The development of microfluidic technologies has enabled chemical and biological analysis systems with increased functionality, complexity, and parallelization. These functionalities often drive the creation and control of complex and dynamic fluidic architectures. Introduced here is a class of microfluidic network based on isotachophoresis (ITP), an electrokinetic process that can extract and purify samples, selectively transport, mix, and aliquot (split) samples in a system with no moving parts. Presented is a theoretical framework to describe these networks. The framework relies on the coupling between a one-dimensional description of ITP and two-dimensional, transient graphs to describe the dynamic evolution of ITP networks. We leverage this framework to create numerical simulations of branched ITP circuits. We build, control, and experimentally study a variety of ITP networks. These systems automatically split and merge ITP zones, enabling complex sample manipulation with minimal external control. The model captures the experimentally observed sample dynamics. We demonstrate an example system where an ITP network is used to control and quantify parallel CRISPR-Cas enzymatic reactions. The methods described here are generally applicable to highly complex topologies and may offer a basis for easily reconfigurable, electric field-driven microfluidic systems. Networks generally offer broad potential for automated chemical and biochemical analysis and lab-on-a-chip integration.