Centre for Systems Biology at Edinburgh

系统生物学是现代生物学的一个令人着迷的发展。我们已经对细胞如何工作有了很好的了解，包括“中心法则”：基因转录为RNA;剪接过程产生成熟的信使RNA; mRNA翻译为蛋白质;蛋白质途径调节基因表达并执行其他功能，如检测细胞间信号。通过人类基因组计划，我们知道了我们的DNA序列，并获得了我们基因的部分图谱。最后，高通量生物学为我们提供了大量的时间序列数据，例如，蛋白质或mRNA的浓度。系统生物学试图通过整合所有这些知识来了解生物系统的功能。系统理论被实现为计算机模型：使用数学模型构建的计算机模拟。生物系统是非常复杂的，有许多相互作用的子系统。因此，我们期望通过组合子模型来构建模型，从途径开始，最终进行到细胞器，细胞，生理系统和整个生物体。人们希望能够预测变化的影响，例如：环境，或由疾病或添加药物引起。这样的系统生物学将极大地提高人们的理解，并导致医学、农业和工业的重大进步。爱丁堡系统生物学中心将通过将先进的计算机科学和数学技术应用于精心选择的重要生物系统的范围来提高我们制作和使用这些模型的能力，这些生物系统的差异足以测试我们的建模能力。我们最大的此类系统是干扰素途径，这是巨噬细胞（主要免疫系统细胞）中的重要信号传导途径。在这里已经很难方便地描述路径的复杂性，我们将开发新的国际图形标准。中等规模的系统是RNA代谢，即从原始RNA到成熟RNA的过程。应该可以对这个仍然复杂的系统进行详细建模，将模型与高通量数据相关联。最后是昼夜节律，生物钟，在这里小的遗传电路调节大部分基因表达。在这里，我们可以进行数学分析，例如，研究光和温度如何在嘈杂的环境中使时钟同步。数学生物学的传统建模技术使用微分方程系统：系统生物学提出了新的挑战。我们将生产SBSI，一个免费提供给所有人的工业质量的建模设施。概率模型有时比微分模型更现实，例如，蛋白质分子。我们将探讨这些变化，以确保现实而又易于处理的建模。高通量数据是嘈杂的，难以获得足够的数量。我们将应用人工智能中熟悉的贝叶斯技术来帮助发现路径。我们希望从小系统（模块）构建大系统，并有效地对系统变体进行实验。编程语言让我们可以为计算系统做这件事;我们将把学到的经验应用于设计和使用生物语言，并有额外的前景，能够使用特殊逻辑查询和设计系统。总之，我们打算建立一个系统生物学的科学，使用计算机科学和数学来产生模型，并通过生物实验来完善和通知。我们研究的生物学的多样性将确保这些技术的广泛用途;我们将探索的技术的多样性将给企业带来成功的每一个前景。然而，要实现有用性，还需要更多。因此，我们将联合收割机的科学努力与培训和推广方案相结合：一个是为培养下一代系统生物学家作出贡献;另一个是使我们的学术界同事和工业界伙伴能够利用我们的工作。

Systems Biology is a fascinating development in modern biology. We have achieved a good general understanding of how cells work, including the 'central dogma': genes are transcribed to RNA; a splicing process produces mature messenger RNA; mRNA is translated to proteins; and protein pathways regulate gene expression and perform other functions such as detecting intercellular signals. With the Human Genome Project we know our DNA sequence and have a partial map of our genes. And, finally, high-throughput biology is giving us massive amounts of time series data, e.g., of protein or mRNA concentrations. Systems Biology seeks to understand how biological systems function by integrating all this knowledge. System theories are implemented as in silico models: computer simulations built using mathematical models. Biological systems are extraordinarily complex with many levels of interacting subsystems. We therefore expect to construct models by combining submodels, beginning with pathways, and eventually proceeding to organelles, cells, physiological systems and whole organisms. One hopes to be able to predict the effect of variations, e.g.: environmental, or resulting from disease or adding drugs. Such a Systems Biology would produce an enormous increase in understanding and lead to major progress in medicine, agriculture and industry. The Edinburgh Centre for Systems Biology will advance our ability to make and use such models by applying advanced computer science and mathematical techniques to a carefully chosen range of important biological systems which are different enough to test our model-making ability to the limit. Our largest such system is the interferon pathway, an important signaling pathway in macrophages, the main immune system cells. It is already hard here to conveniently describe the pathway intricacies, and we shall develop new international graphical standards. The middle-sized system is RNA metabolism, the process leading from raw to mature RNA. It should be possible to model this still complex system in detail, correlating the models with high-throughput data. Finally comes circadian rhythm, biological clocks, where small genetic circuits regulate large parts of gene expression. Here we may perform mathematical analyses, e.g., investigating how light and temperature synchronise clocks in a noisy environment. The traditional modelling technique of mathematical biology uses systems of differential equations: systems biology presents new challenges. We shall produce SBSI, a modelling facility of industrial quality freely available to all. Probabilistic models are sometimes more realistic than differential ones, e.g., for few protein molecules. We shall explore such variations to ensure realistic yet tractable modelling. High-throughput data are noisy and hard to obtain in sufficient quantity. We shall apply Bayesian techniques, familiar from Artificial Intelligence, to help discover pathways. We wish to construct big systems from small ones (modules) and to experiment efficiently with system variants. Programming languages let one do this for computational systems; we shall apply the lessons learnt to design and use languages for biological ones, with the additional prospect of being able to query and design systems using special logics. In summary, we intend to build a science of Systems Biology using computer science and mathematics to produce models refined by and informing biological experiment. The variety of the biology we do will ensure the wide usefulness of the techniques; the variety of the techniques we will explore will give the enterprise every prospect of success. However to achieve usefulness requires much more. We will therefore combine our scientific effort with training and outreach programmes: the one to contribute to the production of the next generation of systems biologists; and the other to make our work available to our colleagues in academia and our partners in industry.