Bottom-Up Approach Overview

The bottom-up approach is a strategy for processing information, which alongside the top-down approach, is used in a number of research fields.

Credit: Katryna Kon/

By piecing together basic data units the whole system can be formed and modeled. This differs from the top-down approach, which breaks the whole system apart into its constituent units.

The bottom-up approach has the advantage of analyzing base units in detail before being integrated into a larger system. Within the field of systems biology, this approach has produced tissue-specific simulations for modeling responses to condition variability.

By applying a bottom-up approach to pharmacology, the possibility of simulated drug safety assessments is being made into a reality.

Bottom-up approach in systems biology

Systems biology is an example of a field that employs bottom-up and top-down approaches for the understanding of complex systems within living organisms.

The bottom-up approach to systems biology forms detailed models from subunits of data to simulate whole systems under different physiological conditions. To develop an overview of the biological interactions occurring within an organism, various data are integrated into a larger genome-scale model.

The approach starts with draft reconstruction, where organism specific data such as genomics and metabolomics are collated from databases.

Manual curation validates the draft reconstruction by adding and removing data where necessary. This is then  transformed into a mathematical model which is tested and refined by inputting objective functions with known results.

Bottom-up approach to tissue-specific modeling applications

Practical applications of the bottom-up approach can be found in the development of human tissue-specific modeling.

One such example is the formation of HepatoNet1 based modeling; a reconstruction of the human liver from using the bottom-up approach, which tests components of liver function under various conditions.

Greater understanding of optimal conditions for full liver functioning is achieved from detailed recreations of component functions, such as cholesterol biosynthesis and bile formation.

Importantly, the model allows for the assessment of tissue-specific biological functions through mathematical reconstruction alone.

Variations in the homeostasis of blood compounds can be quickly tested to understand the effects on metabolic pathways, without the need for time-consuming experimentation.

Bottom-up approach to systems pharmacology

The ability to integrate data into a whole system model has great implications for the pharmacology industry. A bottom-up approach to assessing drug safety is being developed through modeling and simulation.

Models are being developed using in vitro data to form a mechanistic insight into drug therapy. The bottom-up simulation for drug safety evaluations allows the reconstruction of drug exposure with other potential outcomes such as toxicity level and changes to hemodynamics.

By integrating the drug, system and trial design data, it is also possible to predict the exposure response to variability at the individual and population level.

Bottom-up modeling will therefore be an important tool for assessing drug effects in entire populations.

Comparison of bottom-up and top-down modeling

A disadvantage to the bottom-up approach is that the models developed may not accurately reflect clinical conditions.

For this reason, comparative studies of the bottom-up and top-down approach have been carried out to compare their ability to predict the clinical potency of drugs.

One study found good quantitative agreement between the two approaches, though the top-down approach models are more dependent on the quality of pharmacokinetic data. This indicates that the bottom-up approach of modeling clinical efficacy through in vitro input data is valid.

Furthermore, a ‘middle-out’ approach, where bottom-up and top-down data are combined, can determine uncertain parameters within a drug model.

Accurate pharmacological models for assessing drug safety and therapy outcomes are an important goal in the pharmacology industry because of the potential to reduce the length of clinical trials and the need for animal models.

Further Reading

Last Updated: Aug 23, 2018


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