Agent-based modeling of ductal carcinoma in situ: application to patient-specific breast cancer modeling

TY - CHAP

T1 - Agent-based modeling of ductal carcinoma in situ: application to patient-specific breast cancer modeling

AU - Macklin, Paul

AU - Kim, Jahun

AU - Tomaiuolo, Giovanna

AU - Edgerton, Mary E.

AU - Cristini, Vittorio

PY - 2010

Y1 - 2010

N2 - Ductal carcinoma in situ (DCIS) of the breast is the most common precursor to invasive carcinoma (IC), the second-leading cause of death in women in USA. There has been great progress in modeling DCIS at both the cellular scale (e.g., using cellular automata and agent-based models) and the population scale (e.g., using partial differential equations or systems of ordinary differential equations), but these past efforts have been difficult to calibrate with patient-specific molecular and cellular measurements. We develop a biophysically justified, agent-based cellular model of DCIS that is well-suited to patient-specific calibration. The model is modular in nature and can thus be readily extended to incorporate more advanced biology. We give an example of recently developed, patient-specific calibration of the model and conduct parameter studies that generate testable biological hypotheses.

AB - Ductal carcinoma in situ (DCIS) of the breast is the most common precursor to invasive carcinoma (IC), the second-leading cause of death in women in USA. There has been great progress in modeling DCIS at both the cellular scale (e.g., using cellular automata and agent-based models) and the population scale (e.g., using partial differential equations or systems of ordinary differential equations), but these past efforts have been difficult to calibrate with patient-specific molecular and cellular measurements. We develop a biophysically justified, agent-based cellular model of DCIS that is well-suited to patient-specific calibration. The model is modular in nature and can thus be readily extended to incorporate more advanced biology. We give an example of recently developed, patient-specific calibration of the model and conduct parameter studies that generate testable biological hypotheses.

KW - Computational oncology

KW - Cancer development

KW - Breast cancer

KW - Data modeling

U2 - 10.1007/978-1-4419-0811-7_4

DO - 10.1007/978-1-4419-0811-7_4

M3 - Chapter

SN - 9781441908100

T3 - Applied bioinformatics and biostatistics in cancer research

SP - 77

EP - 111

BT - Computational biology: issues and applications in oncology

A2 - Pham, Tuan

PB - Springer

ER -