Biology subjects

Gomez-Schiavon, M.

Publications and source records attributed to Gomez-Schiavon, M..

3 recordsLinked to original sources

bioRxiv2018

Design and analysis of a Proportional-Integral-Derivative controller with biological molecules

The ability of cells to regulate their function through feedback control is a fundamental underpinning of life. The capability to engineer de novo feedback control with biological molecules is ushering in an era of robust functionality for many applications in biotechnology and medicine. To fulfill their potential, feedback control strategies implemented with biological molecules need to be generalizable, modular and operationally predictable. Proportional-Integral-Derivative (PID) control fulfills this role for technological systems and is a commonly used strategy in engineering. Integral feedback control allows a system to return to an invariant steady-state value after step disturbances, hence enabling its robust operation. Proportional and derivative feedback control used with integral control help sculpt the dynamics of the return to steady-state following perturbation. Recently, a biomolecular implementation of integral control was proposed based on an antithetic motif in which two molecules interact stoichiometrically to annihilate each others function. In this work, we report how proportional and derivative implementations can be layered on top of this integral architecture to achieve a biochemical PID control design. We illustrate through computational and analytical treatments that the addition of proportional and derivative control improves performance, and discuss practical biomolecular implementations of these control strategies.

synthetic biology↗

bioRxiv2018

Complexity-Aware Simple Modeling

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach -- complexity-aware simple modeling -- that can bridge the gap between the small- and large-scale approaches.\n\nHighlightsO_LISimple or small-scale models allow deduction of fundamental principles of biological systems\nC_LIO_LIDetailed or large-scale models can be quantitatively accurate but difficult to analyze\nC_LIO_LIComplexity-aware simple models can extract principles that are robust to the presence of unknown complex interactions\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC=\"FIGDIR/small/248419_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@1cc6c8borg.highwire.dtl.DTLVardef@1ea1eb3org.highwire.dtl.DTLVardef@185a0b4org.highwire.dtl.DTLVardef@1f9c6b9_HPS_FORMAT_FIGEXP M_FIG Graphical abstract\n\nC_FIG

systems biology↗

bioRxiv2017

Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution on the abundance and localization of nascent and mature transcripts in single cells. Gene expression dynamics are typically inferred by measuring mRNA abundance in small numbers of fixed cells sampled from a population at multiple time-points after induction. The sparse data that arise from the small number of cells obtained using smFISH present a challenge for inferring transcription dynamics. Here, we developed a computational pipeline (BayFish) to infer kinetic parameters of gene expression from smFISH data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons. We showed that a 2-state promoter model can recapitulate Npas4 dynamics after induction and we inferred that the transition rate from the promoter OFF state to the ON state is increased by the stimulus.\n\nAuthor SummaryGene expression can exhibit cell-to-cell variability due to the stochastic nature of biochemical reactions. Single cell assays (e.g. smFISH) directly quantify stochastic gene expression by measuring the number of active promoters and transcripts per cell in a population of cells. The data are distributions and their shape and time-evolution contain critical information on the underlying process of gene expression. Recent work has combined models of stochastic gene expression with maximum likelihood methods to infer kinetic parameters from smFISH distributions. However, these approaches do not provide a probability distribution or likelihood of model parameters inferred from the smFISH data. This information is useful because it indicates which parameters are loosely constrained by the data and suggests follow up experiments. We developed a suite of MATLAB programs (BayFish) that estimate the Bayesian posterior probability of model parameters from smFISH data. The user specifies an underlying model of stochastic gene expression with unknown parameters ({theta}) and provides smFISH data (Y). BayFish uses a Monte Carlo algorithm to estimate the Bayesian posterior probability P({theta}|Y) of model parameters. BayFish is easily modified and can be applied to other models of stochastic gene expression and smFISH data sets.

systems biology↗