Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.
Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.
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DOI：
10.1371/journal.pcbi.1002751
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发表时间：
2012
影响因子：
4.3
通讯作者：
Valero-Cuevas FJ
中科院分区：
文献类型：
--
作者：
Saxena A;Lipson H;Valero-Cuevas FJ
在系统和计算生物学中，很多工作致力于在分子或细胞尺度上对系统和网络的功能识别。然而，在解剖学尺度上也存在类似的重要网络，如人类手指的肌腱网络：胶原纤维的复杂排列，将肌肉力量传递和分配到手指关节。这个网络对人类手的多功能性至关重要，至少从16世纪起，它的功能就一直存在争议。在这里，我们通过实验推断该网络的结构（包括拓扑和参数值），通过力输入的稀疏询问。在模拟中，通过捕食者-猎物估计-探索算法，一群代表这种结构的模型与一群信息未来力输入共同进化。模型适应度取决于它们解释实验数据的能力，而未来力输入的适应度取决于在当前模型之间造成最大的功能差异。我们通过推断两个已知的合成乳胶网络和一个从尸体中指采集的解剖肌腱网络来验证我们的方法。我们发现功能相似但结构不同的模型可以存在于一个狭窄的训练集和交叉验证误差范围内。对于Latex网络，具有低训练集误差[<4%]且与已知网络相似的模型具有最小的交叉验证误差[~ 5%]。尸体标本模型的低训练集[<4%]和交叉验证[<7.2%]误差表明，据我们所知，这是复杂解剖网络功能结构的第一个实验推断。这项工作扩展了当前的生物信息学推理方法，证明了生物标本的稀疏但信息丰富的询问在准确和有效的推理方面具有显著的计算优势，而不是随机测试，或假设模型拓扑并仅推断参数值。这些发现也为我们的进化史和多功能机器的发展提供了线索。在科学和医学上都一样，理解有机体工作的关键步骤之一是确定特定个体与其他人的相似或不同之处。只有这样，个体的特定特征才能与该物种的一般特征区分开来。然而，为了获得其功能的可靠描述（即模型），在给定生物体上进行足够的输入输出实验，往往会损害生物体，或者在测试易腐组织或人体受试者时需要太多时间。我们通过证明我们的新算法可以通过不断调整每个连续实验来提供更多信息，从而加速复杂组织中准确功能模型的提取，从而应对了这一挑战。我们将这种新方法应用于描述手指肌腱如何相互作用的问题，这一问题自达芬奇时代以来一直困扰着科学家和临床医生。这种新的计算实验方法现在允许实验提取精确的功能模型，对生物体的损害最小，从而为生物和医学研究提供了新的研究方向。例如，它将使人们更好地了解相关物种之间的异同，以及个性化医疗的发展。
In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines. In science and medicine alike, one of the critical steps to understand the working of organisms is to identify how a given individual is similar or different from others. Only then can the specific features of an individual be distinguished from the general properties of that species. However, doing enough input-output experiments on a given organism to obtain a reliable description of its function (i.e., a model) can often harm the organism, or require too much time when testing perishable tissues or human subjects. We have met this challenge by demonstrating that our novel algorithm can accelerate the extraction of accurate functional models in complex tissues by continually tailoring each successive experiment to be more informative. We apply this new method to the problem of describing how the tendons of the fingers interact, which has puzzled scientists and clinicians since the time of Da Vinci. This new computational-experimental method now enables fresh research directions in biological and medical research by allowing the experimental extraction of accurate functional models with minimal damage to the organism. For example, it will allow a better understanding of similarities and differences among related species, and the development of personalized medical treatment.
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影响因子：
--
作者：
Bianchi, S;Martinoli, C;Fasel, JHD
通讯作者：
Fasel, JHD
DOI：
10.2106/00004623-196749070-00002
发表时间：
1967-01-01
影响因子：
5.3
作者：
LITTLER, JW;EATON, RG
通讯作者：
EATON, RG
影响因子：
64.8
作者：
Hartwell, LH;Hopfield, JJ;Murray, AW
通讯作者：
Murray, AW
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
5.5
作者：
Clavero, JA;Golanó, P;Esplugas, M
通讯作者：
Esplugas, M