Chapter 9
Complexity of Biochemical and Genetic Responses Reduced
Using Simple Theoretical Models
Kumar Selvarajoo
Abstract
Living systems are known to behave in a complex and sometimes unpredictable manner. Humans, for a very
long time, have been intrigued by nature, and have attempted to understand biological processes and
mechanisms using numerous experimental and mathematical techniques. In this chapter, we will look at
simple theoretical models, using both linear and nonlinear differential equations, that realistically capture
complex biochemical and genetic responses of living cells. Even for cases where cellular behaviors are
stochastic, as for single-cell responses, randomness added to well-defined deterministic models has elegantly
been shown to be useful. The data collectively present evidence for further exploration of the self-
organizing rules and laws of living matter.
KeywordsSystems biology, Nonlinear dynamics, Biological networks, Oscillation, Modeling
1 Introduction
Recent estimates suggest that the human body consists of about
4 1013 cells across 200 cell types, covering different organs and
tissues. Within each cell, there are an estimated 20–25,000 genes,
and over 100,000 proteins and metabolites. These molecular spe-
cies, the fundamental building blocks of life, are connected through
a complex series of biochemical reaction networks that process
external information for survival and reproduction (Fig. 1)
[1]. For example, the food that an organism ingests is broken
down into various biochemicals, such as carbohydrates, proteins,
lipids, and nuclei acids through diverse reaction networks, either
spontaneously with binding partners or directed through catalytic
enzymes.
Aberrations to the highly robust biochemical networks, either
through genetic mutations or non-genetically through chronic
poor lifestyle habits, can lead to diseases and pre-mature death.
Thus, understanding the dynamic behaviors of various biochemical
networks is indispensable for biological research and good health,
Mariano Bizzarri (ed.),Systems Biology, Methods in Molecular Biology, vol. 1702,
https://doi.org/10.1007/978-1-4939-7456-6_9,©Springer Science+Business Media LLC 2018
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