Biology subjects

Cummins, B.

Publications and source records attributed to Cummins, B..

8 recordsLinked to original sources

bioRxiv2026

Expanded stoichiometric model of chondrocyte metabolism: response to cyclical shear and compressive loading

Cartilage deterioration is a hallmark osteoarthritis, and there is substantial interest in developing strategies for cartilage repair. Cyclical mechanical stimulation has been known for decades to drive synthesis of cartilage matrix proteins. Matrix synthesis requires activation of central metabolism for producing precursors to non-essential amino acids required for protein translation. However, there are gaps in knowledge regarding how mechanical stimuli affect chondrocyte central metabolism. Here, we find that cyclical shear and compression drive differences in chondrocyte central metabolism in a sex-dependent manner. Based on established biochemistry, we developed and tested a stoichiometric model containing 139 metabolites and 172 reactions from central metabolism that includes production of key cartilage matrix proteins. We then used experimental metabolomics data from shear and compressive stimulation of osteoarthritic chondrocytes to constrain this model and ran multiple simulations examining the potential for producing matrix proteins and ATP. Our results show that both shear and compression can stimulate osteoarthritic chondrocyte metabolism in a manner consistent with production of cartilage matrix proteins, with notable differences between male and female chondrocytes. Additionally, and importantly, our simulation results suggest that nitrogen availability is a key limitation to chondrocyte synthesis of matrix proteins. These results are a starting point for using central metabolism of chondrocytes to optimize synthesis of matrix proteins for cartilage repair. For example, increasing glutamine levels in the presence of cyclical compression has potential to increase production of both types II and VI collagen. These strategies have potential for improving cartilage tissue engineering and repair.

bioengineering↗

bioRxiv2024

Multistability and predominant double-positive states in a four node mutually repressive network: a case study of Th1/Th2/Th17/T-reg differentiation

Elucidating the emergent dynamics of complex regulatory networks enabling cellular differentiation is crucial to understand embryonic development and suggest strategies for synthetic circuit design. A well-studied network motif often driving cellular decisions is a toggle switch - a set of two mutually inhibitory lineage-specific transcription factors A and B. A toggle switch often enables two possible mutually exclusive states - (high A, low B) and (low A, high B) - from a common progenitor cell. However, the dynamics of networks enabling differentiation of more than two cell types from a progenitor cell is not well-studied. Here, we investigate the dynamics of four master regulators A, B, C and D inhibiting each other, thus forming a toggle tetrahedron. Our simulations show that a toggle tetrahedron predominantly allows for co-existence of six double positive or hybrid states where two of the nodes are expressed relatively high as compared to the remaining two - (high A, high B, low C, low D), (high A, low B, high C, low D), (high A, low B, low C, high D), (low A, high B, high C, low D), (low A, low B, high C, high D) and (low A, high B, low C, high D). Stochastic simulations showed state-switching among these phenotypes, indicating phenotypic plasticity. Finally, we apply our results to understand the differentiation of naive CD4+ T cells into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process. Our results reveal multistable dynamics and establish the stable co-existence of hybrid cell-states, offering a potential explanation for simultaneous differentiation of multipotent naive CD4+ T cells.

systems biology↗

bioRxiv2022

Assessing biological network dynamics: Comparing numerical simulations with analytical decomposition of parameter space

Mathematical modeling of the emergent dynamics of gene regulatory networks (GRN) faces a double challenge of (a) dependence of model dynamics on parameters, and (b) lack of reliable experimentally determined parameters. In this paper we compare two complementary approaches for describing GRN dynamics across unknown parameters: (1) parameter sampling and resulting ensemble statistics used by RACIPE (RAndom CIrcuit PErturbation), and (2) use of rigorous analysis of combinatorial approximation of the ODE models by DSGRN (Dynamic Signatures Generated by Regulatory Networks). We find a very good agreement between RACIPE simulation and DSGRN predictions for four different 2- and 3-node networks typically observed in cellular decision making. This observation is remarkable since the DSGRN approach assumes that the Hill coefficients of the models are very high while RACIPE assumes the values in the range 1-6. Thus DSGRN parameter domains, explicitly defined by inequalities between systems parameters, are highly predictive of ODE model dynamics within a biologically reasonable range of parameters.

systems biology↗

bioRxiv2022

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

synthetic biology↗

bioRxiv2022

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Computational tools addressing various components of design-build-test-learn loops (DBTL) for the construction of synthetic genetic networks exist, but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called DART (Design Assemble Round Trip). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection, and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.

synthetic biology↗

bioRxiv2022

Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of S. cerevisiae by Gander, et al. Our experimental campaign used a novel high throughput experimentation framework developed under DARPAs Synergistic Discovery and Design (SD2) program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report, and make some remarks about lessons learned.

synthetic biology↗

bioRxiv2022

Experimental Guidance for Discovering Genetic Networks through Iterative Hypothesis Reduction on Time Series

Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent Dynamics Pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data. Author SummaryIn this work we discuss a method for identifying promising experimental targets for genetic network inference by leveraging different features of time series gene expression data along a chained set of previously published software tools. We aim to locate small networks that control oscillations in the genome-wide expression profile in biological functions such as the circadian rhythm and the cell cycle. We infer the most promising targets for further experimentation, emphasizing that modeling and experimentation are an*Corresponding author: breschine.cummins@montana.edu essential feedback loop for confident predictions of core network structure. Our major offering is the reduction of experimental time and expense by providing targeted guidance from computational methods for the inference of oscillating core networks, particularly in novel organisms.

systems biology↗

bioRxiv2020

Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15-80% of the genome. The gene-regulatory networks (GRNs) controlling these programs were largely identified by genetics and chromosome mapping approaches in model systems, yet it is unlikely that we have identified all of the core GRN components. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Previous work used dynamic gene expression features to identify sets of genes with periodic behavior; our work goes further to distinguish genes by role: core versus their non-regulatory outputs. Here we present a quantitative approach that can identify nodes of GRNs controlling cell or circadian cycles across taxa. There are practical applications of the approach for network biologists, but our findings reveal something unexpected--that there are quantifiable and fundamental shared features of these unrelated GRNs controlling disparate periodic phenotypes. Author summaryCircadian rhythms, cellular division, and the developmental cycles of a multitude of living creatures, including those responsible for infectious diseases, are among the many dynamic phenomena in the natural world that are known to be the eventual output of gene regulatory networks. Identifying the small number of specialized genes that control these dynamic behaviors is of fundamental importance to our understanding of life, and our treatment of disease, but is difficult because of the sheer size of the genomes. We show that the core genes in organisms separated by millions of years of evolution have remarkable similarities that can be used to identify them.

genomics↗