Theoretical perspective on synthetic man-made life:Learning from the origin of life

Creating a man-made life in the laboratory is one of life science's most intriguing yet challenging problems.Advances in synthetic biology and related theories,particularly those related to the origin of life,have laid the groundwork for further exploration and understanding in this field of artificial life or man-made life.But there remains a wealth of quantitative mathematical models and tools that have yet to be applied to this area.In this paper,we review the two main approaches often employed in the field of man-made life:the top-down approach that reduces the complexity of extant and existing living systems and the bottom-up approach that integrates welldefined components,by introducing the theoretical basis,recent advances,and their limitations.We then argue for another possible approach,namely"bottom-up from the origin of life":Starting with the establishment of autocatalytic chemical reaction networks that employ physical boundaries as the initial compartments,then designing directed evolutionary systems,with the expectation that independent compartments will eventually emerge so that the system becomes free-living.This approach is actually analogous to the process of how life originated.With this paper,we aim to stimulate the interest of synthetic biologists and experimentalists to consider a more theoretical perspective,and to promote the communication between the origin of life community and the synthetic man-made life community.

Return just your search:privacy-preserving homoglyph search for arbitrary languages

Searchable encryption is an effective way to ensure the security and availability of encrypted outsourced cloud data.Among existing solutions,the keyword exact search solution is relatively inflexible,while the fuzzy keyword search solution either has a high index overhead or suffers from the falsepositive.Furthermore,no existing fuzzy keyword search solution considers the homoglyph search on encrypted data.In this paper,we propose an efficient privacy-preserving homoglyph search scheme supporting arbitrary languages(POSA,in short).We enhance the performance of the fuzzy keyword search in three aspects.Firstly,we formulate the similarity of homoglyph and propose a privacy-preserving homoglyph search.Secondly,we put forward an index build mechanism without the false-positive,which reduces the storage overhead of the index and is suitable for arbitrary languages.Thirdly,POSA returns just the user’s search,i.e.,all returned documents contain the search keyword or its homoglyph.The theoretical analysis and experimental evaluations on real-world datasets demonstrate the effectiveness and efficiency of POSA.