Biology subjects

Khammash, M.

Publications and source records attributed to Khammash, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Engineering Cybergenetic Cell-Based Therapies

Adaptive, closed-loop control of cellular behavior is essential for next-generation therapies, yet most current treatments operate in an open-loop manner and lack robustness to patient variability and disease dynamics. Here, we establish a controltheoretic platform for rational engineering of closed-loop cell-based therapies that achieve precise and robust regulation. First, we introduce multi-dimensional nullgram profiling, a high-throughput approach that enables quantitative prediction and design of advanced genetic controllers in human cells across circuit topologies and parameter regimes in a single experiment. To evaluate dynamic therapeutic behavior, we next develop Cyberpatient-in-the-loop, an optogenetic digital twin platform that interfaces engineered mammalian cells with computational disease models, enabling systematic testing of closed-loop performance under realistic perturbations. Finally, we leverage these approaches to implement integral feedback cell therapies that sense inflammatory signals and autonomously regulate cytokine levels in primary immune cell cultures. Together, these results establish a general paradigm for engineering cellbased control systems and provide a foundation for next-generation cell therapies.

synthetic biology↗

bioRxiv2025

Control with Practical Guarantees of Stationary Variance in Stochastic Chemical Reaction Networks

Biomolecular integral feedback controllers offer precise regulation of molecular species copy numbers, making them valuable for synthetic biology applications. Antithetic integral feedback controllers, in particular, can be effective in low-copy-number regimes with stochastic dynamics. In this work, we introduce a modified variant of this controller, called the antithetic dual-rein integral feedback motif, and analyze its performance from a stochastic perspective in the presence of intrinsic dynamic randomness. We demonstrate that our controller enables first-moment control while maintaining a tractable steady-state variance bound under specific parametric regimes. Notably, this variance bound is tunable, as it depends solely on the controller parameters. We derive these results using stochastic model-order reduction and validate them through numerical simulations. Our findings provide new insights into achieving both precise regulation and noise suppression in stochastic genetic circuits.

synthetic biology↗

bioRxiv2024

Multi-Layer Autocatalytic Feedback Enables Integral Control Amidst Resource Competition and Across Scales

Integral feedback control strategies have proven effective in regulating protein expression in unpredictable cellular environments. These strategies, grounded in model-based designs and control theory, have advanced synthetic biology applications. Autocatalytic integral feed-back controllers, utilizing positive autoregulation for integral action, are particularly promising due to their similarity to natural behaviors like self-replication and positive feedback seen across biological scales. However, their effectiveness is often hindered by resource competition and context-dependent couplings. This study addresses these challenges with a multi-layer feedback strategy, enabling population-level integral feedback and multicellular integrators. We provide a generalized mathematical framework for modeling resource competition in complex genetic networks, supporting the design of intracellular control circuits. Our controller motif demonstrated precise regulation in tasks ranging from gene expression control to population growth in multi-strain communities. We also explore a variant capable of ratiometric control, proving its effectiveness in managing gene ratios and co-culture compositions in engineered microbial ecosystems. These findings offer a versatile approach to achieving robust adaptation and homeostasis from subcellular to multicellular scales.

synthetic biology↗

bioRxiv2024

Cascaded Antithetic Integral Feedback Motifs for Robust Stability and Performance Improvement

Precise intracellular regulation and robust perfect adaptation can be achieved using biomolecular integral controllers and it holds enormous potential for synthetic biology applications. In this letter, we consider the cascaded implementation of a class of such integrator motifs. Our cascaded integrators underpin proportional-integral-derivative (PID) control structures, which we leverage to suggest ways to improve dynamic performance. Moreover, we demonstrate how our cascaded strategy can be harnessed to enhance robust stability in a class of uncertain reaction networks. We also discuss the genetic implementation of our controllers and the natural occurrence of their cascaded sequestration pairs in bacterial pathogens.

