Biological signal processing filters via engineering allosteric transcription factors

Signal processing is critical to a myriad of biological phenomena (natural and engineered) that involve gene regulation. Biological signal processing can be achieved by way of allosteric transcription factors. In canonical regulatory systems (e.g., the lactose repressor), an INPUT signal results in the induction of a given transcription factor and objectively switches gene expression from an OFF state to an ON state. In such biological systems, to revert the gene expression back to the OFF state requires the aggressive dilution of the input signal, which can take 1 or more d to achieve in a typical biotic system. In this study, we present a class of engineered allosteric transcription factors capable of processing two-signal INPUTS, such that a sequence of INPUTS can rapidly transition gene expression between alternating OFF and ON states. Here, we present two fundamental biological signal processing filters, BANDPASS and BANDSTOP, that are regulated by D-fucose and isopropyl-β-D-1-thiogalactopyranoside. BANDPASS signal processing filters facilitate OFF–ON–OFF gene regulation. Whereas, BANDSTOP filters facilitate the antithetical gene regulation, ON–OFF–ON. Engineered signal processing filters can be directed to seven orthogonal promoters via adaptive modular DNA binding design. This collection of signal processing filters can be used in collaboration with our established transcriptional programming structure. Kinetic studies show that our collection of signal processing filters can switch between states of gene expression within a few minutes with minimal metabolic burden—representing a paradigm shift in general gene regulation.

Ultrasensitivity and bistability in covalent-modification cycles with positive autoregulation

Switch-like behaviours in biochemical networks are of fundamental significance in biological signal processing, and exist as two distinct types: ultra-sensitivity and bistability. Here we propose two new models of a reversible covalent-modification cycle with positive autoregulation (PAR), a motif structure that is thought to be capable of both ultrasensitivity and bistability in different parameter regimes. These new models appeal to a modelling framework that we call complex-complete , which accounts fully for the molecular complexities of the underlying signalling mechanisms. Each of the two new models encodes a specific molecular mechanism for PAR. We demonstrate that the modelling simplifications for PAR models that have been used in previous work, which rely on Michaelian approximations, are unable to accurately recapitulate the qualitative signalling responses supported by our detailed models. Strikingly, we show that complex-complete PAR models are capable of new qualitative responses such as one-way switches and a ‘prozone’ effect, depending on the specific PAR-encoding mechanism, which are not supported by Michaelian simplifications. Our results highlight the critical importance of accurately representing the molecular details of biochemical signalling mechanisms, and strongly suggest that the Michaelian approximation is inadequate for predictive models of enzyme-mediated chemical reactions with added regulations such as PAR.

Phase separation provides a mechanism to reduce noise in cells

Expression of proteins inside cells is noisy, causing variability in protein concentration among identical cells. A central problem in cellular control is how cells cope with this inherent noise. Compartmentalization of proteins through phase separation has been suggested as a potential mechanism to reduce noise, but systematic studies to support this idea have been missing. In this study, we used a physical model that links noise in protein concentration to theory of phase separation to show that liquid droplets can effectively reduce noise. We provide experimental support for noise reduction by phase separation using engineered proteins that form liquid-like compartments in mammalian cells. Thus, phase separation can play an important role in biological signal processing and control.

Oscillatory Models for Biological Signal Processing and Pattern Recognition

Among biomedical signals, repetitive or quasi-periodic signals are particularly widespread. While the periodic component is still presented these signals are characterized by period variations (fundamental frequency, amplitude, etc.). The lack of synchronization or phase shifts results in variations in similar segments’ durations, nominally identical signals demonstrate a variation at peak retention times, etc. The inverse methods of oscillation theory were proposed recently as a tool to solve the problems of modelling of repetitive signals with phase shift. In the article, the inverse method of oscillation theory is considered as a tool to solve the problems of supervised and non-supervised classification, and filtering of repetitive signals with phase shift. Examples of application are presented.

Measurement and monitoring in anaesthesia

This chapter introduces some basic physical principles that contribute to the function of various monitors used in anaesthetic practice. Topics include biological signal processing, operational amplifiers, including single-ended amplifiers, and the benefit of patient-isolated differential amplifiers; it includes filtering, digital processing, and electrodes. The generic principles of transducers are introduced, including resistive, capacitive, and inductance strain gauges used in transducers, photoelectric, piezoelectric, and chemical transducers, calibration of transducers, and the significance of resonance and damping in measurement systems. Since both are widely used in monitoring systems, there is an introduction to spectroscopy and magnetism.

A New Instrumentation Amplifier Architecture Based on Differential Difference Amplifier for Biological Signal Processing

<p>In this paper, a new Instrumentation Amplifier (IA) architecture for biological signal pro-cessing is proposed. First stage of the proposed IA architecture consists of fully balance differential difference amplifier and three resistors. Its second stage was designed by using differential difference amplifier and two resistors. The second stage has smaller number of resistors than that of conventional one. The IA architectures are simulated and compared by using 1P 2M 0:6-m CMOS process. From HSPICE simulation result, lower common-mode voltage can be achieved by the proposed IA architecture. Average common-mode gain (Ac) of the proposed IA architecture is 31:26 dB lower than that of conventional one under 3% resistor mismatches condition. Therefore, the Ac of the proposed IA architecture is more insensitive to resistor mismatches and suitable for biological signal processing.</p>

Low Common-Mode Gain Instrumentation Amplifier Architecture Insensitive to Resistor Mismatches

<pre>In this paper, an instrumentation amplifier architecture for biological <br />signal is proposed. First stage of conventional IA architecture was modified <br />by using fully balanced differential difference amplifier and evaluated by <br />using <span>1P</span> <span>2M</span> 0.6<span>μ</span>m CMOS process. From <span>HSPICE</span> simulation result, lower <br />common-mode voltage can be achieved by proposed IA architecture. <br />Actual fabrication was done and six chips were evaluated. From the evaluation result, average common-mode gain of proposed IA architecture <br />is <span>10.84</span> dB lower than that of conventional one without requiring <br />well-matched resistors. Therefore, the proposed IA architecture <br />is suitable for biological signal processing.<br /><br /></pre>

Low Common-Mode Gain Instrumentation Amplifier Architecture Insensitive to Resistor Mismatches

<pre>In this paper, an instrumentation amplifier architecture for biological <br />signal is proposed. First stage of conventional IA architecture was modified <br />by using fully balanced differential difference amplifier and evaluated by <br />using <span>1P</span> <span>2M</span> 0.6<span>μ</span>m CMOS process. From <span>HSPICE</span> simulation result, lower <br />common-mode voltage can be achieved by proposed IA architecture. <br />Actual fabrication was done and six chips were evaluated. From the evaluation result, average common-mode gain of proposed IA architecture <br />is <span>10.84</span> dB lower than that of conventional one without requiring <br />well-matched resistors. Therefore, the proposed IA architecture <br />is suitable for biological signal processing.<br /><br /></pre>