Abstract Machine learning provides researchers a unique opportunity to make metabolic
engineering more predictable. In this review, we offer an introduction to this discipline in …

Metabolic engineering encompasses several widely-used strategies, which currently hold a
high seat in the field of biotechnology when its potential is manifesting through a plethora of …

New synthetic biology capabilities hold the promise of dramatically improving our ability to
engineer biological systems. However, a fundamental hurdle in realizing this potential is our …

An increasing number of industrial bioprocesses capitalize on living cells by using them as
cell factories that convert sugars into chemicals. These processes range from the production …

Recent interest in modeling biochemical networks raises questions about the relationship
between often complex mathematical models and familiar arithmetic concepts from classical …

Abstract Systems Biology has taken advantage of computational tools and high-throughput
experimental data to model several biological processes. These include signaling, gene …

In contrast to stoichiometric-based models, the development of large-scale kinetic models of
metabolism has been hindered by the challenge of identifying kinetic parameter values and …

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools
that facilitate the analysis of dynamic biological systems. This paper depicts major …

Computational strain design protocols aim at the system-wide identification of intervention
strategies for the enhanced production of biochemicals in microorganisms. Existing …

Constraint‐based modeling of biological networks (metabolism, transcription and signal
transduction), although used successfully in many applications, suffer from specific …