Qualitative Methods for Modeling Biochemical Systems and Datasets : The Logicome and the Reaction Systems Approaches

Abstract

In our everyday life we use a number of complex systems that consist of many closely interconnected components. None of the individual components possess a property of the whole system but when they come together they give rise to special properties which are called emergent properties. A similar scenario one may observe in biological systems. There are many interconnected entities such as genes, proteins, and metabolites involved in biological systems. Through their interactions with one another and also with the environment, they exhibit a number of observable characteristics. In order to understand the complexity in biological processes, it is required to understand not just how individual entities function but also how they interact with one another. The molecular activities involved in biological processes very often remain difficult to understand due to their complex structure. We address the issue in this thesis with the focus on development and demonstration of qualitative approaches, to gain useful insights into several characteristics and dynamics lying within biological phenomena. The first part of the thesis presents the development of logic-based approaches aka logicome, where we use simple heuristics and logical operations to interpret complex scenarios. We apply logicome approaches to capture high-level understanding in terms of mathematical logic of the biological phenomena under study. We demonstrated the logicome approach on two case-studies: (i) the numerical model of Epidermal Growth Factor Receptor (EGFR) signaling pathway (ii) the microarray datasets of Head andNeck/Oral squamous-cell carcinoma (HNOSCC). The logicome proposed for the EGFR signaling pathway investigates activation dependencies within the key species whereas the logicome for HNOSCC microarray datasets produces boolean signatures using the representative genes. The second part of the thesis presents so-called reaction systems, a nature inspired qualitative modeling framework which functions based on two main principles: threshold principle and no permanency principle. The interactive processes within the reaction systems framework are controlled through two main mechanisms: facilitation and inhibition. We developed reaction systems models which are built on the simple concepts of set theory. Our models demonstrated the feasibility and expressive power of the reaction systems framework as a versatile modeling framework for several dynamics that typically emerged through the traditional quantitative modeling framework. We show reaction systems models to be natural correspondents of models of known dynamic systems such as kinetic models of self-assembly of intermediate filaments, and dynamic models of systems exhibiting behavior such as bi-stability, multi-stability and period doubling bifurcation. The doctoral thesis is set out to develop and demonstrate the potential role that qualitative approaches play in understanding complex behaviors which are typically observed in biological systems. The hypotheses derived with our approaches are well consistent with the literature findings and the results obtained in other modeling frameworks. We therefore expect that our approaches can be efficient at providing new biological findings for case-studies with intractable complex details.