This method is challenged in biological physics where, for example, neither proteins nor genes can be identified through symmetries, but nonetheless play a significant role in an organism's behavior. Instead, across biology, data-driven methods are now being used to unravel the physical laws underlying complex living systems. These approaches often take the form of inverse problems, aiming to infer underlying stochastic equations of motion or other statistical mechanics descriptions. Examples of systems where these approaches are applied range from chromosome organization and interacting proteins to cell motility, and from postural dynamics in animals, to the movement of interacting swarms of fish, insects or birds. These inverse problems are notoriously hard, posing exciting challenges for theory. Such data-driven theory can now capitalize on the recent surge in high-quality data coming from rapid advances in quantitative experiments across biology. An example is given by the explosion of available genome sequences, allowing data-driven modeling of biological sequence data. Other example is in the broad area of morphogenesis where a complex interplay of genes, protein and biomechanical fields can be elucidated with neural networks thanks to rapid advances in imaging and optogenetics techniques. |
Sunday, March 5, 2023 11:30AM - 12:30PM |
T4.00004: Machine Learning and Model Inference for Biological Physicists Anne-Florence Bitbol Standard physics modeling approaches rely on symmetries and conservation laws to determine the relevant degrees of freedom and their dynamical rules. This method is challenged in biological physics where, for example, neither proteins nor genes can be identified through symmetries, but nonetheless play a significant role in an organism's behavior. Instead, across biology, data-driven methods are now being used to unravel the physical laws underlying complex living systems. These approaches often take the form of inverse problems, aiming to infer underlying stochastic equations of motion or other statistical mechanics descriptions. Examples of systems where these approaches are applied range from chromosome organization and interacting proteins to cell motility, and from postural dynamics in animals, to the movement of interacting swarms of fish, insects or birds. These inverse problems are notoriously hard, posing exciting challenges for theory. Such data-driven theory can now capitalize on the recent surge in high-quality data coming from rapid advances in quantitative experiments across biology. An example is given by the explosion of available genome sequences, allowing data-driven modeling of biological sequence data. Other example is in the broad area of morphogenesis where a complex interplay of genes, protein and biomechanical fields can be elucidated with neural networks thanks to rapid advances in imaging and optogenetics techniques. |
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