synthetic biology↗

bioRxiv2023

Anti-Windup Protection Circuits for Biomolecular Integral Controllers

Robust Perfect Adaptation (RPA) is a desired property of biological systems wherein a systems output perfectly adapts to a steady state, irrespective of a broad class of perturbations. Achieving RPA typically requires the deployment of integral controllers, which continually adjust the systems output based on the cumulative error over time. However, the action of these integral controllers can lead to a phenomenon known as "windup". Windup occurs when an actuator in the system is unable to respond to the controllers commands, often due to physical constraints, causing the integral error to accumulate significantly. In biomolecular control systems, this phenomenon is especially pronounced due to the positivity of molecular concentrations, inevitable promoter saturation and resource limitations. To protect against such performance deterioration or even instability, we present three biomolecular anti-windup topologies. The underlying architectures of these topologies are then linked to classical control-theoretic anti-windup strategies. This link is made possible due the development of a general model reduction result for chemical reaction networks with fast sequestration reactions that is valid in both the deterministic and stochastic settings. The topologies are realized as chemical reaction networks for which genetic designs, harnessing the flexibility of inteins, are proposed. To validate the efficacy of our designs in mitigating windup effects, we perform simulations across a range of biological systems, including a complex model of Type I diabetic patients and advanced biomolecular proportional-integral-derivative (PID) controllers. This work lays a foundation for developing robust and reliable biomolecular control systems, providing necessary safety and protection against windup-induced instability.

synthetic biology↗

bioRxiv2023

A Hidden Proportional Feedback Mechanism Underlies Enhanced Dynamic Performance and Noise Rejection in Sensor-Based Antithetic Integral Control

Efficient regulation of cellular processes is essential for both endogenous and synthetic biological processes. The design of biomolecular feedback controllers that achieve robust and timely regulation is the subject of considerable research at the interface between synthetic biology and control theory. Integral feedback controllers, known for their ability to confer the property of Robust Perfect Adaptation (RPA), are increasingly becoming common features in biological control design. Antithetic integral feedback (AIF) controllers, in particular, have enabled effective chemical reaction realizations of integral controllers that deliver RPA in both deterministic and stochastic settings. This paved the way to experimental implementations of integral controllers in bacterial and mammalian cells. While AIF controllers deliver favorable adaptation properties, they do not necessarily lead to good transient performance or noise reduction properties and may in some cases lead to increased overshoot or cell-to-cell variability. These limitations are commonly circumvented by augmenting new circuitry that realize proportional or derivative feedback mechanisms to enhance dynamic and noise rejection features without affecting the AIF controllers adaptation properties. In this paper, we report that a sensor-based variant of the basic AIF motif exhibits favorable transient dynamic properties and (as reported elsewhere) reduced noise variance. We show that these features are attributed to a "hidden" proportional feedback component that is inherent in the controller structure and that such mechanism is solely responsible for the controllers underlying enhanced dynamic performance and noise rejection properties. This sensor-based AIF controller hence offers a minimal biomolecular realization of Proportional-Integral (PI) control, whereby both integral and proportional feedback mechanisms are achieved through a single actuation reaction.

synthetic biology↗

bioRxiv2023

Efficient Fisher Information Computation and Policy Search in Sampled Stochastic Chemical Reaction Networks through Deep Learning

Markov jump processes constitute the central class of Chemical Reaction Network models used to account for the intrinsic stochasticity observed in the dynamics of molecular species abundance throughout Molecular Biology. These models are specified in a parametric form, and their identification requires the use of inference procedures, and in particular the estimation of the Fisher Information. Here, a fast and accurate computation method is introduced in the case of partial observations at discrete time points, based on the use of a Mixture Density Network. We also demonstrate how this Neural Network can be used to perform fast policy search. The efficiency of these approaches is illustrated on a set of examples, and is compared to that of the current state-of-the-art.

