Marc Birtwistle, PhD
- ASSISTANT PROFESSOR | Pharmacological Sciences
Research Topics:Cancer, Cell Biology, Computational Biology, Signal Transduction, Systems Biology, Systems Pharmacology
Dr. Birtwistle is an Assistant Professor in the Department of Pharmacology and Systems Therapeutics and the Systems Biology Center New York.
Dr. Birtwistle is formally trained as a chemical engineer, and thus knowledgeable in a wide array of quantitative methods for mathematical modeling of physicochemical processes. During his doctoral work, he applied such methods to understanding cancer as a disease of deregulated control systems, particularly, the ErbB signaling network, which is dysfunctional in many types of human cancer. Such a so-called integrative systems biology approach focuses not on how individual genes and proteins affect the signaling network, but rather on how the connectivity between and states of players in the network create biological function, or in the case of cancer, disease. During this doctoral and consequently postdoctoral experience, Dr. Birtwistle was also trained as an experimental cell and molecular biologist to complement the quantitative modeling background, and thus brings an interdisciplinary approach to cancer research.
Multi-Disciplinary Training AreasBiophysics and Systems Pharmacology [BSP], Cancer Biology [CAB]
BS, Georgia Institute of Technology
PhD, University of Delaware
Postdoctoral Training, University College Dublin
Best Mentor Award
The Birtwistle Lab combines computational and experimental methods to understand how cancer cells make decisions, focusing on single cell methods to elucidate how phenotypic variability arises from noisy signaling. We give particular focus to brain tumors, although the approaches we take are quite flexible and can be applied to a multitude of cancer types. We use chemical kinetics theory to represent the various signaling mechanisms controlling proliferation and death as systems of equations which can be simulated to predict how cancer cells stochastically respond to drugs or other perturbations. We constrain and validate these models with a variety of single cell data from flow and mass cytometry that measures many protein and protein modification levels across a cell population, and live-cell microscopy that allows optogenetic manipulation of cell signaling and the ability to simultaneously measure cell fate and signaling processes in the same single cells with high temporal frequency. Our goal is to use the predictive capability of these models to understand better how the panel of genetic alterations and mutations in particular patient’s brain tumor dictates indications for available chemotherapeutic options, providing a potential avenue towards improved therapies.
Please see the Birtwistle Laboratory website for additional information.
Gallo JM, Birtwistle MR. Network pharmacodynamic models for customized cancer therapy. Wiley interdisciplinary reviews. Systems biology and medicine 2015 Jul; 7(4).
Birtwistle MR. Analytical reduction of combinatorial complexity arising from multiple protein modification sites. Journal of the Royal Society, Interface / the Royal Society 2015 Feb; 12(103).
Bouhaddou M, Birtwistle MR. Dimerization-based control of cooperativity. Molecular Biosystems 2014 Jun; 10(7).
Zhang XY, Birtwistle MR, Gallo JM. A General Network Pharmacodynamic Model-Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway. CPT: Pharmacometrics and Systems Pharmacology 2014; 3.
Birtwistle MR, Mager DE, Gallo JM. Mechanistic vs. Empirical network models of drug action. CPT: Pharmacometrics and Systems Pharmacology 2013; 2.
Birtwistle MR, von Kriegsheim A, Dobrzyński M, Kholodenko BN, Kolch W. Mammalian protein expression noise: scaling principles and the implications for knockdown experiments. Molecular Biosystems 2012 Nov; 8(11).