Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Abstract

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

“…This phenotype is likely derived by S6 additive activity from the MAPK pathway and AKT-mTORC1, which cannot be accurately described using AND/OR gates. AND/OR models may be generated by automated tools (30) and can serve well as an initial model scaffold. However, more complex relationships such as those in our model between BAD, S6, 4EBP1, TSC2, and EIF4B in AML need to be further refined.…”

Models of signalling networks – what cell biologists can gain from them and give to them

“…Discrete logical models provide a formal mechanism for developing context-specific signalling networks that are most consistent with available data. The coarseness of discrete on-off changes in signalling is addressed by more-complicated 'fuzzy' logic models, which allow graded transitions between states (Aldridge et al, 2009;Morris et al, 2011). Luckily, network-level modelling and experiments have combined to show that cells operate by a much simpler set of rules (Janes, 2010).…”

Naturally selecting solutions

“…The results achieved can also be inconsistent, even when rerunning a GA with the same parameters, due to the stochastic nature of the process. 43 A similar phenomenon is observed in the biological sciences in the form of the protein folding problem-more specifically, the folding funnel hypothesis for protein folding, representing a specific version of the energy landscape theory of protein folding, which assumes that a protein's native state corresponds to its free energy minimum under the solution conditions usually encountered in cells. Although the folding funnel hypothesis assumes that the native state is a deep free energy minimum with steep walls, corresponding to a single well-defined tertiary structure, energy landscapes are usually "rough," with many non-native local minima in which partially folded proteins can become trapped.…”