systems biology↗

bioRxiv2022

Enhancing the performance of Magnets photosensors through directed evolution

Photosensory protein domains are the basis of optogenetic protein engineering. These domains originate from natural sources where they fulfill specific functions ranging from the protection against photooxidative damage to circadian rhythms. When used in synthetic biology, the features of these photosensory domains can be specifically tailored towards the application of interest, enabling their full exploitation for optogenetic regulation in basic research and applied bioengineering. In this work, we develop and apply a simple, yet powerful, directed evolution and high-throughput screening strategy that allows us to alter the most fundamental property of the widely used nMag/pMag photodimerization system: its light sensitivity. We identify a set of mutations located within the photosensory domains, which either increase or decrease the light sensitivity at sub-saturating light intensities, while also improving the dark-to-light fold change in certain variants. For some of these variants, photosensitivity and expression levels could be changed independently, showing that the shape of the light-activity dose-response curve can be tuned and adjusted. We functionally characterize the variants in vivo in bacteria on the single-cell and the population levels. We further show that a subset of these variants can be transferred into the mOptoT7 for gene expression regulation in mammalian cells. We demonstrate increased gene expression levels for low light intensities, resulting in reduced potential phototoxicity in long-term experiments. Our findings expand the applicability of the widely used Magnets photosensors by enabling a tuning towards the needs of specific optogenetic regulation strategies. More generally, our approach will aid optogenetic approaches by making the adaptation of photosensor properties possible to better suit specific experimental or bioprocess needs.

synthetic biology↗

bioRxiv2022

A scalable approach for solving chemical master equations based on modularization and filtering

Intracellular gene expression systems are inevitably random due to low molecular counts. Consequently, mechanistic models for gene expression should be stochastic, and central to the analysis and inference of such models is solving the Chemical Master Equation (CME), which characterizes the probability evolution of the randomly evolving copy-numbers of the reacting species. While conventional methods such as Monte-Carlo simulations and finite state projections exist for estimating CME solutions, they suffer from the curse of dimensionality, significantly decreasing their efficacy for high-dimensional systems. Here, we propose a new computational method that resolves this issue through a novel divide-and-conquer approach. Our method divides the system into a leader system and several conditionally independent follower subsystems. The solution of the CME is then constructed by combining Monte Carlo estimation for the leader system with stochastic filtering procedures for the follower subsystems. We develop an optimized system decomposition, which ensures the low-dimensionality of the sub-problems, thereby allowing for improved scalability with increasing system dimension. The efficiency and accuracy of the method are demonstrated through several biologically relevant examples in high-dimensional estimation and inference problems. We demonstrate that our method can successfully identify a yeast transcription system at the single-cell resolution, leveraging mRNA time-course microscopy data, allowing us to rigorously examine the heterogeneity in rate parameters among isogenic cells cultured under identical conditions. Furthermore, we validate this finding using a novel noise decomposition technique introduced in this study. This technique exploits experimental time-course data to quantify intrinsic and extrinsic noise components, without requiring supplementary components, such as dual-reporter systems.

systems biology↗

bioRxiv2022

Optogenetic closed-loop feedback control of the unfolded protein response optimizes protein production

In biotechnological protein production processes, the onset of protein unfolding at high gene expression levels leads to diminishing production yields and reduced efficiency. Here we show that in silico closed-loop optogenetic feedback control of the unfolded protein response (UPR) clamps gene expression rates at intermediate near-optimal values, leading to significantly improved product titers. Specifically, in a fully-automated custom-built 1L-photobioreactor, we used a cybergenetic control system to steer the level of UPR in yeast to a desired set-point by optogenetically modulating the expression of a hard-to-fold protein based on real-time feedback measurements of the UPR, resulting in 60% higher product titers. This proof-of-concept study paves the way for advanced optimal biotechnology production strategies that diverge from and complement current strategies employing constitutive overexpression or genetically hardwired circuits.

bioengineering↗

bioRxiv2022

Filtered finite state projection method for the analysis andestimation of stochastic biochemical reaction networks

