Mathematical Modeling in Systems Biology — Free Preprint and Course Materials
by Brian P. Ingalls · MIT Press
Ingalls hosts the full preprint of his MIT Press textbook alongside exercise solutions, errata and MATLAB/Python code. It teaches ODE modelling of metabolic, signalling, gene regulatory and electrophysiological networks from a standing start.
More resources on Systems Biology
Systems Biology (MIT 8.591J)
Cellular and population-level systems biology: genetic switches and oscillators, network motifs, cellular decision-making, pattern formation, cell-cell communication and evolutionary dynamics, with an emphasis on synthetic biology. Includes 24 lecture videos, problem sets and exams for learning to model genetic networks quantitatively.
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
The standard review of gene expression noise: intrinsic versus extrinsic sources, transcriptional bursting, and how cells exploit or suppress variability in stress response, development and cell fate. Free full text via PubMed Central.
Biological Circuit Design (Caltech BE 150 / Bi 250b)
Caltech's open circuit-design textbook, twenty-two chapters with Python notebooks. Covers repression motifs, amplification, oscillators, stochastic gene expression via Gillespie simulation and patterning. Readers finish able to build, simulate and analyse genetic circuit models in code. The one resource that closes Alon's weakest gap — stochasticity — with executable Gillespie/ODE code and 50+ exercises rather than prose.
Systems Biology 2018 — Uri Alon's Full Course
Alon's complete graduate lecture course, free: twelve recorded sessions covering autoregulation, feed-forward loops, chemotaxis robustness, fold-change detection, oscillators, optimality and modularity, plus downloadable notes, problem sets and worked solutions.
An Introduction to Systems Biology: Design Principles of Biological Circuits (2nd Edition)
The canonical systems biology text, universally cited as THE entry text. Builds network motifs, feedback loops, robustness and optimality from simple differential equations, using measured bacterial and developmental circuits. Readers finish able to derive and analyse gene-circuit dynamics themselves.