When:
Monday, November 3, 2025
8:30 AM - 5:00 PM CT
Where: Suite 3500, Chicago, IL 60611
Contact:
Tiffany Leighton
Group: NSF-Simons National Institute for Theory and Mathematics in Biology
Category: Lectures & Meetings
Machine learning of cytoskeletal machines
Traditional bottom-up physical-mathematical models have longstanding popularity and success in studying cytoskeleton and mechanochemical machines driving cell movements and division. These models brought and will continue to bring mechanistic insights into cell migration. However, such models are either too simple to embrace the complexity of the multiscale cell processes or are hopelessly cumbersome and unwieldy to be used to nimbly test multiple hypotheses. Machine learning and AI approaches have demonstrated immense strength in identifying statistical patterns in cytoskeletal machines and in predicting cytoskeletal dynamics from microscopy data. However, these data-driven approaches largely neglect the laws of physics and chemistry needed to ground the discoveries in biological mechanisms. These complementary strengths and weaknesses between the traditional modeling and modern data-scientific approaches suggest a promising avenue forward: augmenting traditional models with data-scientific and AI methods for the sake of building more complex traditional models that can be directly connected with the enormous volumes of biological data of cytoskeletal machines.
This workshop will convene data scientists, experimental biologists, mathematical modelers and biophysicists using or interested in starting to use ML to study cytoskeletal dynamics, cell migration and mitosis. The goal is to foster an exchange of ideas between these research communities. The workshop is structured to help participants identify the most promising opportunities for developing and using ML tools to answer biological questions. The program includes both overview and research talks, poster sessions and lightning talks by poster presenters, and will have ample time for participants to get to know each other, exchange ideas and foster collaborations.
When:
Tuesday, November 4, 2025
8:30 AM - 5:00 PM CT
Where: Suite 3500, Chicago, IL 60611
Contact:
Tiffany Leighton
Group: NSF-Simons National Institute for Theory and Mathematics in Biology
Category: Lectures & Meetings
Machine learning of cytoskeletal machines
Traditional bottom-up physical-mathematical models have longstanding popularity and success in studying cytoskeleton and mechanochemical machines driving cell movements and division. These models brought and will continue to bring mechanistic insights into cell migration. However, such models are either too simple to embrace the complexity of the multiscale cell processes or are hopelessly cumbersome and unwieldy to be used to nimbly test multiple hypotheses. Machine learning and AI approaches have demonstrated immense strength in identifying statistical patterns in cytoskeletal machines and in predicting cytoskeletal dynamics from microscopy data. However, these data-driven approaches largely neglect the laws of physics and chemistry needed to ground the discoveries in biological mechanisms. These complementary strengths and weaknesses between the traditional modeling and modern data-scientific approaches suggest a promising avenue forward: augmenting traditional models with data-scientific and AI methods for the sake of building more complex traditional models that can be directly connected with the enormous volumes of biological data of cytoskeletal machines.
This workshop will convene data scientists, experimental biologists, mathematical modelers and biophysicists using or interested in starting to use ML to study cytoskeletal dynamics, cell migration and mitosis. The goal is to foster an exchange of ideas between these research communities. The workshop is structured to help participants identify the most promising opportunities for developing and using ML tools to answer biological questions. The program includes both overview and research talks, poster sessions and lightning talks by poster presenters, and will have ample time for participants to get to know each other, exchange ideas and foster collaborations.
When:
Wednesday, November 5, 2025
8:30 AM - 5:00 PM CT
Where: Suite 3500, Chicago, IL 60611
Contact:
Tiffany Leighton
Group: NSF-Simons National Institute for Theory and Mathematics in Biology
Category: Lectures & Meetings
Machine learning of cytoskeletal machines
Traditional bottom-up physical-mathematical models have longstanding popularity and success in studying cytoskeleton and mechanochemical machines driving cell movements and division. These models brought and will continue to bring mechanistic insights into cell migration. However, such models are either too simple to embrace the complexity of the multiscale cell processes or are hopelessly cumbersome and unwieldy to be used to nimbly test multiple hypotheses. Machine learning and AI approaches have demonstrated immense strength in identifying statistical patterns in cytoskeletal machines and in predicting cytoskeletal dynamics from microscopy data. However, these data-driven approaches largely neglect the laws of physics and chemistry needed to ground the discoveries in biological mechanisms. These complementary strengths and weaknesses between the traditional modeling and modern data-scientific approaches suggest a promising avenue forward: augmenting traditional models with data-scientific and AI methods for the sake of building more complex traditional models that can be directly connected with the enormous volumes of biological data of cytoskeletal machines.
This workshop will convene data scientists, experimental biologists, mathematical modelers and biophysicists using or interested in starting to use ML to study cytoskeletal dynamics, cell migration and mitosis. The goal is to foster an exchange of ideas between these research communities. The workshop is structured to help participants identify the most promising opportunities for developing and using ML tools to answer biological questions. The program includes both overview and research talks, poster sessions and lightning talks by poster presenters, and will have ample time for participants to get to know each other, exchange ideas and foster collaborations.