Time-lapse microscopy has become increasingly prevalent in biological experimentation, as it provides single-cell trajectories that unveil valuable insights into underlying networks and their stochastic dynamics. However, the limited availability of fluorescent reporters typically constrains tracking to only a few network species. Addressing this challenge, the dynamic estimation of hidden state-components becomes crucial, for which stochastic filtering presents a robust mathematical framework. Yet, the complexity of biological networks often renders direct solutions to the filtering equation intractable due to high dimensionality and nonlinear interactions. In this study, we establish and rigorously prove the well-posedness of the filtering equation for the time-evolution of the conditional distribution of hidden species. Focusing on continuous-time, noise-free observations within a continuous-time discrete state-space Markov chain model, we develop the Filtered Finite State Projection (FFSP) method. This computational approach offers an approximated solution by truncating the hidden species state space, accompanied by computable error bounds. We illustrate the effectiveness of FFSP through diverse numerical examples, comparing it with established filtering techniques such as the Kalman filter, Extended Kalman filter, and particle filter. Finally, we show an application of our methodology with real time-lapse microscopy data. This work not only advances the application of stochastic filtering to biological systems but also contributes towards more accurate implementation of biomolecular feedback controllers. Author SummaryThe aim of this paper is to introduce a novel computational approach for numerically solving high-dimensional filtering problems associated with stochastic reaction network models in intracellular processes. This method, termed the Filtered Finite State Projection (FFSP) method, can reliably predict the dynamics of hidden species in reaction systems based on time-course measurements of the stochastic trajectories of certain species. While stochastic filtering is extensively utilised in engineering, its application in biology has been limited, primarily due to the nonlinear nature of biological interactions and the discrete, non-Gaussian nature of state variables. Traditional filtering techniques, such as the Kalman filter, often encounter difficulties under these conditions. We demonstrate that the FFSP method provides an accurate solution to the stochastic filtering problem, complete with a computable error bound. We present several numerical examples to showcase the effectiveness of FFSP and its superior performance compared to other filtering methodologies. Additionally, we apply FFSP to biological data, successfully reconstructing the hidden dynamics of a yeast transcription system from partial measurements obtained through time-lapse microscopy. We believe that FFSP could be a valuable tool for elucidating hidden intracellular dynamics and understanding stochastic cellular behaviours.

systems biology↗

bioRxiv2022

Inteins in the Loop: A Framework for Engineering Advanced Biomolecular Controllers for Robust Perfect Adaptation

Homeostasis is one of the cornerstones of life shaped by billions of years of evolution. A notion that is similar to homeostasis, but yet more stringent, is Robust Perfect Adaptation (RPA). A system is endowed with RPA if it is capable of driving a variable of interest to a prescribed level despite the presence of disturbances and uncertainties in the environment. Designing and building biomolecular controllers capable of achieving RPA have been identified as an important task which has immediate implications for various disciplines. Here, we develop systematic theoretical and experimental frameworks for custom-built proteins that exploit split inteins -- short amino acid sequences capable of performing protein-splicing reactions -- to design, genetically build and analyze a wide class of RPA-achieving integral feedback controllers. We first lay down a theoretical foundation that facilitates the screening of intein-based controller networks for RPA, and then usher an easy-to-use recipe to simplify their, otherwise complex, underlying mathematical models. Furthermore, we genetically engineer and test various controller circuits based on commonly used transcription factors in mammalian cells. We experimentally and theoretically demonstrate their ability of robustly rejecting external disturbances (that is achieving RPA) over an exquisitely broad dynamic range. Due to their small size, flexibility, modularity, lack of side effects and applicability across various forms of life, inteins serve as promising genetic parts to implement RPA-achieving controllers. To this end, we believe "inteins in the control loop" will leave a significant impact on various disciplines spanning synthetic biology, biofuel production, metabolic engineering and cell therapy among others.

synthetic biology↗

bioRxiv2022

Exploiting the Nonlinear Structure of the Antithetic Integral Controller to Enhance Dynamic Performance

The design of biomolecular feedback controllers has been identified as an important goal across a broad range of biological applications spanning synthetic biology, cell therapy, metabolic engineering, etc. This originates from the need to regulate various cellular processes in a robust and timely fashion. Recently, antithetic integral controllers found their way into synthetic biology due to the Robust Perfect Adaptation (RPA) property they endow -- the biological analogue of robust steady-state tracking. The antithetic integral motif hinges on a sequestration reaction between two molecules that annihilates their function. Here, we demonstrate that the complex resulting from the nonlinear sequestration reaction can be leveraged as an inhibitor to enhance the dynamic performance while maintaining the RPA property. We establish that this additional inhibition by the sequestration complex gives rise to a filtered Proportional-Integral (PI) controller thus offering more flexibility in shaping the dynamic response and reducing cell-to-cell variability. Furthermore, we explore the effect of various biological inhibitory mechanisms on the overall performance. The various analyses in the paper are carried out using analytical tools and are supported by numerical simulations. Finally, an experimental validation is performed using the cyberloop -- a hybrid platform where the controller is implemented in silico to control a genetic circuit in vivo.

