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The boltzmann-exponential thermodynamic laws govern the noisy molecular flux in chemical reactions as well as the noisy subthreshold electron current flux in transistors. These common mathematical laws enable one to map and simulate arbitrary stochastic biochemical reaction networks in highly efficient cytomorphic systems built on subthreshold analog circuits. Such simulations can accurately and automatically model noisy, nonlinear, asynchronous, stiff, and nonmodular feedback dynamics in interconnected networks in physical circuits. The scaling in simulation time for stochastic networks with the number of reactions or molecules is constant in cytomorphic systems. By contrast, it grows rapidly in digital systems, which are not parallelizable. Therefore, cytomorphic systems enable large-scale supercomputing systems-biology simulations of arbitrary and highly computationally intensive biochemical reaction networks that can nevertheless be compiled via digitally programmable parameters and connectivity. [ABSTRACT FROM AUTHOR]

A quasi-stable threshold voltage (V[sub t]) shift is imparted onto field-effect transistors (FETs) with organic semiconductors and polymer dielectrics. Adjustment of V[sub t] from accumulation mode to zero or depletion mode is demonstrated for both p-channel and n-channel FETs, and is accomplished by applying a depletion voltage to the gate prior to device operation. Hydrophobic dielectrics and dopant-resistant semiconductors were advantageous. A pixel circuit that utilizes this nonvolatile memory element is proposed. © 2002 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The analysis and simulation of complex interacting biochemical reaction pathways in cells is important in all of systems biology and medicine. Yet, the dynamics of even a modest number of noisy or stochastic coupled biochemical reactions is extremely time consuming to simulate. In large part, this is because of the expensive cost of random number and Poisson process generation and the presence of stiff, coupled, nonlinear differential equations. Here, we demonstrate that we can amplify inherent thermal noise in chips to emulate randomness physically, thus alleviating these costs significantly. Concurrently, molecular flux in thermodynamic biochemical reactions maps to thermodynamic electronic current in a transistor such that stiff nonlinear biochemical differential equations are emulated exactly in compact, digitally programmable, highly parallel analog “cytomorphic” transistor circuits. For even small-scale systems involving just 80 stochastic reactions, our 0.35-μm BiCMOS chips yield a 311× speedup in the simulation time of Gillespie's stochastic algorithm over COPASI, a fast biochemical-reaction software simulator that is widely used in computational biology; they yield a 15 500× speedup over equivalent MATLAB stochastic simulations. The chip emulation results are consistent with these software simulations over a large range of signal-to-noise ratios. Most importantly, our physical emulation of Poisson chemical dynamics does not involve any inherently sequential processes and updates such that, unlike prior exact simulation approaches, they are parallelizable, asynchronous, and enable even more speedup for larger-size networks. [ABSTRACT FROM PUBLISHER]

Prior work has shown that compact analog circuits can faithfully represent and model fundamental biomolecular circuits via efficient log-domain cytomorphic transistor equivalents. Such circuits have emphasized basis functions that are dominant in genetic transcription and translation networks and deoxyribonucleic acid (DNA)-protein binding. Here, we report a system featuring digitally programmable 0.35 μm BiCMOS analog cytomorphic chips that enable arbitrary biochemical reaction networks to be exactly represented thus enabling compact and easy composition of protein networks as well. Since all biomolecular networks can be represented as chemical reaction networks, our protein networks also include the former genetic network circuits as a special case. The cytomorphic analog protein circuits use one fundamental association-dissociation-degradation building-block circuit that can be configured digitally to exactly represent any zeroth-, first-, and second-order reaction including loading, dynamics, nonlinearity, and interactions with other building-block circuits. To address a divergence issue caused by random variations in chip fabrication processes, we propose a unique way of performing computation based on total variables and conservation laws, which we instantiate at both the circuit and network levels. Thus, scalable systems that operate with finite error over infinite time can be built. We show how the building-block circuits can be composed to form various network topologies, such as cascade, fan-out, fan-in, loop, dimerization, or arbitrary networks using total variables. We demonstrate results from a system that combines interacting cytomorphic chips to simulate a cancer pathway and a glycolysis pathway. Both simulations are consistent with conventional software simulations. Our highly parallel digitally programmable analog cytomorphic systems can lead to a useful design, analysis, and simulation tool for studying arbitrary large-scale biological networks in systems and synthetic biology. [ABSTRACT FROM PUBLISHER]

