Tanevski Jovan, Todorovski Ljupčo, Džeroski Sašo
Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia.
Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, 1000, Slovenia.
BMC Syst Biol. 2016 Mar 22;10:30. doi: 10.1186/s12918-016-0273-4.
Identifying a proper model structure, using methods that address both structural and parameter uncertainty, is a crucial problem within the systems approach to biology. And yet, it has a marginal presence in the recent literature. While many existing approaches integrate methods for simulation and parameter estimation of a single model to address parameter uncertainty, only few of them address structural uncertainty at the same time. The methods for handling structure uncertainty often oversimplify the problem by allowing the human modeler to explicitly enumerate a relatively small number of alternative model structures. On the other hand, process-based modeling methods provide flexible modular formalisms for specifying large classes of plausible model structures, but their scope is limited to deterministic models. Here, we aim at extending the scope of process-based modeling methods to inductively learn stochastic models from knowledge and data.
We combine the flexibility of process-based modeling in terms of addressing structural uncertainty with the benefits of stochastic modeling. The proposed method combines search trough the space of plausible model structures, the parsimony principle and parameter estimation to identify a model with optimal structure and parameters. We illustrate the utility of the proposed method on four stochastic modeling tasks in two domains: gene regulatory networks and epidemiology. Within the first domain, using synthetically generated data, the method successfully recovers the structure and parameters of known regulatory networks from simulations. In the epidemiology domain, the method successfully reconstructs previously established models of epidemic outbreaks from real, sparse and noisy measurement data.
The method represents a unified approach to modeling dynamical systems that allows for flexible formalization of the space of candidate model structures, deterministic and stochastic interpretation of model dynamics, and automated induction of model structure and parameters from data. The method is able to reconstruct models of dynamical systems from synthetic and real data.
在生物学系统方法中，使用能够同时处理结构和参数不确定性的方法来确定合适的模型结构是一个关键问题。然而，它在近期文献中的出现却很少。虽然许多现有方法集成了单一模型的模拟和参数估计方法来解决参数不确定性，但其中只有少数方法同时解决结构不确定性。处理结构不确定性的方法通常通过让人类建模者明确列举相对较少的替代模型结构来过度简化问题。另一方面，基于过程的建模方法提供了灵活的模块化形式主义来指定大量合理的模型结构，但其范围仅限于确定性模型。在此，我们旨在扩展基于过程的建模方法的范围，以便从知识和数据中归纳学习随机模型。
我们将基于过程的建模在处理结构不确定性方面的灵活性与随机建模的优势相结合。所提出的方法结合了在合理模型结构空间中的搜索、简约原则和参数估计，以识别具有最优结构和参数的模型。我们在基因调控网络和流行病学这两个领域的四个随机建模任务中展示了所提出方法的效用。在第一个领域中，使用合成生成的数据，该方法成功地从模拟中恢复了已知调控网络的结构和参数。在流行病学领域，该方法成功地从真实、稀疏且有噪声的测量数据中重建了先前建立的疫情爆发模型。
该方法代表了一种统一的动态系统建模方法，它允许对候选模型结构空间进行灵活形式化，对模型动态进行确定性和随机解释，并从数据中自动归纳模型结构和参数。该方法能够从合成数据和真实数据中重建动态系统模型。
