Fundamentals of Systems Biology

From Synthetic Circuits to Whole-cell Models

Markus W. Covert

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9781138459878, 9781138459878
• eBook ISBN: 9781420084115
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

For decades biology has focused on decoding cellular processes one gene at a time, but many of the most pressing biological questions, as well as diseases such as cancer and heart disease, are related to complex systems involving the interaction of hundreds, or even thousands, of gene products and other factors. How do we begin to understand this complexity?

Fundamentals of Systems Biology: From Synthetic Circuits to Whole-cell Models introduces students to methods they can use to tackle complex systems head-on, carefully walking them through studies that comprise the foundation and frontier of systems biology. The first section of the book focuses on bringing students quickly up to speed with a variety of modeling methods in the context of a synthetic biological circuit. This innovative approach builds intuition about the strengths and weaknesses of each method and becomes critical in the book's second half, where much more complicated network models are addressed—including transcriptional, signaling, metabolic, and even integrated multi-network models.

The approach makes the work much more accessible to novices (undergraduates, medical students, and biologists new to mathematical modeling) while still having much to offer experienced modelers--whether their interests are microbes, organs, whole organisms, diseases, synthetic biology, or just about any field that investigates living systems.

II
From Circuits to Networks

Chapter 7
Transcriptional Regulation

Learning Objectives

• Identify the major motifs in gene transcriptional regulatory networks
• Calculate the dynamic properties associated with these motifs using multiple methods
• Compare model outputs with corresponding experimental data
• Understand how simpler motifs combine to form larger, more complex motifs

You’ve seen most of the commonly used approaches to modeling biological networks, and you’ve applied these approaches to some relatively simple circuits. Now it’s time to extend these techniques to larger, more complicated biological systems! Section II will lead you through the strategies that researchers have used to tackle three kinds of biological networks at a larger scale: transcriptional regulation, or the modification of transcription factor activity to affect gene expression; signal transduction, or how cells sense their surrounding environments and initiate appropriate responses; and carbon-energy metabolism, which breaks down nutrients from the environment to produce all of the building blocks to make a new cell.

To address these topics, I’ll have to cover much more biology. You’ll also learn another analysis method or two along the way, but by and large, we will be applying methods that you have already learned to problems that are more difficult. You will soon find that you are already well equipped to understand and critique existing models as well as to create your own!

Transcriptional Regulation and Complexity

As an example, let’s consider gene expression again. We’ve already looked at regulation extensively in Section I, but this chapter is going to add a new layer of complexity, including the interactions of multiple transcription factors to produce more complex expression dynamics.

Let’s start with one of the most exciting events in the history of biology: the publication of the human genome in 2001. David Baltimore, a preeminent biologist and Nobel laureate, commented on the event: “I’ve seen a lot of exciting biology emerge over the past 40 years. But chills still ran down my spine when I first read the paper that describes the outline of our genome” (Baltimore, 2001; reprinted by permission from Macmillan Publishers Ltd.: Nature, 2001).

At the time, I remember that a number of interesting aspects of the sequence had us talking. As Baltimore wrote: “What interested me most about the genome? The number of genes is high on the list. … It is clear that we do not gain our undoubted complexity over worms and plants by using many more genes. Understanding what does give us our complexity … remains a challenge for the future” (Baltimore, 2001; reprinted by permission from Macmillan Publishers Ltd.: Nature, 2001).

It’s not only the number of the genes in these genomes that was surprising; many genes encode proteins that are essentially the same and carry out the same functions, even in different organisms. Many scientists had previously assumed that the differences between species depended mostly on different genes: A human had human genes, a mouse had mouse genes, a fish had fish genes, and a sea urchin had sea urchin genes. However, the genomic sequences of all of these organisms suggested that differences in the gene complement played a much smaller role than first anticipated. For example, we share nearly all of our genes with mice.

So, what makes us different? The key is not primarily in the genes themselves, but how they are expressed. Humans have approximately eight time...

Table of contents

• Cover
• Half Title
• Title
• Copyright
• Contents
• Preface
• Acknowledgments
• About the Author
• Section I Building Intuition
• Section II From Circuits to Networks
• GLOSSARY
• INDEX