Jian-Qin Liu

Molecular Computation and Evolutionary Wetware: A Cutting-Edge Technology for Artificial Life and Nanobiotechnologies

Focusing on the new frontiers opened by the integration of artificial life and nanobiotechnologies, this paper reviews mainstream biomolecular computation from the viewpoint of an information processing mechanism, computing methods, and problem-solving algorithms. We also discuss evolutionary wetware as a tool for unconventional computing, inspired by biomolecular systems in nature. Biomolecular computation uses a different paradigm of computing than that of the semiconductor computer. It includes several branches based on different molecular materials or molecular structures. Wetware can be used to demonstrate molecular evolution by engineered operations in test tubes. This makes evolutionary wetware capable of bridging the two domains of molecular computation and artificial life so that molecular information processing methods can be extended from carrying out computational tasks to modeling scalable complex systems. From a systematic study of nanobiomachines, we expect to designate models of artificial life, and to search for a novel methodology of nanobioICT (Information and Communication Technology) in the near future

A biomolecular computing method based on Rho family GTPases

In this paper, we propose a new biomolecular computing method based on Rho family GTPases, and discuss the schemes of representation and operations of molecular computing by Rho family GTPases applied to solve large-scale 3-SAT problems. We also present the optimal condition for the regulation schemes dependent on the temperature, kinase activity, and types of cells. This work is important for potential implementation of biomolecular computers using Rho family GTPases in which an optimized controlling scheme can make the best use of the interactions of signaling pathways in a computing system made by the large-scale abundance of kinases and phosphatases in cells.

Brain activity in the resting state: default mode network as an inside story within the brain

As a promising research field after the turn of the new century, a default mode network (DMN) in the brain shows strong potential as a new breakthrough in neuroscience. This approach emphasizes the baseline of the brain’s activities when the brain is awake but is not receiving any external input signal. The study of DMN has recently been highlighted, and is expected to provide a key to understanding mental disorders. This article consists of two sections: (1) a brief tutorial on DMN is presented, together with some necessary fundamental knowledge of neuroscience, and (2) a framework of network informatics for the DMN is proposed based on network dynamics. Models of information networks are discussed to bridge the gap between the level of regions and the level of neurons in the brain, and major issues about analyzing the DMN by brain imaging technologies are also discussed. One of the inspirations arising from the DMN approach is how spontaneous collective behavior emerges within an autonomous system. This is crucial to a systematic understanding of the brain’s function, and to exploring some new design principles of autonomous robotics in order to demonstrate complex life-like behaviors in engineering.

Partially interacted phosphorylation/dephosphorylation trees extracted from signaling pathways in cells

In order to reconstruct a network that consists of partially interacted phosphorylation/dephosphorylation pathways, combinatorial operations are designed based on the binary phosphorylation/dephosphorylation trees. By using these operations, a polynomial time complexity can be achieved from the bottom-up processes of interacted phosphorylation/dephosphorylation trees. To study the signaling mechanism of cell communication in terms of information theory, a kind of code describing the structure of partially interacted phosphorylation/dephosphorylation trees are also proposed.

Biomolecular Computing by Encoding of Regulated Phosphorylation-Dephosphorylation and Logic of Kinase-Phosphatase in Cells

As the first step in studying cell-based computing, a new method of biomolecular computing by cells is proposed based on signaling pathways of kinases and phosphatases for phosphorylation-dephosphorylation (we call this method ”kinase computing” for short in the latter parts of this paper). As opposed to the Adleman-Lipton paradigm of DNA computing and other types of cell-based computing, the core mechanism of kinase computing that carries out recursive computation at the biological level is based on (1) encoding the information by phosphorylation and dephosphorylation, (2) running the selection operators by coupled pathways of kinases and phosphatases under certain conditions, and (3) readout by immunofluorescence analysis. The control schemes for the related synchronization processes in 3-SAT computation is studied to clarify the biological feasibility of kinase computing, in which the control-space complexity and time complexity are linear.

An evolvable proteomic computing method for robust artificial chemistry systems

This paper proposes a novel computational model based on proteomic computing and leading to the construction of a robust artificial chemistry system. the dynamic description for pathways, the evolutionary mechanism, and the robustness are discussed. Furthermore, a preliminary simulation experiment shows the merits of our method for potential applications.

Molecular Computing by Signaling Pathways

To reduce the computing cost (i.e., the molecular number and time) of molecular computers by using DNA, RNA, and other biomolecules is an important task for enhancing their computing performance with parallelism obtained by biological implementation. For this purpose, we propose a new molecular computing method, namely, computing with Rho family GTPases, which differs from the Adleman-Lipton paradigm of DNA computing [1,9] and surfaced-based techniques [2]. This method employs the signaling pathways (the pathways of Rho family GTPases) of in situ cells that are formalized as a special kind of hypergraph rewriting, thus forming “conceptualized pathway objects” that systematically guarantee the rigorousness of massive parallel computing processes.

A Novel Programmable Molecular Computing Method Based on Signaling Pathways Regulated by Rho-GTPases in Living MDCK Epithelial Mammalian Cells

In this paper, we propose a new biomolecular computing method based on the crosstalked pathways of living cells and the corresponding kinase-phosphatase networks under the regulation of Rho family GTPases. Owing to their merits of efficient regulation in controlled pathway complexity and low cost in implementation, we propose a feasible protocol (the algorithm) for kinase-and-phosphatase-based computers (called kinase computers for short) and the materials and methods for their implementation. In order to obtain high programmability in molecular computation, we have successfully designed pathway regulation schemes for computation. Here we report our latest simulation results on a designed controllable crosstalk mechanism and efficient schemes for engineered GTPase-based signaling communications for stable kinase computing under the dynamical environment of a cell culture assay. This is significant for the application of molecular computing to medical nano-bioinformatics and also provides a testbed for new and unconventional computing paradigms inspired by nature.

On Scalable Biomolecular Computers Based on Crosstalked Phosphorylation and Dephosphorylation Pathways Regulated by Rho Family GTPases of Cells

Based on the living cell, which is one of the most promising functional materials for building nanobiomachines for massively parallel computation, we propose a new biomolecular computing method based on the signaling pathways of phosphorylation and dephosphorylation switched by kinases and phosphates and regulated by upstream pathways of Rho family GTPases in living cells, a method that differs from the Adleman-Lipton paradigm of DNA computers. The two main merits of this type of biomolecular computing process based on Rho family GTPases are the low cost of pathway control for cells and the high efficiency of the related computing processes, when certain pathway controllers are designed for the engineered pathway units of biomolecular computers. In this paper, we report our latest results on designing experimentally feasible operators and the related computer architecture of the engineered pathways in cells under the regulation of Rho family GTPases for solving large-scale benchmark problems by biomolecular computers, where the crosstalking processes among the pathways, feedback between the downstream and upstream pathways, and interaction with the nuclear receptors of cells are employed. This is a prerequisite for experimental implementation of a computing nanobiomachine based on the signaling pathways of Rho family GTPases in the form of living cells, which can cut costs in the number of controlled molecules for engineered pathways when the interaction ratings of pathways is regulated on the scale of an entire cell.

A logical method of cell-based molecular computation using the pathways regulated by Rho family GTPases

We propose a new logical method of molecular computing based on the engineered signaling pathways regulated by Rho family GTPases in vitro, in which the logical operators and related design schemes are discussed. Preliminary results on complexity and scalability are also given.