Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming

A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data

Notions of Knowledge Management

{Excerpt} Knowledge management is getting the right knowledge to the right people at the right time, and helping them (with incentives) to apply it in ways that strive to improve organizational performance. Data are facts, and information is interpreted data. Knowledge is created and organized by flows of information, shaped by their holder. It is tacit or explicit. Tacit knowledge is nonverbalized, intuitive, and unarticulated knowledge that people carry in their heads. It is hard to formalize and communicate because it is rooted in skills, experiences, insight, intuition, and judgment, but it can be shared in discussion, storytelling, and personal interactions. It has a technical dimension, which encompasses skills and capabilities referred to as know-how. It has a cognitive dimension, which consists of beliefs, ideals,values, schemata, or mental models. Explicit knowledge is codified knowledge that can be expressed in writing, drawings, or computer programs, for example, and transmitted in various forms. Tacit knowledge and explicit knowledge are mutually complementary forms of meaning

Edge vulnerability in neural and metabolic networks

Biological networks, such as cellular metabolic pathways or networks of corticocortical connections in the brain, are intricately organized, yet remarkably robust toward structural damage. Whereas many studies have investigated specific aspects of robustness, such as molecular mechanisms of repair, this article focuses more generally on how local structural features in networks may give rise to their global stability. In many networks the failure of single connections may be more likely than the extinction of entire nodes, yet no analysis of edge importance (edge vulnerability) has been provided so far for biological networks. We tested several measures for identifying vulnerable edges and compared their prediction performance in biological and artificial networks. Among the tested measures, edge frequency in all shortest paths of a network yielded a particularly high correlation with vulnerability, and identified inter-cluster connections in biological but not in random and scale-free benchmark networks. We discuss different local and global network patterns and the edge vulnerability resulting from them.Comment: 8 pages, 4 figures, to appear in Biological Cybernetic

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function

Structure and stereochemistry of the base excision repair glycosylase MutY reveal a mechanism similar to retaining glycosidases.

MutY adenine glycosylases prevent DNA mutations by excising adenine from promutagenic 8-oxo-7,8-dihydroguanine (OG):A mismatches. Here, we describe structural features of the MutY active site bound to an azaribose transition state analog which indicate a catalytic role for Tyr126 and approach of the water nucleophile on the same side as the departing adenine base. The idea that Tyr126 participates in catalysis, recently predicted by modeling calculations, is strongly supported by mutagenesis and by seeing close contact between the hydroxyl group of this residue and the azaribose moiety of the transition state analog. NMR analysis of MutY methanolysis products corroborates a mechanism for adenine removal with retention of stereochemistry. Based on these results, we propose a revised mechanism for MutY that involves two nucleophilic displacement steps akin to the mechanisms accepted for 'retaining' O-glycosidases. This new-for-MutY yet familiar mechanism may also be operative in related base excision repair glycosylases and provides a critical framework for analysis of human MutY (MUTYH) variants associated with inherited colorectal cancer

Metabolic Profiling of IDH Mutation and Malignant Progression in Infiltrating Glioma.

Infiltrating low grade gliomas (LGGs) are heterogeneous in their behavior and the strategies used for clinical management are highly variable. A key factor in clinical decision-making is that patients with mutations in the isocitrate dehydrogenase 1 and 2 (IDH1/2) oncogenes are more likely to have a favorable outcome and be sensitive to treatment. Because of their relatively long overall median survival, more aggressive treatments are typically reserved for patients that have undergone malignant progression (MP) to an anaplastic glioma or secondary glioblastoma (GBM). In the current study, ex vivo metabolic profiles of image-guided tissue samples obtained from patients with newly diagnosed and recurrent LGG were investigated using proton high-resolution magic angle spinning spectroscopy (1H HR-MAS). Distinct spectral profiles were observed for lesions with IDH-mutated genotypes, between astrocytoma and oligodendroglioma histologies, as well as for tumors that had undergone MP. Levels of 2-hydroxyglutarate (2HG) were correlated with increased mitotic activity, axonal disruption, vascular neoplasia, and with several brain metabolites including the choline species, glutamate, glutathione, and GABA. The information obtained in this study may be used to develop strategies for in vivo characterization of infiltrative glioma, in order to improve disease stratification and to assist in monitoring response to therapy

Isotopic and genetic methods reveal the role of the gut microbiome in mammalian host essential amino acid metabolism.

Intestinal microbiota perform many functions for their host, but among the most important is their role in metabolism, especially the conversion of recalcitrant biomass that the host is unable to digest into bioavailable compounds. Most studies have focused on the assistance gut microbiota provide in the metabolism of carbohydrates, however, their role in host amino acid metabolism is poorly understood. We conducted an experiment on Mus musculus using 16S rRNA gene sequencing and carbon isotope analysis of essential amino acids (AAESS) to quantify the community composition of gut microbiota and the contribution of carbohydrate carbon used by the gut microbiome to synthesize AAESS that are assimilated by mice to build skeletal muscle tissue. The relative abundances of Firmicutes and Bacteroidetes inversely varied as a function of dietary macromolecular content, with Firmicutes dominating when mice were fed low-protein diets that contained the highest proportions of simple carbohydrates (sucrose). Mixing models estimated that the microbial contribution of AAESS to mouse muscle varied from less than 5% (threonine, lysine, and phenylalanine) to approximately 60% (valine) across diet treatments, with the Firmicute-dominated microbiome associated with the greatest contribution. Our results show that intestinal microbes can provide a significant source of the AAESS their host uses to synthesize structural tissues. The role that gut microbiota play in the amino acid metabolism of animals that consume protein-deficient diets is likely a significant but under-recognized aspect of foraging ecology and physiology