We review the field of synthetic biology from an analog circuits and analog computation perspective, focusing on circuits that have been built in living cells. This perspective is well suited to pictorially, symbolically, and quantitatively representing the nonlinear, dynamic, and stochastic (noisy) ordinary and partial differential equations that rigorously describe the molecular circuits of synthetic biology. This perspective enables us to construct a canonical analog circuit schematic that helps unify and review the operation of many fundamental circuits that have been built in synthetic biology at the DNA, RNA, protein, and small-molecule levels over nearly two decades. We review 17 circuits in the literature as particular examples of feedforward and feedback analog circuits that arise from special topological cases of the canonical analog circuit schematic. Digital circuit operation of these circuits represents a special case of saturated analog circuit behavior and is automatically incorporated as well. Many issues that have prevented synthetic biology from scaling are naturally represented in analog circuit schematics. Furthermore, the deep similarity between the Boltzmann thermodynamic equations that describe noisy electronic current flow in subthreshold transistors and noisy molecular flux in biochemical reactions has helped map analog circuit motifs in electronics to analog circuit motifs in cells and vice versa via a ‘cytomorphic’ approach. Thus, a body of knowledge in analog electronic circuit design, analysis, simulation, and implementation may also be useful in the robust and efficient design of molecular circuits in synthetic biology, helping it to scale to more complex circuits in the future. [ABSTRACT FROM PUBLISHER]

We describe a 0.35~\mum BiCMOS silicon chip that quantitatively models fundamental molecular circuits via efficient log-domain cytomorphic transistor equivalents. These circuits include those for biochemical binding with automatic representation of non-modular and loading behavior, e.g., in cascade and fan-out topologies; for representing variable Hill-coefficient operation and cooperative binding; for representing inducer, transcription-factor, and DNA binding; for probabilistic gene transcription with analogic representations of log-linear and saturating operation; for gain, degradation, and dynamics of mRNA and protein variables in transcription and translation; and, for faithfully representing biological noise via tunable stochastic transistor circuits. The use of on-chip DACs and ADCs enables multiple chips to interact via incoming and outgoing molecular digital data packets and thus create scalable biochemical reaction networks. The use of off-chip digital processors and on-chip digital memory enables programmable connectivity and parameter storage. We show that published static and dynamic MATLAB models of synthetic biological circuits including repressilators, feed-forward loops, and feedback oscillators are in excellent quantitative agreement with those from transistor circuits on the chip. Computationally intensive stochastic Gillespie simulations of molecular production are also rapidly reproduced by the chip and can be reliably tuned over the range of signal-to-noise ratios observed in biological cells. [ABSTRACT FROM PUBLISHER]

We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 \mum CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49–66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 \mu Vrms (total power of 5.4 \muW) down to 5.4 \mu Vrms (total power of 20 \muW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 \muW. The neural amplifier and the ADC occupy areas of 0.03 mm^2 and 0.02 mm^2 respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems. [ABSTRACT FROM PUBLISHER]

Abstract-Fast wideband spectrum analysis is expensive in power and hardware resources. We show that the spectrum-analysis architecture used by the biological cochlea is extremely efficient: analysis time, power and hardware usage all scale linearly with N, the number of output frequency bins, versus N log(N) for the Fast Fourier Transform. We also demonstrate two on-chip radio frequency (RF) spectrum analyzers inspired by the cochlea. They use exponentially-tapered transmission lines or filter cascades to model cochlear operation: Inductors map to fluid mass, capacitors to membrane stiffness and active elements (transistors) to active outer hair cell feedback mechanisms. Our RF cochlea chips, implemented in a 0.13 μm CMOS process, are 3 mm x 1.5 mm in size, have 50 exponentially-spaced output channels, have 70 dB of dynamic range, consume <300 mW of power and analyze the radio spectrum from 600 MHz to 8 GHz. Our work, which delivers insight into the efficiency of analog computation in the ear, may be useful in the front ends of ultra-wideband radio systems for fast, power-efficient spectral decomposition and analysis. Our novel rational cochlear transfer functions with zeros also enable improved audio silicon cochlea designs with sharper rolloff slopes and lower group delay than prior all-pole versions. [ABSTRACT FROM AUTHOR]

We present a novel and simple subthreshold tunable resistor (sinh R) which exhibits a sinh I-V characteristic. This compact 8-transistor circuit generates an output current that is proportional to the sinh of its input differential voltage and has an offset-free characteristic, i.e., zero current at zero differential voltage, like a real resistor. In a 1.5-µm CMOS chip implementation, we achieved a common-mode rejection ratio (CMRR) of 46 dB. As an example application, we use the expansive properties of our sinh R to linearize the compressive properties of a tanh differential pair by degeneration and cancel all nonlinearities up to fifth order; We demonstrate good agreement between theory and experimental results. [ABSTRACT FROM AUTHOR]