Cancer is a collection of diseases characterized by misregulation of the biomolecular pathways that control cellular processes of metabolism and growth, DNA replication and repair, mitosis and cell division, autophagy and apoptosis (programmed cell death), de-differentiation, motility and angiogenesis1
. Molecular cell biologists have amassed a large body of information about the genes and proteins involved in these pathways and have some good ideas about how they go awry in certain types of cancers. However, most of our understanding of the molecular basis of cancer relies on intuitive reasoning about highly complex networks of biochemical interactions2–4
. Intuition is clearly not the most reliable tool for querying the behavior of complex regulatory networks. Wouldn’t it be better if we could frame a reaction network in precise mathematical terms and use computer simulations to work out the implications of how the network functions in normal cells and malfunctions in cancer cells?
Of primary interest to cancer biologists is how cancer cells differ from normal cells in their responses to endogenous signals (such as growth and death factors, cell-cell and cell-matrix contacts) and to exogenous treatments (including cytotoxins, radiation, endocrine therapy). Cell responses—signal transduction, cell-fate decisions, adaptation—are intrinsically dynamic phenomena, so it is essential to understand the temporal evolution of biochemical signaling networks in response to particular stimuli. Ordinary differential equations, based on biochemical reaction kinetics, are an appropriate tool for addressing these questions. In principle, ODE models can provide a comprehensive, unified account of many experimental results, and a reliable tool for predicting novel cell behaviors. ODE models of yeast cell growth and division have lived up to these expectations5–8
. But is it possible to build useful models of the considerably more complex regulatory networks in mammalian cells? We intend, in this article, to provide a roadmap for a detailed mathematical model of the estrogen signaling network in breast epithelial cells.
Our roadmap is built on the idea that a cell is an information processing system, receiving signals from its environment and its own internal state, interpreting these signals, and making appropriate cell-fate decisions, such as growth and division, movement, differentiation, self-replication, or cell death9
. In plants and animals, these cell-level decisions are crucial to the growth, development, survival and reproduction of the organism. A hallmark of cancer cells is faulty decision-making: they proliferate when they should be quiescent, they survive when they should die, they move around when they should stay put1
. To understand the origin, pathology and vulnerabilities of cancer cells, we must understand how normal cells make decisions that promote the survival of the organism as a whole, and how cancer cells make decisions that promote their own survival and reproduction with fatal results for the organism they inhabit10
Viewing the living cell as an information processing system, we can (conceptually, at least) distinguish an input level, a processing core, and output devices (). As input, a cell receives information from its surroundings (such as extracellular ligands that bind to cell-surface receptors or to nuclear hormone receptors) and from its internal state (such as DNA damage, misfolded proteins, low energy level and oxidative stress). These signals are processed by chemical reaction networks that integrate information from many sources and compute a response. A response could take the form of the activation or inactivation of key integrator or effector proteins that drive the cell’s functional output devices. Of most interest to cancer biologists are the functional modules that control cell growth and division, motility and invasion, stress responses and apoptosis.
The estrogen receptor signaling network in breast epithelial cells
Although there may be many ways to subdivide the information processing system of a cell, there is clearly a need to divide and conquer the staggering complexity of the system11–13
. Fortunately, it is not necessary to model the protein reaction networks in all their complexity, because it is usually possible to identify a set of key ‘integrator’ and ‘decision-making’ proteins that determine the cell’s response to input signals. Unfortunately, living cells are not like human-engineered systems, where modules are designed not to interfere much with one another14
. Cellular modules have significant crosstalk and shared components. So although we must divide the system into modules to reduce the initial modeling complexity, we must also put the modules back together into a complete system that properly captures the information processing capabilities of living cells.
A comprehensive model of the information processing system of mammalian cells is not yet available, but we can provide a roadmap of how a modeler might capture, in mathematical form, the molecular events controlling cell growth, proliferation, damage responses and programmed death. Our approach is illustrated by simple mathematical models of the mechanisms involved in the initial susceptibility of breast cancer cells to anti-estrogen therapy and their subsequent development of anti-estrogen resistance. The value of this enterprise will be measured ultimately by new insights provided by the model into the logic and functionality of estrogen-receptor signaling pathways and by the effectiveness of the model as a tool for experimental prediction and design.