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The role of molecular discreteness in normal and cancerous growth
1Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Massachusetts General Hospital Cancer Center, Charlestown, Boston 02129, USA. michaelj@helix.mgh.harvard.edu
Abstract:
The physicochemical events that underlie biological processes are inevitably either/or events. Either a growth factor molecule binds to a cell, or it doesn't. Either a site on a cyclin molecule is phosphorylated, or it isn't. Either a regulatory molecule binds to a DNA sequence, or it doesn't. These molecular either/or events lead to cellular either/or events. Either a cell divides, or it doesn't. Either a cell dies, or it doesn't. Either a cell turns on a particular gene, or it doesn't. Either a tumor cell stays where it is, or it forms a distant metastasis. By considering biological processes as the macroscopic aggregate results of these many individual microscopic either/or events, we can gain considerable insight into both normal and cancerous growth. In fact, as will be outlined here, such discrete modeling may allow us to see how the normal cellular populations of the body can grow to predictable sizes, at predictable times, and to predictable shapes. Such modeling can also allow us to gain insight into how normal cellular populations may become cancerous cellular populations. Indeed, such an approach allows us do a sufficiently good job of imitating the growth and spread of tumors as to be able to make estimates the most effective ways to both detect and treat cancer.
Insights
Biological processes can be understood as the sum of many discrete molecular events. This discrete modeling approach offers insights into normal and cancerous cell growth, aiding in cancer detection and treatment strategies.
Area of Science:
- Cellular biology
- Biophysics
- Cancer research
Background:
- Biological processes involve numerous molecular interactions.
- These interactions can be simplified as binary (either/or) events.
- Understanding these discrete events is key to comprehending cellular functions.
Purpose of the Study:
- To model biological processes as aggregate results of microscopic either/or events.
- To gain insight into normal cellular population growth dynamics.
- To understand the transition from normal to cancerous cellular populations.
Main Methods:
- Discrete modeling of molecular and cellular events.
- Analyzing the macroscopic outcomes of microscopic binary processes.
- Simulating tumor growth and metastasis based on discrete event principles.
Main Results:
- Biological processes can be predicted based on the aggregation of discrete molecular events.
- Normal cellular populations exhibit predictable growth in size, timing, and shape.
- The discrete modeling approach effectively imitates tumor growth and spread.
Conclusions:
- Discrete modeling provides a powerful framework for understanding normal and cancerous growth.
- This approach can illuminate mechanisms of tumor formation and metastasis.
- Insights gained can inform strategies for early cancer detection and effective treatment.