Integrating heterogeneous knowledges for understanding biological behaviors: a probabilistic approach

TL;DR

This paper introduces a probabilistic modeling approach that integrates qualitative and quantitative biological knowledge to better understand complex biological behaviors, demonstrated through modeling E. coli's carbon starvation response.

Contribution

It presents a novel probabilistic method combining qualitative and quantitative data for biological system modeling, validated on E. coli's response to carbon starvation.

Findings

The probabilistic model aligns with biological expectations.

Qualitative properties are formally verified.

Quantitative results confirm the model's effectiveness.

Abstract

Despite recent molecular technique improvements, biological knowledge remains incomplete. Reasoning on living systems hence implies to integrate heterogeneous and partial informations. Although current investigations successfully focus on qualitative behaviors of macromolecular networks, others approaches show partial quantitative informations like protein concentration variations over times. We consider that both informations, qualitative and quantitative, have to be combined into a modeling method to provide a better understanding of the biological system. We propose here such a method using a probabilistic-like approach. After its exhaustive description, we illustrate its advantages by modeling the carbon starvation response in Escherichia coli. In this purpose, we build an original qualitative model based on available observations. After the formal verification of its qualitative properties, the probabilistic model shows quantitative results corresponding to biological expectations which confirm the interest of our probabilistic approach.