Biology subjects

Gutmann, M. U.

Publications and source records attributed to Gutmann, M. U..

5 recordsLinked to original sources

bioRxiv2024

Risk-averse optimization of genetic circuits under uncertainty

Synthetic biology aims to engineer biological systems with specified functions. This requires navigating an extensive design space, which is challenging to achieve with wet lab experiments alone. To expedite the design process, mathematical modelling is typically employed to predict circuit function in silico ahead of implementation, which when coupled with computational optimization can be used to automatically identify promising designs. However, circuit models are inherently inaccurate which can result in sub-optimal or non-functional in vivo performance. To mitigate this issue, here we propose to combine Bayesian inference, Thompson sampling, and risk management to find optimal circuit designs. Our approach employs data from non-functional designs to estimate the distribution of the model parameters and then employs risk-averse optimization to select design parameters that are expected to perform well given parameter uncertainty and biomolecular noise. We illustrate the approach by designing robust adaptation circuits and genetic oscillators with a prescribed frequency. The proposed approach provides a novel methodology for the design of robust genetic circuitry.

synthetic biology↗

bioRxiv2022

Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models

AO_SCPLOWBSTRACTC_SCPLOWEstimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of nontrivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.

systems biology↗

bioRxiv2022

Systematic comparison of ranking aggregation methods for gene lists in experimental results

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The results of a group of studies answering the same, or similar, questions can be combined by meta-analysis to find a consensus or a more reliable answer. Ranking aggregation methods can be used to combine gene lists from various sources in meta-analyses. Evaluating a ranking aggregation method on a specific type of dataset before using it is required to support the reliability of the result since the property of a dataset can influence the performance of an algorithm. Evaluation of aggregation methods is usually based on a simulated database especially for the algorithms designed for gene lists because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations which are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data was used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomics data, with various heterogeneity of quality, noise level, and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (NSCLC), and bacteria (macrophage apoptosis) was performed. We summarise our evaluation results in terms of a simple flowchart to select a ranking aggregation method for genomics data.

bioinformatics↗

bioRxiv2019

Multiomic definition of generalizable endotypes in human acute pancreatitis.

Acute pancreatitis (AP) is sudden onset pancreas inflammation that causes multiple organ dysfunction syndrome (MODS) and death in certain individuals who develop AP yet minimal systemic inflammation in others. Here, we show that this observed diversity in systemic response and outcome is accompagnied by diversity in molecular subtypes that can be identified using computational analysis of clinical and multiomic data. We integrated co-incident whole blood transcriptomic, plasma proteomic, and serum metabolomic data at serial time points from a cohort of patients presenting with AP and systematically evaluated four different metrics for patient similarity, using unbiased mathematical, biological and clinical measures of internal and external validity. Our results identify four distinct and stable AP endotypes that are characterized by pathway and biomarker combination stereotypes into hypermetabolic, hepatopancreaticobiliary, catabolic and innate immune endotypes. The catabolic endotype in AP strikingly recapitulates a disease endotype previously reported in acute respiratory distress syndrome, a recognized complication of AP. Our findings demonstrate that clinically-relevant and generalizable endotypes exist in AP.

systems biology↗

bioRxiv2017

Resolving outbreak dynamics using Approximate Bayesian Computation for stochastic birth-death models

Earlier research has suggested that Approximate Bayesian Computation (ABC) makes it possible to fit simulator-based intractable birth-death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters such as the reproductive number R may remain poorly identifiable. Here we show that the identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case-study, we consider the situation where the genotype data are generated as a mixture of three stochastic processes, each with their distinct dynamics and clear epidemiological interpretation.\n\nThe ABC inference yields stable and accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. We also show that under the proposed model, the infectious population size can be reliably inferred from the data. The estimate is approximately two orders of magnitude smaller compared to assumptions made in the earlier ABC studies, and is much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three-fold compared with the previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model.

bioinformatics↗