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Title: New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling


RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.

 [1];  [2];  [3];  [4];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. College Dublin (United Kingdom)
  3. Northern Arizona Univ., Flagstaff, AZ (United States)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of New Mexico Comprehensive Cancer Center, Albuquerque, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1044-579X
Grant/Contract Number:  
Resource Type:
Accepted Manuscript
Journal Name:
Seminars in Cancer Biology
Additional Journal Information:
Journal Volume: 54; Journal ID: ISSN 1044-579X
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES; Biological Science; RAS; ERK cascade; Mechanistic modeling; Mathematical modeling; Systems biology

Citation Formats

Erickson, Keesha E., Rukhlenko, Oleksii S., Posner, Richard G., Hlavacek, William S., and Kholodenko, Boris N. New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling. United States: N. p., 2018. Web. doi:10.1016/j.semcancer.2018.02.008.
Erickson, Keesha E., Rukhlenko, Oleksii S., Posner, Richard G., Hlavacek, William S., & Kholodenko, Boris N. New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling. United States.
Erickson, Keesha E., Rukhlenko, Oleksii S., Posner, Richard G., Hlavacek, William S., and Kholodenko, Boris N. Mon . "New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling". United States.
title = {New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling},
author = {Erickson, Keesha E. and Rukhlenko, Oleksii S. and Posner, Richard G. and Hlavacek, William S. and Kholodenko, Boris N.},
abstractNote = {RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.},
doi = {10.1016/j.semcancer.2018.02.008},
journal = {Seminars in Cancer Biology},
number = ,
volume = 54,
place = {United States},
year = {2018},
month = {3}

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Cited by: 13 works
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Figures / Tables:

Figure 1 Figure 1: The RAS activation cycle. RAS can bind either GTP or GDP, and is active when bound to GTP. In the active configuration, it is able to interact with downstream effectors. RAS activation/deactivation can occur through multiple processes. Processes of interest are labeled with circled numbers: 1 shows freemore » nucleotide exchange, 2 describes RAS-catalyzed hydrolysis of GTP to GDP, 3 depicts GTP hydrolysis stimulated by GAP, and 4 is GEF-induced GDP release from RAS to facilitate GTP binding. Rate constants for each step are labeled; variable names correspond to those in Table 1.« less

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