Abstract
During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition.
Highlights
The complexity of high-throughput datasets has reached a level, where mathematical models are needed to understand biochemical networks
Three case studies will be presented as proof of concept of the advantages of the Flux Balance Analysis (FBA)/Thermodynamic Feasibility Analysis (TFA)/Biochemical Systems Theory (BST) workflow
Case study 2 will deal with another well studied system: the unbranched pathway with feedback inhibition. This example will show how the incorporation of thermodynamic considerations to a dynamic model can lead to the discovery of a new design principle
Summary
The complexity of high-throughput datasets has reached a level, where mathematical models are needed to understand biochemical networks. The most common way of modeling metabolic networks, if regulation is to be included, is a system of differential equations describing the dynamics of certain variables, normally metabolite concentrations. These state variables are collected as a vector of dependent variables xd (t). The connectivity of the network is represented by the stoichiometric matrix S and can be determined by collecting a list of the active reactions in the cell and their respective stoichiometries Such a list can be obtained from a genome and refined by experiments and literature searches as is explained in many texts about metabolic reconstructions [1].
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