Saiz Lab


Recent/Selected News

May 2024-present: Currently we have positions for graduate students, undergraduate students, and postdocs to do research in our lab.

May 2024: Our latest paper on “Nanoparticle Assemblies: Current Advances and Open Problems” has been accepted in ACS Nano.

April 2024: Check out Prof. Saiz preprint on “Actionable forecasting as a determinant of function in noisy biological systems” published in arXiv.

November 2023: Check out Prof. Saiz report with the National Academies of Sciences, Engineering, and Medicine in The National Academies Press.

September 2023: Our best wishes to Ninghui Hao, who just started her Master of Science in Biomedical Informatics at Harvard Medical School.

July 2023: Check out Andy Fell’s piece entitled “Bringing COVID-19 Data into Focus” on our latest paper “Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series“, just published in Science Advances.

June 2023: Our latest paper on “Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series” has been accepted in Science Advances.

 

Research Synopsis— Modeling of Biological Networks & Systems Therapeutics Laboratory —

Computational Molecular Systems Biomedicine

The research of the Saiz Lab focuses on the integration of the molecular properties of the cellular components into the dynamics of relevant cellular processes, including signal transduction and gene regulation and their combined networks, with special interest in those altered in cancer and other diseases. Integration of the events that follow from the sensing of extracellular signals to the resulting cellular responses is needed to faithfully understand the functioning of the cell as a unit. Our work is highly interdisciplinary, drawing from techniques and tools from chemistry, physics, mathematics, computer science, biomedical sciences, and engineering; a key feature required for successful approaches to molecular systems biology. We combine computational and theoretical approaches together with experimental data to build models for accurately predicting the cellular behavior in terms of molecular properties. This type of models is also used “in reverse” to infer detailed molecular properties, such as the in vivo DNA mechanics, from physiological measurements in cell populations.In general, we want to understand and to follow the impact of molecular perturbations in the cellular components, such as a mutation in a protein or interactions with small molecules or drugs, through the different cellular processes up to the cellular behavior. [read more… Leonor Saiz profile at Biomedical Engineering]

Research Interests
Computational and theoretical approaches to the study of biological networks at the cellular and molecular level. Molecular systems biomedicine. Multiscale and multilevel approaches to biomolecular processes. Macromolecular complex assembly on DNA, membranes, and scaffolds.  Statistical mechanics basis of gene regulation and signal transduction. Noise in cellular processes. In vivo biomolecular mechanics. Molecular biophysics: membranes, membrane associated proteins, and their interactions with small molecules and drugs.

 

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