AI 中文总结
该研究提出基于规则建模的系统方法，评估生化系统所有酶子集的途径可行性与碳效率，应用于三类代谢系统，可识别关键酶与高效子集，助力途径设计决策。
AI 中文摘要
计算途径设计通常聚焦于评估选定途径或优化固定网络中的通量，但难以直接探究网络中哪些其他酶子集可支持可行的替代途径这一组合问题。对这些网络进行结构化计算分析可作为途径设计过程的重要前置步骤。本文提出一种系统方法，用于探究不同酶子集下的生化替代途径，该方法基于酶的规则建模：对于酶集为$S$的生化系统，我们通过生成化学反应空间、搜索从指定底物到目标产物的整数超流途径，并按集合包含关系组织可行子集，来评估所有子集$s /subseteq S$。这一过程产生了按包含关系排序的途径可行性与碳效率图谱。我们将该方法应用于非氧化磷酸戊糖途径、非氧化糖酵解及糖酵解。在这些系统中，可行子集仅占所有酶子集的中等比例，但不同系统间可行区域的结构差异显著。当要求测试子集的每个酶都必须参与途径时，更大的酶集并不总能提升碳效率，相反，性能取决于特定的酶组合。由此得到的子集图谱是识别必需酶、候选冗余酶及小型高性能酶子集的重要工具。通过将酶子集图谱本身作为分析对象，该方法填补了单个候选途径详细评估与早期阶段确定值得研究的酶组合这一设计决策之间的空白。
英文摘要
Computational pathway design often focuses on evaluating selected pathways or optimizing fluxes in a fixed network, but gives less direct access to the combinatorial question of which other enzyme subsets of the network can support productive alternative pathways. A structured computational analysis of these networks can act as a valuable pre-step to the pathway design process. We present here a systematic approach for exploring biochemical pathway alternatives across enzyme subsets, using a computational methodology based on a rule-based modeling of the enzymes: for a biochemical system with enzyme set $S$, we evaluate all subsets $s \subseteq S$ by generating chemical reaction spaces, searching for integer-hyperflow pathways from prescribed inputs to target products, and organizing feasible subsets by set inclusion. This yields an inclusion-ordered landscape of pathway feasibility and carbon efficiency. We apply the approach to the non-oxidative pentose phosphate pathway, to non-oxidative glycolysis, and to glycolysis. Across these systems, feasible subsets occupy only a moderate fraction of all enzyme subsets, but the structure of this feasible region differs strongly between the systems. Larger enzyme sets do not consistently improve carbon efficiency when every enzyme in the tested subset is required to participate in the pathway. Instead, performance depends on specific enzyme combinations. The resulting subset landscapes are valuable means for identifying essential enzymes, candidate redundancies, and small high-performing enzyme subsets. By making the enzyme-subset landscape itself the object of analysis, the approach addresses the gap between detailed evaluation of individual candidate pathways and early-stage design decisions about which enzyme combinations are worth investigating at all.
