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Single- and multi-layer complex networks have been proven as a powerful tool to study the dynamics within social, technological, or natural systems. An often observed common goal is to optimize these systems for specific purposes by minimizing certain costs while maximizing a desired output. Acknowledging that especially real-world systems from the coupled socio-ecological realm are highly intertwined this work exemplifies that in such systems the optimization of a certain subsystem, e.g. to increase the resilience against external pressure in an ecological network, may unexpectedly diminish the stability of the whole coupled system. For this purpose we utilize an adaptation of a previously proposed conceptual bi-layer network model composed of an ecological network of diffusively coupled resources co-evolving with a social network of interacting agents that harvest these resources and learn each other’s strategies depending on individual success. We derive an optimal coupling strength that prevents collapse in as many resources as possible if one assumes that the agents’ strategies remain constant over time.
We then show that if agents socially learn and adapt strategies according to their neighbors’ success, this optimal coupling strength is revealed to be a critical parameter above which the probability for a global collapse in terms of irreversibly depleted resources is high—an effect that we denote the tragedy of the optimizer. We thus find that measures which stabilize the Dynamics within a certain part of a larger co-evolutionary system may unexpectedly cause the emergence of novel undesired globally stable states. Our results therefore underline the importance of holistic approaches for managing socio-ecological systems because stabilizing effects which focus on single subsystems may be counter-beneficial for the system as a whole.
Motivation: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples.
Results: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores.
Availability and implementation: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM.
Probability based taxonomic profiling of viral and microbiome samples using PepGM and Unipept
(2022)
In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates
taxonomic inference in samples of unknown taxonomic origin. PepGM uses a graphical model for taxonomic profiling of viral proteomes and
metaproteomic datasets providing taxonomic confidence scores. To build the graphical model, a list of potentially present taxa needs to be
inferred. To this end, we integrate Unipept, which enables the fast querying of potentially present taxa. Together, they allow for taxonomic
inference with statistically sound confidence scores.
In mass spectrometry based proteomics, protein homology leads to
many shared peptides within and between species. This complicates
taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets.
Using the graphical model, our approach computes statistically sound
scores for taxa based on peptide scores from a previous database
search, eliminating the need for commonly used heuristics. heuristics.
In mass spectrometry based proteomics, protein homology leads to
many shared peptides within and between species. This complicates
taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets.
Using the graphical model, our approach computes statistically sound
scores for taxa based on peptide scores from a previous database
search, eliminating the need for commonly used heuristics. heuristics.
In mass spectrometry based proteomics, protein homology leads to
many shared peptides within and between species. This complicates
taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets.
Using the graphical model, our approach computes statistically sound
scores for taxa based on peptide scores from a previous database
search, eliminating the need for commonly used heuristics.
In mass spectrometry based proteomics, protein homology leads to
many shared peptides within and between species. This complicates
taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets.
Using the graphical model, our approach computes statistically sound
scores for taxa based on peptide scores from a previous database
search, eliminating the need for commonly used heuristics.
In mass spectrometry based proteomics, protein homology leads to
many shared peptides within and between species. This complicates
taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets.
Using the graphical model, our approach computes statistically sound
scores for taxa based on peptide scores from a previous database
search, eliminating the need for commonly used heuristics.