synthetic biology↗

bioRxiv2022

Dynamic cybergenetic control of bacterial co-culturecomposition via optogenetic feedback

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. Additionally, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities.

synthetic biology↗

bioRxiv2022

Pad&eacute SSA: A frequency domain method for estimating the dynamics of stochastic reaction networks

Dynamic analysis and control of living cells relies on mathematical representations of cellular processes that are themselves modelled as biomolecular reaction networks. Stochastic models for biomolecular reaction networks are commonly employed for analysing intracellular networks having constituent species with low-copy numbers. In such models, the main object of interest is the probability distribution of the state vector of molecular counts which evolves according to a set of ordinary differential equations (ODEs) called the Chemical Master Equation (CME). Typically this set is very large or even infinite, making the CME practically unsolvable in most cases. Hence the outputs based on the CME solution, like the statistical moments of various state components, are generally estimated with Monte Carlo (MC) procedures by simulating the underlying Markov chain with Gillespies Stochastic Simulation Algorithm (SSA). However to obtain statistical reliability of the MC estimators, often a large number of simulated trajectories are required, which imposes an exorbitant computational burden. The aim of this paper is to present a frequency domain method for mitigating this burden by exploiting a small number of simulated trajectories to robustly estimate certain intrinsic eigenvalues of the stochastic dynamics. This method enables reliable estimation of time-varying outputs of interest from a small number of sampled trajectories and this estimation can be carried out for several initial states without requiring additional simulations. We demonstrate our method with a couple of examples.

systems biology↗

bioRxiv2022

Universal structural requirements for maximal robust perfect adaptation in biomolecular networks

Consider a biomolecular reaction network that exhibits robust perfect adaptation to disturbances from several parallel sources. The well-known Internal Model Principle of control theory suggests that such systems must include a subsystem (called the "internal model") that is able to recreate the dynamic structure of the disturbances. This requirement poses certain structural constraints on the network which we elaborate in this paper for the scenario where constant-in-time disturbances maximally affect network interactions and there is model uncertainty and possible stochasticity in the dynamics. We prove that these structural constraints are primarily characterized by a simple linear-algebraic stoichiometric condition which remains the same for both deterministic and stochastic descriptions of the dynamics. Our results reveal the essential requirements for maximal robust perfect adaptation in biology, with important implications for both systems and synthetic biology. We exemplify our results through many known examples of robustly adapting networks and we construct new examples of such networks with the aid of our linear-algebraic characterization.

systems biology↗

bioRxiv2021

Implementation of a novel optogenetic tool in mammalian cells based on a split T7 RNA polymerase

Optogenetic tools are widely used to control gene expression dynamics both in prokaryotic and eukaryotic cells. These tools are used in a variety of biological applications from stem cell differentiation to metabolic engineering. Despite some tools already available in bacteria, no light-inducible system currently exists to orthogonally control gene expression in mammalian cells. Such a tool would be particularly important in synthetic biology, where orthogonality is advantageous to achieve robust activation of synthetic networks. Here we implement, characterize and optimize a new orthogonal optogenetic tool in mammalian cells based on a previously published system in bacteria called Opto-T7RNAPs. The tool consists of a split T7 RNA polymerase coupled with the blue light-inducible magnets system (mammalian OptoT7 - mOptoT7). In our study we exploited the T7 polymerases viral origins to tune our systems expression level, reaching up to 20-fold change activation over the dark control. mOptoT7 is used here to generate mRNA for protein expression, shRNA for protein inhibition and Pepper aptamer for RNA visualization. Moreover, we show that mOptoT7 can mitigate gene expression burden when compared to other optogenetic constructs. These properties make mOptoT7 a new powerful tool to use when orthogonality and viral-like RNA species are desired in both synthetic biology and basic science applications.

synthetic biology↗

bioRxiv2021

Optogenetic actuator/biosensor circuits for large-scale interrogation of ERK dynamics identify sources of MAPK signaling robustness.

Combining single-cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single-cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK-RAF and the ERK-RSK2-SOS negative feedbacks operate simultaneously to regulate ERK dynamics. Bypassing the RSK2-mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2-dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2-mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs and its perturbation can enhance the efficiency of MAPK inhibitors.

systems biology↗